PDBminer to Find and Annotate Protein Structures for Computational Analysis
Open accessPreprint — published as 10.1021/acs.jcim.3c00884 · Paper page
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 .
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 11,511 · see citing works
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · see citing works
UniProt: a worldwide hub of protein knowledge
The UniProt Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1049 · Cited by 7,698 · see citing works
Exploring Network Structure, Dynamics, and Function Using NetworkX
Hagberg, A. A., Schult, D. A., & Swart, P. J. (2008) · Proceedings of the Python in Science Conference · doi:10.25080/tcwv9851 · Cited by 7,477 · see citing works
seaborn: statistical data visualization
Waskom, M. (2021) · Journal of Open Source Software · doi:10.21105/joss.03021 · Cited by 7,310 · see citing works
UniProt: the Universal Protein Knowledgebase in 2023
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1052 · Cited by 7,210 · see citing works
UniProt: the universal protein knowledgebase in 2021
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372 · see citing works
Biopython: freely available python tools for computational molecular biology and bioinformatics
Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080 · see citing works
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works
Comparative Protein Structure Modeling Using MODELLER
Webb, B., & Šali, A. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.3 · Cited by 4,349 · see citing works
Blinded by the Light: The Growing Complexity of p53
Vousden, K. H., & Prives, C. (2009) · Cell · doi:10.1016/j.cell.2009.04.037 · Cited by 3,163 · 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
Burley, S. K., Berman, H. M., Bhikadiya, C., Bi, C., Chen, L., Costanzo, L. D., Christie, C. H., Dalenberg, K., Duarte, J. M., Dutta, S., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1004 · Cited by 1,249 · see citing works
Cytoplasmic functions of the tumour suppressor p53
Green, D. R., & Kroemer, G. (2009) · Nature · doi:10.1038/nature07986 · Cited by 1,106 · see citing works
The PDB_REDO server for macromolecular structure model optimization
Joosten, R. P., Long, F., Murshudov, G. N., & Anastassis Perrakis, A. (2014) · IUCrJ · doi:10.1107/s2052252514009324 · Cited by 842 · see citing works
Sustainable data analysis with Snakemake
Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., Sochat, V., Forster, J., Lee, S., Twardziok, S. O., Kanitz, A., et al. (2021) · F1000Research · doi:10.12688/f1000research.29032.1 · Cited by 786 · see citing works
Why are there hotspot mutations in the TP53 gene in human cancers?
Baugh, E. H., Ke, H., Levine, A. J., Bonneau, R., & Chan, C. S. (2017) · Cell Death and Differentiation · doi:10.1038/cdd.2017.180 · Cited by 609 · see citing works
AlphaFill: enriching AlphaFold models with ligands and cofactors
Hekkelman, M. L., de Vries, I., Joosten, R. P., & Perrakis, A. (2023) · Nature Methods · doi:10.1038/s41592-022-01685-y · Cited by 511 · see citing works
Structure of the E6/E6AP/p53 complex required for HPV-mediated degradation of p53
Martinez-Zapien, D., Ruiz, F. X., Poirson, J., Mitschler, A., Ramirez, J., Forster, A., Cousido-Siah, A., Masson, M., Pol, S. V., PODJARNY, A. D., et al. (2016) · Nature · doi:10.1038/nature16481 · Cited by 487 · see citing works
Structural Basis of DNA Recognition by p53 Tetramers
Kitayner, M., Rozenberg, H., Kessler, N., Rabinovich, D., Shaulov, L., Haran, T. E., & Shakked, Z. (2006) · Molecular Cell · doi:10.1016/j.molcel.2006.05.015 · Cited by 408 · see citing works
RCSB Protein Data Bank: Enabling biomedical research and drug discovery
