ArticleThe journal of physical chemistry. B2021
Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1.
Article in The journal of physical chemistry. B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 31 citations in OpenAlex.
- Protein structural dynamics in covalent drug design: insights from irreversible and reversible covalent inhibitors.RSC chemical biology · 2026Review
- New allosteric modulators of molecular chaperone TRAP1 from the integration of computational biology, medicinal chemistry, and biophysics.Cell stress & chaperones · 2026Review
- The structural and functional dynamics of BiP and Grp94: opportunities for therapeutic discovery.Trends in pharmacological sciences · 2025Review
- Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics.Molecules (Basel, Switzerland) · 2024Article
- A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors.International journal of molecular sciences · 2024Article
- Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 MRSC advances · 2023Article
- Predicting allosteric pockets in protein biological assemblages.Bioinformatics (Oxford, England) · 2023Article
- Machine learning and protein allostery.Trends in biochemical sciences · 2023Review
- It's ok to be outnumbered - sub-stoichiometric modulation of homomeric protein complexes.RSC medicinal chemistry · 2023Review
- A Structure-Based Allosteric Modulator Design Paradigm.Health data science · 2023Review
- Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs.Journal of molecular biology · 2022Review
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein-ligand binding affinities.Communications biology · 2022Article
- From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output.Frontiers in pharmacology · 2022Review
- New perspectives in cancer drug development: computational advances with an eye to design.RSC medicinal chemistry · 2021Review
- Design and Synthesis of TRAP1 Selective Inhibitors: H-Bonding with Asn171 Residue in TRAP1 Increases Paralog Selectivity.ACS medicinal chemistry letters · 2021Article
- Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics.The journal of physical chemistry letters · 2021Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Allosteric molecules provide a powerful means to modulate protein function. However, the effect of such ligands on distal orthosteric sites cannot be easily described by classical docking methods. Here, we applied machine learning (ML) approaches to expose the links between local dynamic patterns and different degrees of allosteric inhibition of the ATPase function in the molecular chaperone TRAP1. We focused on 11 novel allosteric modulators with similar affinities to the target but with inhibitory efficacy between the 26.3 and 76%. Using a set of experimentally related local descriptors, ML enabled us to connect the molecular dynamics (MD) accessible to ligand-bound (perturbed) and unbound (unperturbed) systems to the degree of ATPase allosteric inhibition. The ML analysis of the comparative perturbed ensembles revealed a redistribution of dynamic states in the inhibitor-bound versus inhibitor-free systems following allosteric binding. Linear regression models were built to quantify the percentage of experimental variance explained by the predicted inhibitor-bound TRAP1 states. Our strategy provides a comparative MD-ML framework to infer allosteric ligand functionality. Alleviating the time scale issues which prevent the routine use of MD, a combination of MD and ML represents a promising strategy to support
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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.