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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.
Overview
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.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Distributions of predicted models relative to the experimental structures
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In the paper
“this approach showed limited success when applied to transporters whose structures were used to train AF2”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Concluding remarks: proposed workflow and future directions
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In the paper
“In every case, a correlation was evident between each model’s PC1 values and their TM scores.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Distributions of predicted models relative to the experimental structures
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In the paper
“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”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > AF2 generates multiple conformations of all eight target proteins
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In the paper
“Correlation between these two measures was observed in most cases and was notable for ASCT2, LAT1, CGRPR, and MCT1 with templates (R2 ≥ 0.75).”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Predicted conformational fluctuations correlate with implied conformational dynamics
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In the paper
“MSAs with hundreds or thousands of sequences were generally observed to engender tighter clustering in conformations specific to each protein.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > AF2 generates multiple conformations of all eight target proteins
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In the paper
“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”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Limited conformational sampling is observed for proteins with structures in the AF2 training set
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In the paper
“Moreover, the experimental structures virtually always flanked the AF2 models along PC1.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Distributions of predicted models relative to the experimental structures
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In the paper
“The increased diversity coincided with the generation of misfolded or outlier models.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > AF2 generates multiple conformations of all eight target proteins
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“comparison of PC1/PC2 values suggested that the predicted dynamics deviate from simple interpolation of two end states”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Distributions of predicted models relative to the experimental structures
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- It may state two separate claims as one. 16
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Distributions of predicted models relative to the experimental structures
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In the paper
“Similar results were observed when we modeled PTH1R using either inactive or active templates, as well as LAT1 using either OF or IF templates”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > AF2 generates multiple conformations of all eight target proteins
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In the paper
“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.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > AF2 generates multiple conformations of all eight target proteins
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > AF2 generates multiple conformations of all eight target proteins
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In the paper
“misfolded models virtually never coclustered and could thus be identified and excluded from further analysis”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > AF2 generates multiple conformations of all eight target proteins
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In the paper
“The inclusion of templates restored this correlation in MCT1 but contributed negligibly to those of PTH1R and LAT1”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Predicted conformational fluctuations correlate with implied conformational dynamics
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In the paper
“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).”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Abstract
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In the paper
“Accurate OF models were obtained only with MSAs containing 16–32 sequences and constituted a minor population in an ensemble dominated by IF models.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > AF2 generates multiple conformations of all eight target proteins
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Abstract
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“while models of CCR5 were less biased toward the training set conformation, deep MSAs reduced conformational diversity.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Limited conformational sampling is observed for proteins with structures in the AF2 training set
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In the paper
“because the optimal MSA depth and choice of templates varied for each protein”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > AF2 generates multiple conformations of all eight target proteins
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None of the sources cited for this claim have been checked yet.
In the paper
“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”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Introduction
What the sources say
- Not checked yetcites Jumper et al., 2021b
- Not checked yetcites Del Alamo et al., 2021a
- Not checked yetcites Debruycker et al., 2020 source
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Limited conformational sampling is observed for proteins with structures in the AF2 training set
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In the paper
“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.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Concluding remarks: proposed workflow and future directions
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In the paper
“hinting at the possibility that traditional methods may still be required to capture alternative conformers”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Concluding remarks: proposed workflow and future directions
What the sources say
- Not checked yetcites Crawley et al., 2011
- Not checked yetcites Ollikainen et al., 2013
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None of the sources cited for this claim have been checked yet.
In the paper
“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).”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Introduction
What the sources say
- Not checked yetcites Ovchinnikov et al., 2015
- Not checked yetcites Nicoludis and Gaudet, 2018
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In the paper
“The physiological relevance of these proposed structural movements nonetheless requires experimental validation.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Concluding remarks: proposed workflow and future directions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: AF2 does not directly account for the lipid environment.
None of the sources cited for this claim have been checked yet.
