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R14-OPT1 for COVID-19

R14-OPT1 has been reported as potentially beneficial for COVID-19 in the following study. We have not reviewed R14-OPT1 in detail.
COVID-19 involves the interplay of 300+ viral and host proteins and factors providing many therapeutic targets. Scientists have proposed 10,000+ potential treatments. c19early.org analyzes 170+ treatments.
Cao et al., From immune evasion to broad in silico binding: computational optimization of SARS-CoV-2 RBD-targeting nanobody, Frontiers in Immunology, doi:10.3389/fimmu.2025.1637955
IntroductionThe rapid evolution of SARS-CoV-2 Omicron variants highlights the urgent need for therapeutic strategies that can target viral evolution and leverage host immune recognition mechanisms. This study uses molecular dynamics (MD) simulations to analyze the immune evasion mechanisms of class 1 nanobodies against emerging SARS-CoV-2 variants, and to develop an efficient in silico pipeline for rapid affinity optimization.MethodsWe employed MD simulations and binding free energy calculations to investigate the immune evasion mechanisms of four class 1 nanobodies (R14, DL4, VH ab6, and Nanosota9) against wild-type (WT) and Omicron variants, including BA.2, JN.1, and KP.3/XEC. Building on these findings, we established a streamlined nanobody optimization pipeline integrating high-throughput mutagenesis of complementarity-determining regions (CDRs) and hotspot residues, protein-protein docking, and MD simulations.ResultsMD analysis confirmed that the immune evasion mechanism of KP.3/XEC is significantly associated with the Q493E mutation, which weakens electrostatic interactions between the nanobodies and the receptor binding domain (RBD). Through our pipeline, we identified high-affinity mutants including 3 for R14, 3 for DL4, 11 for VH ab6, and 9 for Nanosota9. The optimized R14 variant L29W/S52C/A101V demonstrated exceptional performance, achieving a 62.6% binding energy improvement against JN.1 (-76.88 kcal/mol compared to -47.3 kcal/mol for original R14 nanobody) while maintaining < 15% affinity variation across variants (compared to > 40% for original R14 nanobody).DiscussionThis study demonstrates that in silico affinity enhancement is a rapid and resource-efficient approach to repurpose nanobodies against SARS-CoV-2 variants, significantly accelerating affinity optimization while reducing experimental demands. This computational approach expedites the optimization of nanobody binding affinities while minimizing experimental resource requirements. By enhancing nanobody efficacy, our method provides a viable framework for developing targeted countermeasures against evolving SARS-CoV-2 variants and other pathogens.
Please send us corrections, updates, or comments. c19early involves the extraction of 200,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. IMA and WCH provide treatment protocols.
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