PDBminer to Find and Annotate Protein Structures for Computational Analysis
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Abstract
Abstract Structural bioinformatics and molecular modeling of proteins strongly depend on the protein structure selected for investigation. The choice of protein structure relies on direct application from the Protein Data Bank (PDB), homology- or de-novo modeling. Recent de-novo models, such as AlphaFold2, require little preprocessing and omit the need to navigate the many parameters of choosing an experimentally determined model. Yet, the experimentally determined structure still has much to offer, why it should be of interest to the community to ease the choice of experimentally determined models. We provide an open-source software package, PDBminer, to mine both the AlphaFold Database (AlphaFoldDB) and the PDB based on search criteria set by the user. This tool provides an up-to-date, quality-ranked table of structures applicable for further research. PDBminer provides an overview of the available protein structures to one or more input proteins, parallelizing the runs if multiple cores are specified. The output table reports the coverage of the protein structures aligned to the UniProt sequence, overcoming numbering differences in PDB structures, and providing information regarding model quality, protein complexes, ligands, and nucleotide binding. The PDBminer2coverage and PDBminer2network tools assist in visualizing the results. We suggest that PDBminer can be applied to overcome the tedious task of choosing a PDB structure without losing the wealth of additional information available in the PDB. As developers, we will guarantee the introduction of new functionalities, assistance, training of new contributors, and package maintenance. The package is available at http://github.com/ELELAB/PDBminer .
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1038 · Cited by 1,581 · see citing works
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Varadi, M., Nair, S., Sillitoe, I., Tauriello, G., Anyango, S., Bienert, S., Borges, C., Deshpande, M., Green, T., Hassabis, D., et al. (2022) · GigaScience · doi:10.1093/gigascience/giac118 · Cited by 32 · see citing works
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- Authors
- Degn, K., Beltrame, L., Tiberti, M., & Papaleo, E.
- Affiliations
- Technical University of Denmark (DK)
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
- Corresponding author
- Papaleo, Elena
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-11-17
- Volume
- 63
- Issue
- 23
- Pages
- 7274-7281
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W4388749535
- Cited by
- 6
- References
- 4
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2022: 1
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
- Funders
- Danmarks Grundforskningsfond (DNRF-125)
- LEO Fondet (LF17006)
- Novo Nordisk Fonden (NNF20OC0065262)
- Hartmann Fonden (R241-A33877)
- Carlsbergfondet (CF18-0314)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- AlphaFold, protein structure database search, structure selection, structure annotation, model quality assessment, protein complexes, ligand binding, nucleic acid binding, p53, open source software
- MeSH terms
- Computer Simulation, Ligands, Proteins, Software, Databases, Protein
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