High-resolution protein modeling through Cryo-EM and AI: current trends and future perspectives – a review - Jeyaraj, Gnanaprakash; Rajendran, Anith Kumar; Sathishkumar, Kuppusamy; Almutairi, Bader O.; Vadivelu, Aanand; Chokkakula, Santosh; Tu, Yanyang; Xie, Weimim (Frontiers in Molecular Biosciences, 2025)
The structural elucidation of proteins is fundamental to understanding their biological functions and advancing drug discovery. Recent breakthroughs in cryo-electron microscopy (cryo-EM) and artificial intelligence (AI)-based structure prediction have revolutionized protein modeling by enabling near-atomic resolution visualization and highly accurate computational predictions from amino acid sequences. This review summarizes the latest advances that have transformed protein structural biology from a predominantly structure-solving endeavour to a discovery-driven science. We discuss the complementary roles of cryo-EM and AI, including developments in direct electron detectors, advanced image processing, and deep learning algorithms exemplified by AlphaFold 2 and the emerging AlphaFold 3. These technologies facilitate detailed insights into challenging protein targets such as membrane proteins, flexible and intrinsically disordered proteins, and large macromolecular complexes. Furthermore, we highlight applications of integrative approaches in drug design, enzymatic mechanism elucidation, and functional predictions, illustrated by examples including hemoglobin, which demonstrates both the strengths and current limitations of AI–cryo-EM integration, and cytochrome P450 enzymes, where AlphaFold predictions have been combined with cryo-EM maps to explore conformational diversity. The review also addresses ongoing challenges and promising future directions for integrating experimental and computational methods to accelerate the exploration of protein structure–function relationships, ultimately impacting biomedical research and therapeutic development.