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Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction
In recent years, although many ligand-binding site prediction methods have been developed, there has still been a great demand to improve the prediction accuracy and compare different prediction algorithms to evaluate their performances. In this work, in order to improve the performance of the prote...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3430263/ https://www.ncbi.nlm.nih.gov/pubmed/22942732 http://dx.doi.org/10.3390/ijms13078752 |
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author | Gao, Jun Liu, Qi Kang, Hong Cao, Zhiwei Zhu, Ruixin |
author_facet | Gao, Jun Liu, Qi Kang, Hong Cao, Zhiwei Zhu, Ruixin |
author_sort | Gao, Jun |
collection | PubMed |
description | In recent years, although many ligand-binding site prediction methods have been developed, there has still been a great demand to improve the prediction accuracy and compare different prediction algorithms to evaluate their performances. In this work, in order to improve the performance of the protein-ligand binding site prediction method presented in our former study, a comparison of different binding site ranking lists was studied. Four kinds of properties, i.e., pocket size, distance from the protein centroid, sequence conservation and the number of hydrophobic residues, have been chosen as the corresponding ranking criterion respectively. Our studies show that the sequence conservation information helps to rank the real pockets with the most successful accuracy compared to others. At the same time, the pocket size and the distance of binding site from the protein centroid are also found to be helpful. In addition, a multi-view ranking aggregation method, which combines the information among those four properties, was further applied in our study. The results show that a better performance can be achieved by the aggregation of the complementary properties in the prediction of ligand-binding sites. |
format | Online Article Text |
id | pubmed-3430263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-34302632012-08-31 Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction Gao, Jun Liu, Qi Kang, Hong Cao, Zhiwei Zhu, Ruixin Int J Mol Sci Article In recent years, although many ligand-binding site prediction methods have been developed, there has still been a great demand to improve the prediction accuracy and compare different prediction algorithms to evaluate their performances. In this work, in order to improve the performance of the protein-ligand binding site prediction method presented in our former study, a comparison of different binding site ranking lists was studied. Four kinds of properties, i.e., pocket size, distance from the protein centroid, sequence conservation and the number of hydrophobic residues, have been chosen as the corresponding ranking criterion respectively. Our studies show that the sequence conservation information helps to rank the real pockets with the most successful accuracy compared to others. At the same time, the pocket size and the distance of binding site from the protein centroid are also found to be helpful. In addition, a multi-view ranking aggregation method, which combines the information among those four properties, was further applied in our study. The results show that a better performance can be achieved by the aggregation of the complementary properties in the prediction of ligand-binding sites. Molecular Diversity Preservation International (MDPI) 2012-07-16 /pmc/articles/PMC3430263/ /pubmed/22942732 http://dx.doi.org/10.3390/ijms13078752 Text en © 2012 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Gao, Jun Liu, Qi Kang, Hong Cao, Zhiwei Zhu, Ruixin Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction |
title | Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction |
title_full | Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction |
title_fullStr | Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction |
title_full_unstemmed | Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction |
title_short | Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction |
title_sort | comparison of different ranking methods in protein-ligand binding site prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3430263/ https://www.ncbi.nlm.nih.gov/pubmed/22942732 http://dx.doi.org/10.3390/ijms13078752 |
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