Quadratic Optimization-Based Nonlinear Control for Protein Conformation Prediction

TL;DR

This paper introduces a quadratic optimization approach to predict protein folding pathways, ensuring low-entropy routes and energy reduction by leveraging kinetostatic compliance dynamics.

Contribution

It formulates protein conformation prediction as a quadratic program using KCM dynamics, providing a novel control synthesis method to avoid high-entropy folding routes.

Findings

Control torques approximate KCM reference vectors

High-entropy-loss routes are effectively avoided

Protein energy decreases during folding simulation

Abstract

Predicting the final folded structure of protein molecules and simulating their folding pathways is of crucial importance for designing viral drugs and studying diseases such as Alzheimer's at the molecular level. To this end, this paper investigates the problem of protein conformation prediction under the constraint of avoiding high-entropy-loss routes during folding. Using the well-established kinetostatic compliance (KCM)-based nonlinear dynamics of a protein molecule, this paper formulates the protein conformation prediction as a pointwise optimal control synthesis problem cast as a quadratic program (QP). It is shown that the KCM torques in the protein folding literature can be utilized for defining a reference vector field for the QP-based control generation problem. The resulting kinetostatic control torque inputs will be close to the KCM-based reference vector field and guaranteed to be constrained by a predetermined bound; hence, high-entropy-loss routes during folding are avoided while the energy of the molecule is decreased.