Protofold II: Enhanced Model and Implementation for Kinetostatic Protein Folding

TL;DR

Protofold II advances protein folding prediction by integrating entropic effects, optimizing algorithms for linear complexity, and leveraging GPU acceleration, enabling efficient simulation of large proteins with improved accuracy.

Contribution

It introduces an enhanced free energy model including entropic effects and a redesigned, parallelized implementation that significantly speeds up protein folding simulations.

Findings

Simulations align with expected folding behaviors in water.

Model improvements improve accuracy of secondary and tertiary structure prediction.

GPU implementation achieves up to 100x speed-up.

Abstract

A reliable prediction of 3D protein structures from sequence data remains a big challenge due to both theoretical and computational difficulties. We have previously shown that our kinetostatic compliance method (KCM) implemented into the Protofold package can overcome some of the key difficulties faced by other de novo structure prediction methods, such as the very small time steps required by the molecular dynamics (MD) approaches or the very large number of samples needed by the Monte Carlo (MC) sampling techniques. In this article, we improve the free energy formulation used in Protofold by including the typically underrated entropic effects, imparted due to differences in hydrophobicity of the chemical groups, which dominate the folding of most water-soluble proteins. In addition to the model enhancement, we revisit the numerical implementation by redesigning the algorithms and introducing efficient data structures that reduce the expected complexity from quadratic to linear. Moreover, we develop and optimize parallel implementations of the algorithms on both central and graphics processing units (CPU/GPU) achieving speed-ups up to two orders of magnitude on the GPU. Our simulations are consistent with the general behavior observed in the folding process in aqueous solvent, confirming the effectiveness of model improvements. We report on the folding process at multiple levels; namely, the formation of secondary structural elements and tertiary interactions between secondary elements or across larger domains. We also observe significant enhancements in running times that make the folding simulation tractable for large molecules.