Research Fellow  |  Sung, Ting-Yi  
Research Descriptions

        My current research interest is in bioinformatics with focus on protein structure prediction, protein subcellular localization prediction, quantitative proteomics based on high-throughput mass spectrometry data, and interpretation of proteomics data.

        [Prediction of protein structure and interaction]: We have developed a knowledge-based approach for protein secondary structure prediction which is the core of our proposed hybrid prediction method. Using similar approach, we develop a local structure prediction method and use it for subsequent tertiary structure prediction. Moreover, we use machine learning approaches to predict specific structures, e.g., transmembrane helices and their topology, and beta turns. In addition to protein structure prediction, we also work on protein subcellular localization prediction on different species, and domain prediction.

        [Quantitative proteomics based on high-throughput mass spectrometry data]: Various stable isotope labeling techniques, e.g., ICAT and iTRAQ, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is frequently used to quantify protein expressions. We have developed three automated tools for protein quantitation analysis, including Multi-Q for multiplexed quantitation using iTRAQ labeling, MaXIC-Q for quantitaiton using ICAT, SILAC and other stable isotope labeling methods, IDEAL-Q for label-free proteomic experiments. Notably, these three quantitation tools, on one hand, cover all available labeling approaches, and on the other hand, cover the two types of quantitation analysis, i.e., quantitation based on MS data and MS/MS data. More quantitation and visualization tools for large-scale proteomics studies will be developed.

        [Identification of post-translational modifications in mass spectrometry proteomics]: Mass spectrometry data arising from cancer proteomics experiments very likely contian post-translational modifications (PTMs). There are many PTM types; among them, we are interested in single residue mutation, phosphorylation, and glycolization. Specifically, we will work on identifying single residue mutation in a peptide, identifying phosphorylation sites in phosphorated peptides, and identifying glycopeptides and determing some glycon structures.

        [Interpretation of proteomics data]: As protein quantitation is not the ultimate goal of proteomics research, interpretation of proteomics data obtained from high-throughput experiments is essential for biomarker discovery and identifying further targets for biomedical research. We are developing an integrated tool for users to compare quantitation results from different experiments or samples, and to efficiently extract proteins of interest from high-throughput proteomics experiment results or data and retrieve their functions and other proteomic information.

        [Biomarker discovery]: Using our quantitation tools and proteome data analysis platform, we can identify differentially expressed proteins related to cancers that can be possible biomarker candidates. We will further study criteria for good biomarkers.