ArticleJournal of computational chemistry2023
PASSerRank: Prediction of allosteric sites with learning to rank.
Article in Journal of computational chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Targeting frizzled receptors (FZDs) for anti-tumor therapy: From orthosteric to allosteric inhibition.Acta pharmaceutica Sinica. B · 2026Review
- Biophysical Sensing Tools in Drug Discovery: Integrating Kinetics, Thermodynamics, Cellular Target Engagement and Structure.Sensors (Basel, Switzerland) · 2026Review
- A systematic evaluation of protein allosteric site prediction tools with independent datasets.Journal of computer-aided molecular design · 2026Article
- AI-Driven Design of Miniproteins as Potential Allosteric Modulators.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives.Journal of molecular biology · 2025Review
- DeepAllo: allosteric site prediction using protein language model (pLM) with multitask learning.Bioinformatics (Oxford, England) · 2025Article
- AlloBench: A Data Set Pipeline for the Development and Benchmarking of Allosteric Site Prediction Tools.ACS omega · 2025Article
- RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models.Journal of chemical information and modeling · 2024Article
- 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
- Exploring Binding Pockets in the Conformational States of the SARS-CoV-2 Spike Trimers for the Screening of Allosteric Inhibitors Using Molecular Simulations and Ensemble-Based Ligand Docking.International journal of molecular sciences · 2024Article
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- Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variant Complexes with the Host Receptor: The Interplay of Dynamics and Convergent Evolution Modulates Allostery and Functional Mechanisms.Journal of chemical information and modeling · 2023Article
- PASSer: fast and accurate prediction of protein allosteric sites.Nucleic acids research · 2023Article
- From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, that is, how well a pocket meets the characteristics of known allosteric sites. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top three positions for 83.6% and 80.5% of test proteins, respectively. The model outperforms other common machine learning models with higher F1 scores (0.662 in ASD and 0.608 in CASBench) and Matthews correlation coefficients (0.645 in ASD and 0.589 in CASBench). The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.
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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.