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© Oxford University Press

Using substitution probabilities to improve position-specific scoring matrices

Jorja G. Henikoff and Steven Henikoff 1

Howard Hughes Medical Institute, Basic Sciences Division and Fred Hutchinson Cancer Research Center Seattle, WA 98104, USA

1To whom correspondence should be addressed E-mail:henikoff{at}howard.fhcrc.org

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.

Received on September 11, 1995; revised on January 4, 1996; accepted on January 4, 1996

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