Diagnostics
Exploring NS5A Protein of Hepatitis C Virus Through Computational Deep Learning Models and Phylogenetic Analysis for Sequence Variability
By Nabeeha Abid, Obaidullah Qazi, Hania Ahmed and 2 others.
Abstract
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.