Using linear algebra for protein structural comparison and classification

Genetics and Molecular Biology

Janaína Gomide²  Raquel Melo-minardi²  Marcos Augusto Dos Santos²  Goran Neshich¹  Wagner Meira Jr.²  Júlio César Lopes¹  Marcelo Santoro¹ 

[1] ,Universidade Federal de Minas Gerais Departamento de Ciência da Computação Belo Horizonte MG ,Brazil

关键词: protein classification;    contact maps;    linear algebra;    singular value decomposition;    latent semantic indexing;   

DOI  :  10.1590/S1415-47572009000300032

来源: SciELO

【 摘 要 】

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

【 授权许可】

CC BY   
 All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License