We have developed a systematic strategy for drug target identification. This consists of the following sequential steps: (1) enrichment of total binding proteins using two differential affinity matrixes upon which are immobilized positive and negative chemical structures for drug activity, respectively; (2) covalent labeling of the proteins with a new cleavable isotope-coded affinity tag (ICAT) reagent, followed by proteolysis of the combined proteins; (3) isolation, identification, and relative quantification of the tagged peptides by liquid chromatography-mass spectrometry; (4) array-based transcription profiling to select candidate proteins; and (5) confirmation of direct interaction between the activity-associated structure and the selected proteins by using surface plasmon resonance. We present a typical application to identify the primary binding protein of a novel class of anticancer agents exemplified by E7070. Our results suggest that this approach provides a new aspect of quantitative proteomics to find specific binding proteins from protein mixture and should be applicable to a wide variety of biologically active small molecules with unidentified target proteins.

As abundant and user-friendly as computer-aided drug design (CADD) software may seem, there is still a large underserved population of biomedical researchers around the world, particularly those with no computational training and limited research funding. To address this important need and help scientists overcome barriers that impede them from leveraging CADD in their drug discovery work, we have developed ezCADD, a web-based CADD modeling environment that manifests four simple design concepts: easy, quick, user-friendly, and 2D/3D visualization-enabled. In this paper, we describe the features of three fundamental applications that have been implemented in ezCADD: small-molecule docking, protein–protein docking, and binding pocket detection, and their applications in drug design against a pathogenic microbial enzyme as an example. To assess user experience and the effectiveness of our implementation, we introduced ezCADD to first-year pharmacy students as an active learning exercise in the Principles of Drug Action course. The web service robustly handled 95 simultaneous molecular docking jobs. Our survey data showed that among the 95 participating students, 97% completed the molecular docking experiment on their own at least partially without extensive training; 88% considered ezCADD easy and user-friendly; 99–100% agreed that ezCADD enhanced the understanding of drug–receptor structures and recognition; and the student experience in molecular modeling and visualization was significantly improved from zero to a higher level. The student feedback represents the baseline data of user experience from noncomputational researchers. It is demonstrated that in addition to supporting drug discovery research, ezCADD is also an effective tool for promoting science, technology, engineering, and mathematics (STEM) education. More advanced CADD applications are being developed and added to ezCADD, available at .