Sampling alternative conformational states of transporters and receptors with AlphaFold2
Open accessPublished version of a preprint
Abstract
Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.
AI snapshot
Written from the paper’s full text.
TL;DR
Reducing the depth of input MSAs by stochastic subsampling drives AlphaFold2 to sample alternative, native-like conformations of transporters and GPCRs whose structures were absent from its training set. The approach recovers experimentally observed inward/outward or inactive/active states, but it is a methodological “hack” with limited success for proteins already represented in the AF2 training set.
Key findings
- For all eight benchmark targets—five transporters (LAT1, ZnT8, MCT1, STP10, ASCT2) and three GPCRs (CGRPR, PTH1R, FZD7)—accurate models of at least one conformation were obtained (TM score ≥0.9), and both conformations could be predicted with shallow MSAs.
- Models at the extremes of the first principal component (PC1) were among the most accurate: average TM score was 0.94 for the top 1, top 3, and top 10 PC1 models; PC1 captured 64.9% ± 16.1% of structural variation among models generated with MSAs of 32 or more sequences.
- Predicted structural fluctuations correlated with experimental conformational changes for ASCT2, LAT1, CGRPR, and MCT1 with templates (R² ≥ 0.75); Pearson correlation between PC1 and TM score exceeded 0.8 for all transporters.
- MCT1 was an exception: over 99% of template-free models had TM ≥0.9 to the inward-facing structure and <0.9 to the outward-facing structure regardless of MSA depth; accurate outward-facing models required templates and MSAs of 16–32 sequences.
- For four proteins with at least one conformation in the AF2 training set (CCR5, SERT, PfMATE, MurJ), conformational diversity was far more limited, and none adopted the alternative conformer.
Methods
- AF2 v2.0.1 with modified ColabFold was used; MSAs were generated with MMseqs2, template search was disabled except for explicit template-based tests, recycles were set to one, and final MD refinement was skipped.
- For template-free predictions, all five neural networks were used, with 10 predictions per network per MSA size, yielding 50 models per protein per MSA depth; MSA depth was controlled via `max_msa_clusters` and `max_extra_msa`.
- Benchmark set: eight membrane proteins absent from the PDB before April 30, 2018 and determined in two or more conformations, plus four proteins with at least one conformation in the AF2 training set; structural similarity was assessed by TM-align, and PCA/RMSF were calculated in CPPTRAJ with loop residues omitted from PCA.
Limitations
- Stated: the approach is a methodological “hack,” not an explicit AF2 objective; AF2 likely does not learn energy landscapes, does not account for the lipid environment, and showed limited success for proteins whose structures were in the training set.
- Stated: optimal MSA depth and template choice varied by protein, with no one-size-fits-all solution; the GPR114/ADGRG5 predictions require experimental validation.
- Evident from the text: the benchmark is small (eight targets plus four training-set proteins), validation depends on already-known experimental structures, and the authors skipped the final MD simulation to reduce computational cost.
Model-written summary. Check it against the paper before relying on it.
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Cites
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Claims the paper makes
0 of 30 cited sources checked · 0 supported · 0 flagged
- Findingown data
The first principal component (PC1) captured 64.9% ± 16.1% of the structural variations among models generated using MSAs with 32 or more sequences.
“the first principal component (PC1) captured 64.9% ± 16.1% of the structural variations among the models generated using MSAs with 32 or more sequences”
Quote in the paperSubstantial claim - Findingnone given
This approach showed limited success when applied to transporters whose structures were used to train AF2.
“this approach showed limited success when applied to transporters whose structures were used to train AF2”
Quote in the paperSubstantial claim - Findingown data
In every case, a correlation was evident between each model's PC1 values and their TM scores.
“In every case, a correlation was evident between each model’s PC1 values and their TM scores.”
Quote in the paperSubstantial claim - Findingown data
No general pattern was readily apparent regarding the ideal MSA depth required to balance diversity and native folding, even when accounting for sequence length of the target.
“No general pattern was readily apparent regarding the ideal MSA depth required to achieve this balance, even when accounting for sequence length of the target”
Quote in the paperNot a substantial claim - Findingown data
Correlation between each residue's Cα distance between two superimposed experimental structures and its RMSF among all 50 models was observed in most cases and was notable for ASCT2, LAT1, CGRPR, and MCT1 with templates (R2 ≥ 0.75).
“Correlation between these two measures was observed in most cases and was notable for ASCT2, LAT1, CGRPR, and MCT1 with templates (R2 ≥ 0.75).”
Quote in the paperSubstantial claim
- Findingown data
MSAs with hundreds or thousands of sequences generally engender tighter clustering in conformations specific to each protein.
“MSAs with hundreds or thousands of sequences were generally observed to engender tighter clustering in conformations specific to each protein.”
Quote in the paperSubstantial claim - Findingown data
The conformational diversity of these models, including those generated using shallow MSAs, was far more limited than what was generally observed for the proteins discussed above, with the exception of MCT1.
“The conformational diversity of these models, including those generated using shallow MSAs, was far more limited than what was generally observed for the proteins discussed above, with the exception of MCT1”
Quote in the paperSubstantial claim - Findingown data
The experimental structures virtually always flanked the AF2 models along PC1.
“Moreover, the experimental structures virtually always flanked the AF2 models along PC1.”
Quote in the paperSubstantial claim - Findingown data
The increased diversity from shallow MSAs coincided with the generation of misfolded or outlier models.
“The increased diversity coincided with the generation of misfolded or outlier models.”
Quote in the paperSubstantial claim - Findingown data
Comparison of PC1/PC2 values suggested that the predicted dynamics deviate from simple interpolation of two end states.
“comparison of PC1/PC2 values suggested that the predicted dynamics deviate from simple interpolation of two end states”
Quote in the paperSubstantial claim - Findingown data
Accurate models (TM score ≥0.9) of all eight protein targets were obtained for at least one conformation.
“Accurate models of all eight protein targets were obtained for at least one conformation (TM score ≥0.9), consistent with published performance statistics”
Quote in the paperSubstantial claim - Findingown data
In one case (MCT1), accurate modeling of alternative conformations also required the manual curation of template structures.
“in one case (MCT1) accurate modeling of alternative conformations also required the manual curation of template structures.”
Quote in the paperSubstantial claim - Findingown data
The experimental structures virtually always occupied well-defined extreme positions.
“The experimental structures virtually always occupied well-defined extreme positions.”
Quote in the paperSubstantial claim - Findingown data
For proteins with one conformation in the AF2 training set, the majority of transporter models generated were more similar to the conformation present in the training set than to the conformation absent from the training set.
“the majority of the transporter models generated this way were more similar to the conformation present in the training set than the conformation absent from the training set (i.e., their TM scores were greater”
Quote in the paperSubstantial claim - Findingown data
Pearson correlation coefficients between PC1 and TM scores of the ensemble of models exceeded 0.8 for all transporters in this dataset.
“Pearson’s correlation coefficients between PC1 and TM scores of the ensemble of models exceeded 0.8 for all transporters in this dataset.”
Quote in the paperSubstantial claim - Findingown data
Both conformations of all eight protein targets could be predicted with AF2 to high accuracy (TM score ≥0.9) by using MSAs that are far shallower than the default.
“Overall, these results demonstrate that both conformations of all eight protein targets could be predicted with AF2 to high accuracy (TM score ≥0.9) by using MSAs that are far shallower than the default.”
Quote in the paperSubstantial claim - Findingown data
Whereas the DeepMind team reportedly required templates to obtain models of LmrP in an OF conformation, this procedure was usually unnecessary in the present study.
“whereas the DeepMind team reportedly required templates to obtain models of LmrP in an OF conformation (Jumper et al., 2021b), we found that this procedure was usually unnecessary.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Jumper et al., 2021b
Not checked in the free run.
- Findingown data
Decreasing the depth of subsampled MSAs promotes the generation of alternative conformations in most proteins.
“Decreasing the depth of the subsampled MSAs, by contrast, appeared to promote the generation of alternative conformations in most proteins.”
Quote in the paperSubstantial claim - Findingnone given
The predicted flexibility values failed to correlate with their pLDDT values.
“the predicted flexibility values failed to correlate with their pLDDT values”
Quote in the paperNot a substantial claim - Findingown data
The use of shallow MSAs (≤64 sequences) yielded a range of intermediate conformations distributed across three well-separated clusters.
“The use of shallow MSAs (≤64 sequences), by contrast, yielded a range of intermediate conformations distributed across three well-separated clusters (Figure 3—figure supplement 2).”
Quote in the paperSubstantial claim - Findingown data
The structural model of GPR114/ADGRG5 deposited in the AF2 database could be recapitulated by using deep MSAs.
“The structural model deposited in the AF2 database, which likely depicts an active conformation that diverges from the structure of the homolog GPR97 (Ping et al., 2021), could be recapitulated by using deep MSAs.”
Quote in the paperSubstantial claim - Findingown data
One of the clusters from shallow MSAs contains models with an orientation of TM6 and TM7 that fully occludes the orthosteric site and partially blocks the cytosolic pocket where G proteins bind.
“One of these clusters contains models with an orientation of TM6 and TM7 that fully occludes the orthosteric site and partially blocks the cytosolic pocket where G proteins bind.”
Quote in the paperSubstantial claim - Interpretationown data
The use of shallow MSAs was instrumental to obtaining structurally diverse models in most proteins.
“The use of shallow MSAs was instrumental to obtaining structurally diverse models in most proteins”
Quote in the paperSubstantial claim - Premisecitation
The neural networks used for prediction were trained on all structures deposited in the Protein Data Bank on or before April 30, 2018.
“The neural networks used for prediction were trained on all structures deposited in the Protein Data Bank (PDB) on or before April 30, 2018 (Jumper et al., 2021a).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Jumper et al., 2021a
Not checked in the free run.
- Interpretationreasoning
Models extending beyond the partially inactive state along PC1 may represent the fully inactive conformation of PTH1R.
“suggesting that models extending beyond the former state along PC1 may represent the fully inactive conformation.”
Quote in the paperSubstantial claim - Interpretationown data
The exception of MCT1 without templates was likely due to a lack of conformational diversity among the sampled models.
“The exception was MCT1 without templates, which was likely due to a lack of conformational diversity among the sampled models.”
Quote in the paperNot a substantial claim - Interpretationcitation
The divergence in performance may stem from the composition of the AF2 training set, which featured structures of many active GPCRs but no structures of IF MATEs.
