Sensitive pattern discovery with 'fuzzy' alignments of distantly related proteins.
BACKGROUND
Evolutionary comparison leads to efficient functional characterisation of hypothetical proteins. Here, our goal is to map specific sequence patterns to putative functional classes. The evolutionary signal stands out most clearly in a maximally diverse set of homologues. This diversity, however, leads to a number of technical difficulties. The targeted patterns-as gleaned from structure comparisons-are too sparse for statistically significant signals of sequence similarity and accurate multiple sequence alignment.


RESULTS
We address this problem by a fuzzy alignment model, which probabilistically assigns residues to structurally equivalent positions (attributes) of the proteins. We then apply multivariate analysis to the 'attributes x proteins' matrix. The dimensionality of the space is reduced using non-negative matrix factorization. The method is general, fully automatic and works without assumptions about pattern density, minimum support, explicit multiple alignments, phylogenetic trees, etc. We demonstrate the discovery of biologically meaningful patterns in an extremely diverse superfamily related to urease.
Heger
Andreas
A
Institute of Biotechnology, P.O. Box 56, 00014 University of Helsinki Finland.
Holm
Liisa
L
D000465:Algorithms; D000595:Amino Acid Sequence; D016000:Cluster Analysis; D019143:Evolution, Molecular; D017143:Fuzzy Logic; D020869:Gene Expression Profiling; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000737:chemistry* / Q000145:classification* / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D014510:Urease / Q000737:chemistry / Q000145:classification / Q000235:genetics
D011506:Proteins; D014510:Urease