Quantum Algorithm for Protein Side-Chain Optimisation: Comparing Quantum to Classical Methods

TL;DR

This paper introduces a quantum algorithm for protein side-chain configuration optimization, formulating it as a QUBO problem and demonstrating potential computational advantages over classical methods like simulated annealing.

Contribution

It develops a resource-efficient quantum algorithm for rotamer optimization, mapping the problem to an Ising model and benchmarking quantum advantages over classical approaches.

Findings

Quantum method reduces computational cost compared to classical simulated annealing.

Formulates rotamer optimization as a QUBO problem suitable for quantum encoding.

Demonstrates scalability and efficiency of quantum approach for protein structure optimization.

Abstract

Modelling and predicting protein configurations is crucial for advancing drug discovery, enabling the design of treatments for life-threatening diseases. A critical aspect of this challenge is rotamer optimisation - the determination of optimal side-chain conformations given a fixed protein backbone. This problem, involving the internal degrees of freedom of amino acid side-chains, significantly influences the protein's overall structure and function. In this work, we develop a resource-efficient optimisation algorithm to compute the ground state energy of protein structures, with a focus on side-chain configuration. We formulate the rotamer optimisation problem as a Quadratic Unconstrained Binary Optimisation problem and map it to an Ising model, enabling efficient quantum encoding. Building on this formulation, we propose a quantum algorithm based on the Quantum Approximate Optimisation Algorithm to explore the conformational space and identify low-energy configurations. To benchmark our approach, we conduct a classical study using custom-built libraries tailored for structural characterisation and energy optimisation. Our quantum method demonstrates a reduction in computational cost compared to classical simulated annealing techniques, offering a scalable and promising framework for protein structure optimisation in the quantum era.