“This divergence in performance may stem from the composition of the AF2 training set, which featured the structures of many active GPCRs but no structures, for example, of IF MATEs (Claxton et al., 2021).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Claxton et al., 2021 source
Not checked in the free run.
- Interpretationown data
The results argue against an optimal one-size-fits-all approach for sampling the conformational space of every protein with high accuracy.
“they also argue against an optimal one-size-fits-all approach for sampling the conformational space of every protein with high accuracy.”
Quote in the paperSubstantial claim - Interpretationown data
Predicted flexibility by AF2 is related to the protein's dynamics inferred from the experimental structures.
“The correlation demonstrates that predicted flexibility by AF2 is related to the protein’s dynamics inferred from the experimental structures.”
Quote in the paperSubstantial claim - Interpretationown data
Accurate representative models of conformations of interest can be selected from the extreme positions along PC1.
“Therefore, these results indicate that accurate representative models of conformations of interest can be selected from the extreme positions along PC1.”
Quote in the paperSubstantial claim - Interpretationown data
The generation of large numbers of models appeared to be necessary to yield intermediate conformations of interest.
“Thus, the generation of large numbers of models appeared to be necessary to yield intermediate conformations of interest.”
Quote in the paperSubstantial claim - Interpretationown data
The information content provided by templates diminishes as the depth of the MSA increases.
“further indicating that the information content provided by the templates diminishes as the depth of the MSA increases.”
Quote in the paperSubstantial claim - Interpretationown data
AF2 can be manipulated to accurately model alternative conformations of transporters and GPCRs whose structures were not available in the training set.
“Our results indicate that the state-of-the-art de novo structural modeling algorithm AF2 can be manipulated to accurately model alternative conformations of transporters and GPCRs whose structures were not available in the training set.”
Quote in the paperSubstantial claim - Premisecitation
PTH1R was determined in a partially inactive and active conformation.
“The exception, PTH1R, was determined in a partially inactive and active conformation (Zhao et al., 2019)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Zhao et al., 2019 source
Not checked in the free run.
- Premisecitation
Traditional computational prediction pipelines reliant on inter-residue distance restraints calculated from deep MSAs have historically struggled to accurately predict the structures of membrane proteins and their movements.
“Traditional computational prediction pipelines reliant on inter-residue distance restraints calculated from deep multiple sequence alignments (MSAs) have historically struggled to accurately predict the structures of these proteins and their movements.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Ovchinnikov et al., 2015 source
Not checked in the free run.
- cites Nicoludis and Gaudet, 2018
Not checked in the free run.
- Study design and conductreasoning
This study is restricted to proteins whose structures were absent from the PDB before April 30, 2018 and have since been determined at atomic resolution in two or more conformations.
“Therefore, by necessity, this study is restricted to proteins whose structures were absent from the PDB before this date and have since been determined at atomic resolution in two or more conformations.”
Quote in the paperSubstantial claim - Interpretationreasoning
The results represent a methodological 'hack', rather than an explicit objective built into the algorithm's architecture.
“While these results reinforce the notion that AF2 can provide models to guide biophysical studies of conformationally heterogeneous membrane proteins, they represent a methodological ‘hack’, rather than an explicit objective built into the algorithm’s architecture.”
Quote in the paperSubstantial claim- Rests on an assumption: AF2 does not directly account for the lipid environment.
- Premisecitation
AF2, despite its accuracy, likely does not learn the energy landscapes underpinning protein folding and function.
“Several preprints have provided evidence that AF2, despite its accuracy, likely does not learn the energy landscapes underpinning protein folding and function (Saldaño et al., 2021; Pak et al., 2021; Akdel et al., 2021).”
Quote in the paperSubstantial claim0 of 3 sources checked - Interpretationown data
These results reinforce the notion that AF2 can provide models to guide biophysical studies of conformationally heterogeneous membrane proteins.
“While these results reinforce the notion that AF2 can provide models to guide biophysical studies of conformationally heterogeneous membrane proteins, they represent a methodological ‘hack’, rather than an explicit objective built into the algorithm’s architecture.”
Quote in the paperSubstantial claim - Interpretationown data
The exploration of the conformational space is in part a byproduct of low sequence information provided for inference.
“As our results show, the exploration of the conformational space is in part a byproduct of low sequence information provided for inference.”
Quote in the paperSubstantial claim - Premisecitation
The lipid environment has been experimentally shown to bias the conformational equilibria of membrane proteins.
“which has been experimentally shown to bias the conformational equilibria of membrane proteins (Martens et al., 2018; Muller et al., 2019; Immadisetty et al., 2019).”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Dynamic interconversion between multiple conformations underpins the functions of integral membrane proteins in all domains of life.
“Dynamic interconversion between multiple conformations underpins the functions of integral membrane proteins in all domains of life (Campbell et al., 2016; Cournia et al., 2015; Shaw et al., 2010; Boehr et al., 2009).”
Quote in the paperSubstantial claim0 of 3 sources checked
- May combine two claims. 16
- Classified as probably not a substantial claim. 9
- The quote only partly states this claim. 1
May combine two claims.
Findingown dataModels with the most extreme PC1 values were among the most accurate: average TM scores were 0.94 for the top 1, top 3, and top 10 PC1 models.
“Indeed, the models with the most extreme PC1 values were also among the most accurate: average TM scores were 0.94 for the top 1, top 3, and top 10 PC1 models”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSimilar results were observed when modeling PTH1R using either inactive or active templates, as well as LAT1 using either OF or IF templates.
“Similar results were observed when we modeled PTH1R using either inactive or active templates, as well as LAT1 using either OF or IF templates”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIncreasing the depth of subsampled MSAs eliminated misfolded models while also limiting the extent to which alternative conformations were sampled.
“Increasing the depth of subsampled MSAs had the desirable effect of eliminating these models, while also limiting the extent to which alternative conformations were sampled.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMCT1 was exclusively modeled by AF2 in either inward-facing (IF) or fully occluded conformations; over 99% of the models had TM scores ≥0.9 to the IF structure and <0.9 to the outward-facing (OF) structure, regardless of MSA depth.
“One target, MCT1, was exclusively modeled by AF2 in either inward-facing (IF) or fully occluded conformations; over 99% of the models had TM scores of ≥0.9 and <0.9 to the IF and outward-facing (OF) structures, respectively, regardless of MSA depth.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMisfolded models virtually never coclustered and could thus be identified and excluded from further analysis.
“misfolded models virtually never coclustered and could thus be identified and excluded from further analysis”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe inclusion of templates restored the correlation in MCT1 but contributed negligibly to those of PTH1R and LAT1.
“The inclusion of templates restored this correlation in MCT1 but contributed negligibly to those of PTH1R and LAT1”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the benchmark, the predicted conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average TM score 0.94).
“In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataProviding templates of homologs in exclusively OF conformations alongside MSAs of various sizes yielded accurate OF models only with MSAs containing 16-32 sequences, and these constituted a minor population in an ensemble dominated by IF models.
“Accurate OF models were obtained only with MSAs containing 16–32 sequences and constituted a minor population in an ensemble dominated by IF models.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Findingnone givenModels of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another.
“Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenModels of CCR5 were less biased toward the training set conformation, while deep MSAs reduced conformational diversity.
“while models of CCR5 were less biased toward the training set conformation, deep MSAs reduced conformational diversity.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe optimal MSA depth and choice of templates varied for each protein.
“because the optimal MSA depth and choice of templates varied for each protein”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationAF2 modeled the multidrug transporter LmrP (CASP14 target T1024) in multiple conformations, two of which were individually consistent with published experimental data.
“Upon examining the list of CASP14 targets and corresponding models, we found that AF2 modeled the multidrug transporter LmrP (target T1024) in multiple conformations, two of which were individually consistent with published experimental data”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Jumper et al., 2021b
Not checked in the free run.
- cites Del Alamo et al., 2021a
Not checked in the free run.
- cites Debruycker et al., 2020 source
Not checked in the free run.
The quote only partly states this claim.
Findingown dataReducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations for topologically diverse transporters and GPCRs absent from the AF2 training set.
“reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAlthough conformational diversity was demonstrated to a limited extent by the generation of occluded models of MurJ and PfMATE, none of the models observed adopted the alternative conformer.
“Although conformational diversity was demonstrated to a limited extent by the generation of occluded models of MurJ and PfMATE, none of the models observed adopted the alternative conformer.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataAccurate representatives of distinct conformers were generally obtainable with exhaustive sampling and could be identified by performing PCA and selecting models at the extreme positions of PC1.
“Accurate representatives of distinct conformers were generally obtainable with exhaustive sampling and could be identified by performing PCA and selecting models at the extreme positions of PC1.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Interpretationown dataTraditional methods may still be required to capture alternative conformers for transporters whose structures were used to train AF2.
“hinting at the possibility that traditional methods may still be required to capture alternative conformers”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Crawley et al., 2011
Not checked in the free run.
- cites Ollikainen et al., 2013
Not checked in the free run.
May combine two claims.
PremisecitationThe resulting models from traditional pipelines are unnaturally compact and frequently distorted, preventing critical questions about ligand and/or drug binding modes from being addressed.
“The resulting models are unnaturally compact and frequently distorted, preventing critical questions about ligand and/or drug binding modes from being addressed (Ovchinnikov et al., 2015; Nicoludis and Gaudet, 2018).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Ovchinnikov et al., 2015
Not checked in the free run.
- cites Nicoludis and Gaudet, 2018
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe physiological relevance of the proposed structural movements requires experimental validation.
“The physiological relevance of these proposed structural movements nonetheless requires experimental validation.”
Quote in the paperNot a substantial claim- Rests on an assumption: AF2 does not directly account for the lipid environment.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationFor GPCRs, ligand binding on the extracellular side triggers structural rearrangements on the intracellular side that initiate downstream signaling.
“For G-protein-coupled receptors (GPCRs), ligand binding on the extracellular side triggers structural rearrangements on the intracellular side that initiate downstream signaling (Wang et al., 2020; Gusach et al., 2020).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Wang et al., 2020
Not checked in the free run.
- cites Gusach et al., 2020
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationFor transporters, vectorial translocation of substrates is mediated by movements that open and close extra- and intracellular gates.
“the vectorial translocation of substrates by transporters is mediated by movements that open and close extra- and intracellular gates (Drew and Boudker, 2016; Kazmier et al., 2017).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Drew and Boudker, 2016
Not checked in the free run.
- cites Kazmier et al., 2017
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenAll templates were manually inspected, and those with structures similar to the desired conformation of interest were retained.