Goodsell, D. S., Zardecki, C., Costanzo, L. D., Duarte, J. M., Hudson, B. P., Persikova, I., Segura, J., Shao, C., Voigt, M., Westbrook, J., et al. (2019) · Protein Science · doi:10.1002/pro.3730 · Cited by 357 · see citing works
Transcription-independent p53 apoptosis: an alternative route to death
Speidel, D. (2009) · Trends in Cell Biology · doi:10.1016/j.tcb.2009.10.002 · Cited by 356 · see citing works
ETAA1 acts at stalled replication forks to maintain genome integrity
Bass, T. E., Luzwick, J. W., Kavanaugh, G. M., Carroll, C. M., Dungrawala, H., Glick, G. G., Feldkamp, M. D., Putney, R., Chazin, W., & Cortez, D. (2016) · Nature Cell Biology · doi:10.1038/ncb3415 · Cited by 258 · see citing works
Activation of the ATR kinase by the RPA-binding protein ETAA1
Haahr, P., Hoffmann, S., Tollenaere, M. A. X., Ho, T., Toledo, L. I., Mann, M., Bekker‐Jensen, S., Räschle, M., & Mailand, N. (2016) · Nature Cell Biology · doi:10.1038/ncb3422 · Cited by 255 · see citing works
Diversity in DNA recognition by p53 revealed by crystal structures with Hoogsteen base pairs
Kitayner, M., Rozenberg, H., Rohs, R., Suad, O., Rabinovich, D., Honig, B. H., & Shakked, Z. (2010) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1800 · Cited by 230 · see citing works
Wolff, S., Erster, S. H., Palacios, G. F., & Moll, U. M. (2008) · Cell Research · doi:10.1038/cr.2008.62 · Cited by 186 · see citing works
PDBe-KB: a community-driven resource for structural and functional annotations
Váradi, M., Berrisford, J. M., Deshpande, M., Nair, S. S., Gutmanas, A., Armstrong, D. R., Pravda, L., Al‐Lazikani, B., Anyango, S., Barton, G. J., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz853 · Cited by 124 · see citing works
Natan, E., Baloğlu, Ç., Pagel, K., Freund, S. M. V., Morgner, N., Robinson, C. V., Fersht, A. R., & Joerger, A. C. (2011) · Journal of Molecular Biology · doi:10.1016/j.jmb.2011.03.047 · Cited by 98 · see citing works
Of the many cellular responses activated by TP53, which ones are critical for tumour suppression?
Thomas, A., Kelly, G. L., & Strasser, A. (2022) · Cell Death and Differentiation · doi:10.1038/s41418-022-00996-z · Cited by 98 · see citing works
Bittrich, S., Bhikadiya, C., Bi, C., Chao, H., Duarte, J. M., Dutta, S., Fayazi, M., Henry, J., Khokhriakov, I., Lowe, R., et al. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.167994 · Cited by 83 · see citing works
Chen, Y., Zhang, X., Machado, A. C. D., Ding, Y. C., Chen, Z., Qin, P. Z., Rohs, R., & Chen, L. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt584 · Cited by 81 · see citing works
Degtjarik, O., Golovenko, D., Diskin-Posner, Y., Abrahmsén, L., Rozenberg, H., & Shakked, Z. (2021) · Nature Communications · doi:10.1038/s41467-021-27142-6 · Cited by 78 · see citing works
Homology‐based hydrogen bond information improves crystallographic structures in the PDB
van Beusekom, B., Touw, W. G., Tatineni, M., Somani, S., Rajagopal, G., Luo, J., Gilliland, G. L., Anastassis Perrakis, A., & Joosten, R. P. (2017) · Protein Science · doi:10.1002/pro.3353 · Cited by 74 · see citing works
BioPandas: Working with molecular structures in pandas DataFrames
Raschka, S. (2017) · The Journal of Open Source Software · doi:10.21105/joss.00279 · Cited by 67 · see citing works
Suad, O., Rozenberg, H., Brosh, R., Diskin‐Posner, Y., Kessler, N., Shimon, L. J. W., Frolow, F., Liran, A., Rotter, V., & Shakked, Z. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.10.063 · Cited by 57 · see citing works
New Insights into the Role of DNA Shape on Its Recognition by p53 Proteins
Golovenko, D., Bräuning, B., Vyas, P., Haran, T. E., Rozenberg, H., & Shakked, Z. (2018) · Structure · doi:10.1016/j.str.2018.06.006 · Cited by 45 · see citing works
CARPs enhance p53 turnover by degrading 14-3-3σ and stabilizing MDM2
