title: Using substitution probabilities to improve position-specific scoring matrices.
abstract: Each column of amino acids in a multiple alignment of protein sequences can be represented as a vector of 20 amino acid counts. For alignment and searching applications, the count vector is an imperfect representation of a position, because the observed sequences are an incomplete sample of the full set of related sequences. One general solution to this problem is to model unobserved sequences by adding artificial 'pseudo-counts' to the observed counts. We introduce a simple method for computing pseudo-counts that combines the diversity observed in each alignment position with amino acid substitution probabilities. In extensive empirical tests, this position-based method out-performed other pseudo-count methods and was a substantial improvement over the traditional average score method used for constructing profiles.
authors: Howard Hughes Medical Institute, Basic Sciences Division, Seattle, WA 98104, USA. henikoff@howard.fhcrc.org
authors: J G
authors: JG
authors: Henikoff
authors: S
authors: S
authors: Henikoff
mesh_terms: D000595:Amino Acid Sequence; D003201:Computers; D016208:Databases, Factual; D005069:Evaluation Studies as Topic; D016017:Odds Ratio; D011336:Probability; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data
chemical_list: D011506:Proteins