A perspective on protein structure prediction using quantum computers

TL;DR

This paper explores the potential of quantum computing to improve protein structure prediction, proposing a framework for selecting suitable problems and demonstrating its effectiveness with a Zika Virus protein case study.

Contribution

It introduces a systematic framework for identifying protein prediction problems that can benefit from quantum computing and validates it with a real quantum hardware experiment.

Findings

Framework accurately predicts Zika Virus protein structure

Quantum resources estimated for utility-scale quantum computers

Proof-of-concept demonstrates potential of quantum advantage in bioinformatics

Abstract

Despite the recent advancements by deep learning methods such as AlphaFold2, \textit{in silico} protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage, and estimating quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.