Exploring NS5A Protein of Hepatitis C Virus Through Computational Deep Learning Models and Phylogenetic Analysis for Sequence Variability

Background: Hepatitis C Virus (HCV) remains a global health threat, causing severe liver diseases such as cirrhosis and hepatocellular carcinoma. Protein sequences are essential for exploring genetic variability and improving detection. This study presents a generative LSTM model, AI-VNA, for generating and analyzing NS5A protein sequences to enhance understanding of sequence diversity and improve predictive modeling. Methods: The AI-VNA model was trained on curated NS5A protein sequences and evaluated against baseline models including CNN, 1D-CNN, GRU, and Simple RNN. Generated sequences were validated using BLAST (version 2.16.0) to assess biological relevance. Additional analyses included a sequence similarity heatmap, phylogenetic clustering using UPGMA, and statistical comparison of sequence similarities via the Mann–Whitney U test. Results: AI-VNA achieved 98.56% accuracy, outperforming other models and demonstrating strong capability in modeling sequence patterns. BLAST validation showed an average query coverage of 82%, an average E-value of 1 × 10−155, and 96.68% identity to known NS5A proteins. The sequence similarity heatmap revealed notable variability among the generated sequences. Phylogenetic analysis showed distinct clustering patterns, indicating both conservation and diversity. A box plot comparing similarity scores of predicted and database sequences showed significant variation (p = 0.01221; Cliff’s Delta = 0.291, small effect). Conclusions: These findings highlight the AI-VNA model’s ability to generate evolutionarily relevant and diverse NS5A protein sequences, supporting its potential for improving HCV sequence analysis and aiding in diagnostic and therapeutic developments.