SCOPe: Manual Curation and Artifact Removal in the Structural Classification of Proteins – extended Database

By John-Marc Chandonia, Naomi Fox, Steven E. Brenner. 2016

101 citations recorded by the source corpus.

SCOPe (Structural Classification of Proteins – extended, http://scop.berkeley.edu) is a database of relationships between protein structures that extends the Structural Classification of Proteins (SCOP) database. SCOP is an expert-curated ordering of domains from the majority of proteins of known structure in a hierarchy according to structural and evolutionary relationships. SCOPe classifies the majority of protein structures released since SCOP development concluded in 2009, using a combination of manual curation and highly precise automated tools, aiming to have the same accuracy as fully hand-curated SCOP releases. SCOPe also incorporates and updates the ASTRAL compendium, which provides several databases and tools to aid in the analysis of the sequences and structures of proteins classified in SCOPe. SCOPe continues high quality manual classification of new superfamilies, a key feature of SCOP. Artifacts such as expression tags are now separated into their own class, in order to distinguish them from the homology-based annotations in the remainder of the SCOPe hierarchy. SCOPe 2.06 contains 77,439 PDB entries, double the 38,221 structures classified in SCOP.

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

1. Robin Pearce
2. Yang Zhang
3. Chengxin Zhang
4. Yang Li
5. Wei Zheng
6. S. Mortuza
7. Eric W. Bell
8. Xiaogen Zhou

In Molecular Biology

1. Robin Pearce
2. Yang Zhang
3. Chengxin Zhang
4. Yang Li
5. Eric W. Bell
6. Wei Zheng
7. Xiaogen Zhou
8. S. Mortuza

Citations in the corpus

• Learning protein sequence embeddings using information from structure (2019, methodology): “However, these methods are only appropriate for matching proteins with high sequence similarity [2, 3, 30].”
• Protein remote homology detection based on bidirectional long short-term memory (2017, methodology): “Moreover, as a demonstration, we also extend the comparison with other methods via an updated independent dataset set constructed based on SCOPe (latest version: 2.06) [61].”
• I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction (2022, methodology): “Several databases, such as the widely used SCOPe 86 and CATH 87 databases, have been developed to classify protein structures on the basis of individual domains, which are considered to be the structural, functional and evolutionary units of proteins 88 .”
• BRENDA in 2019: a European ELIXIR core data resource (2018, methodology): “The 3D-structural classification of enzyme proteins is covered by SCOPe and CATH (10,11).”
• Detecting distant-homology protein structures by aligning deep neural-network based contact maps (2019, methodology): “The second library was collected from SCOPe [36], which consists of 23,000 single-domain structures after filtering the redundant entries at the 95% sequence identity cutoff.”
• 3D deep convolutional neural networks for amino acid environment similarity analysis (2017, background): “6 database [44] and the corresponding 3D structures are downloaded from the PDB.”
• BRENDA, the ELIXIR core data resource in 2021: new developments and updates (2020, background): “CATH and SCOPe cover the 3D structural classification of enzymes (12,13).”
• Fold Evolution before LUCA: Common Ancestry of SH3 Domains and OB Domains (2021, background): “…are classified as separate architectures and in multiple homology groups in classification systems such as ECOD (Schaeffer et al. 2017), CATH (Sillitoe et al. 2019), and SCOPe (Chandonia et al. 2017) that aim to reflect the interrelatedness between protein domains with similar 3D structures.”