PEP-FOLD: an online resource for de novo peptide structure prediction

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W498-503. doi: 10.1093/nar/gkp323. Epub 2009 May 11.


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

MeSH terms

  • Algorithms
  • Internet
  • Models, Molecular
  • Peptides / chemistry*
  • Protein Conformation
  • Reproducibility of Results
  • Sequence Analysis, Protein
  • Software*
  • User-Computer Interface


  • Peptides