Computational Antibody Redesign for Broad SARS-CoV-2 Variant Neutralization
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Solution Overview
Problem
Existing neutralizing antibodies are ineffective against evolving SARS-CoV-2 variants such as Delta and Omicron, leading to significant morbidity and mortality, necessitating rapid development of broad-spectrum antibodies for effective treatment.
Innovation Solution
Development of antibodies with specific mutations at positions 77, 53, 70, and 71 of the variable heavy and light chains, enhanced through computational redesign to improve binding affinity to variant RBDs, particularly for the Delta variant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing neutralizing antibodies are used, then early phase infections can be effectively treated, but they become ineffective against evolving SARS-CoV-2 variants such as Delta and Omicron
Solution Approach 1:
The patent applies parameter changes by introducing specific amino acid mutations at positions 77, 53, 70, and 71 in the variable heavy and light chains of the antibody. These parameter changes in the antibody structure enable it to maintain binding affinity and neutralization efficacy against evolving SARS-CoV-2 variants including Delta and Omicron, resolving the contradiction between reliability against variants and adaptability to different strains
Solution Approach 2:
The patent employs preliminary action through computational redesign and in silico mutagenesis to predict and implement antibody mutations before viral evolution renders existing antibodies ineffective. This proactive approach allows the antibody to be pre-adapted to neutralize variant RBDs, maintaining reliability against emerging variants while preserving broad-spectrum capability
2Reliability
If antibodies are computationally redesigned with specific mutations, then binding affinity to variant RBDs is improved, but the complexity of antibody development increases
Solution Approach 1:
The patent applies local quality by introducing targeted amino acid mutations at specific positions (77, 53, 70, and 71) in the variable regions of the antibody chains rather than redesigning the entire antibody structure. This localized approach improves binding affinity to variant RBDs while minimizing the increase in overall antibody structure complexity
Solution Approach 2:
The patent replaces traditional experimental antibody development methods with computational redesign approaches including in silico mutagenesis and molecular dynamics simulations. This substitution of mechanical/experimental systems with computational methods efficiently identifies beneficial mutations while reducing the complexity and time required for antibody development
3Reliability
If neutralizing antibodies disrupt spike interactions with human ACE2, then viral entry is prevented, but viral evolution enables escape from neutralization
Solution Approach 1:
The patent employs preliminary action by using computational methods to predict viral escape mutations and pre-adapting the antibody structure to counteract these potential changes. Through in silico mutagenesis and binding affinity calculations, the antibody is designed beforehand to maintain disruption of spike-ACE2 interactions even when the virus evolves, preventing viral entry while resisting escape
Solution Approach 2:
The patent applies parameter changes by mutating specific amino acids in the antibody's variable regions to alter its binding parameters and recognize conserved epitopes on the spike protein that are less prone to mutation. This enables the antibody to maintain reliable viral entry prevention while adapting to viral evolution through changes in binding characteristics rather than overall structure
Data Source
AI summary
Provided herein are antibodies and pharmaceutical compositions for treating coronavirus disease, and methods of their use.