Fast, accurate, and system-specific variable-resolution modelling of proteins

TL;DR

The paper introduces CANVAS, a novel multi-resolution protein modelling scheme that allows flexible resolution modulation, fast parametrisation without reference simulations, and easy integration into molecular dynamics platforms, validated on enzymes and antibodies.

Contribution

It presents a new variable-resolution modelling approach for proteins that overcomes limitations of existing methods by enabling user-defined resolution, rapid parametrisation, and broad platform compatibility.

Findings

CANVAS accurately reproduces atomistic simulation results.

The method is validated on adenylate kinase and pembrolizumab.

Software is openly available for community use.

Abstract

In recent years, a few multiple-resolution modelling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, while the remainder of the system is concurrently treated using a coarse-grained model. In most cases, the parametrisation of the latter requires lengthy reference all-atom simulations and/or the usage of off-shelf coarse-grained force fields, whose interactions have to be refined to fit the specific system under examination. Here, we overcome these limitations through a novel multi-resolution modelling scheme for proteins, dubbed coarse-grained anisotropic network model for variable resolution simulations, or CANVAS. This scheme enables the user-defined modulation of the resolution level throughout the system structure; a fast parametrisation of the potential without the necessity of reference simulations; and the straightforward usage of the model on the most commonly used molecular dynamics platforms. The method is presented and validated on two case studies, the enzyme adenylate kinase and the therapeutic antibody pembrolizumab, by comparing results obtained with the CANVAS model against fully atomistic simulations. The modelling software, implemented in python, is made freely available for the community on a collaborative github repository.