Integrating machine learning and statistical methods for prediction and feature-based network characterization of...

Proteins play a crucial role in various biological processes, including enzymatic reactions, structural support, and cellular regulation. External stress factors such as physical, chemical, or environmental changes significantly impact protein expression and function. Stress response proteins (SRPs), including heat shock proteins and molecular chaperones, are essential in maintaining cellular stability under these challenging conditions. While experimental methods have made substantial progress in identifying SRPs, computational approaches for their systematic identification and functional analysis remain limited. To bridge this gap, we introduce an innovative two-phase computational framework that combines machine learning and network analysis for comprehensive SRP characterization. In the first phase, we employ advanced computational techniques to extract sequence-based features from SRP datasets, enabling more accurate identification of stress-related proteins. In the second phase, we perform protein-protein interaction (PPI) network analysis to uncover novel interactions, providing deeper insights into functional associations, regulatory mechanisms, and interaction patterns influenced by stress conditions. By combining classification and network analysis, our approach not only improves SRP identification but also enhances understanding of their role in stress adaptation. This comprehensive framework offers a strong foundation for future research in stress biology and protein function analysis.