Yang, W., Dicker, D. T., Chen, J., & El‐Deiry, W. S. (2008) · Cell Cycle · doi:10.4161/cc.7.5.5701 · Cited by 36 · see citing works
Faezov, B., & Dunbrack, R. L. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253411 · Cited by 35 · see citing works
Cancer-related Mutations with Local or Long-range Effects on an Allosteric Loop of p53
Degn, K., Beltrame, L., Hede, F. D., Sora, V., Nicolaci, V., Vabistsevits, M., Schmiegelow, K., Wadt, K., Tiberti, M., Lambrughi, M., et al. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167663 · Cited by 34 · see citing works
Self-citationLiu, J., Wu, T., Guo, Z., Hou, J., & Cheng, J. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26186 · Cited by 32 · see citing works
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
PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles
Sora, V., Tiberti, M., Beltrame, L., Doğan, D., Robbani, S. M., Rubin, J. B., & Papaleo, E. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00574 · Cited by 29 · see citing works
Self-citationp53: Multiple Facets of a Rubik's Cube
Zhang, Y., & Lozano, G. (2016) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-050216-121926 · Cited by 26 · see citing works
MAVISp: A Modular Structure-Based Framework for Protein Variant Effects
Arnaudi, M., Utichi, M., Degn, K., Tiberti, M., Beltrame, L., Krzesińska, K., Sánchez-Izquierdo Besora, P., Kiachaki, E., Scrima, S., Bauer, L., et al. (2022) · doi:10.1101/2022.10.22.513328 · Cited by 20 · see citing works
Self-citationData Mining of Macromolecular Structures
van Beusekom, B., Anastassis Perrakis, A., & Joosten, R. P. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3572-7_6 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Benchmarking AI-powered docking methods from the perspective of virtual screening
Gu, S., Shen, C., Zhang, X., Sun, H., Cai, H., Luo, H., Zhao, H., Liu, B., Du, H., Zhao, Y., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00993-0 · Cited by 56 · see citing works
MAVISp: A Modular Structure-Based Framework for Protein Variant Effects
Arnaudi, M., Utichi, M., Degn, K., Tiberti, M., Beltrame, L., Krzesińska, K., Sánchez-Izquierdo Besora, P., Kiachaki, E., Scrima, S., Bauer, L., et al. (2022) · doi:10.1101/2022.10.22.513328 · Cited by 20 · see citing works
Self-citationScrima, S., Lambrughi, M., Tiberti, M., Fadda, E., & Papaleo, E. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167260 · Cited by 12 · see citing works
Self-citationProtein 3D Graph Structure Learning for Robust Structure-Based Protein Property Prediction
Huang, Y., Li, S., Wu, L., Su, J., Lin, H., Zhang, O., Liu, Z., Gao, Z., Zheng, J., & Li, S. Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i11.29161 · Cited by 7 · see citing works
MAVISp : A modular structure‐based framework for protein variant effects
(2026) · doi:10.1002/pro.70548 · Cited by 3 · see citing works
Self-citationDecoding ATG9A Variation: A Comprehensive Structural Investigation of All Missense VariantsPreprint
Utichi, M., Marjault, H., Tiberti, M., & Papaleo, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.03.703515
Self-citation
- Authors
- Degn, K., Beltrame, L., Tiberti, M., & Papaleo, E.
- Affiliations
- Technical University of Denmark (DK)
- Corresponding author
- Papaleo, Elena
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- openRxiv
- Published
- 2023-05-07
- Volume
- 63
- Issue
- 23
- Pages
- 7274-7281
- ISSN
- 1549-9596 (ISSN-L)
- OpenAlex
- W4388749535
- Cited by
- 6
- References
- 47
- 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
- cc-by-nc-nd
- 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 (DNRF125)
- Carlsbergfondet (CF18-0314)
- Novo Nordisk Fonden (NNF20OC0065262)
- Hartmann Fonden (R241-A33877)
- LEO Fondet (LF17006)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
