The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Fold recognition by combining sequence profiles derived from evolution and from depth‐dependent structural alignment of fragments
Open accessAbstract
Recognizing structural similarity without significant sequence identity has proved to be a challenging task. Sequence‐based and structure‐based methods as well as their combinations have been developed. Here, we propose a fold‐recognition method that incorporates structural information without the need of sequence‐to‐structure threading. This is accomplished by generating sequence profiles from protein structural fragments. The structure‐derived sequence profiles allow a simple integration with evolution‐derived sequence profiles and secondary‐structural information for an optimized alignment by efficient dynamic programming. The resulting method (called SP 3) is found to make a statistically significant improvement in both sensitivity of fold recognition and accuracy of alignment over the method based on evolution‐derived sequence profiles alone (SP) and the method based on evolution‐derived sequence profile and secondary structure profile (SP 2). SP 3 was tested in SALIGN benchmark for alignment accuracy and Lindahl, PROSPECTOR 3.0, and LiveBench 8.0 benchmarks for remote‐homology detection and model accuracy. SP 3 is found to be the most sensitive and accurate single‐method server in all benchmarks tested where other methods are available for comparison (although its results are statistically indistinguishable from the next best in some cases and the comparison is subjected to the limitation of time‐dependent sequence and/or structural library used by different methods.). In LiveBench 8.0, its accuracy rivals some of the consensus methods such as ShotGun‐INBGU, Pmodeller3, Pcons4, and ROBETTA. SP 3 fold‐recognition server is available on http://theory.med.buffalo.edu. Proteins 2005. © 2004 Wiley‐Liss, Inc.
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Cites
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320
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
Cited by
Comparative protein structure modeling using MODELLER
Webb, B., & Sali, A. (2016) · Current Protocols in Protein Science · doi:10.1002/cpps.20 · Cited by 730
“Commonly used threading programs are GenTHREADER (Jones, 1999; McGuffin and Jones, 2003), 3D-PSSM (Kelley et al., 2000), FUGUE (Shi et al., 2001), SP3 (Zhou and Zhou, 2005), SAM-T08 multi-track HMM (Karchin et al., 2003; Karplus et al., 2003), and MUSTER (Wu and Zhang, 2008).”
BackgroundMethodInfluentialTeo, H., Gill, D. J., Sun, J., Perisic, O., Veprintsev, D. B., Vallis, Y., Emr, S. D., & Williams, R. L. (2006) · Cell · doi:10.1016/j.cell.2006.01.047 · Cited by 242
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
Self-citationOn the possible origin of protein homochirality, structure, and biochemical function
Skolnick, J., Zhou, H., & Gao, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908241116 · Cited by 45
Self-citationZhou, H., Cao, H., & Skolnick, J. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00309 · Cited by 39
Self-citationLee, S. J., Joo, K., Sim, S., Lee, J., Lee, I.-H., & Lee, J. (2022) · Molecules · doi:10.3390/molecules27123711 · Cited by 3
Metadata
- Authors
- Zhou, H., & Zhou, Y.
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2004-11-02
- Volume
- 58
- Issue
- 2
- Pages
- 321-328
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2144357990
- Cited by
- 254
- References
- 2
- FWCI
- 4.81
- Type
- journal article
- Language
- en
- Open access
- green
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