In Silico Design of Peptide-Based SARS-CoV-2 Fusion Inhibitors That Target WT and Mutant Versions of SARS-CoV-2 HR1 Domains

In 2019, novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began infecting humans, resulting in the COVID-19 pandemic. While the push for development of vaccines has yielded some positive results, the emergence of additional variants has led to concerns surrounding sustained vaccine effectiveness as the variants become the dominant strains. This work was undertaken to develop peptide-based antivirals capable of targeting both the wildtype (WT) heptad repeat 1 (HR1) domain of SARS-CoV-2 and the new HR1 variants which have developed. In silico protein mutagenesis, structural characterization, and protein–protein molecular docking were utilized to determine molecular interactions which facilitated binding of peptide-based antivirals targeting the HR1 domains. Molecular dynamics simulations were utilized to predict the final binding affinities of the top five peptide inhibitors designed. This work demonstrated the importance of hydrophobic interactions in the hydrophobic gorge and in the rim of the HR1 domain. Additionally, the placement of charged residues was shown to be essential in maximizing electrostatic interactions. The top five designed peptide inhibitors were all demonstrated to maintain good binding affinity to the WT and the variant HR1 SARS-CoV-2 domains. Therefore, the peptide inhibitors designed in this work could serve as potent antivirals which are effective in targeting both the original SARS-CoV-2 and the HR1 variants that have developed.

In silico Antibody Mutagenesis for Optimizing its Binding to the Spike Protein of SARS-CoV-2

<p>Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic and there are currently no FDA approved medicines for treatment or prevention. Inspired by promising outcomes for convalescent plasma treatment, developing antibody drugs (biologics) to block SARS-CoV-2 infection has been the focus of drug discovery, along with tremendous efforts in repurposing small-molecule drugs. In the last several months, experimentally, many human neutralizing monoclonal antibodies (mAbs) were successfully extracted from plasma of recovered COVID-19 patients. Currently, several mAbs targeting the SARS-CoV-2's spike protein (Spro) are in clinical trials. With known atomic structures of mAb-Spro complex, it becomes possible to <i>in silico</i> investigate the molecular mechanism of mAb's binding with Spro and design more potent mAbs through protein mutagenesis studies, complementary to existing experimental efforts. Leveraging superb computing power nowadays, we propose a fully automated <i>in silico</i> protocol for quickly identifying possible mutations in a mAb (e.g.~CB6) to enhance its binding affinity with Spro for the design of more efficacious therapeutic mAbs.</p>

In silico Antibody Mutagenesis for Optimizing its Binding to the Spike Protein of SARS-CoV-2

<p>Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic and there are currently no FDA approved medicines for treatment or prevention. Inspired by promising outcomes for convalescent plasma treatment, developing antibody drugs (biologics) to block SARS-CoV-2 infection has been the focus of drug discovery, along with tremendous efforts in repurposing small-molecule drugs. In the last several months, experimentally, many human neutralizing monoclonal antibodies (mAbs) were successfully extracted from plasma of recovered COVID-19 patients. Currently, several mAbs targeting the SARS-CoV-2's spike protein (Spro) are in clinical trials. With known atomic structures of mAb-Spro complex, it becomes possible to <i>in silico</i> investigate the molecular mechanism of mAb's binding with Spro and design more potent mAbs through protein mutagenesis studies, complementary to existing experimental efforts. Leveraging superb computing power nowadays, we propose a fully automated <i>in silico</i> protocol for quickly identifying possible mutations in a mAb (e.g.~CB6) to enhance its binding affinity with Spro for the design of more efficacious therapeutic mAbs.</p>

CodonGenie: optimised ambiguous codon design tools

CodonGenie, freely available from http://codon.synbiochem.co.uk, is a simple web application for designing ambiguous codons to support protein mutagenesis applications. Ambiguous codons are derived from specific heterogeneous nucleotide mixtures, which create sequence degeneracy when synthesised in a DNA library. In directed evolution studies, such codons are carefully selected to encode multiple amino acids. For example, the codon NTN, where the code N denotes a mixture of all four nucleotides, will encode a mixture of phenylalanine, leucine, isoleucine, methionine and valine. Given a user-defined target collection of amino acids matched to an intended host organism, CodonGenie designs and analyses all ambiguous codons that encode the required amino acids. The codons are ranked according to their efficiency in encoding the required amino acids while minimising the inclusion of additional amino acids and stop codons. Organism-specific codon usage is also considered.

Rapid and Programmable Protein Mutagenesis Using Plasmid Recombineering

ABSTRACTComprehensive and programmable protein mutagenesis is critical for understanding structure-function relationships and improving protein function. However, current techniques enabling comprehensive protein mutagenesis are based on PCR and require in vitro reactions involving specialized protocols and reagents. This has complicated efforts to rapidly and reliably produce desired comprehensive protein libraries. Here we demonstrate that plasmid recombineering is a simple and robust in vivo method for the generation of protein mutants for both comprehensive library generation as well as programmable targeting of sequence space. Using the fluorescent protein iLOV as a model target, we build a complete mutagenesis library and find it to be specific and unbiased, detecting 99.8% of our intended mutations. We then develop a thermostability screen and utilize our comprehensive mutation data to rapidly construct a targeted and multiplexed library that identifies significantly improved variants, thus demonstrating rapid protein engineering in a simple one-pot protocol.

CodonGenie: optimised ambiguous codon design tools

CodonGenie, freely available from http://codon.synbiochem.co.uk, is a simple web application for designing ambiguous codons to support protein mutagenesis applications. Ambiguous codons are derived from specific heterogeneous nucleotide mixtures, which create sequence degeneracy when synthesised in a DNA library. In directed evolution studies, such codons are carefully selected to encode multiple amino acids. For example, the codon NTN, where the code N denotes a mixture of all four nucleotides, will encode a mixture of phenylalanine, leucine, isoleucine, methionine and valine. Given a user-defined target collection of amino acids matched to an intended host organism, CodonGenie designs and analyses all ambiguous codons that encode the required amino acids. The codons are ranked according to their efficiency in encoding the required amino acids while minimising the inclusion of additional amino acids and stop codons. Organism-specific codon usage is also considered.

CodonGenie: optimised ambiguous codon design tools

CodonGenie, freely available from http://codon.synbiochem.co.uk, is a simple web application for designing ambiguous codons to support protein mutagenesis applications. Ambiguous codons are derived from specific heterogeneous nucleotide mixtures, which create sequence degeneracy when synthesised in a DNA library. In directed evolution studies, such codons are carefully selected to encode multiple amino acids. For example, the codon NTN, where the code N denotes a mixture of all four nucleotides, will encode a mixture of phenylalanine, leucine, isoleucine, methionine and valine. Given a user-defined target collection of amino acids matched to an intended host organism, CodonGenie designs and analyses all ambiguous codons that encode the required amino acids. The codons are ranked according to their efficiency in encoding the required amino acids while minimising the inclusion of additional amino acids and stop codons. Organism-specific codon usage is also considered.