Substituições de aminoácidos como representação de mutações em transformers para análise e classificação de variantes do SARS-CoV-2

Abstract

The COVID-19 pandemic, caused by SARS-CoV-2, highlighted the need for scalable and automatable computational methods for genomic surveillance in the face of the continuous emergence of variants of concern. In this context, this work proposes an innovative approach based on vector representations of amino acid substitutions, leveraging Transformer models for the analysis and classification of SARS-CoV-2 viral variants, with a focus on the Alpha, Beta, Gamma, Delta, and Omicron variants. Sequences of amino acid substitutions were processed into high-dimensional embeddings, which supported two complementary experiments. In the first, unsupervised techniques, such as Fuzzy C-Means clustering and t-SNE projection, were applied to reveal groupings consistent with known variants, as well as transitional regions and ambiguous samples. In the second, supervised learning models, including SVM, Random Forest, k-NN, and XGBoost, were evaluated for variant classification, with XGBoost achieving accuracy above 99% on an external dataset. The results demonstrate that representations derived exclusively from amino acid substitutions are capable of discriminating viral variants with high performance and capturing mutational signatures of biological relevance, without requiring genomic alignment. Thus, the proposed approach represents a scalable, flexible, adaptable, and purely computational alternative for automated pathogen genomic surveillance, with potential applications in public health scenarios.