Protein backbone angle prediction with machine learning approaches

Rui Kuang, Christina S. Leslie, An Suei Yang

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

Motivation: Protein backbone torsion angle prediction provides useful local structural information that goes beyond conventional three-state (α, β and coil) secondary structure predictions. Accurate prediction of protein backbone torsion angles will substantially improve modeling procedures for local structures of protein sequence segments, especially in modeling loop conformations that do not form regular structures as in α-helices or β -strands. Results: We have devised two novel automated methods in protein backbone conformational state prediction: one method is based on support vector machines (SVMs); the other method combines a standard feed-forward back-propagation artificial neural network (NN) with a local structure-based sequence profile database (LSBSP1). Extensive benchmark experiments demonstrate that both methods have improved the prediction accuracy rate over the previously published methods for conformation state prediction when using an alphabet of three or four states.

Original languageEnglish (US)
Pages (from-to)1612-1621
Number of pages10
JournalBioinformatics
Volume20
Issue number10
DOIs
StatePublished - Jul 10 2004

Bibliographical note

Funding Information:
This work was partially supported by the NIH (PAR-00-102). Planning Grant: National Programs of Excellence in Biomedical Computing for Computational Biology and Bioinformatics. C.L. was also supported by an Award in Informatics from PhRMA Foundation.

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