Revisiting amino acid substitution matrices for identifying distantly related proteins.

ABSTRACT:

Motivation

Although many amino acid substitution matrices have been developed, it has not been well understood which is the best for similarity searches, especially for remote homology detection. Therefore, we collected information related to existing matrices, condensed it and derived a novel matrix that can detect more remote homology than ever.

Results

Using principal component analysis with existing matrices and benchmarks, we developed a novel matrix, which we designate as MIQS. The detection performance of MIQS is validated and compared with that of existing general purpose matrices using SSEARCH with optimized gap penalties for each matrix. Results show that MIQS is able to detect more remote homology than the existing matrices on an independent dataset. In addition,

SUBMITTER: Yamada K 

PROVIDER: S-EPMC3904525 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature