Research Resources

The evolution from homology modeling to advanced deep learning models like AlphaFold represents a transformative paradigm shift in structural biology. This shift has enabled accurate, large-scale protein structure prediction, revolutionizing how researchers approach protein-related diseases, drug discovery, and the study of fundamental biological processes.

AlphaFold represents a groundbreaking advancement in the field of structural biology and protein structure prediction. For decades, scientists have been grappling with the challenge of predicting the 3D structure of proteins from their amino acid sequences, a problem essential for understanding biological processes, drug discovery, and disease mechanisms. AlphaFold's evolution, from its first version to its third, has brought transformative insights and revolutionized the field.

AlphaFold3 (AF3) is the latest version of the AlphaFold series, aimed at predicting molecular interactions with high accuracy. AlphaFold2 (AF2) had already revolutionized protein structure prediction, and AF3 extends those capabilities to a much broader scope, including protein-ligand, protein-nucleic acid, and other complex interactions. The AF3 model surpasses previous iterations in its ability to predict the structures of various biomolecules, including small molecules, nucleic acids, proteins, and covalently modified residues.

Drug discovery is a complex and costly process involving multiple stages, from target identification to clinical trials. Traditionally, researchers have relied on experimental techniques such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryo-electron microscopy (Cryo-EM) to determine the 3D structures of proteins. However, these methods are often time-consuming and expensive, especially for proteins that are difficult to crystallize or have complex structural dynamics. The advent of AlphaFold, a deep learning-based protein structure prediction tool, has revolutionized structural biology by offering accurate and rapid predictions of protein structures. The integration of AlphaFold into the drug discovery workflow has the potential to accelerate target identification, structure-based drug design (SBDD), and lead optimization.

Mitochondria are the main place for cells to carry out aerobic respiration and also the "energy factory" of cell metabolism. Mitochondrial uncoupling protein 1 (UCP1) is highly expressed on the inner mitochondrial membrane and confers on mammalian brown adipose tissue the ability to exclusively burn calories as thermoregulatory heat. Activating brown fat thermogenesis has been reported to be effective against obesity and related metabolic

As soon as this year's Nobel Prize was announced, artificial intelligence (AI) emerged victorious.