ArticleBMC bioinformatics2015
AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- RinPy, a Python Package for Residue Interaction Network Model to Analyze Protein Structures and Predict Ligand Binding Sites.Journal of chemical information and modeling · 2026Article
- Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework.Journal of chemical theory and computation · 2026Article
- AI-Driven Design of Miniproteins as Potential Allosteric Modulators.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses.Journal of molecular biology · 2025Review
- The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives.Journal of molecular biology · 2025Review
- STINGAllo: a web server for high-throughput prediction of allosteric site-forming residues using internal protein nanoenvironment descriptors.Briefings in bioinformatics · 2025Article
- DeepAllo: allosteric site prediction using protein language model (pLM) with multitask learning.Bioinformatics (Oxford, England) · 2025Article
- Mapping allosteric rewiring in related protein structures from collections of crystallographic multiconformer models.bioRxiv : the preprint server for biology · 2025Article
- AlloBench: A Data Set Pipeline for the Development and Benchmarking of Allosteric Site Prediction Tools.ACS omega · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery.RSC chemical biology · 2025Review
- Protein allosteric site identification using machine learning and per amino acid residue reported internal protein nanoenvironment descriptors.Computational and structural biotechnology journal · 2024Article
- MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model.Journal of cheminformatics · 2024Article
- Article
- Enzymatic Metabolic Switches of Astrocyte Response to Lipotoxicity as Potential Therapeutic Targets for Nervous System Diseases.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology.Biodesign research · 2024Article
- PASSer: fast and accurate prediction of protein allosteric sites.Nucleic acids research · 2023Article
- AlphaFold, allosteric, and orthosteric drug discovery: Ways forward.Drug discovery today · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundDespite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery.
resultsAlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site.
conclusionsPerturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study.
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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.