SCOPe: Structural Classification of Proteins--extended, integrating SCOP and ASTRAL data and classification of new structures
Open accessAbstract
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.
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Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80134-2 · Cited by 6,412 · see citing works
Self-citationGough, J., Karplus, K. J., Hughey, R., & Chothia, C. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.5080 · Cited by 1,200 · see citing works
Data growth and its impact on the SCOP database: new developments
Andreeva, A. E., Howorth, D., Chandonia, J., Brenner, S. E., Hubbard, T., Chothia, C., & Murzin, A. G. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm993 · Cited by 933 · see citing works
Self-citationChandonia, J. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh034 · Cited by 529 · see citing works
Self-citationSUPERFAMILY—sophisticated comparative genomics, data mining, visualization and phylogeny
Wilson, D. A., Pethica, R., Zhou, Y., Talbot, C. C., Vogel, C., Madera, M., Chothia, C., & Gough, J. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn762 · Cited by 488 · see citing works
The ASTRAL compendium for protein structure and sequence analysis
Brenner, S. E. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.254 · Cited by 482 · see citing works
Self-citationSCOP database in 2002: refinements accommodate structural genomics
Conte, L. L. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.264 · Cited by 459 · see citing works
ASTRAL compendium enhancements
Chandonia, J. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.260 · Cited by 137 · see citing works
Self-citation[37] Understanding protein structure: Using scop for fold interpretation
Brenner, S. E., Chothia, C., Hubbard, T., & Murzin, A. G. (1996) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(96)66039-x · Cited by 100 · see citing works
Self-citationSCOPmap: Automated assignment of protein structures to evolutionary superfamilies
Cheek, S., Qi, Y., Krishna, S. S., Kinch, L. N., & Grishin, N. V. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-197 · Cited by 51 · see citing works
(1996) · Methods · doi:10.1006/meth.1996.0070 · Cited by 6 · see citing works
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Sensitive protein alignments at tree-of-life scale using DIAMOND
Buchfink, B., Reuter, K., & Drost, H.-G. (2021) · Nature Methods · doi:10.1038/s41592-021-01101-x · Cited by 5,097 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1038 · Cited by 1,581 · see citing works
Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131 · see citing works
Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Tian, S., & Hou, T. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp01555g · Cited by 946 · see citing works
Protein Sequence Analysis Using the MPI Bioinformatics Toolkit
Gabler, F., Nam, S., Till, S., Mirdita, M., Steinegger, M., Söding, J., Lupas, A. N., & Alva, V. (2020) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.108 · Cited by 831 · see citing works
Evaluating Protein Transfer Learning with TAPE
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719 · see citing works
Modeling the language of life – Deep Learning Protein Sequences
Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606 · see citing works
US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes
Zhang, C., Shine, M., Pyle, A. M., & Zhang, Y. (2022) · Nature Methods · doi:10.1038/s41592-022-01585-1 · Cited by 511 · see citing works
Learning the protein language: Evolution, structure, and function
Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483 · see citing works
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · see citing works
“RGN2 training was performed using both the ProteinNet12 dataset 38 and a smaller dataset comprised solely of single protein domains derived from the ASTRAL SCOPe dataset (v1.”
MethodSingle-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works
ECOD: an evolutionary classification of protein domains
Cheng, H., Schaeffer, R. D., Liao, Y., Kinch, L. N., Pei, J., Shi, S., Kim, B.-H., & Grishin, N. V. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003926 · Cited by 426 · see citing works
“To partially alleviate this problem, ASTRAL now offers SCOPe, a sequence-based extension of the original SCOP hierarchy [14].”
BackgroundMethodInfluentialVreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jiménez-García, B., Bates, P. A., Fernandez-Recio, J., et al. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.016 · Cited by 424 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · see citing works
“We construct a dataset to evaluate remote homology detection using SCOPe (Fox et al., 2014).”
