Cracking the molecular code of viral RNA proofreading: unveiling structure, function, and dynamics through artificial intelligence-driven multiscale modeling of molecular interactions

The National Institute of Allergy and Infectious Diseases awarded the Research Foundation of the City University of New York $151,000 on September 1, 2026, under the Allergy and Infectious Diseases Research program (CFDA 93.855) to conduct artificial intelligence-driven multiscale computational modeling of the SARS-CoV-2 RNA proofreading mechanism.

The award funds investigation of the exonuclease proofreading domain of the viral non-structural protein NSP14, which is responsible for maintaining the fidelity of coronavirus RNA replication and has been implicated in resistance to antiviral therapeutics including remdesivir. The recipient will combine computational modeling, molecular simulation, computer-aided drug design, and artificial intelligence approaches to characterize the structure, function, and dynamics of the proofreading exon domain and its interactions with the viral replication and transcription complex enzymes and cofactors. The work aims to provide molecular-level understanding of the proofreading mechanism to support structure-based therapeutic targeting.

The Research Foundation of the City University of New York, which operates as the fiscal agent and grants management arm for Queens College, receives the award with place of performance in Flushing, New York. The period of performance runs through August 31, 2030.

Abstract

Coronaviruses (COVS), among which sars-COV-2 (the covid-19 virus), are RNA viruses with exceptionally large genomes (~30 KB). To maintain such enormous genomes, COVS possess a replication and transcription complex (RTC), requiring an intricate synergy among RNA-dependent RNA polymerase (RDRP), capping, methylation and importantly, high-fidelity exonuclease (EXON) proofreading. While being a unique feature among RNA viruses, COV proofreading is held responsible for severely impairing the effectiveness of antiviral drugs, including the FDA approved remdesivir. Considering the potential benefits of therapeutically targeting COV RNA proofreading, IT is imperative to elucidate, at the molecular level, how the sars-COV-2 RNA proofreading enzyme (i.e., the EXON domain of the non-structural protein NSP14) operates, including detailed evaluations of its structure, function and dynamics, as well as macromolecular assemblies with RTC enzymes and cofactors. A growing number of experimental structures now reveal the overall shape and organization of the proofreading EXON domain in sars-COV-2 and other COVS, including how small/fragment-like molecules can bind to IT and other parts of the virus's replication machinery. While these structures offer valuable snapshots, they DON't tell the full story. They can't capture the dynamic movements of EXON during proofreading, how IT interacts with other viral components, or the step-by-step details of how IT edits viral RNA. To bridge this critical GAP in understanding, we will combine computational modeling and molecular simulation with computer-aided drug design and artificial intelligence (AI) approaches to provide a molecular-level description of the principles governing the sars-COV- 2 proofreading mechanism, as well as to decipher a role for this essential enzymatic function in the viral life cycle. Integration with NMR-based EXON assay will provide a proof-of-concept for our findings. The outcomes of this project will provide both guidance for structure-based drug design of improved anti-sars- COV-2 therapies, and new knowledge to ultimately unveil the relationship between genome size and its constraints on viral adaptability and evolution. Moreover, this research will provide computational frameworks and reveal new discoveries relevant to other highly pathogenic COVS and emerging viruses. Importantly, this project is designed to actively involve undergraduate students in every stage of the research process, from computational simulations and AI-based analysis of EXON dynamics to interpreting experimental data from the literature and collaborative studies. By integrating research with education, this project aims to inspire and prepare undergraduates for careers in biomedical research, contributing to workforce development both at cuny queens college and, more broadly, in the life sciences fields.