Targeting the CAPON-NOS Axis: A Computational Strategy for Small Molecule Modulator Discovery

Abstract

The carboxy-terminal PDZ ligand of neuronal nitric oxide synthase (CAPON) serves as a critical regulatory protein controlling nitric oxide (NO) signaling across multiple physiological and pathological processes which encompass neurological, cardiac and metabolic functions. These diverse physiological roles of CAPON marks it as a key therapeutic target for conditions associated with its dysregulation. Despite this therapeutic potential there are no specific CAPON or nNOS/CAPON modulators which have been developed to date, highlighting a significant gap in targeted drug discovery. Herein, we report the first strategy specifically focused on disrupting the nNOS/CAPON protein-protein interface. Through screening of chemical libraries composed of 4.6 million compounds and eight molecular dynamics simulations, two potential hit compounds were identified. Beyond identifying these promising hits, our approach introduces two novel computational tools: a freely available Python-based toolset for NMR structural analysis and visualization and a second toolkit for accelerated ligand preparation. These tools significantly accelerate data preparation timelines while reducing computational costs, providing the research community with accessible resources for structure-based drug discovery efforts. Together, these tools represent a substantial contribution to the computational chemistry toolkit, enabling researchers to conduct high-throughput virtual screening campaigns more efficiently and with greater reproducibility. This work represents a foundational step toward developing targeted therapies for CAPON-mediated disorders and provides a scalable computational framework for future protein-protein interaction drug discovery efforts. HighlightsO_LIA novel structure-based strategy developed to target the CAPON/nNOS protein-protein interaction. C_LIO_LIA Python-based toolset for protein conformation analysis, identification, visualization and separation. C_LIO_LIPython pipeline enables efficient ligand preparation for ultra-large chemical libraries. C_LIO_LIVirtual screening identified 6 promising small-molecule candidates. C_LIO_LIA total of 8*100ns molecular dynamics (MD) simulations performed using DESMOND. C_LIO_LIMM/GBSA and contact-time analysis were conducted to assess binding stability and affinity. C_LI