Alexandre de Brevern - Thèse de Bioinformatique Moléculaire

abstract

The prediction of loop conformations is one of the challenging problems of homology modeling, due 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.

Results show, that our approach ranks 30% of the target words with the best score, 50% within the 5 best scores. Interestingly, our approach is also suited to accept or not the prediction performed. This allows to rank 57% of the target words with the best score, 67% within the 5 best scores, accepting 16% of learned words and rejecting 93% of unknown words.