“All templates were manually inspected, and those with structures similar to the desired conformation of interest were retained.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationAlphaFold2 achieved remarkably accurate de novo structure prediction.
“A performance breakthrough was unveiled during CASP14 by AlphaFold2 (AF2) (Jumper et al., 2021a; Tunyasuvunakool et al., 2021; Pereira et al., 2021), which achieved remarkably accurate de novo structure prediction.”
Quote in the paperSubstantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenMSA subsampling was carried out randomly by AF2.
“MSA subsampling was carried out randomly by AF2”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenNo manual intervention was carried out to fine-tune the composition of the MSAs used for prediction.
“No manual intervention was carried out to fine-tune the composition of these alignments.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationThe structural model of GPR114/ADGRG5 deposited in the AF2 database likely depicts an active conformation that diverges from the structure of the homolog GPR97.
“The structural model deposited in the AF2 database, which likely depicts an active conformation that diverges from the structure of the homolog GPR97 (Ping et al., 2021), could be recapitulated by using deep MSAs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ping et al., 2021
Not checked in the free run.
The paper relies on this without support.
AlphaFold2 was designed to predict static structures of proteins.
“Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity.”
The paper relies on this without support.
The experimental structures used as references are accurate representations of the native conformations.
“we used the active conformation of FZD7 and the inactive conformation of the nearly identical FZD4”
The paper relies on this without support.
The two experimental structures of each target bracket the relevant conformational ensemble.
“In our benchmark, these conformations spanned the range between two experimental structures of interest”
The paper relies on this without support.
AF2 does not directly account for the lipid environment.
“AF2 does not directly account for the lipid environment”
The paper relies on this without support.
The structure of GPR114/ADGRG5 has not been experimentally determined.
“GPR114/ADGRG5, a class B2 adhesion GPCR whose structure has not been experimentally determined.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
del Alamo et al modified AF2 by reducing recycles and limiting MSA depth to sample functionally relevant alternative conformations of transporters and GPCRs.
“del Alamo et al modified the AF2 pipeline by reducing the number of recycles and restricting the depth of randomly subsampled MSAs to sample functionally relevant alternative conformations of transporters and G-protein-coupled receptors46”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [46]
Initial efforts to enhance conformational sampling have involved altering the number of sequences used to generate shallower MSA representations, masking coevolutionary information from MSAs, and splitting conflicting coevolutionary signals by clustering MSAs.
“Initial efforts to enhance sampling of different conformations have involved altering the number of sequences used to generate shallower MSA representations, masking coevolutionary information provided by MSAs, and splitting conflicting coevolutionary signals by clustering MSAs46,47,105.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [46]
Subsampling the input MSA enables AF2 to predict known conformational changes of transporters.
“Despite these demonstrations of shortcomings, it was shown that subsampling the input MSA enables AF2 to predict known conformational changes of transporters14.”
From Predicting multiple conformations via sequence clustering and AlphaFold2 (2024) · cites this paper as [14]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
FZD7 does not meet the stated two-conformation selection criterion
MinorThe paper says the benchmark is restricted to proteins experimentally determined in two or more conformations. For FZD7, it instead uses an active FZD7 structure and an inactive structure of the related protein FZD4.
“Therefore, by necessity, this study is restricted to proteins whose structures were absent from the PDB before this date and have since been determined at atomic resolution in two or more conformations.”
“to serve as points of comparison, we used the active conformation of FZD7 and the inactive conformation of the nearly identical FZD4, Yang et al., 2018”
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288 · cited 2× · see citing works
“The following templates were used: OF MCT1 with FucB (PDB 3O7Pa and 3O7Qa, 18% sequence identity calculated using Needleman Wunsch Needleman and Wunsch, 1970 ; Madeira et al., 2019 ); OF LAT1 with AdiC (3OB6a and 5J4Ib, 22%); IF LAT1 with b(0,+)AT1 (6LI9, 45%), KCC3 (6Y5Ra, 12%), GkApcT (6F34a,…”
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · cited 1× · see citing works
“0.1 and a modified version of ColabFold ( Mirdita et al., 2021 ) that is available at https://github.com/delalamo/af2_conformations, ( Del Alamo, 2021b copy archived at swh:1:rev:d60db86886186e80622deaa91045caccaf4103d3).”
BackgroundInfluentialPTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data.
Roe, D., & Cheatham, T. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400341p · Cited by 6,969 · cited 1× · see citing works
“PCA and RMSF calculations were carried out in CPPTRAJ ( Roe and Cheatham, 2013 ).”
MethodMMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840 · cited 1× · see citing works
“First, all MSAs were obtained using the MMSeqs2 server ( Steinegger and Söding, 2017 ), rather than the default databases.”
The EMBL-EBI search and sequence analysis tools APIs in 2019
Madeira, F., Park, Y. M., Lee, J., Buso, N., Gur, T., Madhusoodanan, N., Basutkar, P., Tivey, A. R. N., Potter, S. C., Finn, R. D., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz268 · Cited by 4,167 · cited 1× · see citing works
“…templates were used: OF MCT1 with FucB (PDB 3O7Pa and 3O7Qa, 18% sequence identity calculated using Needleman Wunsch Needleman and Wunsch, 1970 ; Madeira et al., 2019 ); OF LAT1 with AdiC (3OB6a and 5J4Ib, 22%); IF LAT1 with b(0,+)AT1 (6LI9, 45%), KCC3 (6Y5Ra, 12%), GkApcT (6F34a, 24%), NKCC1…”
MethodTM-align: a protein structure alignment algorithm based on the TM-score
Zhang, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki524 · Cited by 3,774 · cited 3× · see citing works
“…the targets, each model’s similarity to the experimental structures was quantified using template modeling (TM) score ( Zhang and Skolnick, 2004 ; Zhang and Skolnick, 2005 ; Xu and Zhang, 2010 ), a metric ranging from 0 to 1, which indicates how well the two backbone (C α atoms) structures…”
MethodHighly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 1× · see citing works
“A performance breakthrough was unveiled during CASP14 by AlphaFold2 (AF2) ( Jumper et al., 2021a ; Tunyasuvunakool et al., 2021 ; Pereira et al., 2021 ), which achieved remarkably accurate de novo structure prediction.”
Scoring function for automated assessment of protein structure template quality
Zhang, Y., & Skolnick, J. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20264 · Cited by 2,392 · cited 1× · see citing works
“For the targets, each model’s similarity to the experimental structures was quantified using template modeling (TM) score ( Zhang and Skolnick, 2004 ; Zhang and Skolnick, 2005 ; Xu and Zhang, 2010 ), a metric ranging from 0 to 1, which indicates how well the two backbone (C α atoms) structures…”
MethodThe role of dynamic conformational ensembles in biomolecular recognition
Boehr, D. D., Nussinov, R., & Wright, P. E. (2009) · Nature Chemical Biology · doi:10.1038/nchembio.232 · Cited by 2,008 · cited 1× · see citing works
“Dynamic interconversion between multiple conformations underpins the functions of integral membrane proteins in all domains of life ( Campbell et al., 2016 ; Cournia et al., 2015 ; Shaw et al., 2010 ; Boehr et al., 2009 ).”
BackgroundAtomic-level characterization of the structural dynamics of proteins
Shaw, D. E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R. O., Eastwood, M. P., Bank, J. A., Jumper, J. M., Salmon, J. K., Shan, Y., et al. (2010) · Science · doi:10.1126/science.1187409 · Cited by 1,923 · see citing works
Perineuronal nets stabilize the grid cell network
Christensen, A. C., Lensjø, K. K., Lepperød, M. E., Dragly, S.-A., Sutterud, H., Blackstad, J. S., Fyhn, M., & Hafting, T. (2021) · Nature Communications · doi:10.1038/s41467-020-20241-w · Cited by 1,865 · see citing works
Khalifa, A. A. Z., Ichikawa, M., Dai, D., Kubo, S., Black, C. S., Peri, K., McAlear, T. S., Veyron, S., Yang, S. K., Vargas, J., et al. (2020) · eLife · doi:10.7554/elife.52760 · Cited by 1,676 · see citing works
How significant is a protein structure similarity with TM-score = 0.5?
Xu, J., & Zhang, Y. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq066 · Cited by 1,142 · cited 1× · see citing works
“…similarity to the experimental structures was quantified using template modeling (TM) score ( Zhang and Skolnick, 2004 ; Zhang and Skolnick, 2005 ; Xu and Zhang, 2010 ), a metric ranging from 0 to 1, which indicates how well the two backbone (C α atoms) structures superimpose over one another…”
MethodMariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138 · cited 1× · see citing works
“In contrast with a recent preprint ( Saldaño et al., 2021 ), the predicted flexibility values failed to correlate with their pLDDT values, which reflect the confidence of the AF2 prediction of each residue’s local environment ( Mariani et al., 2013 ).”
ResultA structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · see citing works
X-ray structures and mechanism of the human serotonin transporter
Coleman, J. A., Green, E. M., & Gouaux, E. (2016) · Nature · doi:10.1038/nature17629 · Cited by 672 · cited 1× · see citing works
“…of which was included in the AF2 training set: the class A GPCR CCR5 ( Zheng et al., 2017 ; Zhang et al., 2021 ), the serotonin transporter SERT ( Coleman et al., 2016 ; Coleman et al., 2019 ), the multidrug transporter PfMATE ( Tanaka et al., 2013 ; Zakrzewska et al., 2019 ), and the lipid…”
BackgroundShared Molecular Mechanisms of Membrane Transporters
Drew, D., & Boudker, O. (2016) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-060815-014520 · Cited by 506 · cited 2× · see citing works
“We selected five transporters that not only met these criteria but also reflected a range of transport mechanisms characterized in the literature ( Drew and Boudker, 2016 ), including rocking-bundle (LAT1, Yan et al., 2019 ; Yan et al., 2021 ; ZnT8, Xue et al., 2020 ), rocker-switch (MCT1, Wang et…”
BackgroundApplying and improving AlphaFold at CASP14
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26257 · Cited by 390 · cited 4× · see citing works
“A performance breakthrough was unveiled during CASP14 by AlphaFold2 (AF2) ( Jumper et al., 2021a ; Tunyasuvunakool et al., 2021 ; Pereira et al., 2021 ), which achieved remarkably accurate de novo structure prediction.”
