Abstract
One of the major challenges in the computational prediction of protein–peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein–peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor–ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein–peptide complexes show that the proposed MD-based scoring approach can be used to identify protein–peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.
Highlights
Protein–peptides interactions play essential roles in many cellular processes, and in recent years peptides have become attractive scaffolds for the design of new therapeutics [1]
We describe and test the Molecular Dynamics (MD)-based strategy for scoring protein–peptide complex models provided by the CABS-dock method
The benchmark set should contain a large population of alternative structural models of different protein–peptide complexes, with peptides that exhaustively sample the conformational space within the receptor binding site and its Molecules 2021, 26, 3293 vicinity
Summary
Protein–peptides interactions play essential roles in many cellular processes, and in recent years peptides have become attractive scaffolds for the design of new therapeutics [1]. Even small changes in protein receptor three-dimensional structures can have qualitative effects on the binding mechanisms, binding energy, and structures of resulting complexes For these reasons, molecular docking based on classical MD protocols is computationally extremely costly and, in most cases, not practical. The Rosetta FlexPepDock method [3] has proven quite effective in docking flexible peptides, providing the binding site can be approximately pre-assumed. On the contrary, another CG method, CABS-dock [4,5,6], enables simulations of global docking with the efficient exploration of docking poses over the entire receptor protein surface
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