Application of a novel and fast information-theoretic method to the discovery of higher-order correlations in protein databases.
We present a fast, discrete data-mining approach to the problem of finding kappa-tuples of correlated amino acid residues in protein sequence data. When sets of sequence-distant sites display high mutual information, they may bespeak important structural or functional features. Our novel methodology overcomes the limitations of previous methods which examined only single-residue features or pairwise interactions.
Molecular Mining Corporation, Queen's University, Kingston, Ontario, Canada. steeg@qucis.queensu.ca
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D016915:AIDS Vaccines; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D017124:Conserved Sequence; D016208:Databases, Factual; D015195:Drug Design; D015686:Gene Products, env / Q000737:chemistry / Q000235:genetics; D006678:HIV / Q000235:genetics; D007257:Information Theory; D008390:Markov Chains; D009154:Mutation; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D012984:Software
D016915:AIDS Vaccines; D015686:Gene Products, env; D011506:Proteins