Selecting the Right Similarity‐Scoring Matrix

William R. Pearson1

1 University of Virginia School of Medicine, Charlottesville, Virginia
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 3.5
DOI:  10.1002/0471250953.bi0305s43
Online Posting Date:  October, 2013
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library


Protein sequence similarity searching programs like BLASTP, SSEARCH, and FASTA use scoring matrices that are designed to identify distant evolutionary relationships (BLOSUM62 for BLAST, BLOSUM50 for SSEARCH and FASTA). Different similarity scoring matrices are most effective at different evolutionary distances. “Deep” scoring matrices like BLOSUM62 and BLOSUM50 target alignments with 20% to 30% identity, while “shallow” scoring matrices (e.g., VTML10 to VTML80) target alignments that share 90% to 50% identity, reflecting much less evolutionary change. While “deep” matrices provide very sensitive similarity searches, they also require longer sequence alignments and can sometimes produce alignment overextension into nonhomologous regions. Shallower scoring matrices are more effective when searching for short protein domains, or when the goal is to limit the scope of the search to sequences that are likely to be orthologous between recently diverged organisms. Likewise, in DNA searches, the match and mismatch parameters set evolutionary look‐back times and domain boundaries. In this unit, we will discuss the theoretical foundations that drive practical choices of protein and DNA similarity scoring matrices and gap penalties. Deep scoring matrices (BLOSUM62 and BLOSUM50) should be used for sensitive searches with full‐length protein sequences, but short domains or restricted evolutionary look‐back require shallower scoring matrices. Curr. Protoc. Bioinform. 43:3.5.1‐3.5.9. © 2013 by John Wiley & Sons, Inc.

Keywords: similarity scoring matrices; PAM matrices; BLOSUM matrices; sequence alignment

PDF or HTML at Wiley Online Library

Table of Contents

  • Similarity Searching, Homology, and Statistical Significance
  • Amino Acid Substitution Matrices: History and Classification
  • The Algebra of Similarity Scoring (Log‐Odds) Matrices
  • Scoring Matrices and Gap Penalties
  • Long Alignments and Overextension
  • Scoring Matrices for DNA
  • Summary
  • Literature Cited
  • Figures
  • Tables
PDF or HTML at Wiley Online Library


PDF or HTML at Wiley Online Library



Literature Cited

Literature Cited
  Altschul, S.F. 1991. Amino acid substitution matrices from an information theoretic perspective. J. Mol. Biol. 219:555‐565.
  Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. 1990. A basic local alignment search tool. J. Mol. Biol. 215:403‐410.
  Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI‐BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25:3389‐3402.
  Gonnet, G.H., Cohen, M.A., and Benner, S.A. 1992. Exhaustive matching of the entire protein sequence database. Science 256:1443‐1445.
  Gonzalez, M.W. and Pearson, W.R. 2010. Homologous over‐extension: A challenge for iterative similarity searches. Nucleic Acids Res. 38:2177‐2189.
  Henikoff, S. and Henikoff, J.G. 1992. Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. U.S.A. 89:10915‐10919.
  Jones, D.T., Taylor, W.R., and Thornton, J.M. 1992. The rapid generation of mutation data matrices from protein sequences. Comp. Appl. Biosci. 8:275‐282.
  Mueller, T., Spang, R., and Vingron, M. 2002. Estimating amino acid substitution models: A comparison of Dayhoff's estimator, the resolvent approach and a maximum likelihood method. Mol. Biol. Evol. 19:8‐13.
  Pearson, W.R. 1991. Searching protein sequence libraries: Comparison of the sensitivity and selectivity of the Smith‐Waterman and FASTA algorithms. Genomics 11:635‐650.
  Pearson, W.R. and Lipman, D.J. 1988. Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. U.S.A. 85:2444‐2448.
  Reese, J.T. and Pearson, W.R. 2002. Empirical determination of effective gap penalties for sequence comparison. Bioinformatics 18:1500‐1507.
  Schwartz, R.M. and Dayhoff, M. 1978. Matrices for detecting distant relationships. In Atlas of Protein Sequence and Structure, Volume 5, Supplement 3 (M. Dayhoff, ed.), pp. 353‐358. National Biomedical Research Foundation, Silver Spring, Maryland.
  Smith, T.F. and Waterman, M.S. 1981. Identification of common molecular subsequences. J. Mol. Biol. 147:195‐197.
  States, D.J., Gish, W., and Altschul, S.F. 1991. Improved sensitivity of nucleic acid database searches using application‐specific scoring matrices. Methods Enzymol. 3:66‐70.
PDF or HTML at Wiley Online Library