Quantitative Inhibitor Fingerprinting of Metalloproteases Using Small Molecule Microarrays

Abstract

Current methods to identify interactions on small molecule microarrays (SMMs) introduce false positives that are difficult to dissect from the "real" binding events without tedious downstream re-evaluation. To specifically elucidate only activity-dependent ligand binding interactions, we have developed a technique that can be universally applied to present SMM systems. Our method makes use of a dual-color application strategy and is based on the simultaneous application of differentially treated samples. Overcoming the limitations of slide-to-slide variation, this method directly revealed activity-dependent interactions through a one-step application of protein samples on SMMs. Besides providing lead molecules for further development, the high-throughput screening results confer activity-dependent fingerprints for quantitative characterization and differentiation of proteins. The procedure was tested using a synthetic hydroxamate peptide library with 1400 discrete sequences permuted combinatorially across P1', P2', and P3' positions. Functional profiling across a panel of metalloproteases provided 44,800 datapoints within just eight SMM slides. These data were globally analyzed for activities, specificity, potency, and hierarchical clustering providing unique insights into inhibitor design and preference within this group of enzymes. Quantitative K(D) measurements performed on SMMs using one of the enzymes in the panel, Anthrax Lethal Factor, the toxic component of a notorious bioterror agent, unraveled several lead micromolar binders for further development. Overall, the effectiveness of the SMM platform is shown to be enhanced and extended using the strategy presented in this work.