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


Journal

Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192

Informations de publication

Date de publication:
23 Oct 2024
Historique:
received: 02 07 2024
accepted: 11 10 2024
medline: 24 10 2024
pubmed: 24 10 2024
entrez: 24 10 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 39443514
doi: 10.1038/s41597-024-03999-2
pii: 10.1038/s41597-024-03999-2
doi:

Substances chimiques

Proteins 0

Types de publication

Dataset Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1164

Subventions

Organisme : Japan Agency for Medical Research and Development (AMED)
ID : JP23ama121030
Organisme : Japan Agency for Medical Research and Development (AMED)
ID : JP23ama121030
Organisme : Japan Agency for Medical Research and Development (AMED)
ID : JP23ama121030
Organisme : Japan Agency for Medical Research and Development (AMED)
ID : JP23ama121030
Organisme : Japan Agency for Medical Research and Development (AMED)
ID : JP23ama121030
Organisme : MEXT | Japan Society for the Promotion of Science (JSPS)
ID : 23K11320

Informations de copyright

© 2024. The Author(s).

Références

Berman, H., Henrick, K. & Nakamura, H. Announcing the worldwide Protein Data Bank. Nat. Struct. Mol. Biol. 10, 980–980 (2003).
doi: 10.1038/nsb1203-980
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
doi: 10.1093/nar/28.1.235 pubmed: 10592235 pmcid: 102472
Kinjo, A. R. et al. Protein Data Bank Japan (PDBj): maintaining a structural data archive and resource description framework format. Nucleic Acids Res. 40, D453–D460 (2012).
doi: 10.1093/nar/gkr811 pubmed: 21976737
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
doi: 10.1038/s41586-021-03819-2 pubmed: 34265844 pmcid: 8371605
The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531 (2023).
doi: 10.1093/nar/gkac1052
Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022).
doi: 10.1093/nar/gkab1061 pubmed: 34791371
Hollingsworth, S. A. & Dror, R. O. Molecular Dynamics Simulation for All. Neuron 99, 1129–1143 (2018).
doi: 10.1016/j.neuron.2018.08.011 pubmed: 30236283 pmcid: 6209097
Svensson, M. et al. ONIOM: A Multilayered Integrated MO + MM Method for Geometry Optimizations and Single Point Energy Predictions. A Test for Diels−Alder Reactions and Pt(P(t -Bu)
doi: 10.1021/jp962071j
Kitaura, K., Ikeo, E., Asada, T., Nakano, T. & Uebayasi, M. Fragment molecular orbital method: an approximate computational method for large molecules. Chem. Phys. Lett. 313, 701–706 (1999).
doi: 10.1016/S0009-2614(99)00874-X
Galvez Vallejo, J. L. et al. Toward an extreme-scale electronic structure system. J. Chem. Phys. 159, 044112 (2023).
doi: 10.1063/5.0156399 pubmed: 37497819
Fedorov, D. G. Complete Guide to the Fragment Molecular Orbital Method in GAMESS https://doi.org/10.1142/13063 (World Scientific, 2022).
Fedorov, D. G., Nagata, T. & Kitaura, K. Exploring chemistry with the fragment molecular orbital method. Phys Chem Chem Phys 14, 7562–7577 (2012).
doi: 10.1039/c2cp23784a pubmed: 22410762
Mochizuki, Y. et al. Development Status of ABINIT-MP in 2023. J. Comput. Chem. Jpn. 23, 4–8 (2024).
doi: 10.2477/jccj.2024-0001
Tanaka, S., Mochizuki, Y., Komeiji, Y., Okiyama, Y. & Fukuzawa, K. Electron-correlated fragment-molecular-orbital calculations for biomolecular and nano systems. Phys. Chem. Chem. Phys. 16, 10310–10344 (2014).
doi: 10.1039/C4CP00316K pubmed: 24740821
Mochizuki, Y. et al. The ABINIT-MP Program. in Recent Advances of the Fragment Molecular Orbital Method 53–67 https://doi.org/10.1007/978-981-15-9235-5_4 (Springer, 2021).
Fedorov, D. G. & Kitaura, K. Pair interaction energy decomposition analysis. J. Comput. Chem. 28, 222–237 (2007).
doi: 10.1002/jcc.20496 pubmed: 17109433
