Quantum chemical calculation dataset for representative protein folds by the fragment molecular orbital method

The function of a biomacromolecule is not only determined by its three-dimensional structure but also by its electronic state. Quantum chemical calculations are promising non-empirical methods available for determining the electronic state of a given structure. In this study, we used the fragment molecular orbital (FMO) method, which applies to biopolymers such as proteins, to provide physicochemical property values on representative structures in the SCOP2 database of protein families, a subset of the Protein Data Bank. Our dataset was constructed by over 5,000 protein structures, including over 200 million inter-fragment interaction energies (IFIEs) and their energy components obtained by pair interaction energy decomposition analysis (PIEDA) using FMO-MP2/6-31 G*. Moreover, three basis sets, 6-31 G*, 6-31 G**, and cc-pVDZ, were used for the FMO calculations of each structure, making it possible to compare the energies obtained with different basis functions for the same fragment pair. The total data size is approximately 6.7 GB. Our dataset will be useful for functional analyses and machine learning based on the physicochemical property values of proteins.

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Publication Details

Journal
Scientific Data
Published
2024-10-23
DOI
https://doi.org/10.1038/s41597-024-03999-2
Citations
7
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.82

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Quantum chemical calculation dataset for representative protein folds by the fragment molecular orbital method

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Scientific Data
Machine Learning in Materials Science
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article

Quantum chemical calculation dataset for representative protein folds by the fragment molecular orbital method

Koji Okuwaki, T Miyagishi, Shinya Tanaka, Daisuke Takaya, Koichiro Kato, Chiduru Watanabe, Kaori Fukuzawa, Yu‐Shi Tian, Shinichi Ohno
article en
7 citations

Abstract

The function of a biomacromolecule is not only determined by its three-dimensional structure but also by its electronic state. Quantum chemical calculations are promising non-empirical methods available for determining the electronic state of a given structure. In this study, we used the fragment molecular orbital (FMO) method, which applies to biopolymers such as proteins, to provide physicochemical property values on representative structures in the SCOP2 database of protein families, a subset of the Protein Data Bank. Our dataset was constructed by over 5,000 protein structures, including over 200 million inter-fragment interaction energies (IFIEs) and their energy components obtained by pair interaction energy decomposition analysis (PIEDA) using FMO-MP2/6-31 G*. Moreover, three basis sets, 6-31 G*, 6-31 G**, and cc-pVDZ, were used for the FMO calculations of each structure, making it possible to compare the energies obtained with different basis functions for the same fragment pair. The total data size is approximately 6.7 GB. Our dataset will be useful for functional analyses and machine learning based on the physicochemical property values of proteins.

Scientific DataVol. 11(1)
Kyushu University (JP), RIKEN Center for Biosystems Dynamics Research (JP), Osaka University of Pharmaceutical Sciences (JP), The University of Osaka (JP)
Japan Agency for Medical Research and Development, Japan Society for the Promotion of Science
Affordable and clean energy
Openalex Percentile: Top 26%
Machine Learning in Materials Science
0.82
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