The delicate interplay of covalent and noncovalent interactions in proteins and enzymes is inherently quantum mechanical and highly dynamic in nature. While multi-scale QM/MM modeling has emerged as a powerful tool to address the cost accuracy trade-off in enzyme modeling, charge transfer and redistribution can challenge even the most sophisticated embedding schemes. Nevertheless, it is seldom known a priori which residues are most essential to treat quantum mechanically in a protein. To address such challenges, we have developed systematic techniques to quantify the influence of a distant residue on the electronic environment of a specific site (e.g., the active site in an enzyme) within a protein. We have developed charge shift analysis to interrogate the degree of charge transfer between an active site and surrounding residues as well as Fukui shift analysis to identify the extent to which an active site's Fukui function is altered when the surrounding environment changes. Most recently, we have developed an approach derived from the properties of charge distributions during fully ab initio molecular dynamics of proteins. Using the distributions of partial charges sampled by each residue, we compute the mutual information to quantify the nature and length-scale of the coupling of residue-specific charge probability distributions in these proteins. I will describe how these methods provide a promising approach to determine the most atom-economical treatment to constructing sufficiently accurate QM regions in multi-scale modeling to enable DFT-level free energy simulations.

Methods for systematic multi-scale modeling of enzyme catalysis

ACS Spring 2022

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