Protein classification by stochastic modeling and optimal filtering of amino-acid sequences. (English) Zbl 0791.92012
Summary: 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 with three very broad strucural types, while our approach achieves this level across twelve much more detailed classes.
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 with three very broad strucural types, while our approach achieves this level across twelve much more detailed classes.
MSC:
| 92C40 | Biochemistry, molecular biology |
| 60G35 | Signal detection and filtering (aspects of stochastic processes) |
| 62M20 | Inference from stochastic processes and prediction |
| 92-08 | Computational methods for problems pertaining to biology |
Keywords:
time series of symbols; nonlinear discrete generalizations of linear state space modeling; hidden Markov models; protein’s tertiary structural class; amino-acid sequence; stochastic signal models; recognized structural classes of single-domain proteins; nonlinear, optimal filtering algorithmReferences:
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