title: Improving the alignment quality of consistency based aligners with an evaluation function using synonymous protein words.
abstract: Most sequence alignment tools can successfully align protein sequences with higher levels of sequence identity. The accuracy of corresponding structure alignment, however, decreases rapidly when considering distantly related sequences (<20% identity). In this range of identity, alignments optimized so as to maximize sequence similarity are often inaccurate from a structural point of view. Over the last two decades, most multiple protein aligners have been optimized for their capacity to reproduce structure-based alignments while using sequence information. Methods currently available differ essentially in the similarity measurement between aligned residues using substitution matrices, Fourier transform, sophisticated profile-profile functions, or consistency-based approaches, more recently.In this paper, we present a flexible similarity measure for residue pairs to improve the quality of protein sequence alignment. Our approach, called SymAlign, relies on the identification of conserved words found across a sizeable fraction of the considered dataset, and supported by evolutionary analysis. These words are then used to define a position specific substitution matrix that better reflects the biological significance of local similarity. The experiment results show that the SymAlign scoring scheme can be incorporated within T-Coffee to improve sequence alignment accuracy. We also demonstrate that SymAlign is less sensitive to the presence of structurally non-similar proteins. In the analysis of the relationship between sequence identity and structure similarity, SymAlign can better differentiate structurally similar proteins from non- similar proteins. We show that protein sequence alignments can be significantly improved using a similarity estimation based on weighted n-grams. In our analysis of the alignments thus produced, sequence conservation becomes a better indicator of structural similarity. SymAlign also provides alignment visualization that can display sub-optimal alignments on dot-matrices. The visualization makes it easy to identify well-supported alternative alignments that may not have been identified by dynamic programming. SymAlign is available at http://bio-cluster.iis.sinica.edu.tw/SymAlign/.
authors.affiliation: Bioinformatics Lab, Institute of Information Science, Academia Sinica, Taipei, Taiwan.
authors.forename: Hsin-Nan
authors.initials: HN
authors.lastname: Lin
authors.forename: Cédric
authors.initials: C
authors.lastname: Notredame
authors.forename: Jia-Ming
authors.initials: JM
authors.lastname: Chang
authors.forename: Ting-Yi
authors.initials: TY
authors.lastname: Sung
authors.forename: Wen-Lian
authors.initials: WL
authors.lastname: Hsu
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D007802:Language; D015233:Models, Statistical; D009154:Mutation; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D013997:Time Factors
chemical_list: D011506:Proteins