In silico substrate binding profiling for SARS-COV-2 main protease (mpro) using hexapeptide substrates

COVID-19, as a disease resulting from SARS-CoV-2 infection, and a pandemic has had a devastating effect on the world. There are limited effective measures that control the spread and treatment of COVID-19 illness. The homodimeric cysteine main protease (Mpro) is crucial to the life cycle of the virus, as it cleaves the large polyproteins 1a and 1ab into matured, functional non-structural proteins. The Mpro exhibits high degrees of conservation in sequence, structure and specificity across coronavirus species, making it an ideal drug target. The Mpro substrate-binding profiles remain, despite the resolution of its recognition sequence and cleavage points (Leu-Gln↓(Ser/Ala/Gly)). In this study, a series of hexapeptide sequences containing the appropriate recognition sequence and cleavage points were generated and screened against the Mpro to study these binding profiles, and to further be the basis for efficiency-driven drug design. A multi-conformer hexapeptide substrate library comprising optimised 81000 models of 810 unique sequences was generated using RDKit within the context of python. Terminal capping with ACE and NMe was effected using SMILES and SMARTS matching. Multiple hexapeptides were complexed with chain B of crystallographic Mpro (PDS ID: 6XHM), following the validation of chain B for this purpose using AutoDock Vina at high levels of exhaustiveness (480). The resulting Vina scores ranged between -8.7 and -7.0 kcal.mol-1, and the reproducibility of best poses was validated through redocking. Ligand efficiency indices were calculated to identify substrate residues with high binding efficiency at their respective positions, revealing Val (P3), Ala (P1′); and Gly and Ala (P2′ and P3′) as leading efficient binders. Binding efficiencies were lowered by molecular weight. Substrate recognition was assessed by mapping of binding subsites, and Mpro specificity was evaluated through the resolution of intermolecular interaction at the binding interface. Molecular dynamics simulations for 20 ns were performed to assess the stability and behaviour of 132 Mpro systems complexed with KLQ*** substrates. Principal component analysis (PCA), was performed to assess II protein motions and conformational changes during the simulations. A strategy was formulated to classify and evaluate relations in the Mpro PCA motions, revealing four main clades of similarity. Similarity within a clade (Group 2) and dissimilarity between clades were confirmed. Trajectory visualisation revealed complex stability, substrate unbinding and dimer dissociation for various Mpro systems.Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 202