Our research is focused on the development and integration of computational and experimental approaches for high-throughput ligand screening and design of kinase inhibitors, understanding of the molecular chaperone mechanisms at atomic resolution in order to facilitate discovery of novel anti-cancer therapies.
The challenge of understanding biological systems at the molecular and systems level as well as the integration of computational and experimental approaches for bridging basic and clinical cancer research is what motivates our vibrant research group. Our scientific interests and research efforts are in the areas of Computational Cancer Biology and Pharmacogenetics, Computational Genomics and Pharmacology, Translational Bioinformatics and Computational Medicine with the focus on the development and integration of computational and experimental approaches for (a) system-based analysis of evolutionary, genetic, molecular and clinical signatures associated with human disease; (b) modeling of complex phenotypes and prediction of cancer biomarkers; (c) design and discovery of targeted and personalized cancer therapeutics and development of expert systems for personalized medicine; (d) integration of computational biology and translational informatics with chemical biology and chemical genomics in translational cancer research; (e) enabling information-driven biomedical research on the “bench to bedside” path.
Protein Kinases and Cancer ResearchProtein kinase genes are signaling switches with a conserved catalytic domain that phosphorylate protein substrates and play a critical role in cell signaling pathways. A landmark for understanding the molecular basis of kinase function was the elucidation of the crystal structures of protein kinaseA. Since this discovery, more than 1000 crystal structures of 119 unique human protein kinases have been solved, resulting in the growing wealth of structural knowledge about the kinase catalytic domain. The crystal structures have revealed considerable structural differences between closely related active and highly specific inactive kinase forms. Drug discovery against protein kinases has concentrated mainly on small molecules that target the ATP binding site of the conserved catalytic domain. However, with over 500 protein kinase genes identified in the human genome and the highly conserved ATP-binding site, a considerable effort is needed to design drugs that select for individual kinase members. A growing number of kinase inhibitors selectively target the inactive conformation whereas other compounds bind to both conformations with similar affinity. Inhibitors that bind to the inactive conformation face weaker competition from cellular ATP and may act primarily by shifting equilibrium between conformational states in a way that prevents kinase activation, rather than by inhibiting kinase activity directly. The complete sequencing of the human genome and high-throughput generationof genomic data have opened avenues for a systematic approachto understanding the complex biology of cancer and clinical targetingof activated oncogenes in cancer.
The greatest challenge of the postgenomic era is to understand the function of genes and gene products in multiple organisms --including humans– both from fundamental and applied perspectives. This will ultimately enable the design of new diagnostic tools and pharmacological agents and facilitate efficacious treatment of many pathological processes. These developments are largely guided and enabled by recent advances in translational bioinformatics, computer sciences, quantitative biology and system-based medicine focused on understanding of the biological principles underpinning the heterogeneity of human disease and facilitating predictive and personalized medicine solutions. In the course of next decade, health care and medicine will be transformed from reactively treating illness to proactively maintaining health — from an averaged picture of disease to a systems biology view of individualized care. The complexity of human disease and cancer requires an integrated approach using computational and experimental high-throughput genetic, molecular, and clinical phenotype analysis. We are focused on the development and application of an integrated platform of computational and experimental approaches to(a) advance identification, prediction and functional analysis of genetic and molecular signatures associated with cancer (b) enable the design of personalized and system-based cancer medicine to combat specific genomic profiles; and (c) facilitate biomedical and clinical research on the “bench to bedside” path. We also expanded our recent efforts in supporting a new pathway-based and network-centric paradigm for quantitative analysis of human disease and in silico discovery of anti-cancer therapeutic agents that is based on targeted polypharmacology of signal transduction networks.