Efficient Local Protein Structure Prediction

  • Szymon Nowakowski
  • Michał Drabikowski
Conference paper

DOI: 10.1007/978-3-540-72458-2_38

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4481)
Cite this paper as:
Nowakowski S., Drabikowski M. (2007) Efficient Local Protein Structure Prediction. In: Yao J., Lingras P., Wu WZ., Szczuka M., Cercone N.J., Ślȩzak D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science, vol 4481. Springer, Berlin, Heidelberg

Abstract

The methodology which was previously used with success in genomic sequences to predict new binding sites of transcription factors is applied in this paper for protein structure prediction. We predict local structure of proteins based on alignments of sequences of structurally similar local protein neighborhoods. We use Secondary Verification Assessment (SVA) method to select alignments with most reliable models. We show that using Secondary Verification (SV) method to assess the statistical significance of predictions we can reliably predict local protein structure, better than with the use of other methods (log-odds or p-value). The tests are conducted with the use of the test set consisting of the CASP 7 targets.

Keywords

statistical significance SV method SVA method assessing predictions model assessment protein structure prediction CASP 7 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Szymon Nowakowski
    • 1
  • Michał Drabikowski
    • 2
  1. 1.Infobright Inc., ul. Krzywickiego 34 pok. 219, 02-078 WarszawaPoland
  2. 2.Institute of Informatics, Warsaw UniversityPoland

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