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Identifying protein-protein interface via a novel multi-scale local sequence and structural representation

BACKGROUND: Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great...

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Autores principales: Guo, Fei, Zou, Quan, Yang, Guang, Wang, Dan, Tang, Jijun, Xu, Junhai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929278/
https://www.ncbi.nlm.nih.gov/pubmed/31874604
http://dx.doi.org/10.1186/s12859-019-3048-2
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author Guo, Fei
Zou, Quan
Yang, Guang
Wang, Dan
Tang, Jijun
Xu, Junhai
author_facet Guo, Fei
Zou, Quan
Yang, Guang
Wang, Dan
Tang, Jijun
Xu, Junhai
author_sort Guo, Fei
collection PubMed
description BACKGROUND: Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. RESULTS: In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average I(rmsd) value of 3.28Å and overall F(nat) value of 63%, which improves upon I(rmsd) of 3.89Å and F(nat) of 49% for ZRANK, and I(rmsd) of 3.99Å and F(nat) of 46% for ClusPro. On CAPRI targets, our method achieves average I(rmsd) value of 3.45Å and overall F(nat) value of 46%, which improves upon I(rmsd) of 4.18Å and F(nat) of 40% for ZRANK, and I(rmsd) of 5.12Å and F(nat) of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. CONCLUSION: Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.
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spelling pubmed-69292782019-12-30 Identifying protein-protein interface via a novel multi-scale local sequence and structural representation Guo, Fei Zou, Quan Yang, Guang Wang, Dan Tang, Jijun Xu, Junhai BMC Bioinformatics Research BACKGROUND: Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. RESULTS: In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average I(rmsd) value of 3.28Å and overall F(nat) value of 63%, which improves upon I(rmsd) of 3.89Å and F(nat) of 49% for ZRANK, and I(rmsd) of 3.99Å and F(nat) of 46% for ClusPro. On CAPRI targets, our method achieves average I(rmsd) value of 3.45Å and overall F(nat) value of 46%, which improves upon I(rmsd) of 4.18Å and F(nat) of 40% for ZRANK, and I(rmsd) of 5.12Å and F(nat) of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. CONCLUSION: Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface. BioMed Central 2019-12-24 /pmc/articles/PMC6929278/ /pubmed/31874604 http://dx.doi.org/10.1186/s12859-019-3048-2 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Guo, Fei
Zou, Quan
Yang, Guang
Wang, Dan
Tang, Jijun
Xu, Junhai
Identifying protein-protein interface via a novel multi-scale local sequence and structural representation
title Identifying protein-protein interface via a novel multi-scale local sequence and structural representation
title_full Identifying protein-protein interface via a novel multi-scale local sequence and structural representation
title_fullStr Identifying protein-protein interface via a novel multi-scale local sequence and structural representation
title_full_unstemmed Identifying protein-protein interface via a novel multi-scale local sequence and structural representation
title_short Identifying protein-protein interface via a novel multi-scale local sequence and structural representation
title_sort identifying protein-protein interface via a novel multi-scale local sequence and structural representation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929278/
https://www.ncbi.nlm.nih.gov/pubmed/31874604
http://dx.doi.org/10.1186/s12859-019-3048-2
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