Integrative Computational Strategies for Protein Structure Prediction in the Rational Design of Cancer Therapeutics

Abstract

Precision oncology necessitates the identification of actionable molecular targets to facilitate personalized therapeutic strategies. In this study, we implemented an integrative computational framework that synergizes transcriptomic profiling with structural bioinformatics to uncover and validate putative cancer targets. Differentially expressed genes (DEGs) across various malignancies were extracted from NCBI’s Gene Expression Omnibus (GEO) and subjected to functional enrichment and pathway prioritization using the DAVID platform. High-confidence candidate proteins were structurally characterized via homology modeling using SWISS-MODEL, followed by domain architecture analysis and binding site prediction to assess draggability. This multistage pipeline enables the transition from gene-level perturbations to structure-guided therapeutic insights. The integration of gene expression analysis with three-dimensional protein modeling demonstrates a scalable and rational approach to target discovery, offering a foundation for structure-based drug design. Our findings underscore the potential of computational methodologies in advancing individualized cancer therapy and accelerating the preclinical development of targeted interventions.