Data-Driven Many-Body Simulations of Biomolecules with CCSD(T) Accuracy: I. Polyalanine in the Gas Phase

A predictive understanding of how proteins fold, misfold, and stabilize requires accurate molecular-level insights into the thermodynamic and kinetic forces shaping their backbones. While empirical force fields remain the workhorse of biomolecular simulations, their limited functional forms often fall short in capturing the complex many-body interactions that govern protein dynamics. Quantum-mechanical methods, on the other hand, offer high accuracy but are prohibitively expensive for large biomolecules. In this work, we introduce a generalized, intramolecular formulation of the data-driven many-body MB-nrg formalism that achieves “gold standard” coupled cluster accuracy in simulating polyalanine chains in the gas phase. By decomposing polyalanine chains into chemically intuitive building blocks, we develop modular and transferable potential energy functions that accurately reproduce reference energies, normal-mode harmonic frequencies, and conformational free-energy landscapes. Compared to state-of-the-art force fields, the MB-nrg potential energy function yields a smoother and more realistic free energy surface, captures transient structural motifs missed by empirical force fields, and enables flexible sampling of secondary structure transitions in longer peptides. This work paves the way for "gold standard" coupled cluster-level simulations of proteins under physiologically relevant conditions, bridging the gap between chemical accuracy and biological complexity.