Abstract
Animal venoms, distinguished by their unique structural features and potent bioactivities, represent a vast and relatively untapped reservoir of therapeutic molecules. However, limitations associated with comprehensively constructing and expressing highly complex venom and venom-like molecule libraries have precluded their therapeutic evaluation via high-throughput screening. Here, we developed an innovative computational approach to design a highly diverse library of animal venoms and "metavenoms". We used programmable M13 hyperphage display to preserve critical disulfide-bonded structures for highly parallelized single-round biopanning with quantitation via high-throughput DNA sequencing. Our approach led to the discovery of Kunitz-type domain containing proteins that target the human itch receptor Mas-related G-protein coupled receptor member X4, which plays a crucial role in itch perception. Deep learning-based structural homology mining identified two endogenous human homologs, tissue factor pathway inhibitor (TFPI), and serine peptidase inhibitor, Kunitz type 2 (SPINT2), which exhibit agonist-dependent potentiation of Mas-related G-protein coupled receptor member X4. Highly multiplexed screening of animal venoms and metavenoms is therefore a promising approach to uncover new drug candidates.
Keywords:
database mining; itch receptor; ligand discovery; phage display; venom.