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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Angata, K., Yen, T.Y., El-Battari, A., Macher, B.A., Fukuda, M.: Unique disulfide bond structures found in ST8Sia IV polysialyltransferase are required for its activity. J. Biol. Chem. 18, 15369–15377 (2001)
Fariselli, P., Riccobelli, P., Casadio, R.: Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins. Proteins: Structure, Function, and Genetics 36, 340–346 (1999)
Frasconi, P., Passerini, A., Vullo, A.: A Two-Stage SVM Architecture for Predicting the Disulfide Bonding State of Cysteines. In: Proc. of the IEEE Workshop on Neural Networks for Signal Processing, pp. 25–34 (2002)
Martelli, P.L., Fariselli, P., Malaguti, L., et al.: Prediction of the Disulfide Bonding State of Cysteines in Proteins with Hidden Neural Networks. Protein Engineering 15, 951–953 (2002)
Fariselli, P., Casadio, R.: Prediction of disulfide connectivity in proteins. Bioinformatics 17, 957–964 (2001)
Vullo, A., Frasconi, P.: Disulfide connectivity prediction using recursive neural networks and evolutionary information. Bioinformatics 20, 653–659 (2004)
Ferre, F., Clote, P.: DiANNA: A Web Server for Disulfide Connectivity Prediction. Nucleic Acids Research 33, 230–232 (2005)
Cheng, J., Saigo, H., Baldi, P.: Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching. Proteins 62, 617–629 (2006)
Zhao, E., et al.: Cysteine Separation Profiles on Protein Sequences Infer Disulfide Connectivity. Bioinformatics 8, 1415–1420 (2005)
Chen, Y.-C., Hwang, J.-K.: Prediction of Disulfide Connectivity from Protein Sequences. Proteins 61, 507–512 (2005)
Singh, R.: A Review of Algorithmic Techniques for Disulfide-Bond Determination. Briefings in Functional Genomics and Proteomics 1(1) (to appear, 2008)
Fiser, A., Simon, I.: Predicting the Oxidation State of Cysteines by Multiple Sequence Alignment. Bioinformatics 16, 251–256 (2000)
Muskal, S.M., Holbrook, S.R., Kim, S.-H.: Prediction of the Disulfide-bonding state of cysteine in proteins. Protein Engineering 3, 667–672 (1990)
Fariselli, P., Riccobelli, P., Casadio, R.: Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins. Proteins: Structure, Function, and Genetics 36, 340–346 (1999)
Frasconi, P., Passerini, A., Vullo, A.: A Two-Stage SVM Architecture for Predicting the Disulfide Bonding State of Cysteines. In: Proc. of the IEEE Workshop on Neural Networks for Signal Processing, pp. 25–34 (2002)
Martelli, P.L., Fariselli, P., Malaguti, L., et al.: Prediction of the Disulfide Bonding State of Cysteines in Proteins with Hidden Neural Networks. Protein Engineering 15, 951–953 (2002)
Fariselli, P., Casadio, R.: Prediction of disulfide connectivity in proteins. Bioinformatics 17, 957–964 (2001)
Vullo, A., Frasconi, P.: Disulfide connectivity prediction using recursive neural networks and evolutionary information. Bioinformatics 20, 653–659 (2004)
Ceroni, A., Passerini, A., Vullo, A., et al.: DISULFIND: A Disulfide Bonding State and Cysteine Connectivity Prediction Server. Nucleic Acids Research 34, 177–181 (2006)
Ferre, F., Clote, P.: DiANNA: A Web Server for Disulfide Connectivity Prediction. Nucleic Acids Research 33, 230–232 (2005)
Cheng, J., Saigo, H., Baldi, P.: Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching. Proteins 62, 617–629 (2006)
Lenffer, J., Lai, P., Mejaber, W.-E., et al.: CysView: Protein Classification Based on Cysteine Pairing Patterns. Nucleic Acids Research 32, 350–354 (2004)
Lee, T., Singh, R., Yen, T.Y., Macher, B.: An Algorithmic Approach to Automated High-Throughput Identification of Disulfide Connectivity in Proteins Using Tandem Mass Spectrometry. In: 6th Annual International Conference on Computational Systems Bioinformatics (CSB 2007) (2007)
Swiss-Prot database web site, http://expasy.org/sprot/
Gabow, H.: Implementation of Algorithms for Maximum Matching on Nonbipartite Graphs. Ph.D. thesis, Stanford University (1973)
Schilling, B., Row, R.H., Gibson, B.W., et al.: MS2Assign, Automated Assignment and Nomenclature of Tandem Mass Spectra of Chemically Crosslinked Peptides. Journal of American Society of Mass Spectrometry 14, 834–850 (2003)
Ceroni, A., Passerini, A., Vullo, A., et al.: DISULFIND: A Disulfide Bonding State and Cysteine Connectivity Prediction Server. Nucleic Acids Research 34, 177–181 (2006)
Tsai, C.H., Chen, B.J., Chan, C.H., Liu, H.L., Kao, C.Y.: Improving disulfide connectivity prediction with sequential distance between oxidized cysteines. Bioinformatics 21, 4416–4419 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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. BIRD 2008. Communications in Computer and Information Science, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70600-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-70600-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70598-7
Online ISBN: 978-3-540-70600-7
eBook Packages: Computer ScienceComputer Science (R0)