Computational Protein Design for COVID-19 Research and Emerging Therapeutics

Abstract

As the world struggles with the ongoing COVID-19 pandemic, unprecedented obstacles have continuously been traversed as new SARS-CoV-2 variants continually emerge. Infectious disease outbreaks are unavoidable, but the knowledge gained from the successes and failures will help create a robust health management system to deal with such pandemics. Previously, scientists required years to develop diagnostics, therapeutics, or vaccines; however, we have seen that, with the rapid deployment of high-throughput technologies and unprecedented scientific collaboration worldwide, breakthrough discoveries can be accelerated and insights broadened. Computational protein design (CPD) is a game-changing new technology that has provided alternative therapeutic strategies for pandemic management. In addition to the development of peptide-based inhibitors, miniprotein binders, decoys, biosensors, nanobodies, and monoclonal antibodies, CPD has also been used to redesign native SARS-CoV-2 proteins and human ACE2 receptors. We discuss how novel CPD strategies have been exploited to develop rationally designed and robust COVID-19 treatment strategies.

Delineating the Structure–Dynamics–Binding Differences among BA.1, BA.4/5, and BF.7 SARS-CoV-2 Variants through Atomistic Simulations: Correlation with Structural and Epidemiological Features

“…The insights obtained from this study are invaluable in understanding the distinct structural, dynamic, and binding attributes of BA.1, BA.4/5, and BF.7 variants. Moreover, these findings have implications for comprehending the molecular mechanisms of other SARS-CoV-2 variants/subvariants and can contribute to the development of targeted therapeutics …”

“…Moreover, these findings have implications for comprehending the molecular mechanisms of other SARS-CoV-2 variants/subvariants and can contribute to the development of targeted therapeutics. 39 …”

Interface design of SARS-CoV-2 symmetrical nsp7 dimer and machine learning-guided nsp7 sequence prediction reveals physicochemical properties and hotspots for nsp7 stability, adaptation, and therapeutic design

“…In our recent studies, we harnessed the power of high-throughput protein design methodologies to pinpoint critical residues and mutations within intracellular proteins including main protease (M pro ), RdRp, and spike protein receptor binding domain (RBD) of SARS-CoV-2, which could make the virus more tolerant and adaptable to antiviral drugs such as remdesivir, molnupiravir, favipiravir, nirmatrelvir and bebtelovimab. 21–26 While conducting this research, we found that mutational and residue-specific changes can induce relative stabilities and adaptability of the RdRp, for instance, the nsp7 by forming symmetrical dimers. 20 This realization prompted us to perform an extensive protein design and ML-based endeavour, which was further complemented by a comprehensive multi-parametric analysis.…”

A Comprehensive Analysis of Structural and Functional Changes Induced by SARS-CoV-2 Spike Protein Mutations

“…Alterations to the spike protein's binding affinity and specificity for ACE2 can have an impact on the pathogenicity and transmission of viruses [84]. For instance, the RBD of the spike protein has several changes in the B.1.1.7 variation, including N501Y, which has been demonstrated to increase the binding affinity for ACE2 and improve viral infectivity [85]. Similar to the B.1.351 variant, the B.1.351 variant features many mutations in the RBD, including E484K, which decreases the binding affinity for some monoclonal antibodies and might have an impact on how well the host immune system neutralizes the virus [19,86].…”