A fast method for large-scale De Novo peptide and miniprotein structure prediction

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 A Calpha 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 A Calpha 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.

DOI: 10.1002/jcc.21365
01020200920102011201220132014201520162017
Citations per Year

69 Citations

Semantic Scholar estimates that this publication has 69 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Maupetit2010AFM, title={A fast method for large-scale De Novo peptide and miniprotein structure prediction}, author={Julien Maupetit and Philippe Derreumaux and Pierre Tuff{\'e}ry}, journal={Journal of computational chemistry}, year={2010}, volume={31 4}, pages={726-38} }