Abstract
The construction of a suitable QM region is the most crucial step in setting up hybrid quantum mechanics / molecular mechanics (QM/MM) simulations for enzymatic reactions. The QM region should ideally include all important amino acids residues, while being as small as possible to save computational effort. Most available methods for systematic QM region construction are based either on the distance of single amino acids to the active site or on their electrostatic effect. Such approaches might miss non-electrostatic and long-range allosteric interactions. Here, we present a proof of concept study for the application of protein network analysis to tackle this problem. Specifically, we explore the use of the protein network centralities as descriptor for QM region construction. We find that protein network centralities, in particular the betweenness centrality, can be a useful descriptor for systematic QM region construction. We show that in combination with our previously developed point charge variation analysis, they can be used to identify important residues that are missed in purely electrostatic approaches.
Supplementary materials
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Supporting Information
Description
Extended computational details, additional results for triosephosphate isomerase (TIM), supplementary tables showing descriptor rankings as well as the compositions of the different considered QM regions.
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Data Set
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Python script for WISP and centrality analysis, the PDB files of the starting structures, the modified AMBER95 force field for the use with AMS, substrate, and ion AMS fragment files, as well as the AMS input files for all geometry optimizations and single point calculatio
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