Computational Mass Spectrometry
The computational mass spectrometry group acts as a critical bridge between the mass spectrometry and systems biology approaches to gain deep molecular insights into the disease mechanisms during disease progression or in response to perturbations. The molecular details help in aiding the phenotypic drug discovery pipeline by providing fine-grained resolution in molecular phenotypes which can be exploited for drug target discovery.
The group develops algorithms, statistical tools and software for quantitative proteomics and post-translational modifications (PTMs) in a blind-search strategy i.e. without knowing which modifications exist in data. These analyses generate unforeseen hidden regulatory layer of PTMs which are important in maintaining homeostasis while their deregulation is involved in many diseases. The knowledge on PTMs helps in understanding the metabolic, enzymatic and protein-protein interaction networks and their spatial subcellular localization with fine resolution imparted by PTMs. Quantitative proteomic and network analysis can predict drug targets.
The information on PTM sites help in biologically validating their importance, structural homology modeling and docking to reveal interaction mechanisms of such target proteins and ways to disrupt them. Mathematical modeling and simulations help to understand how the target proteins and their associated PTMs regulate the metabolite levels in maintaining homeostasis, and how these can be fine-tuned to restore homeostatic levels in case of diseases.
Additionally, the group also focus on novel ways of interactive data visualizations to decrease the time lag between data generation and interpretation to accelerate the biological discovery process. Towards this end, browser based rich interactive applications and software interfaces that provide more intuitive and immersive biological inference are developed.
High end workstations to analyze multidimensional mass spectrometry data for mining the proteome and the associated modifications on a system-wide scale.
- Amit Yadav (PhD),Scientist C
- Punit Kumar Kadimi,Research Application Developer
- Suruchi Aggarwal (MSc),Senior Research Fellow
- Pallavi Mahajan (MSc),Senior Research Fellow
- Asmita Bharti (MSc) ,Junior Research Fellow