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.