The Biocomputing Research Lab

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Algorithmic Determination of Disulfide Bonding Patterns


Disulfide bonds play an important role in understanding protein folding, evolution, and in studies related to determining structural and functional properties of specific proteins. At the state-of-the-art, liquid chromatography/electrospray ionization-tandem mass spectrometry (LC/ESI-MS/MS) can produce spectra of the peptides in a protein that are putatively joined by a disulfide bond. In this setting, efficient algorithms are required for matching the theoretical mass spaces of all possible bonded peptide fragments to the experimentally derived spectra to determine the number and location of the disulfide bonds. The algorithmic solution must also account for issues associated with interpreting experimental data from mass spectrometry, such as noise, isotopic variation, neutral loss, and charge state uncertainty.
The research under this project seeks to develop algorithms to address the above challenges. Our work is being conducted in collaboration with the Mass Spectrometry Laboratory of the department of Chemistry and Biochemistry at SFSU. This research is supported in part by the National Science Foundation (grants IIS-0644418 and CHE-0619163), the National Institute of Health (P20MD000262) and a grant from the Center for Computing in Life Sciences of SFSU.


MS2DB+: Efficient Multi-Spectral Analysis-based Disulfide Connectivity Determination Using Mass Spectrometry

MS2DB++: Efficient Disulfide Bond Determination Using Multiple Methods


  • R. Singh and W. Murad, "Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory," BMC Bioinformatics, 14 (Suppl 2):S20, 2013. [PDF]
  • W. Murad and R. Singh, "MS2DB+: A Software for Determination of Disulfide Bonds Using Multi-Ion Analysis", IEEE Transactions on NanoBioscience, To Appear, (Corresponding author R. Singh).
  • R. Singh, W. Murad, and T. Lee, "Algorithmic Frameworks for Protein Disulfide-Connectivity Determination", Chapter 10, Algorithmic and AI Methods for Protein Bioinformatics, Yi Pan, J. Wang, and M. Li eds., Wiley 2012.
  • W. Murad, R. Singh, and T-Y. Yen. "An Efficient Algorithmic Approach for Mass Spectrometry-Based Disulfide Connectivity Determination in Proteins Using Multi-Ion Analysis", BMC Bioinformatics, BMC Bioinformatics, 12 (Suppl 1):S12, 2011. [PDF]
  • W. Murad, R. Singh, and T-Y. Yen. "Polynomial Time Disulfide Bond Determination using Mass Spectrometry Data", IEEE Computational Structural Bioinformatics Workshop (CSBW), pp. 79-86, 2009. [PDF]
  • R. Singh. "A Review of Algorithmic Techniques for Disulfide-Bond Determination", Briefings in Functional Genomics and Proteomics, 2008, Vol. 7, pp. 157-172, 2008. [PDF]
  • T. Lee and R. Singh. “Comparative Analysis of Disulfide Bond Determination Using Computational-Predictive Methods and Mass Spectrometry-Based Algorithmic Analysis”, International Conference on Bioinformatics Research and Development (BIRD), Communications in Computer and Information Science, Vol. 13, pp. 140-153, Springer Verlag, 2008. [PDF]
  • T. Lee, R. Singh, R. Yen, and B. Macher. “An Algorithmic Approach to Automated High-Throughput Identification of Disulfide Connectivity in Proteins Using Tandem Mass Spectrometry”, Computational Systems Bioinformatics Conference (CSB), pp. 41 – 51, 2007. [PDF]
  • T. Lee, R. Singh, R. Yen, and B. Macher. “MS2DB: A Mass-Based Hashing Algorithm for the Identification of Disulfide Linkage Patterns in Protein Utilizing Mass Spectrometric Data”, IEEE International Symposium on Computer-Based Medical Systems (CBMS), pp. 397 – 402, 2007. [PDF]
  • T. Lee, R. Singh, R. Yen, and B. Macher. MS2DB: An Algorithmic Approach to Determine Disulfide Linkage Patterns in Proteins by Utilizing Tandem Mass Spectrometric Data, IEEE International Symposium on Computer-Based Medical Systems (CBMS), pp. 947-952, 2006. [PDF]
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