MethodHigh-resolution <i>de novo</i> structure prediction from primary sequence
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al. (2022) · doi:10.1101/2022.07.21.500999 · Cited by 392 · see citing works
BERTology Meets Biology: Interpreting Attention in Protein Language Models
Vig, J., Madani, A., Varshney, L. R., Xiong, C., Socher, R., & Rajani, N. F. (2020) · doi:10.1101/2020.06.26.174417 · Cited by 338 · see citing works
FATCAT 2.0: towards a better understanding of the structural diversity of proteins
Li, Z., Jaroszewski, L., Iyer, M., Sedova, M., & Godzik, A. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa443 · Cited by 231 · see citing works
The BRENDA enzyme information system–From a database to an expert system
Schomburg, I., Jeske, L., Ulbrich, M., Placzek, S., Chang, A., & Schomburg, D. (2017) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2017.04.020 · Cited by 222 · see citing works
Li, Y., Hu, J., Zhang, C., Yu, D.-J., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz291 · Cited by 158 · see citing works
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks
Liu, Y., Palmedo, P., Ye, Q., Berger, B., & Peng, J. (2018) · Cell Systems · doi:10.1016/j.cels.2017.11.014 · Cited by 114 · see citing works
Frozen Pretrained Transformers as Universal Computation Engines
Lu, K., Grover, A., Abbeel, P., & Mordatch, I. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i7.20729 · Cited by 113 · see citing works
“We use the datasets provided by TAPE (Rao et al. 2019; Fox, Brenner, and Chandonia 2013; Hou, Adhikari, and Cheng 2018), where the train/test split is generated by holding out certain evolutionary groups.”
MethodrawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments
Mirabello, C., & Wallner, B. (2019) · PLOS ONE · doi:10.1371/journal.pone.0220182 · Cited by 105 · see citing works
Amino acid encoding methods for protein sequences: a comprehensive review and assessment
Jing, X., Dong, Q., Hong, D., & Lu, R. (2020) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2911677 · Cited by 97 · see citing works
A topological approach for protein classification
Cang, Z., Mu, L., Wu, K., Opron, K., Xia, K., & Wei, G.-W. (2015) · Computational and Mathematical Biophysics · doi:10.1515/mlbmb-2015-0009 · Cited by 93 · see citing works
Protein design via deep learning
Ding, W., Nakai, K., & Gong, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac102 · Cited by 66 · see citing works
Rocafort, M., Bowen, J. K., Hassing, B., Cox, M. P., McGreal, B., de la Rosa, S., Plummer, K. M., Bradshaw, R. E., & Mesarich, C. H. (2022) · BMC Biology · doi:10.1186/s12915-022-01442-9 · Cited by 52 · see citing works
Histones predate the split between bacteria and archaea
Alva, V., & Lupas, A. N. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1000 · Cited by 43 · see citing works
A model for hydrophobic protrusions on peripheral membrane proteins
Fuglebakk, E., & Reuter, N. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006325 · Cited by 29 · see citing works
Zhang, C., Zheng, W., Cheng, M., Omenn, G. S., Freddolino, P. L., & Zhang, Y. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00359 · Cited by 27 · see citing works
A novel sequence alignment algorithm based on deep learning of the protein folding code
Gao, M., & Skolnick, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa810 · Cited by 18 · see citing works
Phylogenetic tree reconstruction via graph cut presented using a quantum-inspired computer
Onodera, W., Hara, N., Aoki, S., Asahi, T., & Sawamura, N. (2023) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2022.107636 · Cited by 13 · see citing works
- Authors
- Fox, N. K., Brenner, S. E., & Chandonia, J.-M.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Brenner, Steven E.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-12-03
- Volume
- 42
- Issue
- D1
- Pages
- D304-D309
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC3965108
- OpenAlex
- W2161072217
- Cited by
- 762
- References
- 12
- Citations by year
- 2026: 37 · 2025: 42 · 2024: 49 · 2023: 49 · 2022: 86 · 2021: 87 · 2020: 88 · 2019: 72 · 2018: 56 · 2017: 60 · 2016: 65 · 2015: 51 · 2014: 18 · 2013: 1
- FWCI
- 20.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- U.S. Department of Energy (-AC02-05CH11231, Contract No. DE-AC02-05-CH11231, 05CH11231, No. DE-AC02-05-CH11231, DE AC02 05CH11231, DE-AC02-)
- National Institutes of Health (5r01gm073109-08, DE-AC02–05CH11231, R01-GM073109)
- National Institute of General Medical Sciences (R01-GM073109)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- scope, SCOP, ASTRAL, protein structure classification, protein structure database, algorithmic curation, benchmarking, Protein Data Bank, protein evolution, structural relationships
- MeSH terms
- Proteins, Protein Structure, Tertiary, Systems Integration, Internet, Databases, Protein
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