Systematic partitioning of proteins for quantum-chemical fragmentation methods using graph algorithms

TL;DR

This paper introduces a graph algorithm-based systematic partitioning method for proteins that minimizes fragmentation errors in quantum-chemical calculations, improving accuracy over naive fixed-size fragment approaches.

Contribution

The authors develop a graph-based partitioning scheme that optimally divides proteins into fragments to reduce errors in local quantum-chemical properties, advancing protein fragmentation techniques.

Findings

Consistently improves fragmentation accuracy over naive methods

Effective in various protein applications

Uses graph algorithms for optimal partitioning

Abstract

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