title: NdPASA: a novel pairwise protein sequence alignment algorithm that incorporates neighbor-dependent amino acid propensities.
abstract: Sequence alignment has become one of the essential bioinformatics tools in biomedical research. Existing sequence alignment methods can produce reliable alignments for homologous proteins sharing a high percentage of sequence identity. The performance of these methods deteriorates sharply for the sequence pairs sharing less than 25% sequence identity. We report here a new method, NdPASA, for pairwise sequence alignment. This method employs neighbor-dependent propensities of amino acids as a unique parameter for alignment. The values of neighbor-dependent propensity measure the preference of an amino acid pair adopting a particular secondary structure conformation. NdPASA optimizes alignment by evaluating the likelihood of a residue pair in the query sequence matching against a corresponding residue pair adopting a particular secondary structure in the template sequence. Using superpositions of homologous proteins derived from the PSI-BLAST analysis and the Structural Classification of Proteins (SCOP) classification of a nonredundant Protein Data Bank (PDB) database as a gold standard, we show that NdPASA has improved pairwise alignment. Statistical analyses of the performance of NdPASA indicate that the introduction of sequence patterns of secondary structure derived from neighbor-dependent sequence analysis clearly improves alignment performance for sequence pairs sharing less than 20% sequence identity. For sequence pairs sharing 13-21% sequence identity, NdPASA improves the accuracy of alignment over the conventional global alignment (GA) algorithm using the BLOSUM62 by an average of 8.6%. NdPASA is most effective for aligning query sequences with template sequences whose structure is known. NdPASA can be accessed online at http://astro.temple.edu/feng/Servers/BioinformaticServers.htm.
authors.affiliation: Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, USA.
authors.forename: Junwen
authors.initials: J
authors.lastname: Wang
authors.forename: Jin-An
authors.initials: JA
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D030562:Databases, Protein; D020407:Internet; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D012241:Rhodopseudomonas / Q000378:metabolism; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D017386:Sequence Homology, Amino Acid
chemical_list: D000596:Amino Acids; D011506:Proteins