PEP-FOLD: an online resource for de novo peptide structure prediction.
Rational peptide design and large-scale prediction of peptide structure from sequence remain a challenge for chemical biologists. We present PEP-FOLD, 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 2.6 A Calpha root mean square deviation from the full NMR structures. PEP-FOLD can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD.
MTi, INSERM UMR-S 973, - Paris 7, 35 rue H. Brion, F75205, Paris, France.
Julien
J
Maupetit
Philippe
P
Derreumaux
Pierre
P
Tuffery
D000465:Algorithms; D020407:Internet; D008958:Models, Molecular; D010455:Peptides / Q000737:chemistry*; D011487:Protein Conformation; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein; D012984:Software; D014584:User-Computer Interface
D010455:Peptides