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3D-Jury: a simple approach to improve protein structure predictions
Open accessAbstract
Motivation: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins. Results: The 3D-Jury system generates meta-predictions from sets of models created using variable methods. It is not necessary to know prior characteristics of the methods. The system is able to utilize immediately new components (additional prediction providers). The accuracy of the system is comparable with other well-tuned prediction servers. The algorithm resembles methods of selecting models generated using ab initio folding simulations. It is simple and offers a portable solution to improve the accuracy of other protein structure prediction protocols. Availability: The 3D-Jury system is available via the Structure Prediction Meta Server (http://BioInfo.PL/Meta/) to the academic community. Contact: leszek@bioinfo.pl Supplementary information: 3D-Jury is coupled to the continuous online server evaluation program, LiveBench (http://BioInfo.PL/LiveBench/) * To whom correspondence should be addressed.
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Citations
Cited by
Comparative modeling for protein structure prediction
Ginalski, K. (2006) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2006.02.003 · Cited by 296
Self-citationChen, Y., Li, F., & Wurtzel, E. T. (2010) · Plant Physiology · doi:10.1104/pp.110.153916 · Cited by 226
Mandal, S. M., Dey, S., Mandal, M., Sarkar, S., Maria-Neto, S., & Franco, O. L. (2009) · Peptides · doi:10.1016/j.peptides.2008.12.001 · Cited by 136
Pelegrini, P. B., Murad, A. M., Silva, L. P., dos Santos, R. C. P., Costa, F. T., Tagliari, P. D., Bloch Jr, C., Noronha, E. F., Miller, R. N. G., & Franco, O. L. (2008) · Peptides · doi:10.1016/j.peptides.2008.03.013 · Cited by 136
Wieczorek, R., Adamala, K., Gasperi, T., Polticelli, F., & Stano, P. (2017) · Life · doi:10.3390/life7020019 · Cited by 53
DeepUMQA: Ultrafast Shape Recognition-based Protein Model Quality Assessment using Deep Learning
Guo, S.-S., Liu, J., Zhou, X.-G., & Zhang, G.-J. (2021) · doi:10.1101/2021.10.02.462491 · Cited by 53
DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning
Guo, S.-S., Liu, J., Zhou, X.-G., & Zhang, G.-J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac056 · Cited by 53
Das, D., Kozbial, P., Han, G. W., Carlton, D., Jaroszewski, L., Abdubek, P., Astakhova, T., Axelrod, H. L., Bakolitsa, C., Chen, C., et al. (2010) · Acta Crystallographica Section F Structural Biology and Crystallization Communications · doi:10.1107/s1744309109018168 · Cited by 2
Metadata
- Authors
- Ginalski, K., Elofsson, A., Fischer, D., & Rychlewski, L.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-05-22
- Volume
- 19
- Issue
- 8
- Pages
- 1015-1018
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2141783940
- Cited by
- 719
- References
- 0
- FWCI
- 19.95
- Type
- journal article
- Language
- en
- Open access
- bronze
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