SCOP2 prototype: a new approach to protein structure mining

By Antonina Andreeva, Dave Howorth, Cyrus Chothia, Eugene Kulesha, Alexey G. Murzin. 2013

303 citations recorded by the source corpus.

We present a prototype of a new structural classification of proteins, SCOP2 (http://scop2.mrc-lmb.cam.ac.uk/), that we have developed recently. SCOP2 is a successor to the Structural Classification of Proteins (SCOP, http://scop.mrc-lmb.cam.ac.uk/scop/) database. Similarly to SCOP, the main focus of SCOP2 is to organize structurally characterized proteins according to their structural and evolutionary relationships. SCOP2 was designed to provide a more advanced framework for protein structure annotation and classification. It defines a new approach to the classification of proteins that is essentially different from SCOP, but retains its best features. The SCOP2 classification is described in terms of a directed acyclic graph in which nodes form a complex network of many-to-many relationships and are represented by a region of protein structure and sequence. The new classification project is expected to ensure new advances in the field and open new areas of research.

Subfields: Molecular Biology

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In Molecular Biology

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Citations in the corpus

• High-resolution de novo structure prediction from primary sequence (2022, background): “( 33 ) argues for scaling the qk -product not only with inverse square root of d k , but also with log n , where n is the number of tokens in one sequence.”
• ECOD: An Evolutionary Classification of Protein Domains (2014, methodology): “In general, the incomplete coverage of SCOP2 makes general statements about differences from ECOD premature.”
• The SCOP database in 2020: expanded classification of representative family and superfamily domains of known protein structures (2019, background): “75 concluded in 2014 and we then initiated the development of the SCOP 2 prototype (2) in an attempt to capture new discoveries and findings or to recreate complex scenarios of protein evolution.”
• AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling (2016, methodology): “To use a set of non-redundant protein sequences for training and test, we pick one representative sequence from each protein superfamily defined in CATH [42] or SCOP [1].”
• Tertiary alphabet for the observable protein structural universe (2016, background): “Features ranging from backbone or side-chain dihedral angles (6, 7) to domains and folds (8, 9) have been classified, and structural basins of attraction have been found in select motifs (10–17), offering a glimpse of a modular space with frequently repeating elements.”
• PDBsum: Structural summaries of PDB entries (2017, background): “28 The combination of a protein’s domains is its “domain architecture”, with some architectures being particularly common while others are rare.”
• SCOPe: Manual Curation and Artifact Removal in the Structural Classification of Proteins – extended Database (2016, background): “SCOP2 [31] is a major redesign of SCOP that enables curators to annotate a richer set of evolutionary relationships between proteins, providing a more precise and accurate characterization of protein relationships.”
• RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning (2022, background): “Structural Classification SCOPe (105) and SCOP2 (104,125) Source Organism NCBI Taxonomy (85)”
• 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, unknown): “• 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).”
• ProteinBERT: a universal deep-learning model of protein sequence and function (2021, unknown): “Fold classes Categorical (7) Global 15,680 [31, 32]”
• Genome3D: exploiting structure to help users understand their sequences (2014, background): “SCOP (9) Murzin MRC-LMB Classification N/A”
• ATOM3D: Tasks On Molecules in Three Dimensions (2020, methodology): “We also prune the set based on structural similarity, removing any pairs in DIPS that map to corresponding SCOP [Andreeva et al., 2014] pairs of superfamilies that are also present across a pair in DB5 (i.e., we remove a DIPS pair if the first subunit in that pair has a chain with a SCOP superfamily”