A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens.

Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen's protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein-drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein.

Targeted design of drug binding sites in the main protease of SARS-CoV-2 reveals potential signatures of adaptation.

Several existing drugs are currently being tested worldwide to treat COVID-19 patients. Recent data indicate that SARS-CoV-2 is rapidly evolving into more transmissible variants. It is therefore highly possible that SARS-CoV-2 can accumulate adaptive mutations modulating drug susceptibility and hampering viral antigenicity. Thus, it is vital to predict potential non-synonymous mutation sites and predict the evolution of protein structural modifications leading to drug tolerance. As two FDA-approved anti-hepatitis C virus (HCV) drugs, boceprevir, and telaprevir, have been shown to effectively inhibit SARS-CoV-2 by targeting the main protease (Mpro), here we used a high-throughput interface-based protein design strategy to identify mutational hotspots and potential signatures of adaptation in these drug binding sites of Mpro. Several mutants exhibited reduced binding affinity to these drugs, out of which hotspot residues having a strong tendency to undergo positive selection were identified. The data further indicated that these anti-HCV drugs have larger footprints in the mutational landscape of Mpro and hence encompass the highest potential for positive selection and adaptation. These findings are crucial in understanding the potential structural modifications in the drug binding sites of Mpro and thus its signatures of adaptation. Furthermore, the data could provide systemic strategies for robust antiviral design and discovery against COVID-19 in the future.