Structural ensembles of disordered proteins from hierarchical chain growth and simulation

TL;DR

This paper reviews recent computational methods, especially hierarchical chain growth, for exploring the conformational dynamics of disordered biomolecules like proteins and nucleic acids, integrating simulations and experimental data.

Contribution

It introduces hierarchical chain growth as a method to generate detailed conformational ensembles of disordered proteins from fragment libraries, enhancing structural modeling capabilities.

Findings

HCG effectively models disordered protein ensembles.

Application to neurodegeneration-related proteins demonstrates method utility.

Integration with AI approaches shows future potential.

Abstract

Disordered proteins and nucleic acids play key roles in cellular function and disease. Here we review recent advances in the computational exploration of the conformational dynamics of flexible biomolecules. We focus on hierarchical chain growth (HCG) from fragment libraries built with atomistic molecular dynamics simulations. HCG combines chain fragments in a statistically reproducible manner into ensembles of full-length atomically detailed biomolecular structures. The input fragment structures are typically collected from molecular dynamics simulations, but could also come from structural databases. Experimental data can be integrated during and after chain assembly. Applications to the neurodegeneration-linked proteins $\alpha$-synuclein, tau, and TDP-43, including as condensate, illustrate the use of HCG. We conclude by highlighting the emerging connections to AI-based structural modeling.