Support vector machines for prediction of dihedral angle regions

Zimmermann, Olav; Hansmann, Ulrich H. E.

doi:10.1093/bioinformatics/btl489

Cited by 49 publications

(46 citation statements)

References 30 publications

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“…The underlying principles of these algorithms can be found in the articles dedicated to these issues. [20][21][22][23][24][25][26][27][28] The results of the analysis are presented in Figure 1. Several potential amyloidogenic determinants (PADs) were predicted at least by four or even by five methods out of six.…”

Section: Resultsmentioning

confidence: 99%

Amyloidogenic peptides of yeast cell wall glucantransferase Bgl2p as a model for the investigation of its pH-dependent fibril formation

Bezsonov

Groenning

Galzitskaya

et al. 2013

Prion

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Section: Resultsmentioning

confidence: 99%

Amyloidogenic peptides of yeast cell wall glucantransferase Bgl2p as a model for the investigation of its pH-dependent fibril formation

Bezsonov

Groenning

Galzitskaya

et al. 2013

Prion

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“…SVM represents a new approach to supervised pattern classification which has been successfully applied to a wide range of pattern recognition problems, including object recognition, speaker identification, gene function prediction with microarray expression profile, etc. It is a good method of protein structure prediction which is based on the theory of SVM [26].…”

Section: Research and Methodologymentioning

confidence: 99%

Protein Structure Prediction Using Support Vector Machine

Mandle¹,

Jain²,

Shrivastava³

2012

IJSC

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“…The highly preferred regions of dihedral angle pairs define the predicted classes. Both ANN-and SVM-based predictions [173,174] have been carried out, with a special focus on the coil regions. Prediction accuracy of above 80% was reported.…”

Section: Beyond Secondary Structuresmentioning

confidence: 99%

From local structure to a global framework: recognition of protein folds

Joseph

Brevern

2014

J. R. Soc. Interface.

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Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.

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Support vector machines for prediction of dihedral angle regions

Abstract: DHPRED has been implemented as a web service, which academic researchers can access from our webpage http://www.fz-juelich.de/nic/cbb

Cited by 49 publications

References 30 publications

Amyloidogenic peptides of yeast cell wall glucantransferase Bgl2p as a model for the investigation of its pH-dependent fibril formation

Amyloidogenic peptides of yeast cell wall glucantransferase Bgl2p as a model for the investigation of its pH-dependent fibril formation

Protein Structure Prediction Using Support Vector Machine

From local structure to a global framework: recognition of protein folds

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