Designing and bioengineering of CDRs with higher affinity against receptor-binding domain (RBD) of SARS-CoV-2 Omicron variant
Vishakha SinghiD, Shweta ChoudharyiD, Mandar BhutkariD, Sanketkumar NehuliD, Sabika Ali, Jitin SinglaiD, Pravindra KumariD, Shailly TomariD
Abstract
The emergence of the SARS-CoV-2 Omicron variant highlights the need for innovative strategies to address evolving viral threats. This study bioengineered three nanobodies H11-H4, C5, and H3 originally targeting the Wuhan RBD, to bind more effectively to the Omicron RBD. A structure-based in silico affinity maturation pipeline was developed to enhance their binding affinities. The pipeline consists of three key steps: high-throughput in silico mutagenesis of complementarity-determining regions (CDRs), protein-protein docking for screening, and molecular dynamics (MD) simulations for assessment of the complex stability. A total of 741, 551, and 684 mutations were introduced in H11-H4, C5, and H3 nanobodies, respectively. Protein-protein docking and MD simulations shortlisted high-affinity mutants for H11-H4(6), C5(5), and H3(6). Further, recombinant production of H11-H4 mutants and Omicron RBD enabled experimental validation through Isothermal Titration Calorimetry (ITC). The H11-H4 mutants R27E, S57D, S107K, D108W, and A110I exhibited improved binding affinities with dissociation constant (KD) values ranging from [~]8.8 to [~]27 {micro}M, compared to the H11-H4 nanobody KD of [~]32 {micro}M, representing a three-fold enhancement. This study demonstrates the potential of the developed in silico affinity maturation pipeline as a rapid, cost-effective method for repurposing nanobodies, aiding the development of robust prophylactic strategies against evolving SARS-CoV-2 variants and other pathogens. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/613860v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1ea8ff7org.highwire.dtl.DTLVardef@91d567org.highwire.dtl.DTLVardef@51629corg.highwire.dtl.DTLVardef@1212752_HPS_FORMAT_FIGEXP M_FIG C_FIG
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 7
- FWCI
- 2.22
- Field percentile
- top 11%
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Enhancing affinity of neutralizing SARS-CoV-2 nanobody through facile structure-guided mutations in CDRs
Vishakha Singh, Mandar Bhutkar, … Shailly Tomar·Biochemical and Biophysical Research CommunicationsPeer-reviewed·Sep 26, 2024
- Development of an Escape-resistant SARS CoV-2 Neutralizing Synthetic Nanobody
Dmitri Dormeshkin, Michail A. Shapira, … Sergei Semyonov·Frontiers in ImmunologyPeer-reviewed·Sep 16, 2022
- The influence of new SARS-CoV-2 variant Omicron (B.1.1.529) on vaccine efficacy, its correlation to Delta Variants: a computational approach
Prashant Ranjan, Neha Neha, … Parimal Kumar Das·Microbial PathogenesisPeer-reviewed·Jan 1, 2022
- Binding of Human ACE2 and RBD of Omicron Enhanced by Unique Interaction Patterns Among SARS-CoV-2 Variants of Concern
- Structural insight into antibody evasion of SARS-CoV-2 omicron variant
Jyoti Kumar Verma, Naidu Subbarao·bioRxiv·Jan 25, 2022
- Deep Mutational Engineering of broadly-neutralizing and picomolar affinity nanobodies to accommodate SARS-CoV-1 & 2 antigenic polymorphism
A. Laroche, M. L. Orsini Delgado, … H. Nozach·bioRxiv·Dec 7, 2021