Revolutionizing structural biology: AI-driven protein structure prediction from AlphaFold to next-generation innovations.

Abstract

Protein structure modeling from the prediction algorithm has become a valuable tool in biology and medicine with computational advances. Accurate protein structure prediction is critical in druglike compound discovery, disease mechanism understanding, and protein engineering because it provides molecular level insights into protein folding and its effects on molecular and cellular function. This chapter covers the evolution of protein structure prediction, from traditional methods like homology modeling, threading, and ab initio procedures and the new emerging AlphaFold's influence. AlphaFold's highly recognized precision level and open-access data democratized structural biology research, and that lead to inspiring new prediction models like RoseTTAFold and OmegaFold tools. Alpha Folds design, methodology, and highly accurate performance are thoroughly examined, and comparisons are performed with similar tools. We also highlight limitations, such as protein complex and dynamics forecasting, post-AlphaFold developments in structural databases, computer resources, and multi-scale modeling. Protein structure modeling and predictions have a wide range of applications in biomedical research, including drug discovery, functional annotation, and synthetic biology. Future directions include the integration of protein structure prediction with systems biology and genomics, as well as the use of next-generation AI and quantum computing to boost prediction accuracy. This research emphasizes AI's importance in structural biology and envisions a future in which predictive tools will provide comprehensive insights into protein function, dynamics, and therapeutic potential.