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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 ).”
Background
Flagged first, then by citations
1 of the 66 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- 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
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Moriwaki, Y., Shiraishi, T., Katsuyama, Y., Matsuda, K., Ose, T., Minami, A., Oikawa, H., Kuzuyama, T., Ishitani, R., & Terada, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.26.684697
Geometry-constrained prediction of catalytically competent kinase domains across the human kinome
Wang, Y., Hu, Q., & Huang, J. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103412
DyVarMap: An Interpretable, Dynamics-Aware Framework for Missense Variant Classification
Lian, Y., & Shehu, A. (2025) · Companion Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics · doi:10.1145/3768322.3769028
Fine-tuning AlphaFold with limited cryo-EM observations
Liao, J., Zheng, D., Zhang, H., Zhang, L., Hu, M., & Bao, C. (2026) · Communications Chemistry · doi:10.1038/s42004-026-01899-7
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Zhang, S., Miller, J. J., & Bowman, G. R. (2026) · doi:10.64898/2026.01.21.700870 · Cited by 7
Triviño, J., Jiménez, E., Grininger, C., Caballero, I., Medina, A., Castellví, A., Petrillo, G., Govantes, F., Sagmeister, T., Alcorlo, M., et al. (2026) · Protein Science · doi:10.1002/pro.70481
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Aranganathan, A., & Beyerle, E. R. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02554
Exploring Protein Conformational Ensembles Using Evolutionary Conditional Diffusion
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Hajnal, Á. B., Gallé, Á., & Csiszár, J. (2026) · Plant Molecular Biology · doi:10.1007/s11103-026-01681-2
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Malik, A. J., Portelli, S., & Ascher, D. B. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103218
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Sampling in structure-token space enables accurate prediction of multiple protein conformationsPreprint
Wang, Z., Yu, Y., Zheng, W., Bu, D., & Bu, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.03.708411
Aoki, T., & HARADA, R. (2026) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6c00466
Medicinal chemistry enabling advances in protein structure prediction
Munasinghe, A., Zhang, Z., & Wu, H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00096-0
IDPForge: deep learning of proteins with global and local regions of disorder
Castro, S. D., Zhang, O., Liu, Z. H., Forman-Kay, J. D., & Head-Gordon, T. L. (2026) · Nature Communications · doi:10.64898/2026.03.25.714313 · Cited by 4
The landscape of machine learning approaches for modeling protein conformational ensembles
Sledzieski, S., & Hanson, S. M. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103253 · Cited by 3
Lidbrink, S. E., Nissen, I., Ahrlind, J. K., Howard, R. J., & Lindahl, E. R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.02.716037
Structural prediction of protein assemblies with deep learning
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Zero-Shot Generation of Protein Conformational Ensembles Through AlphaFold Latent FloodingPreprint
Qian, R., Zhan, R., Song, Z., & Huang, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.16.718914
Mitjavila-Domènech, N., Díaz‐Holguín, A., Hu, H., Kahlous, N. A., de Vaca, I. C., Wallner, B., & Carlsson, J. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00034
Chaudhary, H., Archna, P., & Chowhan, R. K. (2026) · Biochemistry · doi:10.1021/acs.biochem.6c00105
Fold-switching and metamorphic proteins
Ronish, L. A., Chakravarty, D., & Porter, L. L. (2026) · Elsevier eBooks · doi:10.1016/b978-0-323-99507-8.00079-2
Chou, C.-H., Hong, X., & Xu, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.11.724213
RetractedConforFlux: Particle-Guided Trunk Repulsion for Diverse Protein ConformationsPreprint
Suzuki, S., & Amagasa, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.16.725138
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From prediction to discovery: AI as an observatory of physical organization in protein space
Zheng, Y., Zhang, Z., Wang, Y., Kang, W., Ren, W., & Tang, Q.-Y. (2026) · AI for Science · doi:10.1088/3050-287x/ae78ea
Moriwaki, Y., Shiraishi, T., Katsuyama, Y., Matsuda, K., Zhang, J., Ose, T., Minami, A., Oikawa, H., Kuzuyama, T., Ishitani, R., et al. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0149 · Cited by 3
_sampleworks:_ A Modular Platform for Experimentally Guided Biomolecular Ensemble Generation
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DAMPE: Fusing intrinsic and extrinsic information for protein function prediction
Zheng, R., Wang, Z., Qiao, A., Xie, J., Rao, J., & Yang, Y. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104549 · Cited by 4
MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen KineticsPreprint
Teng, D., Pitman, M., Jha, P. K., Sood, A., Rufa, D. A., Ryczko, K., Bortolato, A., & Tiwary, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.10.731383
ProMiSE: Protein Multi-State Evaluation Benchmark in Biological ContextsPreprint
Ku, B., Kim, S., Kim, Y. W., Park, H., & Seok, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.11.731790
Molecular basis of mitochondrial leucine transport by human Sideroflexin 1Preprint
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SteerAF: Distogram-based Steering of AlphaFold2 toward Alternative ConformationsPreprint
Tang, J., Zhu, Z., Yang, S., & Song, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.19.733296 · Cited by 1
D81 Mutants Reveal Hidden EF-Tu Diversity while Natural Sequences Preserve Aspartate
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Optimal transport method for efficient modeling of extended peptide conformations
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Collective variable design for biomolecular conformational dynamics.
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Adapting Co-Folding Models for Structure-Based Protein-Protein Docking Through Flow Matching
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TPS-Flow: Physics-Guided Flow-Based Generative Modeling of Protein Transition Paths
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Multi-state Structure Prediction of G Protein-Coupled Receptor Proteins via Prompting on AlphaFold
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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.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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