ArticleJournal of molecular biology2016
CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites.
Article in Journal of molecular biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.
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Who cites it
133 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
- Fragment-Based Discovery of KLK6 and KLK7 Inhibitors.Journal of chemical information and modeling · 2026Article
- A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A cryptic allosteric pocket shapes isoform-selective inhibition of human malic enzymes.Protein science : a publication of the Protein Society · 2026Article
- Biophysical insights into a cryptic ligand site in the hydrophobic core of human PCNA.Biophysical journal · 2026Article
- The influence of ligands on AlphaFold3 prediction of cryptic pockets.Communications biology · 2026Article
- A graph retrieval-augmented generation pipeline for systematic drug target discovery: validation and application to ocular neovascularization.Briefings in bioinformatics · 2026Article
- Extracellular matrix and proteolysis: mechanisms driving irreversible changes and shaping cell behavior.The FEBS journal · 2026Review
- A Hydrophobic Cluster Modulates Long-Range Allostery in the TRMT2A RNA Recognition Motif.Journal of chemical information and modeling · 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
- CryptoBank: A resource for the identification and prediction of cryptic sites in proteins.Science advances · 2026Article
- Article
- Integrating computational chemistry and machine learning to predict KRAS mutation-induced resistance.bioRxiv : the preprint server for biology · 2026Article
- Decrypting cryptic pockets with physics-based simulations and artificial intelligence.Current opinion in structural biology · 2026Review
- Structure-based Discovery of a Non-competitive FTO Inhibitor Bound to a Cryptic Site at the Domain Interface.Journal of molecular biology · 2026Article
- Computational analysis of CCN1 as a druggable target predicts interactions with bioactive compounds.Scientific reports · 2026Article
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- UniPocket: Unified Ligand and Cryptic Pocket Prediction from Protein Language Model Embeddings.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2026Article
73 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
Many proteins have small-molecule binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially "druggable" human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite Web server is available at http://salilab.org/cryptosite.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.