Topological potentials guiding protein self-assembly

TL;DR

This paper introduces a topological potential based on weighted total persistence combined with a morphometric approach, significantly improving protein self-assembly simulations by overcoming energy landscape ruggedness and kinetic barriers.

Contribution

It presents a novel long-range topological potential that enhances self-assembly simulation success rates and is applicable to arbitrary systems using atomic coordinates.

Findings

Assembly success rate increased up to sixteen-fold.

Enabled simulation of systems that do not assemble within typical timescales.

Method is independent of electrostatics or chemical specificity.

Abstract

The simulated self-assembly of molecular building blocks into functional complexes is a key area of study in computational biology and materials science. Self-assembly simulations of proteins using physically-motivated potentials for non-polar interactions, can identify the biologically correct assembly as the energy-minimizing state. Short-range potentials, however, produce rugged energy landscapes, which lead to simulations becoming trapped in non-functional local minimizers. Successful self-assembly simulations depend on the physical realism of the driving potentials as well as their ability to efficiently explore the configuration space. We introduce a long-range topological potential, quantified via weighted total persistence, and combine it with the morphometric approach to solvation-free energy. This combination improves the assembly success rate in simulations of the tobacco mosaic virus dimer and other protein complexes by up to sixteen-fold compared with the morphometric model alone. It further enables successful simulation in systems that don't otherwise assemble during the examined timescales. Compared to previous topology-based work, which has been primarily descriptive, our approach uses topological measures as an active energetic bias that is independent of electrostatics or chemical specificity and depends only on atomic coordinates. Therefore, the method can, in principle, be applied to arbitrary systems where such coordinates are optimized. Integrating topological descriptions into an energy function offers a general strategy for overcoming kinetic barriers in molecular simulations, with potential applications in drug design, materials development, and the study of complex self-assembly processes.