DSP : A protein shape string and its profile prediction server
(2012) In Nucleic Acids Research 40(W1). p.298-302- Abstract
Many studies have demonstrated that shape string is an extremely important structure representation, since it is more complete than the classical secondary structure. The shape string provides detailed information also in the regions denoted random coil. But few services are provided for systematic analysis of protein shape string. To fill this gap, we have developed an accurate shape string predictor based on two innovative technologies: a knowledge-driven sequence alignment and a sequence shape string profile method. The performance on blind test data demonstrates that the proposed method can be used for accurate prediction of protein shape string. The DSP server provides both predicted shape string and sequence shape string profile... (More)
Many studies have demonstrated that shape string is an extremely important structure representation, since it is more complete than the classical secondary structure. The shape string provides detailed information also in the regions denoted random coil. But few services are provided for systematic analysis of protein shape string. To fill this gap, we have developed an accurate shape string predictor based on two innovative technologies: a knowledge-driven sequence alignment and a sequence shape string profile method. The performance on blind test data demonstrates that the proposed method can be used for accurate prediction of protein shape string. The DSP server provides both predicted shape string and sequence shape string profile for each query sequence. Using this information, the users can compare protein structure or display protein evolution in shape string space. The DSP server is available at both http://cheminfo.tongji.edu.cn/dsp/and its main mirror http://chemcenter. tongji.edu.cn/dsp/.
(Less)
- author
- Sun, Jiangming LU ; Tang, Shengnan ; Xiong, Wenwei ; Cong, Peisheng and Li, Tonghua
- publishing date
- 2012-07
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Nucleic Acids Research
- volume
- 40
- issue
- W1
- pages
- 298 - 302
- publisher
- Oxford University Press
- external identifiers
-
- pmid:22553364
- scopus:84864437069
- ISSN
- 0305-1048
- DOI
- 10.1093/nar/gks361
- language
- English
- LU publication?
- no
- additional info
- Funding Information: National Natural Science Foundation of China (NSFC) [20675057, 20705024]. Funding for open access charge: NSFC.
- id
- 2d4690e4-e35c-474f-8095-f6773c1ef58e
- date added to LUP
- 2023-04-24 15:32:51
- date last changed
- 2024-02-03 12:33:51
@article{2d4690e4-e35c-474f-8095-f6773c1ef58e, abstract = {{<p>Many studies have demonstrated that shape string is an extremely important structure representation, since it is more complete than the classical secondary structure. The shape string provides detailed information also in the regions denoted random coil. But few services are provided for systematic analysis of protein shape string. To fill this gap, we have developed an accurate shape string predictor based on two innovative technologies: a knowledge-driven sequence alignment and a sequence shape string profile method. The performance on blind test data demonstrates that the proposed method can be used for accurate prediction of protein shape string. The DSP server provides both predicted shape string and sequence shape string profile for each query sequence. Using this information, the users can compare protein structure or display protein evolution in shape string space. The DSP server is available at both http://cheminfo.tongji.edu.cn/dsp/and its main mirror http://chemcenter. tongji.edu.cn/dsp/.</p>}}, author = {{Sun, Jiangming and Tang, Shengnan and Xiong, Wenwei and Cong, Peisheng and Li, Tonghua}}, issn = {{0305-1048}}, language = {{eng}}, number = {{W1}}, pages = {{298--302}}, publisher = {{Oxford University Press}}, series = {{Nucleic Acids Research}}, title = {{DSP : A protein shape string and its profile prediction server}}, url = {{http://dx.doi.org/10.1093/nar/gks361}}, doi = {{10.1093/nar/gks361}}, volume = {{40}}, year = {{2012}}, }