Coupling Dynamics and Evolutionary Information with Structure to Identify Protein Regulatory and Functional Binding Sites Mishra, Sambit Kumar Jernigan, Robert Kandoi, Gaurav Jernigan, Robert
dc.contributor.department Biochemistry, Biophysics and Molecular Biology
dc.contributor.department Electrical and Computer Engineering
dc.contributor.department Bioinformatics and Computational Biology 2019-07-17T18:03:21.000 2020-06-29T23:46:41Z 2020-06-29T23:46:41Z Tue Jan 01 00:00:00 UTC 2019 2020-05-29 2019-05-29
dc.description.abstract <p>Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, AR-Pred (Active and Regulatory site Prediction), which supplements protein geometry, evolutionary and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. Since the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median AUC of 91% and MCC of 0.68, whereas the less welldefined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR-Pred is available as a free downloadable package at source.</p>
dc.description.comments <p>This is the peer reviewed version of the following article: Mishra, Sambit Kumar, Gaurav Kandoi, and Robert L. Jernigan. "Coupling Dynamics and Evolutionary Information with Structure to Identify Protein Regulatory and Functional Binding Sites." <em>Proteins: Structure, Function, and Bioinformatics</em> (2019), which has been published in final form at doi: <a href="">10.1002/prot.25749</a>. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 1247
dc.identifier.contextkey 14659074
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath bbmb_ag_pubs/239
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 22:50:04 UTC 2022
dc.source.uri 10.1002/prot.25749
dc.subject.disciplines Biochemistry, Biophysics, and Structural Biology
dc.subject.disciplines Bioinformatics
dc.subject.disciplines Computational Biology
dc.subject.disciplines Electrical and Computer Engineering
dc.subject.disciplines Statistical Models
dc.subject.keywords Regulatory sites
dc.subject.keywords Allostery
dc.subject.keywords Active sites
dc.subject.keywords Proteins dynamics
dc.subject.keywords Machine learning
dc.subject.keywords Coarse-graining
dc.subject.keywords Elastic network models
dc.title Coupling Dynamics and Evolutionary Information with Structure to Identify Protein Regulatory and Functional Binding Sites
dc.type article
dc.type.genre article
dspace.entity.type Publication
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