pH-dependent coarse-grained model of peptides

TL;DR

This paper introduces a novel coarse-grained peptide model that efficiently captures pH effects by integrating atomistic data and experimental results, enabling accurate simulations across diverse peptide sequences.

Contribution

It presents the first pH-dependent coarse-grained peptide modeling strategy that transfers physical features from atomistic and experimental data to the coarse scale.

Findings

Model accurately reproduces experimental and atomistic data.

Demonstrates high transferability across different peptide systems.

Shows universal applicability of the modeling approach.

Abstract

We propose the first, to our knowledge, coarse-grained modeling strategy for peptides where the effect of changes of the pH can be efficiently described. The idea is based on modeling the effects of the pH value on the main driving interactions. We use reference data from atomistic simulations and experimental databases and transfer its main physical features to the coarse-grained resolution according the principle of "consistency across the scales". The coarse-grained model is refined by finding a set of parameters that, when applied to peptides with different sequences and experimental properties, reproduces the experimental and atomistic data of reference. We use the such parameterized model for performing several numerical tests to check its transferability to other systems and to prove the universality of the related modeling strategy. We have tried systems with rather different response to pH variations, showing a highly satisfactory performance of the model.