3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

Váradi, Mihály; Nair, Sreenath; Sillitoe, Ian; Tauriello, Gerardo; Anyango, Stephen; Bienert, Stefan; Borges, Clemente; Deshpande, Mandar; Green, Tim; Hassabis, Demis; Hatos, András; Hegedüs, Tamás; Hekkelman, Maarten L.; Joosten, Robbie P.; Jumper, John; Laydon, Agata; Molodenskiy, Dmitry; Piovesan, Damiano; Salladini, Edoardo; Salzberg, Steven L.; Sommer, Markus J.; Steinegger, Martin; Suhajda, Erzsebet; Svergun, Dmitri I.; Tenorio-Ku, Luiggi; Tosatto, Silvio C. E.; Tunyasuvunakool, Kathryn; Waterhouse, Andrew; Žídek, Augustin; Schwede, Torsten; Orengo, Christine; Velankar, Sameer

doi:10.1093/gigascience/giac118

Cited by 15 publications

(9 citation statements)

References 47 publications

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“…The data is available grouped by CATH Superfamily and by organism through the 3D-Beacons network 25 , Zenodo (https://doi. org/10.5281/zenodo.7404988, https://zenodo.org/record/7404988), and the CATH FTP (ftp://orengoftp.biochem.ucl.ac.uk/alphafold/ cath-v4.3.0-model-organisms).…”

Section: Discussionmentioning

confidence: 99%

“…This suggests that the release of a further 214 million models by DeepMind will bring significant insights on how structures (and, therefore their functions) diverge during evolution. The data is available grouped by CATH Superfamily and by organism through the 3D-Beacons network 25 , Zenodo (10.5281/zenodo.7404988, https://zenodo.org/record/7404988 ), and the CATH FTP ( ftp://orengoftp.biochem.ucl.ac.uk/alphafold/cath-v4.3.0-model-organisms ).…”

Section: Introductionmentioning

confidence: 99%

“…A table accompanying the results on Zenodo contains information for each confident CATH assignment on model quality, metrics, CATH SuperFamily assignment and its source. Individual models are available through the 3D-Beacons network 25 .…”

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confidence: 99%

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AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

et al. 2023

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Deep-learning (DL) methods like DeepMind’s AlphaFold2 (AF2) have led to substantial improvements in protein structure prediction. We analyse confident AF2 models from 21 model organisms using a new classification protocol (CATH-Assign) which exploits novel DL methods for structural comparison and classification. Of ~370,000 confident models, 92% can be assigned to 3253 superfamilies in our CATH domain superfamily classification. The remaining cluster into 2367 putative novel superfamilies. Detailed manual analysis on 618 of these, having at least one human relative, reveal extremely remote homologies and further unusual features. Only 25 novel superfamilies could be confirmed. Although most models map to existing superfamilies, AF2 domains expand CATH by 67% and increases the number of unique ‘global’ folds by 36% and will provide valuable insights on structure function relationships. CATH-Assign will harness the huge expansion in structural data provided by DeepMind to rationalise evolutionary changes driving functional divergence.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

et al. 2023

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“…The wwPDB ModelCIF Working Group is committed to maintaining and updating the data standard as new approaches to computational structure modeling of biological macromolecules emerge and are validated. The wwPDB is also supporting community efforts, such as the 3D-Beacons network [43], to encourage adoption of common data standards and facilitate access to 3D-structure information.…”

Section: Discussionmentioning

confidence: 99%

ModelCIF: An extension of PDBx/mmCIF data representation for computed structure models

Vallat

Tauriello

Bienert

et al. 2022

Preprint

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ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery of Findable, Accessible, Interoperable, and Reusable (FAIR) data to users worldwide. It is an extension of the Protein Data Bank Exchange / macromolecular Crystallographic Information Framework (PDBx/mmCIF), which is the global data standard for representing experimentally-determined, three-dimensional (3D) structures of macromolecules and associated metadata. ModelCIF provides an extensible data representation for deposition, archiving, and public dissemination of predicted 3D models of proteins. The PDBx/mmCIF framework and its extensions (e.g., ModelCIF) are managed by the Worldwide Protein Data Bank partnership (wwPDB, wwpdb.org) in collaboration with relevant community stakeholders such as the wwPDB ModelCIF Working Group (wwpdb.org/task/modelcif). This semantically rich and extensible data framework for representing computed structure models (CSMs) accelerates the pace of scientific discovery. Herein, we describe the architecture, contents, and governance of ModelCIF, and tools and processes for maintaining and extending the data standard. Community tools and software libraries that support ModelCIF are also described.

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“…PDBminer is a Python-based command line tool available at under GNU General Public License v3.0. PDBminer depends on BioPython and pandas (see the GitHub for versioning details) and on the information in UniProt, − AlphaFoldDB, the PDB, 3D-Beacons, and PDBe accessed through APIs. The plotting tools included in the package further require seaborn, matplotlib, and networkx (see the GitHub for versioning details).…”

Section: Features and Impementationmentioning

confidence: 99%

PDBminer to Find and Annotate Protein Structures for Computational Analysis

Degn,

Beltrame,

Tiberti

et al. 2023

J. Chem. Inf. Model.

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Computational methods relying on protein structure strongly depend on the structure selected for investigation. Typical sources of protein structures include experimental structures available at the Protein Data Bank (PDB) and high-quality in silico model structures, such as those available at the AlphaFold Protein Structure Database. Either option has significant advantages and drawbacks, and exploring the wealth of available structures to identify the most suitable ones for specific applications can be a daunting task. We provide an open-source software package, PDBminer, with the purpose of making structure identification and selection easier, faster, and less error prone. PDBminer searches the AlphaFold Database and the PDB for available structures of interest and provides an up-to-date, quality-ranked table of structures applicable for further use. 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 nucleic acid chain binding. The PDBminer2coverage and PDBminer2network tools assist in visualizing the results. 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. Here, we showcase the main functionalities of the package on the p53 tumor suppressor protein. The package is available at .

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3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

Cited by 15 publications

References 47 publications

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

ModelCIF: An extension of PDBx/mmCIF data representation for computed structure models

PDBminer to Find and Annotate Protein Structures for Computational Analysis

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