Topological potentials guiding protein self-assembly

2025-08-21

AI Summary

Protein self-assembly simulations often suffer from rugged energy landscapes induced by short-range hydrophobic interactions, leading to trapping in nonfunctional local minima and poor sampling of native biological conformations. To address this, we propose a topology-guided long-range potential based on persistent homology—specifically, the weighted total persistence—which is the first to directly embed this general geometric descriptor into molecular force fields without relying on electrostatic or chemically specific parameters, using only atomic coordinates as input. Our method synergistically integrates topological metrics with implicit solvation free-energy models to enable efficient navigation toward functional conformational spaces in molecular dynamics. Applied to systems including the tobacco mosaic virus dimer, it improves self-assembly success rates by up to 16-fold and successfully converges previously intractable assembly processes. This significantly extends the feasibility boundary for structural prediction in both coarse-grained and all-atom simulations.

Technology Category

• Machine Learning: Learning with Manifolds
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Embodied AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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, driven by short-range non-polar interactions, can find the biologically correct assembly as the energy minimizing state. Short-ranged potentials produce rugged energy landscapes however, which lead to simulations becoming trapped in non-functional, local minimizers. Successful self-assembly simulations depend both 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.

Problem

• Overcoming kinetic barriers in protein self-assembly simulations
• Improving success rates of functional complex formation
• Reducing trapping in non-functional local energy minima

Innovation

• Long-range topological potential via weighted persistence
• Combined with morphometric solvation-free energy approach
• Overcomes kinetic barriers in molecular simulations