BackgroundMethodResultInfluentialStructure of the human LAT1–4F2hc heteromeric amino acid transporter complex
Yan, R., Zhao, X., Lei, J., & Zhou, Q. (2019) · Nature · doi:10.1038/s41586-019-1011-z · Cited by 358 · cited 1× · see citing works
“…but also reflected a range of transport mechanisms characterized in the literature ( Drew and Boudker, 2016 ), including rocking-bundle (LAT1, Yan et al., 2019 ; Yan et al., 2021 ; ZnT8, Xue et al., 2020 ), rocker-switch (MCT1, Wang et al., 2021 ; STP10, Bavnhøj et al., 2021 ), and elevator…”
BackgroundStructural basis of human monocarboxylate transporter 1 inhibition by anti-cancer drug candidates
Wang, N., Jiang, X. J., Zhang, S., Zhu, A., Yuan, Y., Xu, H., Lei, J., & Yan, C. (2020) · Cell · doi:10.1016/j.cell.2020.11.043 · Cited by 346 · cited 1× · see citing works
“…characterized in the literature ( Drew and Boudker, 2016 ), including rocking-bundle (LAT1, Yan et al., 2019 ; Yan et al., 2021 ; ZnT8, Xue et al., 2020 ), rocker-switch (MCT1, Wang et al., 2021 ; STP10, Bavnhøj et al., 2021 ), and elevator (ASCT2, Garibsingh et al., 2021 ; Garaeva et al., 2019 ).”
BackgroundUsing AlphaFold to predict the impact of single mutations on protein stability and function
Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2023) · PLOS One · doi:10.1371/journal.pone.0282689 · Cited by 322 · cited 1× · see citing works
“Several preprints have provided evidence that AF2, despite its accuracy, likely does not learn the energy landscapes underpinning protein folding and function ( Saldaño et al., 2021 ; Pak et al., 2021 ; Akdel et al., 2021 ).”
BackgroundUsing AlphaFold to predict the impact of single mutations on protein stability and function
Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2021) · doi:10.1101/2021.09.19.460937 · Cited by 322 · see citing works
High‐accuracy protein structure prediction in <scp>CASP14</scp>
Pereira, J., Simpkin, A. J., Hartmann, M. D., Rigden, D. J., Keegan, R. M., & Lupas, A. N. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26171 · Cited by 314 · cited 1× · see citing works
“A performance breakthrough was unveiled during CASP14 by AlphaFold2 (AF2) ( Jumper et al., 2021a ; Tunyasuvunakool et al., 2021 ; Pereira et al., 2021 ), which achieved remarkably accurate de novo structure prediction.”
The role of protein dynamics in the evolution of new enzyme function
Campbell, E., Kaltenbach, M., Correy, G. J., Carr, P. D., Porebski, B. T., Livingstone, E. K., Afriat-Jurnou, L., Buckle, A. M., Weik, M., Hollfelder, F., et al. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.2175 · Cited by 311 · cited 1× · see citing works
“Dynamic interconversion between multiple conformations underpins the functions of integral membrane proteins in all domains of life ( Campbell et al., 2016 ; Cournia et al., 2015 ; Shaw et al., 2010 ; Boehr et al., 2009 ).”
BackgroundMuller, M. P., Jiang, T., Sun, C., Lihan, M., Pant, S., Mahinthichaichan, P., Trifan, A., & Tajkhorshid, E. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00608 · Cited by 282 · cited 1× · see citing works
“Moreover, AF2 does not directly account for the lipid environment, which has been experimentally shown to bias the conformational equilibria of membrane proteins ( Martens et al., 2018 ; Muller et al., 2019 ; Immadisetty et al., 2019 ).”
BackgroundSerotonin transporter–ibogaine complexes illuminate mechanisms of inhibition and transport
Coleman, J. A., Yang, D., Zhao, Z., Wen, P., Yoshioka, C., Tajkhorshid, E., & Gouaux, E. (2019) · Nature · doi:10.1038/s41586-019-1135-1 · Cited by 268 · cited 1× · see citing works
“…in the AF2 training set: the class A GPCR CCR5 ( Zheng et al., 2017 ; Zhang et al., 2021 ), the serotonin transporter SERT ( Coleman et al., 2016 ; Coleman et al., 2019 ), the multidrug transporter PfMATE ( Tanaka et al., 2013 ; Zakrzewska et al., 2019 ), and the lipid flippase MurJ ( Kuk et al.,…”
BackgroundStructure and dynamics of the active human parathyroid hormone receptor-1
Zhao, L.-H., Ma, S., Sutkeviciute, I., Shen, D.-D., Zhou, X. E., de Waal, P. W., Li, C.-Y., Kang, Y., Clark, L. J., Jean-Alphonse, F. G., et al. (2019) · Science · doi:10.1126/science.aav7942 · Cited by 256 · cited 2× · see citing works
“The exception, PTH1R, was determined in a partially inactive and active conformation ( Zhao et al., 2019 ), suggesting that models extending beyond the former state along PC1 may represent the fully inactive conformation.”
BackgroundMethodStructural basis for the drug extrusion mechanism by a MATE multidrug transporter
Tanaka, Y., Hipolito, C. J., Maturana, A. D., Ito, K., Kuroda, T., Higuchi, T., Katoh, T., KATO, H. E., Hattori, M., Kumazaki, K., et al. (2013) · Nature · doi:10.1038/nature12014 · Cited by 250 · cited 1× · see citing works
“…the class A GPCR CCR5 ( Zheng et al., 2017 ; Zhang et al., 2021 ), the serotonin transporter SERT ( Coleman et al., 2016 ; Coleman et al., 2019 ), the multidrug transporter PfMATE ( Tanaka et al., 2013 ; Zakrzewska et al., 2019 ), and the lipid flippase MurJ ( Kuk et al., 2017 ; Kuk et al., 2019 ).”
BackgroundMembrane Protein Structure, Function, and Dynamics: a Perspective from Experiments and Theory
Cournia, Z., Allen, T. W., Andricioaei, I., Antonny, B., Baum, D., Brannigan, G., Buchete, N.-V., Deckman, J. T., Delemotte, L., del Val, C., et al. (2015) · The Journal of Membrane Biology · doi:10.1007/s00232-015-9802-0 · Cited by 235 · cited 1× · see citing works
“Dynamic interconversion between multiple conformations underpins the functions of integral membrane proteins in all domains of life ( Campbell et al., 2016 ; Cournia et al., 2015 ; Shaw et al., 2010 ; Boehr et al., 2009 ).”
BackgroundLarge-scale determination of previously unsolved protein structures using evolutionary information
Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V., & Baker, D. (2015) · eLife · doi:10.7554/elife.09248 · Cited by 222 · cited 1× · see citing works
“The resulting models are unnaturally compact and frequently distorted, preventing critical questions about ligand and/or drug binding modes from being addressed ( Ovchinnikov et al., 2015 ; Nicoludis and Gaudet, 2018 ).”
BackgroundImpact of protein conformational diversity on AlphaFold predictions
Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., García Melani, A., Novomisky Nechcoff, J., Salas, M. N., et al. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac202 · Cited by 196 · see citing works
Impact of protein conformational diversity on AlphaFold predictions
Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., Melani, A. G., Novomisky Nechcoff, J., Salas, M. N., et al. (2021) · doi:10.1101/2021.10.27.466189 · Cited by 196 · see citing works
Structures of the glucocorticoid-bound adhesion receptor GPR97–Go complex
Ping, Y.-Q., Mao, C., Xiao, P., Zhao, R.-J., Jiang, Y., Yang, Z., An, W., Shen, D., Yang, F., Zhang, H., et al. (2021) · Nature · doi:10.1038/s41586-020-03083-w · Cited by 181 · cited 1× · see citing works
“The structural model deposited in the AF2 database, which likely depicts an active conformation that diverges from the structure of the homolog GPR97 ( Ping et al., 2021 ), could be recapitulated by using deep MSAs.”
BackgroundZheng, Y., Han, G. W., Abagyan, R. A., et al. (2017) · Immunity · doi:10.1016/j.immuni.2017.05.002 · Cited by 172 · cited 4× · see citing works
“We investigated this question using four membrane proteins with two experimentally determined conformations, at least one of which was included in the AF2 training set: the class A GPCR CCR5 (Zheng et al., 2017; Zhang et al., 2021), the serotonin transporter SERT (Coleman et al.”
BackgroundInfluentialDirect protein-lipid interactions shape the conformational landscape of secondary transporters
Martens, C., Shekhar, M., Borysik, A. J., Lau, A. M., Reading, E., Tajkhorshid, E., Booth, P. J., & Politis, A. (2018) · Nature Communications · doi:10.1038/s41467-018-06704-1 · Cited by 134 · cited 1× · see citing works
“Moreover, AF2 does not directly account for the lipid environment, which has been experimentally shown to bias the conformational equilibria of membrane proteins ( Martens et al., 2018 ; Muller et al., 2019 ; Immadisetty et al., 2019 ).”
BackgroundA structural biology community assessment of AlphaFold 2 applications
Akdel, M., Pires, D. E. V., Porta Pardo, E., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2021) · doi:10.1101/2021.09.26.461876 · Cited by 122 · see citing works
Ehrenmann, J., Schöppe, J., Klenk, C., Rappas, M., Kummer, L., Dore, A. S., & Plückthun, A. (2018) · Nature Structural & Molecular Biology · doi:10.1038/s41594-018-0151-4 · Cited by 118 · cited 1× · see citing works
“We also included three GPCRs, which were distributed across classes A (CGRPR, Liang et al., 2020 ; Josephs et al., 2021 ), B1 (PTH1R, Ehrenmann et al., 2018 ; Zhao et al., 2019 ), and F (FZD7, Xu et al., 2021 ; to serve as points of comparison, we used the active conformation of FZD7 and the…”
MethodLipids modulate the conformational dynamics of a secondary multidrug transporter
Martens, C., Stein, R. A., Masureel, M., Roth, A., Mishra, S., Dawaliby, R., Konijnenberg, A., Sobott, F., Govaerts, C., & Mchaourab, H. S. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3262 · Cited by 117 · cited 1× · see citing works
“…we found that AF2 modeled the multidrug transporter LmrP (target T1024) in multiple conformations, two of which were individually consistent with published experimental data ( Jumper et al., 2021b ; Del Alamo et al., 2021a ; Debruycker et al., 2020 ; Martens et al., 2016 ; Masureel et al., 2014 ).”
