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J Chem Theory Comput. 2014 Oct 14;10(10):4745-58. doi: 10.1021/ct500592m.

Improved PEP-FOLD Approach for Peptide and Miniprotein Structure Prediction.

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INSERM U973 , MTi, F-75205 Paris, France.
Univ Paris Diderot, Sorbonne Paris Cité , F-75205 Paris, France.
Laboratoire de Biochimie Théorique, UPR 9080 CNRS, Institut de Biologie Physico-Chimique , F-75005 Paris, France.
Institut Universitaire de France , 103 Boulevard Saint-Michel, 75005, Paris, France.


Peptides and mini proteins have many biological and biomedical implications, which motivates the development of accurate methods, suitable for large-scale experiments, to predict their experimental or native conformations solely from sequences. In this study, we report PEP-FOLD2, an improved coarse grained approach for peptide de novo structure prediction and compare it with PEP-FOLD1 and the state-of-the-art Rosetta program. Using a benchmark of 56 structurally diverse peptides with 25-52 amino acids and a total of 600 simulations for each system, PEP-FOLD2 generates higher quality models than PEP-FOLD1, and PEP-FOLD2 and Rosetta generate near-native or native models for 95% and 88% of the targets, respectively. In the situation where we do not have any experimental structures at hand, PEP-FOLD2 and Rosetta return a near-native or native conformation among the top five best scored models for 80% and 75% of the targets, respectively. While the PEP-FOLD2 prediction rate is better than the ROSETTA prediction rate by 5%, this improvement is non-negligible because PEP-FOLD2 explores a larger conformational space than ROSETTA and consists of a single coarse-grained phase. Our results indicate that if the coarse-grained PEP-FOLD2 method is approaching maturity, we are not at the end of the game of mini-protein structure prediction, but this opens new perspectives for large-scale in silico experiments.


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