Exploiting new approaches for selective inhibition of trypsins

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PROJECT SUMMARY/ABSTRACT The human trypsin isoforms, trypsin 1, trypsin 2, and mesotrypsin, are proteases that have been implicated in disease processes in cancer and pancreatitis, and may offer viable therapeutic targets. Trypsins belong to a large family of trypsin-like enzymes with similar active site topology, and hence existing inhibitors lack selectivity. There is a need for selective trypsin inhibitors and isoform-selective trypsin inhibitors as pharmacological tools to better define the functions of these individual enzymes in disease, and to evaluate trypsin inhibition as a therapeutic strategy in preclinical models of disease. In this project, we will take a multipronged approach to develop new strategies for potent and selective inhibition of each of the human trypsin isoforms. (1) Our preliminary data reveal a previously unsuspected auto-inhibited conformation of mesotrypsin with a ligand-targetable allosteric site that may be exploited for inhibitory effect. We will use high- throughput virtual screening and structure-based hit-to-lead optimization to develop potent and selective allosteric inhibitors of mesotrypsin. We will also use structural and molecular dynamics analyses to evaluate whether similar strategies may hold potential for trypsins 1 and 2. (2) Our published studies have shown that Kunitz domains can be engineered to create more selective protein-based inhibitors of trypsin-like proteases by using a yeast surface display (YSD) platform for directed evolution. To enable further optimization of such inhibitors, we seek to generate comprehensive maps of the binding specificity landscapes that can, for any possible combination of mutations within an inhibitor, predict the consequences on inhibitor affinity and specificity toward a set of target proteases. We will accomplish this task by integrating YSD combinatorial library screening with next-generation sequencing (NGS), machine-learning (ML) approaches, and experimental calibration to enable quantitative prediction of the impact of multiple potentially interacting mutations of an inhibitor. These data will enable us to identify the most potent and selective Kunitz domains that can be achieved for targeting each of the human trypsins. (3) Our preliminary data demonstrate an enhancement of trypsin affinity by bivalent inhibitors capable of binding simultaneously to two molecules of mesotrypsin. Here, we will dissect the mechanisms responsible for these affinity enhancements and design strategies to exploit this information toward development of more potent and selective polyvalent trypsin inhibitors. In addition to developing three complementary strategies, each of which has high potential to produce the desired selective inhibitors of human trypsins, our project will provide broader insights that can aid future development of inhibitors for many other important trypsin-like proteases. Finally, the novel methodology developed here for mapping protein-protein interaction (PPI) affinity and specificity landscapes will be of broad utility for characterizing the sequence and structural constraints governing affinity and selectivity of functional protein interactions in many other diverse systems.