ReviewAccounts of chemical research2020
Investigating Cryptic Binding Sites by Molecular Dynamics Simulations.
Review in Accounts of chemical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers.
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Who cites it
101 citing papers in PubMed.
- Conformational Dynamics of Viral Protease Precursors in Maturation, Inhibition, and Drug-Resistance Development.Viruses · 2026Review
- Drug Discovery of Allosteric Molecular Glue Degraders (aMGDs): Expanding the Landscape of Targeted Protein Degradation.Journal of medicinal chemistry · 2026Article
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 2026Article
- Natural Products Beyond Inhibition: A Mechanistic Framework Spanning Pockets, Interfaces, and Kinetic Barriers.Molecules (Basel, Switzerland) · 2026Review
- CryptoBank: A resource for the identification and prediction of cryptic sites in proteins.Science advances · 2026Article
- Assessing the Performance of BioEmu in Understanding Protein Dynamics.International journal of molecular sciences · 2026Article
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- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- MarinePharmaceuticals (Basel, Switzerland) · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- Molecular biophysics of viral proteins: From mechanistic insights to microbiological and clinical applications.AIMS microbiology · 2026Review
- Perspectives on TRPV Channels in the Structural and Computational Revolution Era.Advances in experimental medicine and biology · 2026Review
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- Unveiling structural dynamics and allosteric vulnerabilities in Klebsiella pneumoniae KPHS_11890: an integrated DRKG-MD study.Journal of computer-aided molecular design · 2025Article
- CCD2MD: A Suite of Packages for Preparing Co-Folded Outputs for Molecular Dynamics Simulations.Journal of chemical information and modeling · 2025Article
- Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.Biological cybernetics · 2025Review
41 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
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Abstract
This Account highlights recent advances and discusses major challenges in investigations of cryptic (hidden) binding sites by molecular simulations. Cryptic binding sites are not visible in protein targets crystallized without a ligand and only become visible crystallographically upon binding events. These sites have been shown to be druggable and might provide a rare opportunity to target difficult proteins. However, due to their hidden nature, they are difficult to find through experimental screening. Computational methods based on atomistic molecular simulations remain one of the best approaches to identify and characterize cryptic binding sites. However, not all methods are equally efficient. Some are more apt at quickly probing protein dynamics but do not provide thermodynamic or druggability information, while others that are able to provide such data are demanding in terms of time and resources. Here, we review the recent contributions of mixed-solvent simulations, metadynamics, Markov state models, and other enhanced sampling methods to the field of cryptic site identification and characterization. We discuss how these methods were able to provide precious information on the nature of the site opening mechanisms, to predict previously unknown sites which were used to design new ligands, and to compute the free energy landscapes and kinetics associated with the opening of the sites and the binding of the ligands. We highlight the potential and the importance of such predictions in drug discovery, especially for difficult ("undruggable") targets. We also discuss the major challenges in the field and their possible solutions.
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