Takaya, D. et al. Protein ligand interaction analysis against new CaMKK2 inhibitors by use of X-ray crystallography and the fragment molecular orbital (FMO) method. J. Mol. Graph. Model. 99, 107599 (2020).
doi: 10.1016/j.jmgm.2020.107599 pubmed: 32348940
Watanabe, C. et al. Theoretical Analysis of Activity Cliffs among Benzofuranone-Class Pim1 Inhibitors Using the Fragment Molecular Orbital Method with Molecular Mechanics Poisson–Boltzmann Surface Area (FMO+MM-PBSA) Approach. J. Chem. Inf. Model. 57, 2996–3010 (2017).
doi: 10.1021/acs.jcim.7b00110 pubmed: 29111719
Watanabe, H. et al. Comparison of binding affinity evaluations for FKBP ligands with state-of-the-art computational methods: FMO, QM/MM, MM-PB/SA and MP-CAFEE approaches. Chem-Bio Inform. J. 10, 32–45 (2010).
doi: 10.1273/cbij.10.32
Watanabe, C., Okiyama, Y., Tanaka, S., Fukuzawa, K. & Honma, T. Molecular recognition of SARS-CoV-2 spike glycoprotein: quantum chemical hot spot and epitope analyses. Chem. Sci. 12, 4722–4739 (2021).
doi: 10.1039/D0SC06528E pubmed: 35355624 pmcid: 8892577
Fukuzawa, K. & Tanaka, S. Fragment molecular orbital calculations for biomolecules. Curr. Opin. Struct. Biol. 72, 127–134 (2022).
doi: 10.1016/j.sbi.2021.08.010 pubmed: 34656048
Handa, Y. et al. Prediction of Binding Pose and Affinity of Nelfinavir, a SARS-CoV-2 Main Protease Repositioned Drug, by Combining Docking, Molecular Dynamics, and Fragment Molecular Orbital Calculations. J. Phys. Chem. B 128, 2249–2265 (2024).
doi: 10.1021/acs.jpcb.3c05564 pubmed: 38437183
Takebe, K. et al. Structural and Computational Analyses of the Unique Interactions of Opicapone in the Binding Pocket of Catechol O -Methyltransferase: A Crystallographic Study and Fragment Molecular Orbital Analyses. J. Chem. Inf. Model. 63, 4468–4476 (2023).
doi: 10.1021/acs.jcim.3c00331 pubmed: 37436881
Ramakrishnan, R., Dral, P. O., Rupp, M. & Von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 1, 140022 (2014).
doi: 10.1038/sdata.2014.22 pubmed: 25977779 pmcid: 4322582
Takaya, D. et al. FMODB: The World’s First Database of Quantum Mechanical Calculations for Biomacromolecules Based on the Fragment Molecular Orbital Method. J. Chem. Inf. Model. 61, 777–794 (2021).
doi: 10.1021/acs.jcim.0c01062 pubmed: 33511845
Kato, K. et al. High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning. J. Chem. Inf. Model. 60, 3361–3368 (2020).
doi: 10.1021/acs.jcim.0c00273 pubmed: 32496771
Fukuzawa, K. et al. Special Features of COVID-19 in the FMODB: Fragment Molecular Orbital Calculations and Interaction Energy Analysis of SARS-CoV-2-Related Proteins. J. Chem. Inf. Model. 61, 4594–4612 (2021).
doi: 10.1021/acs.jcim.1c00694 pubmed: 34506132
Kamisaka, K. et al. Statistical analysis of interactions among amino acid residues in apo structures using fragment molecular orbital method. Chem-Bio Inform. J. 24, 25–47 (2024).
doi: 10.1273/cbij.24.25
Andreeva, A., Howorth, D., Chothia, C., Kulesha, E. & Murzin, A. G. SCOP2 prototype: a new approach to protein structure mining. Nucleic Acids Res. 42, D310–D314 (2014).
doi: 10.1093/nar/gkt1242 pubmed: 24293656
Andreeva, A., Kulesha, E., Gough, J. & Murzin, A. G. The SCOP database in 2020: expanded classification of representative family and superfamily domains of known protein structures. Nucleic Acids Res. 48, D376–D382 (2020).
doi: 10.1093/nar/gkz1064 pubmed: 31724711
Fedorov, D. G. & Kitaura, K. Second order Møller-Plesset perturbation theory based upon the fragment molecular orbital method. J. Chem. Phys. 121, 2483–2490 (2004).
doi: 10.1063/1.1769362 pubmed: 15281845
Mochizuki, Y. et al. A parallelized integral-direct second-order Møller-Plesset perturbation theory method with a fragment molecular orbital scheme. Theor. Chem. Acc. 112, 442–452 (2004).
doi: 10.1007/s00214-004-0602-3
