Analysis and prediction of functional sub-types from protein sequence alignments.
The increasing number and diversity of protein sequence families requires new methods to define and predict details regarding function. Here, we present a method for analysis and prediction of functional sub-types from multiple protein sequence alignments. Given an alignment and set of proteins grouped into sub-types according to some definition of function, such as enzymatic specificity, the method identifies positions that are indicative of functional differences by comparison of sub-type specific sequence profiles, and analysis of positional entropy in the alignment. Alignment positions with significantly high positional relative entropy correlate with those known to be involved in defining sub-types for nucleotidyl cyclases, protein kinases, lactate/malate dehydrogenases and trypsin-like serine proteases. We highlight new positions for these proteins that suggest additional experiments to elucidate the basis of specificity. The method is also able to predict sub-type for unclassified sequences. We assess several variations on a prediction method, and compare them to simple sequence comparisons. For assessment, we remove close homologues to the sequence for which a prediction is to be made (by a sequence identity above a threshold). This simulates situations where a protein is known to belong to a protein family, but is not a close relative of another protein of known sub-type. Considering the four families above, and a sequence identity threshold of 30 %, our best method gives an accuracy of 96 % compared to 80 % obtained for sequence similarity and 74 % for BLAST. We describe the derivation of a set of sub-type groupings derived from an automated parsing of alignments from PFAM and the SWISSPROT database, and use this to perform a large-scale assessment. The best method gives an average accuracy of 94 % compared to 68 % for sequence similarity and 79 % for BLAST. We discuss implications for experimental design, genome annotation and the prediction of protein function and protein intra-residue distances.
Hannenhalli
S S
SS
Bioinformatics Research Group, SmithKline Beecham Pharmaceuticals Research & Development, 709 Swedeland Road, King of Prussia, PA 19406, USA.
Russell
R B
RB
D000262:Adenylyl Cyclases / Q000737:chemistry / Q000378:metabolism; D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D019992:Databases as Topic; D019277:Entropy; D006162:Guanylate Cyclase / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D007770:L-Lactate Dehydrogenase / Q000737:chemistry / Q000378:metabolism; D008291:Malate Dehydrogenase / Q000737:chemistry / Q000378:metabolism; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D011494:Protein Kinases / Q000737:chemistry / Q000378:metabolism; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D012697:Serine Endopeptidases / Q000737:chemistry / Q000378:metabolism; D012984:Software; D013329:Structure-Activity Relationship; D013379:Substrate Specificity
D011506:Proteins; D007770:L-Lactate Dehydrogenase; D008291:Malate Dehydrogenase; D011494:Protein Kinases; D012697:Serine Endopeptidases; D000262:Adenylyl Cyclases; D006162:Guanylate Cyclase