New era in structural biology with the AlphaFold program

Abstract

Proteins control all biological processes. Therefore, understanding protein functions is indispensable for elucidating each life phenomenon, including the pathogenic mechanisms of diseases. In structural biology, three-dimensional structures of proteins are used to uncover their functions. Thus far, more than 180,000 structures, determined using X-ray/neutron crystallography, nuclear magnetic resonance, or cryo-electron microscopy, have been deposited in the Protein Data Bank. These structures have significantly contributed to our understanding of life. During the summer of 2021, two artificial intelligence (AI) programs that can predict protein structures were released (AlphaFold and RoseTTAFold). These AI programs can predict highly accurate three-dimensional structures of proteins from their amino acid sequences. AlphaFold can predict protein structures with high accuracy; therefore, structural biologists and other scientists can now easily predict the protein structure of interest without requiring any specialized skill or equipment. Furthermore, AlphaFold accelerates the experimental protein structure determination because the program-generated structures can be excellent starting models for experimental structure determination. In contrast, these AI programs use only information based on amino acid sequences. They cannot predict complex structures and conformational changes the proteins adopt while interacting with other proteins or performing vital biological processes. In this review, we have discussed the significance of AlphaFold in structural biology.