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Abstract
Fragmentation schemes enable the efficient quantum-chemical treatment of large biomolecular systems, and provide an ideal starting point for the development of accurate machine-learning potentials for proteins. Here, we present a fragment-based method that only used calculations for single-amino acids and their dimers, and is able to reduce the fragmentation error in total energies to ca. 1 kJ/mol per amino acid for polypeptides and proteins across different structural motifs. This is achieved by combining a two-body extension of the molecular fractionation with conjugate caps (MFCC) scheme with the density-based many-body expansion (db-MBE), thus extending the applicability of the db-MBE from molecular clusters to polypeptides and proteins.
Supplementary materials
Title
Supporting Information
Description
Explicit expressions for the density-based energy correction in the db-MBE, db-MFCC, and db-MFCC-MBE(2) schemes; additional plot of total absolute errors for the protein test set.
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Supplementary weblinks
Title
Data Set
Description
PDB files of all considered molecular structures, Jupyter notebooks for data analysis (including all raw data) and for generating all figures, as well as PyADF input scripts to perform the eb-MFCC, db-MFCC, eb-MFCC-MBE(2) and db-MFCC-MBE(2) calculations
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