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.