Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship

Abstract

Protein analysis has been completely transformed by the swift growth of bioinformatics, which has improved protein structure prediction, simulated interactions, and clarified functional interactions. To improve our knowledge of proteomics, this review carefully examines the application of diverse bioinformatics methods in protein analysis. We evaluate computational methods such as molecular dynamics simulations and machine learning algorithms critically, with an emphasis on their applicability to modeling protein-protein interactions and protein tertiary structure prediction. Our findings show that these methods are useful for predicting protein functions and interactions, which are important for drug discovery and development. We also talk about the important implications of these developments for our knowledge of complex biological systems and disease mechanisms at the molecular level. This review also provides insights into the existing and future potential of bioinformatics tools, emphasizing their vital role in revolutionizing protein analysis. We additionally offer future strategies to improve our knowledge and management of complex disorders, particularly highlighting the need for integrated, multi-scale approaches and additional research on underrepresented proteins.