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A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction
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Citations
Cited by
Wisdom of crowds for robust gene network inference
The DREAM5 Consortium, Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2016 · Cited by 1,667
The Protein-Folding Problem, 50 Years On
Dill, K. A., & MacCallum, J. L. (2012) · Science · doi:10.1126/science.1219021 · Cited by 1,699
Dill, K. A., Ozkan, S. B., Shell, M. S., & Weikl, T. R. (2008) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.37.092707.153558 · Cited by 1,163
Protein 3D Structure Computed from Evolutionary Sequence Variation
Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144
Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138
Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Comparative protein structure modeling using MODELLER
Webb, B., & Sali, A. (2016) · Current Protocols in Protein Science · doi:10.1002/cpps.20 · Cited by 730
“Improving the performance and accuracy of methods in this regime remains one of the main tasks of comparative modeling today (Moult, 2005).”
MethodComparative modeling for protein structure prediction
Ginalski, K. (2006) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2006.02.003 · Cited by 296
Chapman, W. W., Nadkarni, P. M., Hirschman, L., D'Avolio, L. W., Savova, G. K., & Uzuner, O. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000465 · Cited by 296
Progress in Modeling of Protein Structures and Interactions.
Schueler-Furman, O., Wang, C., Bradley, P., Misura, K., & Baker, D. (2005) · Science · doi:10.1126/science.1112160 · Cited by 294
An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models
Reis, A. C., & Salis, H. M. (2020) · ACS Synthetic Biology · doi:10.1021/acssynbio.0c00394 · Cited by 166
β‐Rolls, β‐Helices, and Other β‐Solenoid Proteins
Kajava, A. V., & Steven, A. C. (2006) · Advances in Protein Chemistry · doi:10.1016/s0065-3233(06)73003-0 · Cited by 149
Fang, X., Wang, F., Liu, L., He, J., Lin, D., Xiang, Y., Zhu, K., Zhang, X., Wu, H., Li, H., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00721-6 · Cited by 132
Current structure predictors are not learning the physics of protein folding
Outeiral, C., Nissley, D. A., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab881 · Cited by 82
CASP13 target classification into tertiary structure prediction categories
Kinch, L. N., Kryshtafovych, A., Monastyrskyy, B., & Grishin, N. V. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25775 · Cited by 36
Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Zhao, Z., Chen, H., Wang, L., Golts, A., Khapun, D., Shats, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.01984 · Cited by 35
“One of the first machine learning competitions to use this format was the Critical Assessment of protein Structure Prediction or “CASP” competition which has been followed by a sequel CASP event every even-numbered year since 1994 [39].”
MethodPredicting improved protein conformations with a temporal deep recurrent neural network
Pfeiffenberger, E., & Bates, P. A. (2018) · PLOS ONE · doi:10.1371/journal.pone.0202652 · Cited by 21
Assessing the binding properties of CASP14 targets and models
Egbert, M., Ghani, U., Ashizawa, R., Kotelnikov, S., Nguyen, T., Desta, I., Hashemi, N., Padhorny, D., Kozakov, D., & Vajda, S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26209 · Cited by 10
Bergquist, T., Schaffter, T., Yan, Y., Yu, T., Prosser, J., Gao, J., Chen, G., Charzewski, Ł., Nawalany, Z., Brugere, I., et al. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad159 · Cited by 8
Metadata
- Authors
- Moult, J.
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2005-06
- Volume
- 15
- Issue
- 3
- Pages
- 285-289
- ISSN
- 0959-440X (ISSN-L)
- OpenAlex
- W2073758233
- Cited by
- 484
- References
- 0
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.