Exploring SARS-CoV-2 spike protein mutations through genetic algorithm-driven structural modeling

TL;DR

This paper introduces a genetic algorithm framework to model and optimize SARS-CoV-2 spike protein mutations computationally, focusing on structural stability and binding properties.

Contribution

The novel contribution is a genetic algorithm-based method for structural modeling of spike protein variants, emphasizing thermodynamic properties rather than evolutionary prediction.

Findings

The GA framework generated spike variants with improved structural stability over generations.

Candidate conformations with favorable thermodynamic properties for spike-ACE2 interactions were identified.

The method demonstrates feasibility for in silico exploration of mutational landscapes.

Abstract

The rapid evolution of SARS-CoV-2 highlights the importance of computational approaches to explore mutational effects on the viral spike protein. In this work, we present a genetic algorithm (GA) framework applied to the structural optimization of spike protein variants, with a focus on energetic and binding properties rather than direct evolutionary prediction.

Our GA-driven pipeline generated spike variants with progressively improved structural stability as indicated by lower discrete optimized protein energy scores across generations. The approach also enabled evaluation of Gibbs free energy and binding affinity for spike—Angiotensin-converting enzyme 2 receptor interactions, revealing candidate conformations with favorable thermodynamic properties. These results demonstrate the algorithm’s capacity to refine protein models and explore mutational landscapes in silico, although no validation against naturally emerging variants was performed. This study presents a methodological framework for GA-based structural modeling of SARS-CoV-2 spike mutations. Rather than forecasting specific variants of concern, it demonstrates the feasibility of a computational approach that can be extended and integrated with evolutionary and experimental evidence to strengthen future efforts in variant monitoring and vaccine development.

All the Python and R scripts are available upon request to the authors.

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