Understand Compound Mechanism of Action at a System Biology Scale through Chemical Biology Data Management and Analysis

Understanding drug-target interactions at both protein and pathway signaling network level in healthy vs. disease states is critical for the success of drug discovery and development. In the post-genomic era, quantitative chemical proteomics is emerging as a powerful tool to identify and validate novel druggable targets by means of (i) deconvolution of the molecular mechanism of action (MMoA); (ii) proteome selectivity assessment of bioactive molecules and (iii) druggability assessment for proteins of therapeutic interest with unclear MMoA identified through diverse approaches. Internally, we utilized in biochemical assays, transcriptomics and chemoproteomics experiments to elucidate drug-target interaction from various angles. At its core, chemoproteomics has the unprecedented power to unbiasedly discover, and unambiguously quantify, hundreds to thousands of protein interactions in a disease-relevant biological system perturbed with a controlled chemical insult, such as a drug or investigational molecule. Yet, the effective means to systematically mine, integrate and derive relevant information out of such complex big data sets remains a scientific frontier today. To maximize the value of various chemical biology data, to fuel in the hypothesis generation-testing cycle in Target Identification and Validation (TIDVal), we invested in a Chemical Biology Data Management System (CBDMS) as the infrastructure foundation to capture systems-biology perspectives of drug-proteome and transcriptome dynamics, on-target engagement, off-target effects and polypharmacology. Herein we report current progress of this endeavor, particularly in chemical proteomics data handling, analysis and visualization in the context of several exemplary chemical proteomics experiments to identifying novel targets.