Postdoctoral researcher

Jian Wang

Jian is a post-doctoral fellow at the department of Computer Science and Engineering at the University of California San Diego (UCSD) and a member of the Center for Computational Mass Spectrometry (CCMS).  Jian received his Ph. D in Bioinformatics and System Biology from UCSD under the supervision of Professor Philip E. Bourne and Professor Nuno Baneria.  Before that Jian worked in the center of functional imaging at Lawrence Berkeley National Laboratory.  He received his undergraduate degree in Bioengineering from the University of California, Berkeley.  Jian’s current research focus on developing novel computational tools that can hardness the power of emerging  mass spectrometric techniques to help elucidate the dynamic complexity of the proteome.

 

Jian Wang

Contact Info

Email Address: jiw006@ucsd.edu
Phone Number: (858) 822-2496
Location: CSE (EBU3b) 4216
Department of Computer Science and Engineering
University of California, San Diego
9500 Gilman Drive La Jolla, CA 92093-0404, USA

Key Publications

Peptide identification from mixture tandem mass spectra. Wang J, Pérez-Santiago J, Katz JE, Mallick P, Bandeira N. Mol Cell Proteomics. 2010 Jul;9(7):1476-85. doi: 10.1074/mcp.M000136-MCP201. Epub 2010 Mar 27.
Peptide identification by database search of mixture tandem mass spectra. Wang J, Bourne PE, Bandeira N. Mol Cell Proteomics. 2011 Dec;10(12):M111.010017. doi: 10.1074/mcp.M111.010017. Epub 2011 Aug 23.
A Turn-key Approach for Large-scale Identification of Complex Post-translational Modifications Wang J, Anania VG, Knott J, Rush J, Lill JR, Bourne PE, Bandeira N. J Proteome Res. 2014 Mar 7;13(3):1190-9. doi: 10.1021/pr400368u. Epub 2014 Jan 29.
View All CCMS Publictions

Research Interests

  • Analysis of multiplexed mass spectra: the traditional one-peptide-one-spectrum paradigm used for peptide identification is no longer adequate to handle the high complexity in modern proteomic samples, leading to issues such as long analysis time and low reproducibility.  Advances in instrumentation have allowed us to analyze multiple peptides in the same (multiplexed) MS/MS spectrum with high speed, sensitivity and mass accuracy, but this creates tremendous challenges in data analysis.  We develop computational tools that aim to consistently identify and quantify peptides and proteins from multiplexed MS/MS spectra. Related tools: M-SPLIT, MixDB
  • Identification of peptides with complex PTM: detection of post-translational modifications (PTMs) are important to understand protein functions. Simple PTMs (e.g. methylation, deamidation etc.) can be readily identified in tandem mass spectrometry (MS/MS) by considering characteristic mass shifts in peptide fragment ions, but more complex PTMs such as glycosylation and small ubiquitin-like modification (SUMOylation) changes the fragmentation pattern of the substrate peptide substantially, making current database search methods not suitable to identify these modified peptides.  We develop novel experimental techniques and automatic algorithms that utilizes the PTM-specific fragmentation patterns to improve the identification of these atypical peptides.  Related tools: Specialize
  • Identification of linked peptides: both chemically-conjugated and naturally occurred (e.g. disulfide-bridged) linked peptides,  have been shown to constitute a powerful tool to study protein-protein interactions and to help elucidate the structure of large macromolecular complexes. However, computational methods to interpret the complex MS/MS spectra from linked peptides are still in their infancy, thus making the high-throughput application of this approach largely impractical. We develop novel computational methods to identify various type of linked peptides in complex biological samples and utilize them to elucidate protein-protein interactions and protein structures. Related tools: MXDB