Recent Work In The Development And Application Of Protein–Peptide Docking

Abstract

Interest in the development of novel peptide-based drugs is growing. There is, thus, a pressing need for the development of effective methods to enable novel peptide-based drug discovery. A cogent case can be made for the development and application of computational or in silico methods to assist with peptide discovery. In particular, there is a need for the development of effective protein-peptide docking methods. Here, recent work in the area of protein-peptide docking method development is reviewed and several drug-discovery projects that benefited from protein-peptide docking are discussed. In the present review, special attention is given to the search and scoring problems, the use of peptide docking to enable hit identification, and the use of peptide docking to help rationalize experimental results, and generate and test structure-based hypotheses. Finally, some recommendations are made for improving the future development and application of protein-peptide docking.

PaFlexPepDock: Parallel Ab-Initio Docking of Peptides onto Their Receptors with Full Flexibility Based on Rosetta

“…A comparison of the results shown in Table 2 (a summary of results using recently developed protein-peptide docking methods) from ref. [19] showed that PaFlexPepDock was better than some of other docking protocols in terms of the Ave_rmsd. But it was worse than some methods on P(LE), it is our future working to improve it.…”

“…Moreover, the scoring problems and search problems are two basic and important considerations for understanding protein-peptide docking [3]. From the modeling perspective, the problem of flexibility is an un-solved problem for conventional protein docking algorithms [4].…”

Understanding the challenges of protein flexibility in drug design

“…The most common ones involve (i) a systematic search (e.g., exhaustive search, ensemble-based search, fragment-based search) or (ii) a stochastic search (e.g., Monte Carlo, evolutionary algorithm, swarm optimization, tabu search) [2], [5], [10]. Large ligands and peptides represent bigger challenges for docking, which might exceed the capabilities of many current approaches [11]- [15]. The treatment of ligand flexibility has been extensively reviewed elsewhere [2], [5], [10] and will not be specifically discussed here.…”

PepVis: An integrated peptide virtual screening pipeline for ensemble and flexible docking protocols

“…In this present study, DockQ (Basu & Wallner, 2016) was used to calculate the backbone RMSD of the peptide in native versus docked complex. The predicted pose is considered to be near-native if the backbone RMSD with respect to the native structure is ≤2.5 Å (Audie & Swanson, 2012). The success rate of the protocol was calculated by the F I G U R E 1 Flowchart describing the modified protocol for peptide modeling and ensemble docking number of predicted conformation cases that are near-native to the 53 native protein-peptide complexes in the LEADS-PEP dataset.…”