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.