# Design of Mimetic Antibodies Targeting the SARS-CoV-2 Spike Glycoprotein Based on the GB1 Domain: A Molecular Simulation and Experimental Study

## TL;DR

This study shows how a genetic algorithm can design mimetic antibodies targeting SARS-CoV-2, combining molecular simulations with experimental validation.

## Contribution

A novel genetic algorithm protocol for designing mimetic antibodies without relying on preexisting databases.

## Key findings

- The genetic algorithm rapidly converges by selecting initial populations based on antigenic surface interactions.
- Experimental tests confirmed the algorithm's ability to optimize molecular recognition in mimetic antibodies.
- New structural motifs were discovered that can be designed directly from the mimetic antibody structure.

## Abstract

In the context of fast and significant technological
transformations,
it is natural for innovative artificial intelligence (AI) methods
to emerge for the design of bioactive molecules. In this study, we
demonstrated that the design of mimetic antibodies (MA) can be achieved
using a combination of software and algorithms traditionally employed
in molecular simulation. This combination, organized as a genetic
algorithm (GA), has the potential to address one of the main challenges
in the design of bioactive molecules: GA convergence occurs rapidly
due to the careful selection of initial populations based on intermolecular
interactions at antigenic surfaces. Experimental immunoenzymatic tests
prove that the GA successfully optimized the molecular recognition
capacity of one of the MA. One of the significant results of this
study is the discovery of new structural motifs, which can be designed
in an original and innovative way based on the MA structure itself,
eliminating the need for preexisting databases. Through the GA developed
in this study, we demonstrated the application of a new protocol capable
of guiding experimental methods in the development of new bioactive
molecules.