Cargo Recognition Mechanisms of Yeast Myo2 Revealed by AlphaFold2-Powered Protein Complex Prediction

Autoinhibition and activation of myosin VI revealed by its cryo-EM structure

Myosin VI is the only molecular motor that moves towards the minus end along actin filaments. Numerous cellular processes require myosin VI and tight regulations of the motor's activity. Defects in myosin VI activity are known to cause genetic diseases such as deafness and cardiomyopathy. However, the molecular mechanisms underlying the activity regulation of myosin VI remain elusive. Here, we determined the high-resolution cryo-electron microscopic structure of myosin VI in its autoinhibited state. Our structure reveals that autoinhibited myosin VI adopts a compact, monomeric conformation via extensive interactions between the head and tail domains, orchestrated by an elongated single-α-helix region resembling a "spine". This autoinhibited structure effectively blocks cargo binding sites and represses the motor's ATPase activity. Certain cargo adaptors such as GIPC can release multiple inhibitory interactions and promote motor activity, pointing to a cargo-mediated activation of the processive motor. Moreover, our structural findings allow rationalization of disease-associated mutations in myosin VI. Beyond the activity regulation mechanisms of myosin VI, our study also sheds lights on how activities of other myosin motors such as myosin VII and X might be regulated.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.