Support vector machines for prediction of dihedral angle regions
Motivation: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such order...
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| Published in: | Bioinformatics Vol. 22; no. 24; pp. 3009 - 3015 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Oxford University Press
15-12-2006
Oxford Publishing Limited (England) |
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| Abstract | Motivation: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information. Results: We propose a multi-step support vector machine (SVM) procedure, dihedral prediction (DHPRED), to predict the dihedral angle state of residues from sequence. Trained on 20 000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs. Availability: DHPRED has been implemented as a web service, which academic researchers can access from our webpage Contact:u.hansmann@fz-juelich.de |
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| AbstractList | Motivation: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information. Results: We propose a multi-step support vector machine (SVM) procedure, dihedral prediction (DHPRED), to predict the dihedral angle state of residues from sequence. Trained on 20 000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs. Availability: DHPRED has been implemented as a web service, which academic researchers can access from our webpage http://www.fz-juelich.de/nic/cbb Contact: u.hansmann@fz-juelich.de Motivation: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information. Results: We propose a multi-step support vector machine (SVM) procedure, dihedral prediction (DHPRED), to predict the dihedral angle state of residues from sequence. Trained on 20 000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs. Availability: DHPRED has been implemented as a web service, which academic researchers can access from our webpage Contact: u.hansmann@fz-juelich.de Motivation: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information. Results: We propose a multi-step support vector machine (SVM) procedure, dihedral prediction (DHPRED), to predict the dihedral angle state of residues from sequence. Trained on 20 000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs. Availability: DHPRED has been implemented as a web service, which academic researchers can access from our webpage Contact:u.hansmann@fz-juelich.de MOTIVATION: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information. RESULTS: We propose a multi-step support vector machine (SVM) procedure, dihedral prediction (DHPRED), to predict the dihedral angle state of residues from sequence. Trained on 20 000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs. AVAILABILITY: DHPRED has been implemented as a web service, which academic researchers can access from our webpage http://www.fz-juelich.de/nic/cbb CONTACT: u.hansmannz-juelich.de MOTIVATIONMost secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information.RESULTSWe propose a multi-step support vector machine (SVM) procedure, dihedral prediction (DHPRED), to predict the dihedral angle state of residues from sequence. Trained on 20,000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs.AVAILABILITYDHPRED has been implemented as a web service, which academic researchers can access from our webpage http://www.fz-juelich.de/nic/cbb Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information. We propose a multi-step support vector machine (SVM) procedure, dihedral prediction (DHPRED), to predict the dihedral angle state of residues from sequence. Trained on 20,000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs. DHPRED has been implemented as a web service, which academic researchers can access from our webpage http://www.fz-juelich.de/nic/cbb |
| Author | Hansmann, Ulrich H. E. Zimmermann, Olav |
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| Cites_doi | 10.1002/prot.340190108 10.1006/jmbi.1999.3091 10.1093/protein/gzg072 10.1093/bioinformatics/btg223 10.1016/j.jmb.2004.06.091 10.1006/jmbi.2001.4580 10.1016/0005-2795(75)90109-9 10.1016/S0076-6879(96)66034-0 10.1111/j.1399-3011.1988.tb01261.x 10.1006/jsbi.2001.4336 10.1093/nar/25.17.3389 10.1016/0167-4838(84)90312-1 10.1093/nar/28.1.235 10.1093/bioinformatics/18.4.608 10.1002/1097-0134(20001001)41:1<17::AID-PROT40>3.0.CO;2-F 10.1093/bioinformatics/bth476 10.1021/bi00699a002 10.1002/bip.360220105 10.1093/protein/14.8.525 10.1002/prot.340190207 10.1111/j.1399-3011.1988.tb01258.x 10.1016/0022-2836(88)90564-5 10.1093/nar/27.1.368 10.1110/ps.051479505 10.1016/0005-2795(73)90350-4 10.1016/j.jmb.2004.04.005 10.1006/jmbi.2000.3837 10.1016/0022-2836(78)90297-8 10.1002/prot.10286 10.1006/jmbi.1999.2829 |
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| Copyright | 2007 INIST-CNRS Copyright Oxford University Press(England) Dec 2006 |
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| Keywords | Order Prediction Web service Support vector machine Secondary structure Premises Original document Angle Aminoacid Region Dihedron Random coil Bioinformatics |
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| Snippet | Motivation: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random... Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo... MOTIVATION: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random... MOTIVATIONMost secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil... |
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| SubjectTerms | Algorithms Amino Acid Sequence Artificial Intelligence Biological and medical sciences Computer Simulation Fundamental and applied biological sciences. Psychology General aspects Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Chemical Models, Molecular Molecular Sequence Data Pattern Recognition, Automated - methods Protein Structure, Secondary Proteins - chemistry Proteins - ultrastructure Sequence Alignment - methods Sequence Analysis, Protein - methods |
| Title | Support vector machines for prediction of dihedral angle regions |
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