The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Ab initio modeling of small proteins by iterative TASSER simulations
Open accessAbstract
Background: Predicting 3-dimensional protein structures from amino-acid sequences is an important unsolved problem in computational structural biology. The problem becomes relatively easier if close homologous proteins have been solved, as high-resolution models can be built by aligning target sequences to the solved homologous structures. However, for sequences without similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch. Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER (t hreading/asse mbly/r efinement) method for the ab initio modeling and examine systemically its ability to fold small single-domain proteins. Results: We developed I-TASSER by iteratively implementing the TASSER method, which is used in the folding test of three benchmarks of small proteins. First, data on 16 small proteins (< 90 residues) were used to generate I-TASSER models, which had an average C α-root mean square deviation (RMSD) of 3.8Å, with 6 of them having a C α-RMSD < 2.5Å. The overall result was comparable with the all-atomic ROSETTA simulation, but the central processing unit (CPU) time by I-TASSER was much shorter (150 CPU days vs. 5 CPU hours). Second, data on 20 small proteins (< 120 residues) were used. I-TASSER folded four of them with a C α-RMSD < 2.5Å. The average C α-RMSD of the I-TASSER models was 3.9Å, whereas it was 5.9Å using TOUCHSTONE-II software. Finally, 20 non-homologous small proteins (< 120 residues) were taken from the PDB library. An average C α-RMSD of 3.9Å was obtained for the third benchmark, with seven cases having a C α-RMSD < 2.5Å. Conclusion: Our simulation results show that I-TASSER can consistently predict the correct folds and sometimes high-resolution models for small single-domain proteins. Compared with other ab initio modeling methods such as ROSETTA and TOUCHSTONE II, the average performance of I-TASSER is either much better or is similar within a lower computational time. These data, together with the significant performance of automated I-TASSER server (the Zhang-Server) in the 'free modeling' section of the recent Critical Assessment of Structure Prediction (CASP)7 experiment, demonstrate new progresses in automated ab initio model generation. The I-TASSER server is freely available for academic users http://zhang.bioinformatics.ku.edu/I-TASSER.
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Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
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Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288
A new approach to protein fold recognition
Jones, D. T., Taylort, W. R., & Thornton, J. M. (1992) · Nature · doi:10.1038/358086a0 · Cited by 1,221
The protein structure prediction problem could be solved using the current PDB library
Zhang, Y., & Skolnick, J. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0407152101 · Cited by 297
Self-citation
Cited by
I-TASSER server for protein 3D structure prediction
Zhang, Y. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-40 · Cited by 5,124
Self-citationProtein structure prediction: when is it useful?
Zhang, Y. (2009) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2009.02.005 · Cited by 277
Self-citationProtein-Protein Complex Structure Predictions by Multimeric Threading and Template Recombination
Mukherjee, S., & Zhang, Y. (2011) · Structure · doi:10.1016/j.str.2011.04.006 · Cited by 174
Self-citationPredicting the accuracy of protein–ligand docking on homology models
Bordogna, A., Pandini, A., & Bonati, L. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21601 · Cited by 107
DESTINI: A deep-learning approach to contact-driven protein structure prediction
Gao, M., Zhou, H., & Skolnick, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-40314-1 · Cited by 61
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Metadata
- Authors
- Wu, S., Skolnick, J., & Zhang, Y.
- Venue
- BMC Biology
- Published
- 2007-05-08
- Volume
- 5
- Issue
- 1
- Pages
- 17-17
- ISSN
- 1741-7007 (ISSN-L)
- OpenAlex
- W2124371326
- Cited by
- 514
- References
- 5
- FWCI
- 15.33
- Type
- journal article
- Language
- en
- Open access
- gold
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