I propose to develop a generalized method for designing reagents that when incubated with a serine protease of interest will result in the specific and efficient labeling of the substrates of the protease. I will specifically focus on members of the membrane-type serine proteases (MTSPs). Members of this recently discovered family of extracellular serine proteases share a conserved proteolytic domain. I will use the membrane-type serine protease 1 (MT-SP1) catalytic domain as a model system to develop techniques that will be generalizable amongst the MT-SPs by focusing on highly conserved regions of the MT-SP1 catalytic domain. I will first test a panel of stronger nucleophilic functional groups to increase the rate of labeling of MT-SP1 substrates. Next, I will use synthetic prime-side libraries and substrate phage display libraries to exhaustively profile the prime-side sequence preference of MT-SPI. I will use the data from the first two experiments to design optimized nucleophilic labeling reagents (NLRs), These reagents will be tested in with purified MT-SP1 and the substrate sc-uPA for labeling efficiency. NLRs that exhibit efficient labeling of substrate will then be tested in complex mixtures to assay labeling specificity. Optimized NLRs will also be used as scaffolds for molecular modeling and protein engineering efforts. I will design mutations in conserved amino acids of the catalytic domain that can be complemented with corresponding alterations in the NLRs to increase specificity for the labeling reagents.
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| Goetz, D H; Choe, Y; Hansell, E et al. (2007) Substrate specificity profiling and identification of a new class of inhibitor for the major protease of the SARS coronavirus. Biochemistry 46:8744-52 |
