Improving protein secondary-structure prediction by predicting ends of secondary-structure segments

Uros Midic, A. Keith Dunker, Zoran Obradovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Motivated by known preferences for certain amino acids in positions around a-helices, we developed neural network-based predictors of both N and C a-helix ends, which achieved about 88% accuracy. We applied a similar approach for predicting the ends of three types of secondary structure segments. The predictors for the ends of H, E and C segments were then used to create input for protein secondary-structure prediction. By incorporating this new type of input, we significantly improved the basic one-stage predictor of protein secondary structure in terms of both per-residue (Q3) accuracy (+0.8%) and segment overlap (SOV3) measure (+1.4).

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
StatePublished - Dec 1 2005
Event2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05 - La Jolla, CA, United States
Duration: Nov 14 2005Nov 15 2005

Publication series

NameProceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
Volume2005

Other

Other2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05
CountryUnited States
CityLa Jolla, CA
Period11/14/0511/15/05

ASJC Scopus subject areas

  • Engineering(all)

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