Skip to Main content Skip to Navigation
Journal articles

Exploring the use of a structural alphabet for a structural prediction of protein loops

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.
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

https://www.hal.inserm.fr/inserm-00134555
Contributor : Alexandre G. de Brevern <>
Submitted on : Friday, March 2, 2007 - 3:52:52 PM
Last modification on : Saturday, March 28, 2020 - 2:24:53 AM
Long-term archiving on: : Tuesday, April 6, 2010 - 9:56:35 PM

Identifiers

Collections

Citation

Anne-Claude Camproux, Alexandre de Brevern, Serge Hazout, Pierre Tufféry. Exploring the use of a structural alphabet for a structural prediction of protein loops. Theoretical Chemistry Accounts: Theory, Computation, and Modeling, Springer Verlag, 2001, 106 (1/2), pp.28-35. ⟨10.1007/s002140100261⟩. ⟨inserm-00134555⟩

Share

Metrics

Record views

257

Files downloads

527