ResultSelf-citationCrystal structure of the Frizzled 4 receptor in a ligand-free state
Yang, S., Wu, Y., Xu, T.-H., de Waal, P. W., He, Y., Pu, M., Chen, Y., DeBruine, Z. J., Zhang, B., Zaidi, S. A., et al. (2018) · Nature · doi:10.1038/s41586-018-0447-x · Cited by 113 · cited 2× · see citing works
“We also included three GPCRs, which were distributed across classes A (CGRPR, Liang et al., 2020 ; Josephs et al., 2021 ), B1 (PTH1R, Ehrenmann et al., 2018 ; Zhao et al., 2019 ), and F (FZD7, Xu et al., 2021 ; to serve as points of comparison, we used the active conformation of FZD7 and the inactive conformation of t…”
MethodStructural basis for chemokine recognition and receptor activation of chemokine receptor CCR5
Zhang, H., Chen, K., Tan, Q., Shao, Q., Han, S., Zhang, C., Yi, C., Chu, X., Zhu, Y., Xu, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24438-5 · Cited by 108 · cited 1× · see citing works
“…experimentally determined conformations, at least one of which was included in the AF2 training set: the class A GPCR CCR5 ( Zheng et al., 2017 ; Zhang et al., 2021 ), the serotonin transporter SERT ( Coleman et al., 2016 ; Coleman et al., 2019 ), the multidrug transporter PfMATE ( Tanaka et…”
BackgroundA one-gate elevator mechanism for the human neutral amino acid transporter ASCT2
Garaeva, A. A., Guskov, A., Slotboom, D. J., & Paulino, C. (2019) · Nature Communications · doi:10.1038/s41467-019-11363-x · Cited by 103 · cited 1× · see citing works
“…characterized in the literature ( Drew and Boudker, 2016 ), including rocking-bundle (LAT1, Yan et al., 2019 ; Yan et al., 2021 ; ZnT8, Xue et al., 2020 ), rocker-switch (MCT1, Wang et al., 2021 ; STP10, Bavnhøj et al., 2021 ), and elevator (ASCT2, Garibsingh et al., 2021 ; Garaeva et al., 2019 ).”
BackgroundMechanism of substrate transport and inhibition of the human LAT1-4F2hc amino acid transporter
Yan, R., Li, Y., Müller, J., Zhang, Y., Singer, S. A., Xia, L., Zhong, X., Gertsch, J., Altmann, K., & Zhou, Q. (2021) · Cell Discovery · doi:10.1038/s41421-021-00247-4 · Cited by 103 · cited 1× · see citing works
“…a range of transport mechanisms characterized in the literature ( Drew and Boudker, 2016 ), including rocking-bundle (LAT1, Yan et al., 2019 ; Yan et al., 2021 ; ZnT8, Xue et al., 2020 ), rocker-switch (MCT1, Wang et al., 2021 ; STP10, Bavnhøj et al., 2021 ), and elevator (ASCT2, Garibsingh…”
BackgroundCrystal structure of the MOP flippase MurJ in an inward-facing conformation
Kuk, A. C. Y., Mashalidis, E. H., & Lee, S. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3346 · Cited by 91 · cited 1× · see citing works
“…the class A GPCR CCR5 ( Zheng et al., 2017 ; Zhang et al., 2021 ), the serotonin transporter SERT ( Coleman et al., 2016 ; Coleman et al., 2019 ), the multidrug transporter PfMATE ( Tanaka et al., 2013 ; Zakrzewska et al., 2019 ), and the lipid flippase MurJ ( Kuk et al., 2017 ; Kuk et al., 2019 ).”
BackgroundStructure and dynamics of the CGRP receptor in apo and peptide-bound forms
Josephs, T. M., Belousoff, M. J., Liang, Y.-L., Piper, S. J., Cao, J., Garama, D. J., Leach, K., Gregory, K. J., Christopoulos, A., Hay, D. L., et al. (2021) · Science · doi:10.1126/science.abf7258 · Cited by 89 · cited 1× · see citing works
“We also included three GPCRs, which were distributed across classes A (CGRPR, Liang et al., 2020 ; Josephs et al., 2021 ), B1 (PTH1R, Ehrenmann et al., 2018 ; Zhao et al., 2019 ), and F (FZD7, Xu et al., 2021 ; to serve as points of comparison, we used the active conformation of FZD7 and the…”
MethodLiang, Y.-L., Belousoff, M. J., Fletcher, M. M., Zhang, X., Khoshouei, M., Deganutti, G., Koole, C., Furness, S. G. B., Miller, L. J., Hay, D. L., et al. (2020) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.9b00080 · Cited by 87 · see citing works
Kazmier, K. N., Claxton, D. P., & Mchaourab, H. S. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.12.006 · Cited by 79 · cited 1× · see citing works
“For example, the vectorial translocation of substrates by transporters is mediated by movements that open and close extra-and intracellular gates ( Drew and Boudker, 2016 ; Kazmier et al., 2017 ).”
BackgroundSelf-citationCryo-EM structures of human ZnT8 in both outward- and inward-facing conformations
Xue, J., Xie, T., Zeng, W., Jiang, Y., & Bai, X.-C. (2020) · eLife · doi:10.7554/elife.58823 · Cited by 78 · cited 2× · see citing works
“…mechanisms characterized in the literature ( Drew and Boudker, 2016 ), including rocking-bundle (LAT1, Yan et al., 2019 ; Yan et al., 2021 ; ZnT8, Xue et al., 2020 ), rocker-switch (MCT1, Wang et al., 2021 ; STP10, Bavnhøj et al., 2021 ), and elevator (ASCT2, Garibsingh et al., 2021 ; Garaeva et…”
BackgroundVisualizing conformation transitions of the Lipid II flippase MurJ
Kuk, A. C. Y., Hao, A., Guan, Z., & Lee, S. (2019) · Nature Communications · doi:10.1038/s41467-019-09658-0 · Cited by 76 · cited 1× · see citing works
“…the class A GPCR CCR5 ( Zheng et al., 2017 ; Zhang et al., 2021 ), the serotonin transporter SERT ( Coleman et al., 2016 ; Coleman et al., 2019 ), the multidrug transporter PfMATE ( Tanaka et al., 2013 ; Zakrzewska et al., 2019 ), and the lipid flippase MurJ ( Kuk et al., 2017 ; Kuk et al., 2019 ).”
BackgroundStructural features of activated GPCR signaling complexes
Wang, J., Hua, T., & Liu, Z. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.04.008 · Cited by 73 · cited 1× · see citing works
“For G- protein-coupled receptors (GPCRs), ligand binding on the extra-cellular side triggers structural rearrangements on the intracellular side that initiate downstream signaling ( Wang et al., 2020 ; Gusach et al., 2020 ).”
BackgroundProtonation drives the conformational switch in the multidrug transporter LmrP
Masureel, M., Martens, C., Stein, R. A., Mishra, S., Ruysschaert, J., Mchaourab, H. S., & Govaerts, C. (2013) · Nature Chemical Biology · doi:10.1038/nchembio.1408 · Cited by 73 · cited 1× · see citing works
“…we found that AF2 modeled the multidrug transporter LmrP (target T1024) in multiple conformations, two of which were individually consistent with published experimental data ( Jumper et al., 2021b ; Del Alamo et al., 2021a ; Debruycker et al., 2020 ; Martens et al., 2016 ; Masureel et al., 2014 ).”
ResultSelf-citationEhrenmann, J., Schöppe, J., Klenk, C., Rappas, M., Kummer, L., Dore, A. S., & Plückthun, A. (2018) · Worldwide Protein Data Bank · doi:10.2210/pdb6fj3/pdb · Cited by 72 · see citing works
An embedded lipid in the multidrug transporter LmrP suggests a mechanism for polyspecificity
Debruycker, V., Hutchin, A., Masureel, M., Ficici, E., Martens, C., Legrand, P., Stein, R. A., Mchaourab, H. S., Faraldo-Gómez, J. D., Remaut, H., et al. (2020) · Nature Structural & Molecular Biology · doi:10.1038/s41594-020-0464-y · Cited by 69 · cited 1× · see citing works
“…we found that AF2 modeled the multidrug transporter LmrP (target T1024) in multiple conformations, two of which were individually consistent with published experimental data ( Jumper et al., 2021b ; Del Alamo et al., 2021a ; Debruycker et al., 2020 ; Martens et al., 2016 ; Masureel et al., 2014 ).”
ResultSelf-citationBavnhøj, L., Paulsen, P. A., Flores‐Canales, J. C., Schiøtt, B., & Pedersen, B. P. (2021) · Nature Plants · doi:10.1038/s41477-021-00992-0 · Cited by 64 · cited 2× · see citing works
“…characterized in the literature ( Drew and Boudker, 2016 ), including rocking-bundle (LAT1, Yan et al., 2019 ; Yan et al., 2021 ; ZnT8, Xue et al., 2020 ), rocker-switch (MCT1, Wang et al., 2021 ; STP10, Bavnhøj et al., 2021 ), and elevator (ASCT2, Garibsingh et al., 2021 ; Garaeva et al., 2019 ).”
BackgroundBeyond structure: emerging approaches to study GPCR dynamics
Gusach, A., Maslov, I., Luginina, A. P., Borshchevskiy, V. I., Mishin, A., & Cherezov, V. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.03.004 · Cited by 60 · cited 1× · see citing works
“For G- protein-coupled receptors (GPCRs), ligand binding on the extra-cellular side triggers structural rearrangements on the intracellular side that initiate downstream signaling ( Wang et al., 2020 ; Gusach et al., 2020 ).”
BackgroundRational design of ASCT2 inhibitors using an integrated experimental-computational approach
Garibsingh, R. A., Ndaru, E., Garaeva, A. A., Shi, Y., Zielewicz, L. J., Zakrepine, P., Bonomi, M., Slotboom, D. J., Paulino, C., Grewer, C. T., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2104093118 · Cited by 56 · cited 1× · see citing works
“…characterized in the literature ( Drew and Boudker, 2016 ), including rocking-bundle (LAT1, Yan et al., 2019 ; Yan et al., 2021 ; ZnT8, Xue et al., 2020 ), rocker-switch (MCT1, Wang et al., 2021 ; STP10, Bavnhøj et al., 2021 ), and elevator (ASCT2, Garibsingh et al., 2021 ; Garaeva et al., 2019 ).”
BackgroundInward-facing conformation of a multidrug resistance MATE family transporter
Zakrzewska, S., Mehdipour, A. R., Malviya, V. N., Nonaka, T., Koepke, J., Muenke, C., Hausner, W., Hummer, G., Safarian, S., & Michel, H. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1904210116 · Cited by 49 · cited 1× · see citing works
“…the class A GPCR CCR5 ( Zheng et al., 2017 ; Zhang et al., 2021 ), the serotonin transporter SERT ( Coleman et al., 2016 ; Coleman et al., 2019 ), the multidrug transporter PfMATE ( Tanaka et al., 2013 ; Zakrzewska et al., 2019 ), and the lipid flippase MurJ ( Kuk et al., 2017 ; Kuk et al., 2019 ).”
