Molecular Dynamics Scoring of Protein–Peptide Models Derived from Coarse-Grained Docking

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.

Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

“…These rigid-body methods are often used as a first docking step, followed by scoring [ 12 , 13 , 14 , 15 , 16 ], using experimental data [ 17 ] and/or structural refinement to capture backbone flexibility [ 5 , 18 ]. Molecular Dynamics is perhaps the most common refinement strategy, either in classic or enhanced sampling versions [ 17 , 19 , 20 , 21 , 22 ]. Other tools use rotamer libraries to address side-chain flexibility [ 23 ] and Elastic Network Models (ENMs) for modeling backbone rearrangements [ 24 , 25 , 26 , 27 , 28 ].…”

Protein-protein docking with large-scale backbone flexibility using coarse-grained Monte-Carlo simulations

“…Molecular Dynamics is perhaps the most common refinement strategy, either in classic or enhanced sampling versions [17,[19][20][21][22]. Other tools use rotamer libraries to address side-chain flexibility [23] and Elastic Network Models (ENM) for modeling backbone rearrangements [24][25][26][27][28].…”

“…These rigid-body methods are often used as a first docking step, followed by scoring [12–16], using experimental data [17] and/or structural refinement to capture backbone flexibility [5,18]. Molecular Dynamics is perhaps the most common refinement strategy, either in classic or enhanced sampling versions [17,19–22]. Other tools use rotamer libraries to address side-chain flexibility [23] and Elastic Network Models (ENM) for modeling backbone rearrangements [24–28].…”

Multiscale Modeling of Amyloid Fibrils Formed by Aggregating Peptides Derived from the Amyloidogenic Fragment of the A-Chain of Insulin

“…However, it needs to be stressed that the presented models were the best (with the lowest values of RMSD when compared to the reference structure) found in the set of 100 top-scored structures resulting from 10 independent docking simulations for each system. The identification of the most accurate structure in the generated model set of top-scored structures is always a challenging task [14,23,24], and many different scoring methods have been developed for such a purpose [12,40]. The obtained fibril models reveal a high degree of similarity to the reference experimentally determined structures.…”