ArticlePLoS computational biology2011
Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
40 citing papers in PubMed, 66 citations in OpenAlex.
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- Unsupervised learning reveals landscape of local structural motifs across protein classes.Bioinformatics (Oxford, England) · 2025Article
- DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.Nucleic acids research · 2025Article
- Novel therapeutic agents in clinical trials: emerging approaches in cancer therapy.Discover oncology · 2024Review
- COLLAPSE: A representation learning framework for identification and characterization of protein structural sites.Protein science : a publication of the Protein Society · 2023Article
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- Druggable Transient Pockets in Protein Kinases.Molecules (Basel, Switzerland) · 2021Review
- AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters.Journal of chemical information and modeling · 2020Article
- Binding site matching in rational drug design: algorithms and applications.Briefings in bioinformatics · 2019Review
- A deep learning framework to predict binding preference of RNA constituents on protein surface.Nature communications · 2019Article
- PatchSearch: a web server for off-target protein identification.Nucleic acids research · 2019Article
- High precision protein functional site detection using 3D convolutional neural networks.Bioinformatics (Oxford, England) · 2019Article
- Pocket similarity identifies selective estrogen receptor modulators as microtubule modulators at the taxane site.Nature communications · 2019Article
- Computational analysis of kinase inhibitor selectivity using structural knowledge.Bioinformatics (Oxford, England) · 2019Article
- Molecular characterization of the interaction of sialic acid with the periplasmic binding protein fromThe Journal of biological chemistry · 2018Article
- PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development.PLoS computational biology · 2018Article
- A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs).PLoS computational biology · 2018Article
- A scientist engineer's contribution to therapeutic discovery and development.Experimental biology and medicine (Maywood, N.J.) · 2018Article
- Comparative assessment of strategies to identify similar ligand-binding pockets in proteins.BMC bioinformatics · 2018Article
- Biological and functional relevance of CASP predictions.Proteins · 2018Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway.
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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.