A Fast Method for Large-Scale De Novo Peptide and Miniprotein Structure Prediction

MTi, INSERM UMR-S973 and RPBS, Université Paris Diderot - Paris 7, 5 rue Marie-Andrée Lagroua Weill-Halle, 75205 Paris, Cedex 13, France.
Journal of Computational Chemistry (Impact Factor: 3.59). 11/2009; 31(4):726-38. DOI: 10.1002/jcc.21365
Source: PubMed

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.

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    • "The folding protocol and force field parameters were calibrated in the folding step (rather than using existing peptide folding methods; e.g. Maupetit et al., 2010) and then used also in the docking step. Implicit solvent, virtual hydrogen representation, and a large integration time step of 5 fs were used. "
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    ABSTRACT: The huge conformational space stemming from the inherent flexibility of peptides is among the main obstacles to successful and efficient computational modeling of protein-peptide interactions. Current peptide docking methods typically overcome this challenge using prior knowledge from the structure of the complex. Here we introduce AnchorDock, a peptide docking approach, which automatically targets the docking search to the most relevant parts of the conformational space. This is done by precomputing the free peptide's structure and by computationally identifying anchoring spots on the protein surface. Next, a free peptide conformation undergoes anchor-driven simulated annealing molecular dynamics simulations around the predicted anchoring spots. In the challenging task of a completely blind docking test, AnchorDock produced exceptionally good results (backbone root-mean-square deviation ≤ 2.2Å, rank ≤15) for 10 of 13 unbound cases tested. The impressive performance of AnchorDock supports a molecular recognition pathway that is driven via pre-existing local structural elements. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Structure 04/2015; 23(5). DOI:10.1016/j.str.2015.03.010 · 5.62 Impact Factor
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    • "Based on the highly interacting residues between the Chain A and B of a-hemolysin, twenty peptides were designed using Mobyle server portal (Maupetit et al. 2010) by randomly altering the position of the residues. Property of the peptides such as molecular weight, isoelectric point, pH, and solubility were predicted using innovagen tool (http:// "
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    ABSTRACT: Staphylococcus aureus self-assembling α-hemolysin heptamer is an acute virulence factor that determines the severity of S. aureus infections. Hence, inhibiting the heptamer formation is of considerable interest. However, both natural and chemical inhibitors reported so far has difficulties related to toxicity, bioavailability, and solubility, which necessitate in identifying some alternatives. Hence, in this study, potential peptides for α-hemolysin inhibition was developed using in silico based approach. Haddock server was used to understand the residues involved in complex formation. Based on the key residues involved in the interaction, 20 peptides were designed and docked with the α-hemolysin monomer (Chain A). Further, the best scored Chain A-peptide complex was chosen and docked with Chain B to identify the ability of dimer formation in the presence of designed peptide. The stability of the Chain A–B dimer, Chain A-peptide and Chain A-peptide-Chain B complex was studied by performing molecular dynamic simulation over 3,000 ps. The peptide IYGSKANRQTDK was found to be binding efficiently with Chain A of α-hemolysin with highest binding energy and also revealed that the designed peptide disturbed the dimer formation, which provided useful information in developing promising lead for inhibiting α-hemolysin assembly in the future.
    International Journal of Peptide Research and Therapeutics 08/2014; 20(4). DOI:10.1007/s10989-014-9424-x · 0.91 Impact Factor
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    • "Studying protein structures can reveal relevant structural and functional information which may not be derived from protein sequences alone. During recent years, various methods that study protein structures have been elaborated based on diverse types of descriptor such as profiles [15] and motifs [11] and others [17] [10]. "
    Journées Ouvertes en Biologie, Informatique et Mathématiques, Centre de Congrès Pierre Baudis, Toulouse, France; 07/2013
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