RinQ: Towards predicting central sites in proteins on current quantum computers

TL;DR

RinQ is a hybrid quantum-classical method that models protein structures as networks and uses quantum-inspired algorithms to identify key residues, showing promising accuracy and robustness across various proteins.

Contribution

This work introduces RinQ, a novel quantum-inspired framework for predicting central sites in proteins by formulating the problem as a QUBO and solving it with simulated annealing.

Findings

RinQ accurately identifies central residues in proteins.

The approach aligns well with classical benchmarks.

RinQ demonstrates robustness across diverse protein structures.

Abstract

We introduce RinQ, a hybrid quantum-classical framework for identifying functionally critical residues in proteins by formulating centrality detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Protein structures are modeled as residue interaction networks (RINs), and the QUBO formulations are solved using D-Wave's simulated annealing. Applied to a diverse set of proteins, RinQ consistently identifies central residues that closely align with classical benchmarks, demonstrating both the accuracy and robustness of the approach.