Bioinformatics advances · 2025
Exploring SARS-CoV-2 spike protein mutations through genetic algorithm-driven structural modeling.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Di Salvatore Valentina, Maleki Avisa, Mohajer Babak, Ras-Carmona Alvaro, Russo Giulia, Reche Pedro Antonio, Pappalardo Francesco
Аннотация
MOTIVATION: 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.
RESULTS: 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.
AVAILABILITY AND IMPLEMENTATION: All the Python and R scripts are available upon request to the authors.
RESULTS: 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.
AVAILABILITY AND IMPLEMENTATION: All the Python and R scripts are available upon request to the authors.