Mochizuki, Y., Koikegami, S., Nakano, T., Amari, S. & Kitaura, K. Large scale MP2 calculations with fragment molecular orbital scheme. Chem. Phys. Lett. 396, 473–479 (2004).
doi: 10.1016/j.cplett.2004.08.082
Umezawa, Y. & Nishio, M. CH/p Interactions as Demonstrated in the Crystal Structure of Guanine-nucleotide Binding Proteins, Src Homology-2 Domains and Human Growth Hormone in Complex with their Speci
Yuan, Z. et al. Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation, and molecular dynamic simulation. J. Mol. Model. 30, 131 (2024).
doi: 10.1007/s00894-024-05935-y pubmed: 38613643
Watanabe, K. et al. Intermolecular Interaction Analyses on SARS-CoV-2 Spike Protein Receptor Binding Domain and Human Angiotensin-Converting Enzyme 2 Receptor-Blocking Antibody/Peptide Using Fragment Molecular Orbital Calculation. J. Phys. Chem. Lett. 12, 4059–4066 (2021).
doi: 10.1021/acs.jpclett.1c00663 pubmed: 33881894
Otsuka, T., Okimoto, N. & Taiji, M. Assessment and acceleration of binding energy calculations for protein–ligand complexes by the fragment molecular orbital method. J. Comput. Chem. 36, 2209–2218 (2015).
doi: 10.1002/jcc.24055 pubmed: 26400829
Dunning, T. H. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J. Chem. Phys. 90, 1007–1023 (1989).
doi: 10.1063/1.456153
Watanabe, C. et al. Development of an automated fragment molecular orbital (FMO) calculation protocol toward construction of quantum mechanical calculation database for large biomolecules. Chem-Bio Inform. J. 19, 5–18 (2019).
doi: 10.1273/cbij.19.5
Molecular Operating Environment (MOE), 2022.02; Chemical Computing Group ULC, 1010 Sherbrooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2022.
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).
doi: 10.1038/s41592-022-01488-1 pubmed: 35637307 pmcid: 9184281
Nakano, T. et al. Fragment molecular orbital method: use of approximate electrostatic potential. Chem. Phys. Lett. 351, 475–480 (2002).
doi: 10.1016/S0009-2614(01)01416-6
Fedorov, D. G., Olson, R. M., Kitaura, K., Gordon, M. S. & Koseki, S. A new hierarchical parallelization scheme: Generalized distributed data interface (GDDI), and an application to the fragment molecular orbital method (FMO). J. Comput. Chem. 25, 872–880 (2004).
doi: 10.1002/jcc.20018 pubmed: 15011259
Takaya, D. & Ohno, S. FMO-SCOP-29Jun2022. figshare https://doi.org/10.6084/m9.figshare.25980112.v2 (2024).
Monteleone, S. et al. Hotspot Identification and Drug Design of Protein–Protein Interaction Modulators Using the Fragment Molecular Orbital Method. J. Chem. Inf. Model. 62, 3784–3799 (2022).
doi: 10.1021/acs.jcim.2c00457 pubmed: 35939049

Auteurs

Daisuke Takaya (D)

Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan. takaya-d@phs.osaka-u.ac.jp.

Shu Ohno (S)

Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan.

Toma Miyagishi (T)

Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan.

Sota Tanaka (S)

Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan.

Koji Okuwaki (K)

Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan.

Chiduru Watanabe (C)

Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.

Koichiro Kato (K)

Department of Applied Chemistry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.

Yu-Shi Tian (YS)

Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan.

Kaori Fukuzawa (K)

Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan. fukuzawa-k@phs.osaka-u.ac.jp.

Articles similaires

Databases, Protein Protein Domains Protein Folding Proteins Deep Learning
Humans Molecular Chaperones Brain Protein Folding Mutation

Brain malformations and seizures by impaired chaperonin function of TRiC.

Florian Kraft, Piere Rodriguez-Aliaga, Weimin Yuan et al.
1.00
Humans Chaperonin Containing TCP-1 Brain Seizures Protein Folding
Humans Computational Biology ROC Curve Algorithms Proteins

Classifications MeSH