BackgroundCryo-EM structure of constitutively active human Frizzled 7 in complex with heterotrimeric Gs
Xu, L., Chen, B., Schihada, H., et al. (2021) · Cell Research · doi:10.1038/s41422-021-00525-6 · Cited by 48 · see citing works
Flexible Backbone Sampling Methods to Model and Design Protein Alternative Conformations
Ollikainen, N., Smith, C. A., Fraser, J. S., & Kortemme, T. (2013) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-394292-0.00004-7 · Cited by 44 · cited 1× · see citing works
“Moreover, this approach showed limited success when applied to transporters whose structures were used to train AF2, hinting at the possibility that traditional methods may still be required to capture alternative conformers ( Crawley et al., 2011 ; Ollikainen et al., 2013 ).”
MethodLipid-Dependent Alternating Access Mechanism of a Bacterial Multidrug ABC Exporter
Immadisetty, K., Hettige, J., & Moradi, M. (2019) · ACS Central Science · doi:10.1021/acscentsci.8b00480 · Cited by 38 · cited 1× · see citing works
“Moreover, AF2 does not directly account for the lipid environment, which has been experimentally shown to bias the conformational equilibria of membrane proteins ( Martens et al., 2018 ; Muller et al., 2019 ; Immadisetty et al., 2019 ).”
BackgroundApplications of sequence coevolution in membrane protein biochemistry
Nicoludis, J. M., & Gaudet, R. (2017) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2017.10.004 · Cited by 31 · cited 1× · see citing works
“The resulting models are unnaturally compact and frequently distorted, preventing critical questions about ligand and/or drug binding modes from being addressed ( Ovchinnikov et al., 2015 ; Nicoludis and Gaudet, 2018 ).”
BackgroundPrinciples of Alternating Access in Multidrug and Toxin Extrusion (MATE) Transporters
Claxton, D. P., Jagessar, K. L., & Mchaourab, H. S. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.166959 · Cited by 29 · cited 1× · see citing works
“This divergence in performance may stem from the composition of the AF2 training set, which featured the structures of many active GPCRs but no structures, for example, of IF MATEs ( Claxton et al., 2021 ).”
BackgroundAlphaFold2 predicts the inward‐facing conformation of the multidrug transporter LmrP
del Alamo, D., Govaerts, C., & Mchaourab, H. S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26138 · Cited by 29 · cited 1× · see citing works
“…we found that AF2 modeled the multidrug transporter LmrP (target T1024) in multiple conformations, two of which were individually consistent with published experimental data ( Jumper et al., 2021b ; Del Alamo et al., 2021a ; Debruycker et al., 2020 ; Martens et al., 2016 ; Masureel et al., 2014 ).”
ResultSelf-citationCrawley, S. W., Gharaei, M. S., Ye, Q., Yang, Y., Raveh, B., London, N., Schueler‐Furman, O., Jia, Z., & Côté, G. P. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.177014 · Cited by 23 · cited 1× · see citing works
“Moreover, this approach showed limited success when applied to transporters whose structures were used to train AF2, hinting at the possibility that traditional methods may still be required to capture alternative conformers ( Crawley et al., 2011 ; Ollikainen et al., 2013 ).”
Method
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- Del Alamo D. 2021b. Prediction of alternative conformations using AlphaFold 2. Software Heritage. swh:1:rev:d60db86886186e80622deaa91045caccaf4103d3. https://archive.softwareheritage.org/swh:1:dir:732ab369a1b141030fbc5728d9ab3f50e222b3ea;origin=https://github.com/delalamo/af2_conformations;visit=swh:1:snp:cb0ae8b45df2c5f548867364bbfe52debb482bda;anchor=swh:1:rev:d60db86886186e80622deaa91045caccaf4103d3.
Cited by
Sorted by citations
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works
AlphaFold2 and its applications in the fields of biology and medicine
Yang, Z., Zeng, X., Zhao, Y. C., & Chen, R. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01381-z · Cited by 606 · see citing works
Predicting multiple conformations via sequence clustering and AlphaFold2
Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., & Kern, D. (2024) · Nature · doi:10.1038/s41586-023-06832-9 · Cited by 545 · see citing works
“Despite these demonstrations of shortcomings, it was shown that subsampling the input MSA enables AF2 to predict known conformational changes of transporters 14 .”
InfluentialHigh-resolution <i>de novo</i> structure prediction from primary sequence
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al. (2022) · doi:10.1101/2022.07.21.500999 · Cited by 392 · see citing works
“…lines will continue to enable a wide spectrum of protein science applications, such as multi-state conformational sampling, where AlphaFold2 has had some success to regions for which predictions are challenging ( 41 ), variant effect prediction, protein-protein interactions and protein docking.”
BackgroundScalable emulation of protein equilibrium ensembles with generative deep learning
Lewis, S., Hempel, T., Jiménez-Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., Satorras, V. G., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2025) · Science · doi:10.1126/science.adv9817 · Cited by 335 · see citing works
SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2
Stein, R. A., & Mchaourab, H. S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010483 · Cited by 258 · see citing works
“To enable direct comparison, the twelve membrane proteins were the same as those used in a previous study exploring conformational sampling with AF2 [7].”
BackgroundMethodResultInfluentialSelf-citationMulti‐state modeling of G‐protein coupled receptors at experimental accuracy
Heo, L., & Feig, M. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26382 · Cited by 215 · see citing works
<scp>AlphaFold2</scp> fails to predict protein fold switching
Chakravarty, D., & Porter, L. L. (2022) · Protein Science · doi:10.1002/pro.4353 · Cited by 193 · see citing works
Modeling conformational states of proteins with AlphaFold
Sala, D., Engelberger, F., Mchaourab, H. S., & Meiler, J. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102645 · Cited by 182 · see citing works
Self-citationHigh-throughput prediction of protein conformational distributions with subsampled AlphaFold2
Monteiro da Silva, G., Cui, J. Y., Dalgarno, D. C., Lisi, G. P., & Rubenstein, B. M. (2024) · Nature Communications · doi:10.1038/s41467-024-46715-9 · Cited by 196 · see citing works
Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
Stahl, K., Graziadei, A., Dau, T., Brock, O., & Rappsilber, J. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01704-z · Cited by 145 · see citing works
“Unlike other methods that rely on manipulation of the MSA 25,31,32 , AlphaLink uses experimental information to drive the prediction of multiple conformational states.”
MethodState-specific protein–ligand complex structure prediction with a multiscale deep generative model
Qiao, Z., Nie, W., Vahdat, A., Miller, T. F., & Anandkumar, A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00792-z · Cited by 178 · see citing works
“During model training, an MSA bootstrapping technique 73 was applied to sample diverse template structures from AF2 as auxiliary model inputs complementary to alternative experimentally determined conformations of the same proteins retrieved from the PDB (‘Datasets and training summary’).”
Easy and accurate protein structure prediction using ColabFold
Kim, G., Lee, S., Karin, E. L., Kim, H., Moriwaki, Y., Ovchinnikov, S., Steinegger, M., & Mirdita, M. (2024) · Nature Protocols · doi:10.1038/s41596-024-01060-5 · Cited by 167 · see citing works
“Although AF2 was designed for predicting static structures, del Alamo et al. 29 have recently shown that certain manipulations to its input can, in some cases, tweak it to predict a landscape of structures spanning several conformations.”
InfluentialDisProt in 2024: improving function annotation of intrinsically disordered proteins
Aspromonte, M. C., Nugnes, M. V., Quaglia, F., Bouharoua, A., Sagris, V., Promponas, V. J., Chasapi, A., Fichó, E., Balatti, G. E., Parisi, G. D., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad928 · Cited by 142 · see citing works
“Shallow MSAs derived from protein families with a limited conservation at the species level have been shown to decrease the confidence of the AlphaFold prediction and increase the structural diversity in the top ranking solutions ( 26 ).”
BackgroundThe power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155 · see citing works
Predicting equilibrium distributions for molecular systems with deep learning
Zheng, S., He, J., Liu, C., Shi, Y., Lu, Z., Feng, W., Ju, F., Wang, J., Zhu, J., Min, Y., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00837-3 · Cited by 178 · see citing works
Accelerating Cryptic Pocket Discovery Using AlphaFold
Meller, A., Bhakat, S., Solieva, S. O., & Bowman, G. R. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01189 · Cited by 132 · see citing works
“Previous studies have shown that AlphaFold samples diverse conformations of transporters and receptors when its input MSA is stochastically subsampled to only include 16 sequences 10,11 .”
BackgroundAlphaFold2 protein structure prediction: Implications for drug discovery
Borkakoti, N., & Thornton, J. M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102526 · Cited by 137 · see citing works
Exploring structural diversity across the protein universe with The Encyclopedia of Domains
Lau, A. M., Bordin, N., Kandathil, S. M., Sillitoe, I., Waman, V. P., Wells, J., Orengo, C. A., & Jones, D. T. (2024) · Science · doi:10.1126/science.adq4946 · Cited by 140 · see citing works
“Figure 15b where we highlight examples where for some domains, the difference can be as large as some structures being completely unfolded and predicted with very low plDDT.”
AlphaFold2-RAVE: From Sequence to Boltzmann Ranking
Vani, B. P., Aranganathan, A., Wang, D., & Tiwary, P. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00290 · Cited by 120 · see citing works
Structure prediction of alternative protein conformations
Bryant, P., & Noé, F. (2024) · Nature Communications · doi:10.1038/s41467-024-51507-2 · Cited by 105 · see citing works
“The first uses a decreasing amount of sequence clusters [5] and the second a clustering procedure with DBscan [6] to generate diverse clusters [7].”
