Utilizing artificial intelligence for precision exploration of N protein targeting phenanthridine sars-cov-2 inhibitors: A novel approach

Abstract

The persistent mutation of the novel coronavirus presents a continual threat of infections and associated illnesses. While considerable research efforts have concentrated on the functional proteins of SARS-CoV-2 in the development of anti-COVID-19 therapeutics, the structural proteins, particularly the N protein, have received comparatively less attention. This study focuses on the N protein, a critical structural component of the virus, and employs advanced deep learning models, including EMPIRE and DeepFrag, to optimize the structures of phenanthridine-based compounds. More than 10,000 small molecules, derived through deep learning, underwent high-throughput virtual screening, resulting in the synthesis of 44 compounds. Compound 38 showed a binding potential energy of -8.2 kcal/mol in molecular docking. Surface Plasmon Resonance (SPR) and Microscale Thermophoresis (MST) validation yielded dissociation constants of 353 nM and 726 nM, confirming strong binding to the N protein. Compound 38 demonstrated antiviral activity in vitro and exhibited anti-COVID-19 effects by interfering with the binding of N proteins to RNA. This research underscores the potential of targeting the SARS-CoV-2 N protein for therapeutic intervention and illustrates the efficacy of deep learning model in the design of lead compounds. The application of these deep learning models represents a promising approach for accelerating the discovery and development of antiviral agents.

Keywords: Deep learning; Nucleocapsid protein; Phenanthridine; SARS-CoV-2.