SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures

By Naomi Fox, Steven E. Brenner, John-Marc Chandonia. 2013

743 citations recorded by the source corpus.

Structural Classification of Proteins—extended (SCOPe, http://scop.berkeley.edu) is a database of protein structural relationships that extends the SCOP database. SCOP is a manually curated ordering of domains from the majority of proteins of known structure in a hierarchy according to structural and evolutionary relationships. Development of the SCOP 1.x series concluded with SCOP 1.75. The ASTRAL compendium provides several databases and tools to aid in the analysis of the protein structures classified in SCOP, particularly through the use of their sequences. SCOPe extends version 1.75 of the SCOP database, using automated curation methods to classify many structures released since SCOP 1.75. We have rigorously benchmarked our automated methods to ensure that they are as accurate as manual curation, though there are many proteins to which our methods cannot be applied. SCOPe is also partially manually curated to correct some errors in SCOP. SCOPe aims to be backward compatible with SCOP, providing the same parseable files and a history of changes between all stable SCOP and SCOPe releases. SCOPe also incorporates and updates the ASTRAL database. The latest release of SCOPe, 2.03, contains 59 514 Protein Data Bank (PDB) entries, increasing the number of structures classified in SCOP by 55% and including more than 65% of the protein structures in the PDB.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, methodology): “We construct a dataset to evaluate remote homology detection using SCOPe (Fox et al., 2014).”
High-resolution de novo structure prediction from primary sequence (2022, methodology): “(32) introduce a hand-craft relative positional bias term added to the pre-softmax logits.”
Protein Sequence Analysis Using the MPI Bioinformatics Toolkit (2020, positive): “…HHblits (Remmert, Biegert, Hauser, & Soding, 2011), HHpred (Soding, 2005; Steinegger et al., 2019)]; protein sequence and domain databases [SCOPe (Fox, Brenner, & Chandonia, 2014), ECOD (Cheng et al., 2014; Schaeffer, Liao, & Grishin, 2018), Pfam (Coggill, Finn, & Bateman, 2008; El-Gebali et…”
RUPEE: A fast and accurate purely geometric protein structure search (2018, background): “Among protein structure classification databases for which corresponding structure searches exist, SCOPe [2] and CATH [3] are the most popular.”
ECOD: An Evolutionary Classification of Protein Domains (2014, methodology): “To partially alleviate this problem, ASTRAL now offers SCOPe, a sequence-based extension of the original SCOP hierarchy [14].”
BERTology Meets Biology: Interpreting Attention in Protein Language Models (2020, methodology): “We use two protein sequence datasets from the TAPE repository for the analysis: the ProteinNet dataset [3, 10, 25, 50] and the Secondary Structure dataset [10, 36, 50, 58].”
Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “3d; classifications in panels 3b-3d based on SCOPe [54]).”
Learning protein sequence embeddings using information from structure (2019, methodology): “We first benchmark our model’s ability to correctly predict structural similarity between pairs of sequences using the SCOPe ASTRAL dataset [5].”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “We evaluated the performance of DeepContact on the ASTRAL validation set (660 proteins), on a set of 228 previous CASP targets (CASP228), on a set of 220 CAMEO targets, and by participating in the blind prediction tasks from the wellestablished CASP12 experiment (Fox et al., 2013; Moult et al., 2014”
JPred4: a protein secondary structure prediction server (2015, methodology): “However, in JPred4, the JNet 2.0 neural network-based predictor has been retrained to make JNet 2.3.1 by 7-fold cross-validation using one representative for each of the 1358 SCOPe / ASTRAL v.2.04 superfamily domain sequences (32).”
ProteinNet: a standardized data set for machine learning of protein structure (2019, methodology): “In addition to full length PDB structures and sequences, single domain entries are created by extracting domain boundaries from ASTRAL [20] to enable training of both single and multi-domain models.”
Dali server update (2016, background): “Dali’s scoring function belongs to the latter category, and it has been shown to yield structural dendrograms that agree well with expert classifications (13,16–18).”
RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences (2020, background): “• Annotations tab (6), which takes the user to an extensive collection of annotations for the macromolecules in the structure, including Domain Annotation: SCOP Classification (42) from SCOPe (43); Domain Annotation: CATH (44); Protein Family Annotation (45); and Gene Product Annotation (46).”
SCOPe: Manual Curation and Artifact Removal in the Structural Classification of Proteins – extended Database (2016, methodology): “We use a combination of manual curation and a rigorously validated software pipeline [5] to add new structures from the Protein Data Bank (PDB) [6,7], and we have also developed software to identify errors in SCOP, which are then corrected in new releases of SCOPe.”