CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks
<p>Abstract</p> <p>Background</p> <p>One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate predict...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2006-09-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/7/401 |
| _version_ | 1818564854121758720 |
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| author | Kinjo Akira R Nishikawa Ken |
| author_facet | Kinjo Akira R Nishikawa Ken |
| author_sort | Kinjo Akira R |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes.</p> <p>Results</p> <p>We implemented a program CRNPRED which predicts secondary structures, contact numbers and residue-wise contact orders. This program is based on a novel machine learning scheme called critical random networks. Unlike most conventional one-dimensional structure prediction methods which are based on local windows of an amino acid sequence, CRNPRED takes into account the whole sequence. CRNPRED achieves, on average per chain, <it>Q</it><sub>3 </sub>= 81% for secondary structure prediction, and correlation coefficients of 0.75 and 0.61 for contact number and residue-wise contact order predictions, respectively.</p> <p>Conclusion</p> <p>CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions.</p> |
| first_indexed | 2024-12-14T01:33:58Z |
| format | Article |
| id | doaj.art-b218dfc5195e4b68ba435109c240f059 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T01:33:58Z |
| publishDate | 2006-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-b218dfc5195e4b68ba435109c240f0592022-12-21T23:21:57ZengBMCBMC Bioinformatics1471-21052006-09-017140110.1186/1471-2105-7-401CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networksKinjo Akira RNishikawa Ken<p>Abstract</p> <p>Background</p> <p>One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes.</p> <p>Results</p> <p>We implemented a program CRNPRED which predicts secondary structures, contact numbers and residue-wise contact orders. This program is based on a novel machine learning scheme called critical random networks. Unlike most conventional one-dimensional structure prediction methods which are based on local windows of an amino acid sequence, CRNPRED takes into account the whole sequence. CRNPRED achieves, on average per chain, <it>Q</it><sub>3 </sub>= 81% for secondary structure prediction, and correlation coefficients of 0.75 and 0.61 for contact number and residue-wise contact order predictions, respectively.</p> <p>Conclusion</p> <p>CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions.</p>http://www.biomedcentral.com/1471-2105/7/401 |
| spellingShingle | Kinjo Akira R Nishikawa Ken CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks BMC Bioinformatics |
| title | CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks |
| title_full | CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks |
| title_fullStr | CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks |
| title_full_unstemmed | CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks |
| title_short | CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks |
| title_sort | crnpred highly accurate prediction of one dimensional protein structures by large scale critical random networks |
| url | http://www.biomedcentral.com/1471-2105/7/401 |
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