AlphaFold which builds in deep learning-based methods based on large amounts of protein data allows us highly accurate structure prediction/modeling for most proteins. AlphaFold models of proteins can be useful for diverse applications such as predictions of intrinsically disordered regions, modeling of structures based on cryo-EM maps and ligand binding site predictions. Here, we introduce and review the backgrounds, effects and caveats of protein structure/complex structure prediction using AlphaFold/AlphaFold-Multimer.
Iron is ubiquitously contained in all the living things on the earth. Iron plays many essential roles in our body as iron ions and heme with proteins. At the same time, labile iron and heme, designated as protein-free or weakly protein-bound species, also play essential roles in physiological and pathological aspects. However, the subcellular dynamics of labile iron/heme have remained unclear due to a lack of useful imaging probes. This review presents new chemical tools enabling live-cell imaging of labile iron and labile heme and their applications.