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Prediction of protein-mannose binding sites using random forest

Mannose is an abundant cell surface monosaccharide and has an important role in many biochemical processes. It binds to a great diversity of receptor proteins. In this study we have employed Random Forest for prediction of mannose binding sites. Mannosebinding site is taken to be a sphere around the...

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Detalles Bibliográficos
Autores principales: Khare, Harshvardan, Ratnaparkhi, Vivek, Chavan, Sonali, Jayraman, Valadi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3530872/
https://www.ncbi.nlm.nih.gov/pubmed/23275720
http://dx.doi.org/10.6026/97320630081202
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author Khare, Harshvardan
Ratnaparkhi, Vivek
Chavan, Sonali
Jayraman, Valadi
author_facet Khare, Harshvardan
Ratnaparkhi, Vivek
Chavan, Sonali
Jayraman, Valadi
author_sort Khare, Harshvardan
collection PubMed
description Mannose is an abundant cell surface monosaccharide and has an important role in many biochemical processes. It binds to a great diversity of receptor proteins. In this study we have employed Random Forest for prediction of mannose binding sites. Mannosebinding site is taken to be a sphere around the centroid of the ligand and the sphere is subdivided into different layers and atom wise and residue wise features were extracted for each layer. The method achieves 95.59 % of accuracy using Random Forest with 10 fold cross validation. Prediction of mannose binding site analysis will be quite useful in drug design.
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spelling pubmed-35308722012-12-28 Prediction of protein-mannose binding sites using random forest Khare, Harshvardan Ratnaparkhi, Vivek Chavan, Sonali Jayraman, Valadi Bioinformation Hypothesis Mannose is an abundant cell surface monosaccharide and has an important role in many biochemical processes. It binds to a great diversity of receptor proteins. In this study we have employed Random Forest for prediction of mannose binding sites. Mannosebinding site is taken to be a sphere around the centroid of the ligand and the sphere is subdivided into different layers and atom wise and residue wise features were extracted for each layer. The method achieves 95.59 % of accuracy using Random Forest with 10 fold cross validation. Prediction of mannose binding site analysis will be quite useful in drug design. Biomedical Informatics 2012-12-08 /pmc/articles/PMC3530872/ /pubmed/23275720 http://dx.doi.org/10.6026/97320630081202 Text en © 2012 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Hypothesis
Khare, Harshvardan
Ratnaparkhi, Vivek
Chavan, Sonali
Jayraman, Valadi
Prediction of protein-mannose binding sites using random forest
title Prediction of protein-mannose binding sites using random forest
title_full Prediction of protein-mannose binding sites using random forest
title_fullStr Prediction of protein-mannose binding sites using random forest
title_full_unstemmed Prediction of protein-mannose binding sites using random forest
title_short Prediction of protein-mannose binding sites using random forest
title_sort prediction of protein-mannose binding sites using random forest
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3530872/
https://www.ncbi.nlm.nih.gov/pubmed/23275720
http://dx.doi.org/10.6026/97320630081202
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