In the paper
“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).”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Introduction
What the sources say
- Not checked yetcites Wang et al., 2020
- Not checked yetcites Gusach et al., 2020
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“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).”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Introduction
What the sources say
- Not checked yetcites Drew and Boudker, 2016
- Not checked yetcites Kazmier et al., 2017
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All templates were manually inspected, and those with structures similar to the desired conformation of interest were retained.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Materials and methods > Template-based predictions
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None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Introduction
What the sources say
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In the paper
“MSA subsampling was carried out randomly by AF2”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Materials and methods > MSA subsampling
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No manual intervention was carried out to fine-tune the composition of these alignments.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Materials and methods > MSA subsampling
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In the paper
“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.”
§ Sampling alternative conformational states of transporters and receptors with AlphaFold2 > Results and discussion > Concluding remarks: proposed workflow and future directions
What the sources say
- Not checked yetcites Ping et al., 2021
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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”
Citations
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
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×
“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×
“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. R., & Cheatham, T. E. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400341p · Cited by 7,466 · cited 1×
“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×
“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×
“…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×
“…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×
“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×
“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×
“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 ).”
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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.
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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
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 655
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
“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 de novo 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
“…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 390
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
“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
AlphaFold2 fails to predict protein fold switching
Chakravarty, D., & Porter, L. L. (2022) · Protein Science · doi:10.1002/pro.4353 · Cited by 193
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
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
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 172
“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 210
“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 170
“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 147
“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
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 206
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 136
“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 140
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
“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 127
Structure prediction of alternative protein conformations
Bryant, P., & Noé, F. (2024) · Nature Communications · doi:10.1038/s41467-024-51507-2 · Cited by 105
“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 109
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
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
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
AFsample2 predicts multiple conformations and ensembles with AlphaFold2
Kalakoti, Y., & Wallner, B. (2025) · Communications Biology · doi:10.1038/s42003-025-07791-9 · Cited by 126
“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
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
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
Zhang, X., Ibrahim, Z., Khaskheli, M. B., Raza, H., Zhou, F., & Shamsi, I. H. (2024) · Sustainability · doi:10.3390/su16177651 · Cited by 92
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 117
“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
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 73
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 90
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 76
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 69
“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
“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 76
“…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 70
Casadevall, G., Duran, C., & Osuna, S. (2023) · JACS Au · doi:10.1021/jacsau.3c00188 · Cited by 70
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 73
“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 77
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 70
“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 AlphaFold in CASP15
Wallner, B. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26562 · Cited by 53
“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 65
“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 62
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 60
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 50
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 48
Improved Multimer Prediction using Massive Sampling with AlphaFold in CASP15
Wallner, B. (2023) · doi:10.22541/au.168170986.60158084/v1 · Cited by 53
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 55
Gumpper, R. H., Fay, J. F., & Roth, B. L. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111211 · Cited by 50
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
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 51
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 43
Molecular Docking in the Study of Ligand-Protein Recognition: An Overview
Azad, I. (2023) · Biomedical engineering · doi:10.5772/intechopen.106583 · Cited by 41
Qian, R., Xue, J., Xu, Y. C., & Huang, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01024 · Cited by 47
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
Self-citationRecent 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 48
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 33
“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
“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
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
“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 53
Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry
(2024) · doi:10.1016/j.mcpro.2024.100724 · Cited by 33
“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 34
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 34
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 37
“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
Aranganathan, A., Gu, X., Wang, D., Vani, B. P., & Tiwary, P. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103000 · Cited by 39
“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 42
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 38
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 34
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
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 28
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 38
“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 39
“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 42
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
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
“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
Raisinghani, N., Alshahrani, M., Gupta, G., & Verkhivker, G. M. (2024) · Viruses · doi:10.3390/v16091458 · Cited by 33
“…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
Mitrovic, D., McComas, S. E., Alleva, C., Bonaccorsi, M., Drew, D., & Delemotte, L. (2023) · eLife · doi:10.7554/elife.84805 · Cited by 31
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
“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 27
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
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 23
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
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 34
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
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 29
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
“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 29
Ketter, S., & Joseph, B. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c10080 · Cited by 25
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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
“…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
“…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
“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.”
Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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