Computational Protein Modelling, Refinement, Validation, and Its Application

In the era of computational drug discovery, Protein modelling has emerged as a cornerstone methodology for predicting the three-dimensional (3D) structures of proteins based on evolutionary relatedness. This chapter explores the detailed pipeline of computational protein modelling, focusing on comparative (homology) modelling approaches that integrate bioinformatics tools, structural databases, and algorithmic innovations to accelerate drug development. Emphasizing the application of artificial intelligence (AI) and advanced sequence alignment strategies, the chapter elaborates on steps ranging from query sequence identification and template selection to model building, loop refinement, and structural validation. The integration of machine learning has significantly enhanced modelling accuracy, bridging gaps where experimental techniques such as X-ray crystallography, NMR, and cryo-EM are limited by cost and complexity. Moreover, the chapter evaluates the utility of molecular dynamics simulations and quality metrics such as RMSD, GDT_TS, and Z-score in refining protein structures for downstream drug discovery applications. By providing a comprehensive examination of software tools, databases, and validation techniques, this chapter offers valuable insights into how AI-driven protein modelling is revolutionizing target identification, ligand docking, and rational drug design within the larger framework of bioinformatics.