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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10600 - Biological sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-1-4503-5794-4

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

6

Strana od-do

645-650

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Washington, DC, USA

Datum konání akce

29. 8. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—