Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization

TL;DR

This paper presents a novel protein design approach integrating machine learning with quantum-inspired algorithms, demonstrating rapid learning of scoring functions and outperforming traditional methods on benchmark models.

Contribution

It introduces a general iterative protein design scheme combining machine learning and quantum-inspired optimization, with a proof-of-concept on lattice models showing promising results.

Findings

Quantum-inspired reformulation outperforms classical sequence optimization.

Rapid learning of physics-based scoring functions achieved.

Scheme is adaptable to various models and computational platforms.

Abstract

The protein design problem involves finding polypeptide sequences folding into a given threedimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure searches can now be bypassed thanks to recent machine learning breakthroughs, which have enabled accurate and rapid structure predictions. Similarly, sequence searches might be entirely transformed by the advent of quantum annealing machines and by the required new encodings of the search problem, which could be performative even on classical machines. In this work, we introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated, and an optimal physics-based scoring function is iteratively learned. In this first proof-of-concept application, we apply the iterative method to a lattice protein model amenable to exhaustive benchmarks, finding that it can rapidly learn a physics-based scoring function and achieve promising design performances. Strikingly, our quantum-inspired reformulation outperforms conventional sequence optimization even when adopted on classical machines. The scheme is general and can be easily extended, e.g., to encompass off-lattice models, and it can integrate progress on various computational platforms, thus representing a new paradigm approach for protein design.