Brief Research Report ARTICLE
Improved Modeling of Peptide-Protein Binding through Global Docking and Accelerated Molecular Dynamics Simulations
- 1University of Kansas, United States
- 2Stony Brook University, United States
- 3Russian Academy of Sciences (RAS), Russia
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 Å – 4.8 Å, 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 Å – 2.7 Å, 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.
Keywords: Peptide-Protein Binding, Peptide docking, PeptiDock, Gaussian accelerated molecular dynamics (GaMD), Peptide flexibility
Received: 22 Jul 2019;
Accepted: 09 Oct 2019.
Copyright: © 2019 Wang, Alekseenko, Kozakov and Miao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Yinglong Miao, University of Kansas, Lawrence, 66045, Kansas, United States, firstname.lastname@example.org