Quanta-Bind: A Quantum Computing Pipeline for Modeling Strongly Correlated Metal-Protein Interactions

To understand biological processes and design drugs effectively, it is necessary to understand the mechanisms of interaction between metals and proteins. These interactions in many cases involve strong electronic correlation which can be computationally challenging to describe for classical computing. For this reason, strongly correlated protein-metal interactions is an area where advancements in quantum technologies can be of great value. We present Quanta-Bind, an algorithmic pipeline designed to investigate the mechanisms of coordination between metals and proteins using a combination of classical dynamics, quantum chemistry, and quantum computing techniques. The pipeline combines the Fragment Molecular Orbital (FMO) method to create manageable protein fragments, the Localized Active Space (LAS) and Quantum Bootstrap Embedding (QBE) methods to solve for the electronic structure, alongside the Generalized Superfast Encoding (GSE) to map fermions to qubits. Moreover, Hamiltonian truncation schemes are integrated to optimize the pipeline and improve performance. Given that the intrinsic framework of these methods relies on subdividing (fragmenting) the target system, the overall pipeline is ideal for current and developing quantum devices. We implemented and optimized this pipeline on qBraid-Lab. Initial results of utilizing this pipeline are presented. It provides a promising tool in our toolkit to better understand metal-protein interactions, and ultimately, to design drugs more effectively.