ArticleProceedings of the National Academy of Sciences of the United States of America2024
Predicting multiple conformations of ligand binding sites in proteins suggests that AlphaFold2 may remember too much.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Uncovering hidden druggable sites: computational approaches to allosteric and cryptic pocket discovery-from molecular dynamics to AI.Journal of computer-aided molecular design · 2026Review
- Reimagining computational macromolecular modeling: AI-driven approaches.Biophysical journal · 2026Review
- Synthetic sequence alignments as programmable probes of learned conformational landscapes in deep learning protein structure predictors.Bioinformatics (Oxford, England) · 2026Article
- The influence of ligands on AlphaFold3 prediction of cryptic pockets.Communications biology · 2026Article
- Improving AlphaFold2 Performance in Virtual Screens Targeting GPCRs by Enhancing Binding-Site Conformational Sampling.Journal of chemical information and modeling · 2026Article
- The latest AI breakthroughs in structural biology: protein binder design and conformational state prediction.Communications biology · 2026Article
- Hidden structural states of proteins revealed by conformer selection.Nature communications · 2026Article
- Bias in the AlphaFold3 prediction of ligand-induced domain motion in enzymes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Master of Metals2: a graph neural network based architecture for the prediction of zinc binding sites in protein structures.Briefings in bioinformatics · 2026Article
- Article
- Modeling Alternative Conformational States in CASP16.Proteins · 2026Article
- Assessing the relation between protein phosphorylation, AlphaFold3 models, and conformational variability.Protein science : a publication of the Protein Society · 2026Article
- Article
- Memorization bias impacts modeling of alternative conformational states of solute carrier membrane proteins with methods from deep learning.PLoS computational biology · 2025Article
- Modeling Alternative Conformational States in CASP16.bioRxiv : the preprint server for biology · 2025Article
- Memorization Bias Impacts Modeling of Alternative Conformational States of Symmetric Solute Carrier Membrane Proteins with Methods from Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Emerging frontiers in protein structure prediction following the AlphaFold revolution.Journal of the Royal Society, Interface · 2025Review
- Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold2.Research square · 2025Article
- Predicting multiple conformations of ligand binding sites in proteins suggests that AlphaFold2 may remember too much.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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7 authors.
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Abstract
The goal of this paper is predicting the conformational distributions of ligand binding sites using the AlphaFold2 (AF2) protein structure prediction program with stochastic subsampling of the multiple sequence alignment (MSA). We explored the opening of cryptic ligand binding sites in 16 proteins, where the closed and open conformations define the expected extreme points of the conformational variation. Due to the many structures of these proteins in the Protein Data Bank (PDB), we were able to study whether the distribution of X-ray structures affects the distribution of AF2 models. We have found that AF2 generates both a cluster of open and a cluster of closed models for proteins that have comparable numbers of open and closed structures in the PDB and not too many other conformations. This was observed even with default MSA parameters, thus without further subsampling. In contrast, with the exception of a single protein, AF2 did not yield multiple clusters of conformations for proteins that had imbalanced numbers of open and closed structures in the PDB, or had substantial numbers of other structures. Subsampling improved the results only for a single protein, but very shallow MSA led to incorrect structures. The ability of generating both open and closed conformations for six out of the 16 proteins agrees with the success rates of similar studies reported in the literature. However, we showed that this partial success is due to AF2 "remembering" the conformational distributions in the PDB and that the approach fails to predict rarely seen conformations.
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