4.6 Article

Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations

Journal

FRONTIERS IN MOLECULAR BIOSCIENCES
Volume 6, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fmolb.2019.00112

Keywords

peptide-protein binding; peptide docking; PeptiDock; gaussian accelerated molecular dynamics (GaMD); peptide flexibility

Funding

  1. National Institutes of Health [R01GM132572]
  2. National Science Foundation [AF 1816314, DBI 1759277, ACI-1548562, TG-MCB180049]
  3. Binational Science Foundation Grant [2015207]
  4. American Heart Association [17SDG33370094]
  5. College of Liberal Arts and Sciences at the University of Kansas (KU)
  6. National Energy Research Scientific Computing Center (NERSC) [M2874, DE-AC02-05CH11231]

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Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology, and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3-4.8 angstrom, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6-2.7 angstrom, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.

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