期刊
NUCLEIC ACIDS RESEARCH
卷 37, 期 -, 页码 W498-W503出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkp323
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资金
- INSERM [UMR-S 973]
Rational peptide design and large-scale prediction of peptide structure from sequence remain a challenge for chemical biologists. We present PEPFOLD, an online service, aimed at de novo modelling of 3D conformations for peptides between 9 and 25 amino acids in aqueous solution. Using a hidden Markov model-derived structural alphabet (SA) of 27 four-residue letters, PEP-FOLD first predicts the SA letter profiles from the amino acid sequence and then assembles the predicted fragments by a greedy procedure driven by a modified version of the OPEP coarse-grained force field. Starting from an amino acid sequence, PEP-FOLD performs series of 50 simulations and returns the most representative conformations identified in terms of energy and population. Using a benchmark of 25 peptides with 9-23 amino acids, and considering the reproducibility of the runs, we find that, on average, PEP-FOLD locates lowest energy conformations differing by 2.6A Ca root mean square deviation from the full NMR structures. PEP-FOLD can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD
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