Skip to main content
Log in

A non-local gap-penalty for profile alignment

  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

The length of an alignment of biological sequences is typically longer than the mean length of its component sequences. (This arises from the insertion of gaps in the alignment.) When such an alignment is used as a profile for the alignment of further sequences (or profiles), it will have a bias toward additional sequences that match the length of the profile, rather than the mean length of sequences in the profile, as the alignment of these well entail fewer (or smaller) insertions) so avoiding gap-penalties). An algorithm is described to correct this bias that entails monitoring the correspondence, for every pair of positions, of the mean separations in both profiles as they are aligned. The correction was incorporated into a standard dynamic programming algorithm through a modification of the gap-penalty, but, unlike other approaches, this modification is not local and takes into consideration the overall alignment of the sequences. This implies that the algorithm cannot guarantee to find the optimal alignment, but tests suggest that close approximations are obtained. The method was tested on protein families by measuring the area in the parameter space of the phase containing the correct multiple alignment. No improvement (increase in phase area) was found with a family that required few gaps to be aligned correctly. However, for highly gapped alignments, a 50% increase in area was obtained with one family and the correct alignment was found for another that could not be aligned with the unbiased method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Barton, G. J. and M. J. E. Sternberg, 1987a. Evaluation and improvements in the automatic alignment of protein sequences.Protein Eng. 1, 89–94.

    Google Scholar 

  • Barton, G. J. and M. J. E. Sternberg, 1987b. A strategy for the rapid multiple alignment of protein sequences.J. Mol. Biol. 198, 327–337.

    Article  Google Scholar 

  • Dayhoff, M. O., R. M. Schwartz and B. C. Orcutt, 1978. A model of evolutionary change in proteins.In Atlas of Protein Sequence and Structure, M. O. Dayhoff (Ed), Vol. 5, Suppl. 3, pp. 345–352. Washington DC: Nat. Biomed. Res. Foundation.

    Google Scholar 

  • Doolittle, R. F., D. F. Feng, M. S. Johnson and M. A. McClure, 1989. Origins and evolutionary relationships of retroviruses.Quart. Rev. Biol. 64, 1–30.

    Article  Google Scholar 

  • Feng, D. F. and R. F. Doolittle, 1987. Progressive sequence alignment as a prerequisite to correct phylogenetic trees.J. Mol. Evol. 25, 351–360.

    Google Scholar 

  • Gribskov, M., A. D. McLachlan and D. Eisenberg, 1987. Profile analysis: detection of distantly related proteins.Proc. Natl. Acad. Sci. U.S.A. 84, 4355–4358.

    Article  Google Scholar 

  • Higgins, D. G. and P. M. Sharp, 1988. Clustal: a package for performing multiple sequence alignment on a microcomputer.Gene 73, 237–244.

    Article  Google Scholar 

  • Jones, D. T., W. R. Taylor and J. M. Thornton, 1992. A new approach to protein fold recognition.Nature 358, 86–89.

    Article  Google Scholar 

  • Lathrop, R. H., 1994. The protein threading problem with sequence amino acid interaction preferences is NP-complete.Protein Eng. 7, 1059–1068.

    Google Scholar 

  • Lesk, A., M. Levitt and C. Chothia, 1986. Alignment of the amino acid sequences of distantly related proteins using variable gap penalties.Protein Eng. 1, 77–78.

    Google Scholar 

  • McClure, M. A., T. K. Vasi and W. M. Fitch, 1994. Comparative analysis of multiple protein-sequence alignment methods.Mol. Biol. Evol. 11 571–592.

    Google Scholar 

  • Musacchio, A., T. J. Gibson, V. P. Lehto and M. Saraste, 1992. SH3—an abundant protein domain in search of a function.FEBS Lett. 307, 55–61.

    Article  Google Scholar 

  • Needleman, S. B. and C. D. Wunsch, 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins.J. Mol. Biol. 48, 443–453.

    Article  Google Scholar 

  • Taylor, W. R. and C. A. Orengo, 1989. A protein structure alignment.J. Mol. Biol. 208, 1–22.

    Article  Google Scholar 

  • Taylor, W. R., 1988. A flexible method to align large numbers of biological sequences.J. Mol. Evol. 28, 161–169.

    Article  Google Scholar 

  • Taylor, W. R., 1989. A template based method of pattern matching in protein sequences.Prog. Biophys. Mol. Biol. 54, 159–252.

    Article  Google Scholar 

  • Taylor, W. R. 1994. Motif-biased protein sequence alignment.J. Comp. Biol. 1.

  • Thompson, J. D., D. G. Higgins and T. J. Gibson, 1994a. Clustal-W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.Nucleic Acids Res. 22, 4673–4680.

    Google Scholar 

  • Thompson, J. D., D. G. Higgins and T. J. Gibson, 1994b. Improved sensitivity of profile searches through the use of sequence weights and gap excision.CABIOS 10, 19–29.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Taylor, W.R. A non-local gap-penalty for profile alignment. Bltn Mathcal Biology 58, 1–18 (1996). https://doi.org/10.1007/BF02458279

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02458279

Keywords

Navigation