Probing Mechanisms of Polycystin-1 Regulation Using Peptide Modulators Designed by Sequence- and Structure-Based Learning

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Autosomal dominant polycystic kidney disease (ADPKD) is the most common potentially lethal genetic disease. ADPKD is caused mainly by mutations in the PKD1 gene, which encodes the polycystin-1 (PC1) protein. Therapeutic treatment of ADPKD that targets the proximal signaling functions of PC1 has yet to be discovered. PC1 is an important unusual G-protein-coupled receptor (GPCR) with 11 transmembrane (TM) domains. PC1 shares multiple characteristics with Adhesion GPCRs. These include a GPCR proteolysis site that autocatalytically divides these proteins into extracellular, N-terminal and membrane-embedded, C-terminal (CTF) fragments. A tethered peptide agonist (TA) within the N-terminal stalk of the CTF has been suggested to activate signaling of PC1. Using the cryo-EM structure of PC1, we have recently revealed a novel allosteric TA/stalk-mediated signaling mechanism of PC1 by combining complementary all-atom Gaussian accelerated molecular dynamics (GaMD) simulations and biochemical and cellular assay experiments. Moreover, we have uncovered unique features of activation and allosteric modulation in the A and B classes of GPCRs from sequence coevolutionary “Potts” models and structural contact analysis. We have shown how “Potts” models fit to homologous sequences can be used to generate and detect cryptic functionality of multiresidue sequence motifs involved in allosteric binding and signaling. In addition, we have developed the GaMD, Deep Learning and free energy prOfiling Workflow (GLOW) to predict molecular determinants and map free energy landscapes of functional biomolecules. Building upon these advances, we will design and test novel peptide modulators to probe mechanisms of PC1 signaling regulation by combining state-of-the-art computational techniques (including sequence coevolutionary Potts models, GaMD, GLOW and peptide docking) and complementary cellular signaling experiments. Our specific aims include: (1) Characterize the binding mechanisms of known TA/stalk-derived peptide modulators of PC1 through sequence coevolution analysis, peptide docking, and AI modeling; and (2) Predict and validate new peptide modulators of PC1 through Potts modeling, peptide virtual screening, and cellular signaling assays. Therefore, we will implement a unique computational sequence- and structure-based learning approach coupled with relevant in vitro experimental analyses to develop novel peptide modulators of PC1. Our long-term goals are (1) to develop robust computational and experimental methodologies to characterize protein-peptide interactions, (2) to understand mechanisms of signaling in the wildtype and ADPKD disease mutants of PC1, and (3) to lay the foundation for the future design of effective therapeutics for treatment of ADPKD.