Exploring the use of a structural alphabet for structural prediction of protein loops
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The prediction of loop conformations is one of the challenging problems of homology modeling, owing to the large sequence variability associated with these parts of protein structures. In the present study, we introduce a search procedure that evolves in a structural alphabet space deduced from a hidden Markov model to simplify the structural information. It uses a Bayesian criterion to predict, from the amino acid sequence of a loop region, its corresponding word in the structural alphabet space. The results show that our approach ranks 30% of the target words with the best score, 50% within the five best scores. Interestingly, our approach is also suited to accept or not the prediction performed. This allows the ranking of 57% of the target words with the best score, 67% within the five best scores, accepting 16% of learned words and rejecting 93% of unknown words.
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