Name
Abstract | ||
|---|---|---|
Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large-scale experiments is still missing. We introduce a new approach-PEP-FOLD-to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model-derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse-grained energy score. On a benchmark of 52 peptides with 9-23 amino acids, PEP-FOLD generates lowest-energy conformations within 2.8 and 2.3 angstrom C alpha root-mean-square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27-49 amino acids, PEP-FOLD reaches an accuracy of 3.6 and 4.6 angstrom C alpha root-mean-square deviation for the most-native and lowest-energy conformations, using the nonflexible regions identified by NMR. PEP-FOLD simulations are fast-a few minutes only-opening therefore, the door to in silico large-scale rational design of new bioactive peptides and miniproteins. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 31: 726-738, 2010 |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1002/jcc.21365 | JOURNAL OF COMPUTATIONAL CHEMISTRY |
Keywords | Field | DocType |
peptides,miniprotein,structural alphabet,structure prediction,coarse-grained force field | Amino acid,Peptide,Computational chemistry,Combinatorial chemistry,Chemistry,Computational biology,Hidden Markov model,Rational design,In silico,Alphabet | Journal |
Volume | Issue | ISSN |
31 | 4 | 0192-8651 |
Citations | PageRank | References |
13 | 0.82 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julien Maupetit | 1 | 132 | 8.72 |
| Philippe Derreumaux | 2 | 70 | 14.13 |
| Pierre Tufféry | 3 | 456 | 42.46 |