Comparative Analysis of Disulfide Bond Determination Using Computational-Predictive Methods and Mass Spectrometry-Based Algorithmic Approach

  • Timothy Lee
  • Rahul Singh
Conference paper

DOI: 10.1007/978-3-540-70600-7_11

Part of the Communications in Computer and Information Science book series (CCIS, volume 13)
Cite this paper as:
Lee T., Singh R. (2008) Comparative Analysis of Disulfide Bond Determination Using Computational-Predictive Methods and Mass Spectrometry-Based Algorithmic Approach. In: Elloumi M., Küng J., Linial M., Murphy R.F., Schneider K., Toma C. (eds) Bioinformatics Research and Development. Communications in Computer and Information Science, vol 13. Springer, Berlin, Heidelberg

Abstract

Identifying the disulfide bonding pattern in a protein is critical to understanding its structure and function. At the state-of-the-art, a large number of computational strategies have been proposed that predict the disulfide bonding pattern using sequence-level information. Recent past has also seen a spurt in the use of Mass spectrometric (MS) methods in proteomics. Mass spectrometry-based analysis can also be used to determine disulfide bonds. Furthermore, MS methods can work with lower sample purity when compared with x-ray crystallography or NMR. However, without the assistance of computational techniques, MS-based identification of disulfide bonds is time-consuming and complicated. In this paper we present an algorithmic solution to this problem and examine how the proposed method successfully deals with some of the key challenges in mass spectrometry. Using data from the analysis of nine eukaryotic Glycosyltransferases with varying numbers of cysteines and disulfide bonds we perform a detailed comparative analysis between the MS-based approach and a number of computational-predictive methods. These experiments highlight the tradeoffs between these classes of techniques and provide critical insights for further advances in this important problem domain.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Timothy Lee
    • 1
  • Rahul Singh
    • 1
  1. 1.Department of Computer ScienceSan Francisco State UniversitySan FranciscoU.S.A.

Personalised recommendations