Systematic Partitioning of Proteins for Quantum-Chemical Fragmentation Methods Using Graph Algorithms

Quantum-chemical fragmentation methods offer an efficient approach for the treatment of large proteins, in particular if local target quantities such as protein–ligand interaction energies, enzymatic reaction or spectroscopic properties embedded chromophores are sought. However, accuracy that is achievable intricately depends on how protein partitioned into smaller fragments. While commonly employed naı̈ve using fragments with a fixed size widely used, it can result and unpredictable errors when varying fragment size. Here, we present systematic partitioning scheme aims at minimizing error quantity given maximum To this end, construct weighted graph representation protein, which amino acids constitute nodes. These nodes connected by edges estimate expected cutting edge. This allows us to employ algorithms provided computer science determine near-optimal partitions protein. We apply test set six proteins representing various prototypical applications quantum-chemical simplified molecular fractionation conjugate caps (MFCC) hydrogen caps. show our graph-based consistently improves upon approach.