title: Protein classification by stochastic modeling and optimal filtering of amino-acid sequences.
abstract: The prediction of a protein's tertiary structural class from its amino-acid sequence is formulated as a signal-processing problem. The amino-acid sequence is treated as a "time series" of symbols containing signals that determine the protein's structural class. A methodology is described for building detailed stochastic signal models for recognized structural classes of single-domain proteins. We solve the problem of determining that model, from a set of candidates, which is the most probable generator of a protein's entire amino-acid sequence. The solution employs a nonlinear, optimal filtering algorithm, which is suited for implementation on parallel computer architectures. Previous approaches have only been able to classify correctly 80% of single-domain proteins within three very broad structural types, while our approach achieves this level across twelve much more detailed classes.
authors.affiliation: TASC, Reading, Massachusetts 01867.
authors.forename: J V
authors.initials: JV
authors.lastname: White
authors.forename: C M
authors.initials: CM
authors.lastname: Stultz
authors.forename: T F
authors.initials: TF
authors.lastname: Smith
mesh_terms: D000595:Amino Acid Sequence; D008390:Markov Chains; D008433:Mathematics; D008961:Models, Structural; D008969:Molecular Sequence Data; D011336:Probability; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
chemical_list: D011506:Proteins