title: Discrete profile comparison using information bottleneck.
abstract: Sequence homologs are an important source of information about proteins. Amino acid profiles, representing the position-specific mutation probabilities found in profiles, are a richer encoding of biological sequences than the individual sequences themselves. However, profile comparisons are an order of magnitude slower than sequence comparisons, making profiles impractical for large datasets. Also, because they are such a rich representation, profiles are difficult to visualize. To address these problems, we describe a method to map probabilistic profiles to a discrete alphabet while preserving most of the information in the profiles. We find an informationally optimal discretization using the Information Bottleneck approach (IB). We observe that an 80-character IB alphabet captures nearly 90% of the amino acid occurrence information found in profiles, compared to the consensus sequence's 78%. Distant homolog search with IB sequences is 88% as sensitive as with profiles compared to 61% with consensus sequences (AUC scores 0.73, 0.83, and 0.51, respectively), but like simple sequence comparison, is 30 times faster. Discrete IB encoding can therefore expand the range of sequence problems to which profile information can be applied to include batch queries over large databases like SwissProt, which were previously computationally infeasible.
authors.affiliation: Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Dr., San Diego, CA 92093, USA. seano@cs.ucsd.edu
authors.forename: Sean
authors.initials: S
authors.lastname: O'Rourke
authors.forename: Gal
authors.initials: G
authors.lastname: Chechik
authors.forename: Robin
authors.initials: R
authors.lastname: Friedman
authors.forename: Eleazar
authors.initials: E
authors.lastname: Eskin
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008954:Models, Biological; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011336:Probability; D017510:Protein Folding; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software