ArticleProceedings of the National Academy of Sciences of the United States of America2015
Discovery of multiple hidden allosteric sites by combining Markov state models and experiments.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.
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Who cites it
120 citing papers in PubMed, 234 citations in OpenAlex.
- Uncovering hidden druggable sites: computational approaches to allosteric and cryptic pocket discovery-from molecular dynamics to AI.Journal of computer-aided molecular design · 2026Review
- The influence of ligands on AlphaFold3 prediction of cryptic pockets.Communications biology · 2026Article
- Metastable Protein-Protein Interactions as a Design Principle for PROTACs: Insights from the RIPK1-VHL System.JACS Au · 2026Article
- Decrypting cryptic pockets with physics-based simulations and artificial intelligence.Current opinion in structural biology · 2026Review
- Dynamic signature of activity-stability tradeoff in lactamase evolution.Nature communications · 2026Article
- Article
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- A Practical Guide to Transition State Analysis in Biomolecular Simulations with TS-DAR.The journal of physical chemistry. B · 2025Article
- Markovian state models uncover casein kinase 1 dynamics that govern circadian period.Biophysical journal · 2025Article
- Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge.Journal of molecular biology · 2025Review
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Opening and closing of a cryptic pocket in VP35 toggles it between two different RNA-binding modes.eLife · 2025Article
- Exploring binding and allosteric energy landscapes for the KRAS interactions with effector proteins using Markov state modeling of conformational ensembles and allosteric network modeling.Protein science : a publication of the Protein Society · 2025Review
- Protein Catalysis Through Structural Dynamics: A Comprehensive Analysis of Energy Conversion in Enzymatic Systems and Its Computational Limitations.Pharmaceuticals (Basel, Switzerland) · 2025Review
- AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.Journal of chemical theory and computation · 2025Article
- Systematic analysis of biomolecular conformational ensembles with PENSA.The Journal of chemical physics · 2025Article
- Recent computational advances in the identification of cryptic binding sites for drug discovery.Bioinformatics advances · 2025Review
- 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
- Which cryptic sites are feasible drug targets?Drug discovery today · 2024Review
- MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model.Journal of cheminformatics · 2024Article
60 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
The discovery of drug-like molecules that bind pockets in proteins that are not present in crystallographic structures yet exert allosteric control over activity has generated great interest in designing pharmaceuticals that exploit allosteric effects. However, there have only been a small number of successes, so the therapeutic potential of these pockets--called hidden allosteric sites--remains unclear. One challenge for assessing their utility is that rational drug design approaches require foreknowledge of the target site, but most hidden allosteric sites are only discovered when a small molecule is found to stabilize them. We present a means of decoupling the identification of hidden allosteric sites from the discovery of drugs that bind them by drawing on new developments in Markov state modeling that provide unprecedented access to microsecond- to millisecond-timescale fluctuations of a protein's structure. Visualizing these fluctuations allows us to identify potential hidden allosteric sites, which we then test via thiol labeling experiments. Application of these methods reveals multiple hidden allosteric sites in an important antibiotic target--TEM-1 β-lactamase. This result supports the hypothesis that there are many as yet undiscovered hidden allosteric sites and suggests our methodology can identify such sites, providing a starting point for future drug design efforts. More generally, our results demonstrate the power of using Markov state models to guide experiments.
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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.