ArticleNucleic acids research2009
PEP-FOLD: an online resource for de novo peptide structure prediction.
Article in Nucleic acids research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 167 papers.
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167 citing papers in PubMed.
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- Dysregulated Autophagy and Sarcomere Dysfunction in Patients With Heart Failure With Co-Occurrence of P63A and P380SJournal of the American Heart Association · 2023Article
- Decoy peptides effectively inhibit the binding of SARS-CoV-2 to ACE2 on oral epithelial cells.Heliyon · 2023Article
107 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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