Omidi, A., Møller, M. H., Malhis, N., Bui, J. M., & Gsponer, J. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2406407121 · Cited by 103 · see citing works
From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
Bret, H., Gao, J., Zea, D. J., Andreani, J., & Guerois, R. (2024) · Nature Communications · doi:10.1038/s41467-023-44288-7 · Cited by 103 · see citing works
AlphaFold prediction of structural ensembles of disordered proteins
Brotzakis, Z. F., Zhang, S., Murtada, M. H., & Vendruscolo, M. (2025) · Nature Communications · doi:10.1038/s41467-025-56572-9 · Cited by 99 · see citing works
AlphaFold, allosteric, and orthosteric drug discovery: Ways forward
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103551 · Cited by 96 · see citing works
AFsample2 predicts multiple conformations and ensembles with AlphaFold2
Kalakoti, Y., & Wallner, B. (2025) · Communications Biology · doi:10.1038/s42003-025-07791-9 · Cited by 96 · see citing works
“Speci fi cally, compared to AFvanilla, MSAsubsample, AFcluster, SPEACH_AF, and AFsample, 11, 7, 12, 7, and 9 out of 16 targets are improved, respectively.”
Sala, D., Hildebrand, P. W., & Meiler, J. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1121962 · Cited by 80 · see citing works
Self-citationRanking Peptide Binders by Affinity with AlphaFold**
Chang, L., & Perez, A. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202213362 · Cited by 100 · see citing works
AlphaFold2 Can Predict Single-Mutation Effects
McBride, J. M., Polev, K., Abdirasulov, A., Reinharz, V., Grzybowski, B. A., & Tlusty, T. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.218401 · Cited by 87 · see citing works
Zhang, X., Ibrahim, Z., Khaskheli, M. B., Raza, H., Zhou, F., & Shamsi, I. H. (2024) · Sustainability · doi:10.3390/su16177651 · Cited by 90 · see citing works
Koh, H. Y., Zheng, Y., Yang, M., Arora, R., Webb, G. I., Pan, S., Li, L., & Church, G. M. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00349-8 · Cited by 96 · see citing works
“In a different approach, PhiGNet 95 uses graph neural networks representing residues as nodes connected by co-evolutionary links to improve prediction through graph-based modelling.”
Prediction of multiple conformational states by combining sequence clustering with AlphaFold2Preprint
Wayment-Steele, H. K., Ovchinnikov, S., Colwell, L. J., & Kern, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.17.512570 · Cited by 66 · see citing works
Ghafouri, H., Lázár, T., Conte, A. D., Tenorio, L., Aspromonte, M. C., Bernadó, P., Chaves‐Arquero, B., Chemes, L. B., Clementel, D., Cordeiro, T. N., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad947 · Cited by 71 · see citing works
AlphaFold two years on: Validation and impact
Kovalevskiy, O. V., Mateos-Garcia, J. C., & Tunyasuvunakool, K. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315002121 · Cited by 81 · see citing works
Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
Chakravarty, D., Lee, M., & Porter, L. L. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102973 · Cited by 74 · see citing works
A structure and evolutionary-based classification of solute carriers
Ferrada, E., & Superti-Furga, G. (2022) · iScience · doi:10.1016/j.isci.2022.105096 · Cited by 65 · see citing works
“The impact and possible applications of these recent advancements are still expanding in scope, from drug and protein design to the prediction of protein complexes and their dynamics (Del Alamo et al., 2022).”
BackgroundNovel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72 · see citing works
“Furthermore, overrepresentation of proteins with a particular folding state results in a model that is not representative of other alternative states [88].”
BackgroundDeep learning for protein structure prediction and design—progress and applications
Jänes, J., & Beltrão, P. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00016-x · Cited by 71 · see citing works
“…conformations has now been successfully applied in a number of different systems (see review (Sala et al, 2023)), including transporters and GPCRs (Del Alamo et al, 2022b), different “ metamorphic ” proteins (KaiB, RfaH, Mad2) (Wayment-Steele et al, 2024), small molecule binding proteins…”
BackgroundAdaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning
Kleiman, D. E., Nadeem, H., & Shukla, D. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c04843 · Cited by 62 · see citing works
Casadevall, G., Duran, C., & Osuna, S. (2023) · JACS Au · doi:10.1021/jacsau.3c00188 · Cited by 68 · see citing works
Díaz‐Holguín, A., Saarinen, M., Vo, D. D., Sturchio, A., Branzell, N., de Vaca, I. C., Hu, H., Mitjavila-Domènech, N., Lindqvist, A., Baranczewski, P., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn1524 · Cited by 67 · see citing works
“These results suggest that our approach could be further improved by using methods that generate more diverse ensembles of structures or fold the receptor in the presence of small molecules ( 60 , 61 ).”
From byte to bench to bedside: molecular dynamics simulations and drug discovery
Ahmed, M., Maldonado, A. M., & Durrant, J. D. (2023) · BMC Biology · doi:10.1186/s12915-023-01791-z · Cited by 67 · see citing works
Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era
Cui, X., Ge, L., Chen, X., Lv, Z., Wang, S., Zhou, X., & Zhang, G. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf340 · Cited by 67 · see citing works
“Methods like Subsampled AF2 [22] and af2_conformations [21] reduce the depth of input MSAs through a subsampling strategy and then combine AlphaFold2 to generate multiple conformations (as listed in Table 2).”
Improved multimer prediction using massive sampling with <scp>AlphaFold</scp> in <scp>CASP15</scp>
Wallner, B. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26562 · Cited by 53 · see citing works
“8 Alternative methods to achieve greater diversity among generated models include increasing the number of times the prediction is recycled in the network, 9 randomly perturbing, 10 or altering the input MSA.”
MethodComputational drug development for membrane protein targets
Li, H., Sun, X., Cui, W., Xu, M., Dong, J., Ekundayo, B., Ni, D., Rao, Z., Guo, L., Stahlberg, H., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01987-2 · Cited by 62 · see citing works
“The quality of the prediction of membrane proteins has been investigated in detail for several membrane proteins 12 , including GPCRs 32–35 , channel proteins 36,37 and transporters 35 .”
Predicting protein conformational motions using energetic frustration analysis and AlphaFold2
Guan, X., Tang, Q.-Y., Ren, W., Chen, M., Wang, W., Wolynes, P. G., & Li, W. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2410662121 · Cited by 59 · see citing works
Structure and sucrose binding mechanism of the plant SUC1 sucrose transporter
Bavnhøj, L., Driller, J. H., Zuzic, L., Stange, A. D., Schiøtt, B., & Pedersen, B. P. (2023) · Nature Plants · doi:10.1038/s41477-023-01421-0 · Cited by 58 · see citing works
Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE
Vani, B. P., Aranganathan, A., & Tiwary, P. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01436 · Cited by 51 · see citing works
AlphaFold2 models of the active form of all 437 catalytically competent human protein kinase domainsPreprint
Faezov, B., & Dunbrack, R. L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.21.550125 · Cited by 45 · see citing works
Improved Multimer Prediction using Massive Sampling with AlphaFold in CASP15
Wallner, B. (2023) · doi:10.22541/au.168170986.60158084/v1 · Cited by 53 · see citing works
Overview of AlphaFold2 and breakthroughs in overcoming its limitations
Wang, L., Wen, Z., Liu, S.-W., Zhang, L., Finley, C., Lee, H., & Fan, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108620 · Cited by 53 · see citing works
Gumpper, R. H., Fay, J. F., & Roth, B. L. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111211 · Cited by 50 · see citing works
Effective Molecular Dynamics from Neural Network-Based Structure Prediction Models
Jussupow, A., & Kaila, V. R. I. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01027 · Cited by 42 · see citing works
Tripathi, R. S., Guglani, A., Ghorpade, R., & Wang, B. (2023) · Journal of Enzyme Inhibition and Medicinal Chemistry · doi:10.1080/14756366.2023.2276663 · Cited by 48 · see citing works
The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery
Nguyen, A. T. N., Nguyen, D. T. N., Koh, H. Y., Toskov, J., MacLean, W., Xu, A., Zhang, D., Webb, G. I., May, L. T., & Halls, M. L. (2023) · British Journal of Pharmacology · doi:10.1111/bph.16140 · Cited by 42 · see citing works
Molecular Docking in the Study of Ligand-Protein Recognition: An Overview
Azad, I. (2023) · Biomedical engineering · doi:10.5772/intechopen.106583 · Cited by 41 · see citing works
Qian, R., Xue, J., Xu, Y. C., & Huang, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01024 · Cited by 41 · see citing works
Recent Progress of Protein Tertiary Structure Prediction
Wuyun, Q., Chen, Y., Shen, Y., Cao, Y., Hu, G., Cui, W., Gao, J., & Zheng, W. H. (2024) · Molecules · doi:10.3390/molecules29040832 · Cited by 42 · see citing works
del Alamo, D., DeSousa, L., Nair, R., Rahman, S., Meiler, J., & Mchaourab, H. S. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2206129119 · Cited by 32 · see citing works
“This methodology, which capitalizes on a recent modification of AF2 by our group 65 , is general and is predicted to increase the throughput of spectroscopic and biochemical investigations by enabling optimal experimental design.”
MethodInfluentialSelf-citationRecent Advances in NMR Protein Structure Prediction with ROSETTA
Leman, J. K., & Künze, G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24097835 · Cited by 38 · see citing works
“Within the human proteome, about 30% of regions are disordered [169,170], and they frequently interact with other proteins and function as hubs in protein interaction networks.”
BackgroundAgajanian, S., Alshahrani, M., Bai, F., Tao, P., & Verkhivker, G. M. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01634 · Cited by 37 · see citing works
Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15
Kryshtafovych, A., Montelione, G. T., Rigden, D. J., Mesdaghi, S., Karaca, E., & Moult, J. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26584 · Cited by 31 · see citing works
“The successful groups all used versions of AF2 with enhanced sampling and include methods that performed well in the single molecule and multimer CASP categories.”
BackgroundResultInfluentialModeling protein–ligand interactions for drug discovery in the era of deep learning
Wang, Y. X., Li, Y., Chen, J., & Lai, L. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00415b · Cited by 46 · see citing works
Brown, B. P., Stein, R. A., Meiler, J., & Mchaourab, H. S. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01081 · Cited by 41 · see citing works
Self-citationModeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry
(2024) · doi:10.1016/j.mcpro.2024.100724 · Cited by 33 · see citing works
“1 (1), that limits the MSA depth, resulting in a greater variation in predicted conformers compared to the default conditions (18).”
BackgroundInfluentialA Perspective on the Prospective Use of AI in Protein Structure Prediction
Versini, R., Sritharan, S., Fas, B. A., Tubiana, T., Aimeur, S., Henri, J., Erard, M., Nüße, O., Andréani, J., Baaden, M., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01361 · Cited by 33 · see citing works
Yee, S. W., Macdonald, C. B., Mitrovic, D., Zhou, X., Koleske, M. L., Yang, J. Y., Silva, D. B., Grimes, P. R., Trinidad, D., More, S. S., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.04.008 · Cited by 32 · see citing works
Lazou, M., Khan, O., Nguyen, T., Padhorny, D., Kozakov, D., Joseph‐McCarthy, D., & Vajda, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412719121 · Cited by 31 · see citing works
“Stochastic subsampling of the MSAs led to the generation of conformations that spanned the range between active and inactive structures.”
