ArticleJournal of chemical theory and computation2023
Accelerating Cryptic Pocket Discovery Using AlphaFold.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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Who cites it
73 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
- Enhanced Sampling of Protein Conformations in AlphaFold3 with Repulsive Bias in the Diffusion Generative Model.JACS Au · 2026Article
- CAFE: A Co-folding Approach for Fragment Exploration of Allosteric and Cryptic Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- The influence of ligands on AlphaFold3 prediction of cryptic pockets.Communications biology · 2026Article
- Biophysical insights into a cryptic ligand site in the hydrophobic core of human PCNA.Biophysical journal · 2026Article
- Tyrosine kinases sample unique activation ensembles.bioRxiv : the preprint server for biology · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- From Genome Inspection to Precision Agrochemicals: A Structure-Based Antivirulence Roadmap for Sustainable Crop Protection againstJournal of agricultural and food chemistry · 2026Review
- Insights into the Mechanism Underlying the Alkane Dehydrogenation Capability ofMicroorganisms · 2026Article
- Elucidating the Structure and Activation Mechanisms of GPCRs Using Modern Computational and AI Tools.Cell biochemistry and biophysics · 2026Review
- From "accurate structures" to decision-grade mechanisms: advancing AI-enabled GPCR biophysics toward globally reproducible standards.Cell biochemistry and biophysics · 2026Article
- Distance-Restraint-Guided Diffusion Models for Sampling Protein Conformational Changes and Ligand Dissociation Pathways.Journal of chemical theory and computation · 2026Article
- The intrinsic disorder challenge for AlphaFold: A case study of G3BP1 and pathogenic peptide.iScience · 2026Article
- Martini Mapper: An Automated Fragment-Based Mapping Algorithm for Developing Coarse-Grained Models within the Martini 3 Framework.Journal of chemical information and modeling · 2026Article
- Natural Products Beyond Inhibition: A Mechanistic Framework Spanning Pockets, Interfaces, and Kinetic Barriers.Molecules (Basel, Switzerland) · 2026Review
- Deep mining of the human antibody repertoire identifies frequent and genetically diverse CDRH3 topologies targetable by vaccination.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Conformational Dynamics of NaJournal of chemical information and modeling · 2026Article
- Estimating Protein Conformational States from High-Speed AFM Images with Molecular Dynamics and Deep Learning.Journal of chemical information and modeling · 2026Article
- CryptoBank: A resource for the identification and prediction of cryptic sites in proteins.Science advances · 2026Article
13 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket openings are often beyond the reach of conventional biomolecular simulations because certain cryptic pocket openings involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate cryptic pocket discovery either by generating structures with open pockets directly or generating structures with partially open pockets that can be used as starting points for simulations. We use AlphaFold to generate ensembles for 10 known cryptic pocket examples, including five that were deposited after AlphaFold's training data were extracted from the PDB. We find that in 6 out of 10 cases AlphaFold samples the open state. For plasmepsin II, an aspartic protease from the causative agent of malaria, AlphaFold only captures a partial pocket opening. As a result, we ran simulations from an ensemble of AlphaFold-generated structures and show that this strategy samples cryptic pocket opening, even though an equivalent amount of simulations launched from a ligand-free experimental structure fails to do so. Markov state models (MSMs) constructed from the AlphaFold-seeded simulations quickly yield a free energy landscape of cryptic pocket opening that is in good agreement with the same landscape generated with well-tempered metadynamics. Taken together, our results demonstrate that AlphaFold has a useful role to play in cryptic pocket discovery but that many cryptic pockets may remain difficult to sample using AlphaFold alone.
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