Triage protein fold prediction
| Autor: | Temple F. Smith, Hongxian He, Gregory D. McAllister |
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| Rok vydání: | 2002 |
| Předmět: |
Models
Molecular Protein Folding Fold prediction Protein Conformation Computer science computer.software_genre Bayesian inference Models Biological Biochemistry Protein Structure Secondary Sequence Analysis Protein Structural Biology Prior probability Animals Hidden Markov model Molecular Biology Structural class business.industry Membrane Proteins Proteins Bayes Theorem Pattern recognition Template library Triage Markov Chains Protein Structure Tertiary Data mining Artificial intelligence Threading (protein sequence) business computer |
| Zdroj: | Proteins: Structure, Function, and Genetics. 48:654-663 |
| ISSN: | 1097-0134 0887-3585 |
| DOI: | 10.1002/prot.10194 |
| Popis: | We have constructed, in a completely automated fashion, a new structure template library for threading that represents 358 distinct SCOP folds where each model is mathematically represented as a Hidden Markov model (HMM). Because the large number of models in the library can potentially dilute the prediction measure, a new triage method for fold prediction is employed. In the first step of the triage method, the most probable structural class is predicted using a set of manually constructed, high-level, generalized structural HMMs that represent seven general protein structural classes: all-α, all-β, α/β, α+β, irregular small metal-binding, transmembrane β-barrel, and transmembrane α-helical. In the second step, only those fold models belonging to the determined structural class are selected for the final fold prediction. This triage method gave more predictions as well as more correct predictions compared with a simple prediction method that lacks the initial classification step. Two different schemes of assigning Bayesian model priors are presented and discussed. Proteins 2002;48:654–663. © 2002 Wiley-Liss, Inc. |
| Databáze: | OpenAIRE |
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