Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10369379" target="_blank" >RIV/00216208:11320/17:10369379 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1186/s12859-017-1921-4" target="_blank" >http://dx.doi.org/10.1186/s12859-017-1921-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12859-017-1921-4" target="_blank" >10.1186/s12859-017-1921-4</a>

Result language

angličtina

Original language name

Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites

Original language description

Background: Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. Results: We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE&apos;s behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. Conclusion: In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GP14-29032P" target="_blank" >GP14-29032P: Efficient chemical space exploration using multi-objective optimization</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

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

Name of the periodical

BMC Bioinformatics

ISSN

1471-2105

e-ISSN

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Volume of the periodical

18

Issue of the periodical within the volume

Supplement 15

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

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UT code for WoS article

000417562500006

EID of the result in the Scopus database

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