Literature DB >> 32954566

Substitution scoring matrices for proteins - An overview.

Rakesh Trivedi1,2, Hampapathalu Adimurthy Nagarajaram3,4.   

Abstract

Sequence analysis is the primary and simplest approach to discover structural, functional and evolutionary details of related proteins. All the alignment based approaches of sequence analysis make use of amino acid substitution matrices, and the accuracy of the results largely depends on the type of scoring matrices used to perform alignment tasks. An amino acid substitution matrix is a 20 × 20 matrix in which the individual elements encapsulate the rates at which each of the 20 amino acid residues in proteins are substituted by other amino acid residues over time. In contrast to most globular/ordered proteins whose amino acids composition is considered as standard, there are several classes of proteins (e.g., transmembrane proteins) in which certain types of amino acid (e.g., hydrophobic residues) are enriched. These compositional differences among various classes of proteins are manifested in their underlying residue substitution frequencies. Therefore, each of the compositionally distinct class of proteins or protein segments should be studied using specific scoring matrices that reflect their distinct residue substitution pattern. In this review, we describe the development and application of various substitution scoring matrices peculiar to proteins with standard and biased compositions. Along with most commonly used standard matrices (PAM, BLOSUM, MD and VTML) that act as default parameters in various homologs search and alignment tools, different substitution scoring matrices specific to compositionally distinct class of proteins are discussed in detail.
© 2020 The Protein Society.

Entities:  

Keywords:  amino acid substitution matrix; general purpose matrix; sequence alignments; sequence analysis; specialized matrix

Mesh:

Substances:

Year:  2020        PMID: 32954566      PMCID: PMC7586916          DOI: 10.1002/pro.3954

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  84 in total

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Journal:  BMC Bioinformatics       Date:  2017-06-05       Impact factor: 3.169

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  2 in total

Review 1.  Substitution scoring matrices for proteins - An overview.

Authors:  Rakesh Trivedi; Hampapathalu Adimurthy Nagarajaram
Journal:  Protein Sci       Date:  2020-10-12       Impact factor: 6.725

2.  Quantifying prediction of pathogenicity for within-codon concordance (PM5) using 7541 functional classifications of BRCA1 and MSH2 missense variants.

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Journal:  Genet Med       Date:  2021-11-18       Impact factor: 8.864

  2 in total

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