Optimization of Analytical Potentials for Coarse-Grained Biopolymer Models

Abstract

The increasing trend in the recent literature on coarse grained (CG) models testifies their impact in the study of complex systems. However, the CG model landscape is variegated: even considering a given resolution level, the force fields are very heterogeneous and optimized with very different parametrization procedures. Along the road for standardization of CG models for biopolymers, here we describe a strategy to aid building and optimization of statistics based analytical force fields and its implementation in the software package AsParaGS (Assisted Parameterization platform for coarse Grained modelS). Our method is based on the use and optimization of analytical potentials, optimized by targeting internal variables statistical distributions by means of the combination of different algorithms (i.e., relative entropy driven stochastic exploration of the parameter space and iterative Boltzmann inversion). This allows designing a custom model that endows the force field terms with a physically sound meaning. Furthermore, the level of transferability and accuracy can be tuned through the choice of statistical data set composition. The method-illustrated by means of applications to helical polypeptides-also involves the analysis of two and three variable distributions, and allows handling issues related to the FF term correlations. AsParaGS is interfaced with general-purpose molecular dynamics codes and currently implements the "minimalist" subclass of CG models (i.e., one bead per amino acid, Cα based). Extensions to nucleic acids and different levels of coarse graining are in the course.

Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems

“…A systematic feedback between the CG and the corresponding AA system with iterative improvement of the parameters is achieved through the iterative Boltzmann inversion [ 90 , 91 ] and inverse Monte Carlo (IMC) method [ 92 , 93 ]. A more sophisticated approach is the relative entropy minimization [ 94 , 95 , 96 , 97 ], in which the Kullback–Leibler divergence [ 98 ] between the CG and AA ensembles is minimized. We developed a variant of this method, which we termed maximum-likelihood optimization [ 99 ], replacing the AA-simulated ensembles with the experimental ensembles, obtained from Nuclear Magnetic Resonance (NMR) studies of the training proteins at various temperatures, which were selected to bracket the folding–unfolding transition [ 100 ].…”

Building Minimalist Models for Functionalized Metal Nanoparticles

“…(ii) and (iii) are complex tasks which have been addressed using a large number of different methodologies (Bauer et al, 2017; Lin et al, 2018; Brancolini et al, submitted). Particularly effective are usually combinations of bottom up and top-down strategy (Leonarski et al, 2013; Mereghetti et al, 2016) including both atomistic simulations and experimental data (Trovato and Tozzini, 2014) from different sources (e.g., structural, or thermodynamic). Here we focus on a general strategy to address (i) (Brancolini et al, 2018).…”

Minimalist Model for the Dynamics of Helical Polypeptides: A Statistic-Based Parametrization

“…The building of an automatic parametrization tool based on the principles here described is currently in progress. 47 In conclusion, we propose a model able to reproduce accurately the helical polypeptides and outline a general strategy for extending it to the other secondary structures. The very simple terms involved allow at the same time a clear physical interpretation and a parametrization based on a (even probabilistic) knowledge of the secondary structure.…”