Interpreting the molecular mechanisms of disease variants in human transmembrane proteins
Tiemann, J. K. S., Zschach, H., Lindorff-Larsen, K., & Stein, A. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2022.12.031 · Cited by 35 · see citing works
Aranganathan, A., Gu, X.-Y., Wang, D.-D., Vani, B. P., & Tiwary, P. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103000 · Cited by 37 · see citing works
“Enhanced or rare event sampling methods sample a modified distribution with a more uniform distribution across metastable states, then reweight to obtain the “true” physical distribution [3].”
Recent Advances and Challenges in Protein Structure Prediction
Peng, C., Liang, F., Xia, Y., Zhao, K., Hou, M., & Zhang, G. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01324 · Cited by 35 · see citing works
Lee, M., Schafer, J. W., Prabakaran, J., Chakravarty, D., Clore, M. F., & Porter, L. L. (2025) · Nature Communications · doi:10.1038/s41467-025-60759-5 · Cited by 35 · see citing works
Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data
Li, E. H., Spaman, L. E., Tejero, R., Huang, Y. J., Ramelot, T. A., Fraga, K. J., Prestegard, J. H., Kennedy, M. A., & Montelione, G. T. (2023) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2023.107481 · Cited by 33 · see citing works
Representing structures of the multiple conformational states of proteins
Ramelot, T. A., Tejero, R., & Montelione, G. T. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102703 · Cited by 26 · see citing works
Ellaway, J. I. J., Anyango, S., Nair, S. S., Zaki, H. A., Nadzirin, N., Powell, H. R., Gutmanas, A., Váradi, M., & Velankar, S. S. (2024) · Structural Dynamics · doi:10.1063/4.0000251 · Cited by 27 · see citing works
Confronting the data deluge: How artificial intelligence can be used in the study of plant stress
Koh, E., Sunil, R. S., Lam, H. Y. I., & Mutwil, M. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.09.010 · Cited by 36 · see citing works
“Furthermore, tools such as AlphaFold2 and conformational sub-sampling of such can provide structural insights into how proteins related to plant stress bind and interact with each other [84].”
BackgroundSon, A., Kim, W., Park, J., Lee, W., Lee, Y., Choi, S., & Kim, H. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25179725 · Cited by 34 · see citing works
“The energy landscape framework provides insights into mechanisms of constitutive activity, inverse agonism, and allosteric modulation, offering a powerful tool for understanding GPCR function and drug design [174,179].”
AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles
Bowman, G. R. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102423-011435 · Cited by 35 · see citing works
P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching
Jin, Y., Huang, Q., Song, Z., Zheng, M., Teng, D., & Shi, Q. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01620 · Cited by 33 · see citing works
AFsample: Improving Multimer Prediction with AlphaFold using Aggressive SamplingPreprint
Wallner, B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.20.521205 · Cited by 29 · see citing works
“Another option is to randomly perturb or alter the input MSA, which has been shown to enable better sampling of the conformational landscape and prediction of multiple conformational states (Alamo et al., 2022; Wayment-Steele et al., 2022).”
BackgroundBreaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15
Kryshtafovych, A., Montelione, G., Rigden, D., Mesdaghi, S., Karaca, E., & Moult, J. (2023) · doi:10.22541/au.169199504.48580490/v1 · Cited by 31 · see citing works
Raisinghani, N., Alshahrani, M., Gupta, G., & Verkhivker, G. M. (2024) · Viruses · doi:10.3390/v16091458 · Cited by 32 · see citing works
“…the number of sequences subsampled from the MSA ( max_seqs sets the number of sequences passed to the row/column attention track and extra_seqs the number of sequences additionally processed by the main evoformer stack). and KP. variants using AF2 methodology [72,73] with varied MSA depth [74–76].”
BackgroundMethodInfluentialArtificial intelligence for medicine 2025: Navigating the endless frontier
Dai, J., Xu, H., Chen, T., Huang, T., Liang, W., Zhang, R., Xu, G.-X., Zhang, Z., Xue, L., Gao, Y. Q., et al. (2025) · The Innovation Medicine · doi:10.59717/j.xinn-med.2025.100120 · Cited by 35 · see citing works
Mitrovic, D., McComas, S. E., Alleva, C., Bonaccorsi, M., Drew, D., & Delemotte, L. (2023) · eLife · doi:10.7554/elife.84805 · Cited by 31 · see citing works
Sequence clustering confounds AlphaFold2
Schafer, J. W., Lee, M., Chakravarty, D., Thole, J. F., Chen, E. A., & Porter, L. L. (2025) · Nature · doi:10.1038/s41586-024-08267-2 · Cited by 38 · see citing works
“Thus, randomly sampling diverse MSAs at shallow depths 11 enables more accurate, higher-confidence predictions than clustering by sequence similarity.”
Is the Functional Response of a Receptor Determined by the Thermodynamics of Ligand Binding?
Vögele, M., Zhang, B. W., Kaindl, J. M., & Wang, L. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00899 · Cited by 26 · see citing works
Protein multiple conformations prediction using multi-objective evolution algorithm
Hou, M.-H., Jin, S.-R., Cui, X.-Y., Peng, C.-X., Zhao, K.-L., Song, L., & Zhang, G.-J. (2023) · doi:10.1101/2023.04.21.537776 · Cited by 29 · see citing works
Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning
O’Donnell, T. J., Kanduri, C., Isacchini, G., Limenitakis, J. P., Brachman, R. A., Alvarez, R. A., Haff, I. H., Sandve, G. K., & Greiff, V. (2024) · Cell Systems · doi:10.1016/j.cels.2024.11.006 · Cited by 22 · see citing works
AFsample2: Predicting multiple conformations and ensembles with AlphaFold2Preprint
Kalakoti, Y., & Wallner, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596195 · Cited by 26 · see citing works
Wu, T., Stein, R. A., Kao, T., Brown, B. P., & Mchaourab, H. S. (2025) · Nature Communications · doi:10.1038/s41467-025-62582-4 · Cited by 33 · see citing works
Self-citationFeeding from the sun—Successes and prospects in bioengineering photosynthesis for food security
Long, S. P., Wang, Y., Carmo‐Silva, E., Cavanagh, A. P., Jonikas, M. C., Kromdijk, J., Long, B. M., Marshall‐Colón, A., Shukla, D., Wilson, R. H., et al. (2025) · Cell · doi:10.1016/j.cell.2025.10.033 · Cited by 32 · see citing works
The impact of AlphaFold2 on experimental structure solution
Edich, M., Briggs, D. C., Kippes, O., Gao, Y., & Thorn, A. (2022) · Faraday Discussions · doi:10.1039/d2fd00072e · Cited by 28 · see citing works
AlphaFold encodes the principles to identify high affinity peptide bindersPreprint
Chang, L., & Pérez, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.18.484931 · Cited by 27 · see citing works
“The structural biology community has rapidly used these developments to test neural networks beyond their original application to problems including alternative conformational sampling, disordered protein identification, or complex structure prediction, with very promising results 4,5 .”
BackgroundShahapurmath, S., Sharannavar, B. R., & Koli, R. (2025) · Medicine in Drug Discovery · doi:10.1016/j.medidd.2025.100218 · Cited by 28 · see citing works
Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE
Gu, X., Aranganathan, A., & Tiwary, P. (2024) · eLife · doi:10.7554/elife.99702 · Cited by 25 · see citing works
Ketter, S., & Joseph, B. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c10080 · Cited by 25 · see citing works
Aubel, M., Eicholt, L. A., & Bornberg‐Bauer, E. (2023) · F1000Research · doi:10.12688/f1000research.130443.1 · Cited by 24 · see citing works
Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures
Masri, C. A., Trozzi, F., Lin, S.-H., Tran, O., Sahni, N., Pátek, M., Cichońska, A., Ravikumar, B., & Rahman, R. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad129 · Cited by 24 · see citing works
“…success of AlphaFold2 (AF2) in the prediction of protein structure from sequence (Jumper et al. 2021), and AF2’s accuracy in modeling membrane transporter conformational diversity (Del Alamo et al. 2022), we hypothesized that AF2 may be able to model the conformational landscape of protein kinases.”
Random, de novo, and conserved proteins: How structure and disorder predictors perform differently
Middendorf, L., & Eicholt, L. A. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26652 · Cited by 24 · see citing works
“…low confidence of prediction and to correlate with higher disorder [Akdel et al., 2022, Ruff and Pappu, 2021, Piovesan et al., 2022, Bruley et al., 2022, Wilson et al., 2022] and structural heterogeneity [Del Alamo et al., 2022, Saldaño et al., 2022, Alderson et al., 2022, Bruley et al., 2023].”
BackgroundMethodVats, S., Bobrovs, R., Söderhjelm, P., & Bhakat, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307226 · Cited by 27 · see citing works
“Sampling cryptic pocket opening in apo proteins is of significant interest in drug discovery, as targeting these pockets has the potential to yield highly selective and potent inhibitors with unique structural features distinct from catalytic sites or known binding interfaces.”
- Authors
- del Alamo, D., Sala, D., Mchaourab, H. S., & Meiler, J.
- Affiliations
- Department of Molecular Physiology and Biophysics, Vanderbilt University
- Leipzig University (DE)
- Vanderbilt University (US)
- Corresponding author
- Mchaourab, Hassane S
- Meiler, Jens
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2022-03-03
- Volume
- 11
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC9023059
- OpenAlex
- W4214847177
- Semantic Scholar
- ff9b84297f16760472c791456befb31ff1d224ef
- Cited by
- 511
- References
- 66
- Citations by year
- 2026: 115 · 2025: 121 · 2024: 138 · 2023: 93 · 2022: 42 · 2021: 2
- FWCI
- 32.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Florence Research (University of Florence) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (GM 128087)
- Deutsche Forschungsgemeinschaft (CRC 1423, project number 421152132, subproject Z04)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- AlphaFold2, conformational sampling, membrane transporters, G protein-coupled receptors, structural heterogeneity, protein conformational plasticity, protein structure prediction, conformational ensembles
- MeSH terms
- Algorithms, Furylfuramide, Protein Conformation, Sequence Alignment, Membrane Transport Proteins
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