Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology.
We present a method for condensing the information in multiple alignments of proteins into a mixture of Dirichlet densities over amino acid distributions. Dirichlet mixture densities are designed to be combined with observed amino acid frequencies to form estimates of expected amino acid probabilities at each position in a profile, hidden Markov model or other statistical model. These estimates give a statistical model greater generalization capacity, so that remotely related family members can be more reliably recognized by the model. This paper corrects the previously published formula for estimating these expected probabilities, and contains complete derivations of the Dirichlet mixture formulas, methods for optimizing the mixtures to match particular databases, and suggestions for efficient implementation.
Baskin Center for Computer Engineering and Information Sciences, University of California at Santa Cruz 95064, USA. kimmen@cse.ucsc.edu
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K
Sjölander
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Karplus
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M
Brown
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Hughey
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Krogh
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Mian
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Haussler
D000465:Algorithms; D001499:Bayes Theorem; D016208:Databases, Factual; D005069:Evaluation Studies as Topic; D015233:Models, Statistical; D009010:Monte Carlo Method; D011338:Probability Theory; D011506:Proteins / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data; D017386:Sequence Homology, Amino Acid
D011506:Proteins