Trajectory-Based Parameterization of a Coarse-Grained Forcefield for High-Thoughput Protein Simulation

The traditional trade-off in biomolecular simulation between accuracy and computational efficiency is predicated on the assumption that detailed forcefields are typically well-parameterized (i.e. obtaining a significant fraction of possible accuracy). We re-examine this trade-off in the more realistic regime in which parameterization is a greater source of bias than the level of detail in the forcefield. To address parameterization of coarse-grained forcefields, we use the contrastive divergence technique from machine learning to train directly from simulation trajectories on 450 proteins. In our scheme, the computational efficiency of the model enables high accuracy through precise tuning of the Boltzmann ensemble over a large collection of proteins. This method is applied to our recently developed Upside model [1], where the free energy for side chains are rapidly calculated at every time-step, allowing for a smooth energy landscape without steric rattling of the side chains. After our contrastive divergence training, the model is able to fold proteins up to approximately 100 residues de novo on a single core in CPU core-days. Additionally, the improved Upside model is a strong starting point both for investigation of folding dynamics and as an inexpensive Bayesian prior for protein physics that can be integrated with additional experimental or bioinformatic data.

FF12MC: A revised AMBER forcefield and new protein simulation protocol

ABSTRACTSpecialized to simulate proteins in molecular dynamics (MD) simulations with explicit solvation, FF12MC is a combination of a new protein simulation protocol employing uniformly reduced atomic masses by tenfold and a revised AMBER forcefield FF99 with (i) shortened CH bonds, (ii) removal of torsions involving a nonperipheralsp3atom, and (iii) reduced 1-4 interaction scaling factors of torsionsϕandψThis article reports that in multiple, distinct, independent, unrestricted, unbiased, isobaric-isothermal, and classical MD simulations FF12MC can (i) simulate the experimentally observed flipping between left-and right-handed configurations for C14-C38 of BPTI in solution, (ii) autonomously fold chignolin, CLN025, and Trp-cage with folding times that agree with the experimental values, (iii) simulate subsequent unfolding and refolding of these miniproteins, and (iv) achieve a robust Z score of 1.33 for refining protein models TMR01, TMR04, and TMR07. By comparison, the latest general-purpose AMBER forcefield FF14SB locks the C14-C38 bond to the right-handed configuration in solution under the same protein simulation conditions. Statistical survival analysis shows that FF12MC folds chignolin and CLN025 in isobaric-isothermal MD simulations 2-4 times faster than FF14SB under the same protein simulation conditions. These results suggest that FF12MC may be used for protein simulations to study kinetics and thermodynamics of miniprotein folding as well as protein structure and dynamics.

Force fields for simulating the interaction of surfaces with biological molecules

The interaction of biomolecules with solid interfaces is of fundamental importance to several emerging biotechnologies such as medical implants, anti-fouling coatings and novel diagnostic devices. Many of these technologies rely on the binding of peptides to a solid surface, but a full understanding of the mechanism of binding, as well as the effect on the conformation of adsorbed peptides, is beyond the resolution of current experimental techniques. Nanoscale simulations using molecular mechanics offer potential insights into these processes. However, most models at this scale have been developed for aqueous peptide and protein simulation, and there are no proven models for describing biointerfaces. In this review, we detail the current research towards developing a non-polarizable molecular model for peptide–surface interactions, with a particular focus on fitting the model parameters as well as validation by choice of appropriate experimental data.