Quantifying the Long‐Range Coupling of Electronic Properties in Proteins with ab initio Molecular Dynamics**

Abstract

The delicate interplay of covalent and non-covalent interactions in proteins is inherently quantum mechanical and highly dynamic in nature. To directly interrogate the evolving nature of the electronic structure of proteins, we carry out 100-ps-scale ab initio molecular dynamics simulations of three representative small proteins with range-separated hybrid density functional theory. We quantify the nature and length-scale of the coupling of residue-specific charge probability distributions in these proteins. While some nonpolar residues exhibit expectedly narrow charge distributions, most polar and charged residues exhibit broad, multimodal distributions. Even for nonpolar residues, we observe sequence-specific deviations correspond-ing to charge accumulation or depletion that would be challenging to capture in a fixed charge force field. We quantify the effect of residue-residue interactions on charge distributions first with linear cross-correlations. We then show how additional insight can be gained from evaluating the mutual information of charge distributions. We show that a significant number of residues couple most strongly with residues that are distant in both sequence and space over a range of secondary structures including α-helical, β-sheet, disulfide bridging, and lasso motifs. The mutual information analysis is necessary to capture coupling between some polar and charged residues that would be otherwise missed.

Insights into the stability of engineered mini-proteins from their dynamic electronic properties

“…Both charges and structures were recorded at every timestep. This relatively short timescale was consistent with prior work that showed convergence of charge distributions on the scale of tens of ps [66,109]. While explicit solvation might be preferable, our use of an implicit solvent model avoids challenges associated with equilibrating solvent degrees of freedom, further reducing the time needed to achieve converged solute protein degrees of freedom.…”

“…Mulliken partial charges from GFN2-xTB were collected for 20 ps (i.e., 40 000 steps) for each of the 20 starting structures for both mutants. These charges were summed over each residue to mitigate shortcomings of the Mulliken partial charge scheme [135] as in prior work [109,113,136]. Both charges and structures were recorded at every timestep.…”

Factors That Determine the Variation of Equilibrium and Kinetic Properties of QM/MM Enzyme Simulations: QM Region, Conformation, and Boundary Condition

“…It is therefore encouraging to see extensive efforts in systematically determining QM region dependence with respect to different observables. 15,26,[28][29][30][31]35,123 While our computed KIEs for WT-COMT match experimental values rather well, investigation of the Y68A mutant offers a surprising challenge. On the one hand, although Y68A is more flexible, PBC and the more constrained GSBP boundary conditions lead to remarkably similar free energy results.…”

“…Thus, these results strongly suggest that different properties likely require different QM region sizes, a feature often observed in the analysis of complex systems. It is therefore encouraging to see extensive efforts in systematically determining QM region dependence with respect to different observables. ,,− ,, …”

Convergence in determining enzyme functional descriptors across Kemp eliminase variants

“…The study informs the conditions under which different descriptors can be calculated with high fidelity for predicting the impact of mutations on catalytic functions. In addition, as the interplay between protein dynamics and electronic structures emerges as a new direction of study [81,82], the convergence trend investigated in the current study might inspire the development of new strategies to predict computationally demanding QM properties using MM-derived properties.…”