Identification of mannose interacting residues using local composition.

<h4>Background</h4>Mannose binding proteins (MBPs) play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. Thus, it is important to iden...

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Autores principales:	Sandhya Agarwal, Nitish Kumar Mishra, Harinder Singh, Gajendra P S Raghava
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2d15654df190436088f0e2f45ab6b643
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spelling	oai:doaj.org-article:2d15654df190436088f0e2f45ab6b6432021-11-04T06:08:46ZIdentification of mannose interacting residues using local composition.1932-620310.1371/journal.pone.0024039https://doaj.org/article/2d15654df190436088f0e2f45ab6b6432011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21931639/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Mannose binding proteins (MBPs) play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. Thus, it is important to identify mannose interacting residues (MIRs) in order to understand mechanism of recognition of pathogens by MBPs.<h4>Results</h4>This paper describes modules developed for predicting MIRs in a protein. Support vector machine (SVM) based models have been developed on 120 mannose binding protein chains, where no two chains have more than 25% sequence similarity. SVM models were developed on two types of datasets: 1) main dataset consists of 1029 mannose interacting and 1029 non-interacting residues, 2) realistic dataset consists of 1029 mannose interacting and 10320 non-interacting residues. In this study, firstly, we developed standard modules using binary and PSSM profile of patterns and got maximum MCC around 0.32. Secondly, we developed SVM modules using composition profile of patterns and achieved maximum MCC around 0.74 with accuracy 86.64% on main dataset. Thirdly, we developed a model on a realistic dataset and achieved maximum MCC of 0.62 with accuracy 93.08%. Based on this study, a standalone program and web server have been developed for predicting mannose interacting residues in proteins (http://www.imtech.res.in/raghava/premier/).<h4>Conclusions</h4>Compositional analysis of mannose interacting and non-interacting residues shows that certain types of residues are preferred in mannose interaction. It was also observed that residues around mannose interacting residues have a preference for certain types of residues. Composition of patterns/peptide/segment has been used for predicting MIRs and achieved reasonable high accuracy. It is possible that this novel strategy may be effective to predict other types of interacting residues. This study will be useful in annotating the function of protein as well as in understanding the role of mannose in the immune system.Sandhya AgarwalNitish Kumar MishraHarinder SinghGajendra P S RaghavaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 9, p e24039 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sandhya Agarwal Nitish Kumar Mishra Harinder Singh Gajendra P S Raghava Identification of mannose interacting residues using local composition.
description	<h4>Background</h4>Mannose binding proteins (MBPs) play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. Thus, it is important to identify mannose interacting residues (MIRs) in order to understand mechanism of recognition of pathogens by MBPs.<h4>Results</h4>This paper describes modules developed for predicting MIRs in a protein. Support vector machine (SVM) based models have been developed on 120 mannose binding protein chains, where no two chains have more than 25% sequence similarity. SVM models were developed on two types of datasets: 1) main dataset consists of 1029 mannose interacting and 1029 non-interacting residues, 2) realistic dataset consists of 1029 mannose interacting and 10320 non-interacting residues. In this study, firstly, we developed standard modules using binary and PSSM profile of patterns and got maximum MCC around 0.32. Secondly, we developed SVM modules using composition profile of patterns and achieved maximum MCC around 0.74 with accuracy 86.64% on main dataset. Thirdly, we developed a model on a realistic dataset and achieved maximum MCC of 0.62 with accuracy 93.08%. Based on this study, a standalone program and web server have been developed for predicting mannose interacting residues in proteins (http://www.imtech.res.in/raghava/premier/).<h4>Conclusions</h4>Compositional analysis of mannose interacting and non-interacting residues shows that certain types of residues are preferred in mannose interaction. It was also observed that residues around mannose interacting residues have a preference for certain types of residues. Composition of patterns/peptide/segment has been used for predicting MIRs and achieved reasonable high accuracy. It is possible that this novel strategy may be effective to predict other types of interacting residues. This study will be useful in annotating the function of protein as well as in understanding the role of mannose in the immune system.
format	article
author	Sandhya Agarwal Nitish Kumar Mishra Harinder Singh Gajendra P S Raghava
author_facet	Sandhya Agarwal Nitish Kumar Mishra Harinder Singh Gajendra P S Raghava
author_sort	Sandhya Agarwal
title	Identification of mannose interacting residues using local composition.
title_short	Identification of mannose interacting residues using local composition.
title_full	Identification of mannose interacting residues using local composition.
title_fullStr	Identification of mannose interacting residues using local composition.
title_full_unstemmed	Identification of mannose interacting residues using local composition.
title_sort	identification of mannose interacting residues using local composition.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/2d15654df190436088f0e2f45ab6b643
work_keys_str_mv	AT sandhyaagarwal identificationofmannoseinteractingresiduesusinglocalcomposition AT nitishkumarmishra identificationofmannoseinteractingresiduesusinglocalcomposition AT harindersingh identificationofmannoseinteractingresiduesusinglocalcomposition AT gajendrapsraghava identificationofmannoseinteractingresiduesusinglocalcomposition
_version_	1718445140675133440

Identification of mannose interacting residues using local composition.

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