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ECMIS: computational approach for the identification of hotspots at protein-protein interfaces

BACKGROUND: Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence spa...

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Autores principales: Shingate, Prashant, Manoharan, Malini, Sukhwal, Anshul, Sowdhamini, Ramanathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177600/
https://www.ncbi.nlm.nih.gov/pubmed/25228146
http://dx.doi.org/10.1186/1471-2105-15-303
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author Shingate, Prashant
Manoharan, Malini
Sukhwal, Anshul
Sowdhamini, Ramanathan
author_facet Shingate, Prashant
Manoharan, Malini
Sukhwal, Anshul
Sowdhamini, Ramanathan
author_sort Shingate, Prashant
collection PubMed
description BACKGROUND: Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence space at residue level to highlight interface residues crucial for the protein-protein complex formation. RESULTS: ECMIS (Energetic Conservation Mass Index and Spatial Clustering) algorithm was able to outperform existing hotspot identification methods. It was able to achieve around 80% accuracy with incredible increase in sensitivity and outperforms other existing methods. This method is even sensitive towards the hotspot residues contributing only small-scale hydrophobic interactions. CONCLUSION: Combination of diverse features of the protein viz. energy contribution, extent of conservation, location and surrounding environment, along with optimized weightage for each feature, was the key for the success of the algorithm. The academic version of the algorithm is available at http://caps.ncbs.res.in/download/ECMIS/ECMIS.zip. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-303) contains supplementary material, which is available to authorized users.
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spelling pubmed-41776002014-09-29 ECMIS: computational approach for the identification of hotspots at protein-protein interfaces Shingate, Prashant Manoharan, Malini Sukhwal, Anshul Sowdhamini, Ramanathan BMC Bioinformatics Research Article BACKGROUND: Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence space at residue level to highlight interface residues crucial for the protein-protein complex formation. RESULTS: ECMIS (Energetic Conservation Mass Index and Spatial Clustering) algorithm was able to outperform existing hotspot identification methods. It was able to achieve around 80% accuracy with incredible increase in sensitivity and outperforms other existing methods. This method is even sensitive towards the hotspot residues contributing only small-scale hydrophobic interactions. CONCLUSION: Combination of diverse features of the protein viz. energy contribution, extent of conservation, location and surrounding environment, along with optimized weightage for each feature, was the key for the success of the algorithm. The academic version of the algorithm is available at http://caps.ncbs.res.in/download/ECMIS/ECMIS.zip. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-303) contains supplementary material, which is available to authorized users. BioMed Central 2014-09-16 /pmc/articles/PMC4177600/ /pubmed/25228146 http://dx.doi.org/10.1186/1471-2105-15-303 Text en © Shingate et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 Article
Shingate, Prashant
Manoharan, Malini
Sukhwal, Anshul
Sowdhamini, Ramanathan
ECMIS: computational approach for the identification of hotspots at protein-protein interfaces
title ECMIS: computational approach for the identification of hotspots at protein-protein interfaces
title_full ECMIS: computational approach for the identification of hotspots at protein-protein interfaces
title_fullStr ECMIS: computational approach for the identification of hotspots at protein-protein interfaces
title_full_unstemmed ECMIS: computational approach for the identification of hotspots at protein-protein interfaces
title_short ECMIS: computational approach for the identification of hotspots at protein-protein interfaces
title_sort ecmis: computational approach for the identification of hotspots at protein-protein interfaces
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177600/
https://www.ncbi.nlm.nih.gov/pubmed/25228146
http://dx.doi.org/10.1186/1471-2105-15-303
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