Peptide-Binding Site Prediction From Protein Structure via points on the Solvent Accessible Surface

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10379895" target="_blank" >RIV/00216208:11320/18:10379895 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3233547.3233708" target="_blank" >https://doi.org/10.1145/3233547.3233708</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3233547.3233708" target="_blank" >10.1145/3233547.3233708</a>

Result language

angličtina

Original language name

Peptide-Binding Site Prediction From Protein Structure via points on the Solvent Accessible Surface

Original language description

Protein-peptide binding interactions play an important role in cellular regulation and are functionally important in many diseases. If no prior knowledge of the location of a binding site is available, prediction may be needed as a starting point for further modeling or docking. Existing approaches to prediction either require a sequence of the peptide to be already known or offer an unsatisfactory predictive performance. Here we propose P2Rank-Pept, a new machine learning based method for prediction of peptide-binding sites from protein structure. We show that our method significantly outperforms other evaluated methods, including the most recent structure based prediction method SPRINT-Str published last year (AUC: $0.85 &gt; 0.78$). P2Rank-Pept utilizes local structural and sequence information, including evolutionary conservation, and builds a prediction model based on a Random Forest classifier. The novelty of our approach lies in using points on the solvent accessible surface as a unit of classification (as opposed to the typical approach of focusing on amino acid residues), and in the application of the robust technique of Bayesian optimization to systematically optimize arbitrary parameters of the algorithm. Our results assert that P2Rank software package is a viable framework for developing top-performing binding-site prediction methods for different types of binding partners.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10600 - Biological sciences

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

ISBN

978-1-4503-5794-4

ISSN

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e-ISSN

neuvedeno

Number of pages

6

Pages from-to

645-650

Publisher name

ACM

Place of publication

New York, NY, USA

Event location

Washington, DC, USA

Event date

Aug 29, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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