Gene3D: Extensive prediction of globular domains in proteins

By Tony E. Lewis, Ian Sillitoe, N. Dawson, S. Lam, Tristan Clarke, David A. Lee, Christine A. Orengo, Jonathan G. Lees. 2017

185 citations recorded by the source corpus.

Abstract Gene3D (http://gene3d.biochem.ucl.ac.uk) is a database of globular domain annotations for millions of available protein sequences. Gene3D has previously featured in the Database issue of NAR and here we report a significant update to the Gene3D database. The current release, Gene3D v16, has significantly expanded its domain coverage over the previous version and now contains over 95 million domain assignments. We also report a new method for dealing with complex domain architectures that exist in Gene3D, arising from discontinuous domains. Amongst other updates, we have added visualization tools for exploring domain annotations in the context of other sequence features and in gene families. We also provide web-pages to visualize other domain families that co-occur with a given query domain family.

Subfields: Molecular Biology

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Citations in the corpus

Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models (2022, background): “2 S95 domains [27, 10] from the Protein Data Bank (PDB) [6].”
Protein sequence design with a learned potential (2022, methodology): “To train our classi ﬁ er, we used X-ray crystal structure data from the Protein Data Bank (PDB) 37 , speci ﬁ cally training on CATH 4.2 S95 domains 38,39 .”
InterPro in 2019: improving coverage, classification and access to protein sequence annotations (2018, methodology): “However, the CATH-Gene3D and SUPERFAMILY databases adopt a different approach, using collections of underlying HMMs per entry, which is necessary to encapsulate diverse structural families.”
Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins (2022, unknown): “The consensus sequences from each MSA in the BFD were extracted and scanned against the Pfam (49) and CATH-Gene3D (38, 50)HMM librariesusing hmmscan(51) in order to ﬁ lter out regions corresponding to well-characterized protein families.”
Ensembl 2020 (2019, background): “Variants co-located with a transcript can be displayed and coloured according to their SIFT (41) or PolyPhen-2 (42) predicted pathogenicity alongside exon boundaries and protein domains from Gene3D (43), Smart (44), Pfam (45) and Panther (46).”
Fast and flexible design of novel proteins using graph neural networks (2019, unknown): “We split this dataset into a training subset, containing sequences of 1,029 Gene3D superfamilies, a validation subset, containing sequences of 172 Gene3D superfamilies, and a test subset, containing sequences of another 172 Gene3D superfamilies.”
Protein sequence design with a learned potential (2020, methodology): “The existence of many structural homologs with low sequence identity suggests that there should be many foldable sequences for a given protein backbone [28, 29].”
ProGen2: Exploring the Boundaries of Protein Language Models (2022, methodology): “The P RO G EN 2-large model was ﬁnetuned for two epochs on 1M sequences, from Gene3D (Lewis et al., 2018) and CATH (Sillitoe et al., 2021), adopting a two-layer sandwich architecture (CATH 3.30).”