4.7 Article

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

期刊

BIOMOLECULES
卷 12, 期 8, 页码 -

出版社

MDPI
DOI: 10.3390/biom12081032

关键词

Myo2p; organelle transport; molecular motor; cytoskeleton; globular tail domain (GTD); protein-protein interaction; protein structure prediction

资金

  1. National Natural Science Foundation of China [31971131, 31870757, 32170697]
  2. Shenzhen Science and Technology Program [RCJC20210609104333007]
  3. Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions [2021SHIBS0002]
  4. Shenzhen Science and Technology Innovation Commission [JCYJ20200109141241950]

向作者/读者索取更多资源

The study predicts the complex structures of Myo2-GTD and its cargo adaptors using ColabFold and summarizes the versatile cargo-recognition mechanisms of Myo2 by comparing the interaction details of multiple complexes. The research provides an efficient solution for studying protein-protein interactions.
Myo2, a yeast class V myosin, transports a broad range of organelles and plays important roles in various cellular processes, including cell division in budding yeast. Despite the fact that several structures of Myo2/cargo adaptor complexes have been determined, the understanding of the versatile cargo-binding modes of Myo2 is still very limited, given the large number of cargo adaptors identified for Myo2. Here, we used ColabFold, an AlphaFold2-powered and easy-to-use tool, to predict the complex structures of Myo2-GTD and its several cargo adaptors. After benchmarking the prediction strategy with three Myo2/cargo adaptor complexes that have been determined previously, we successfully predicted the atomic structures of Myo2-GTD in complex with another three cargo adaptors, Vac17, Kar9 and Peat, which were confirmed by our biochemical characterizations. By systematically comparing the interaction details of the six complexes of Myo2 and its cargo adaptors, we summarized the cargo-binding modes on the three conserved sites of Myo2-GTD, providing an overall picture of the versatile cargo-recognition mechanisms of Myo2. In addition, our study demonstrates an efficient and effective solution to study protein-protein interactions in the future via the AlphaFold2-powered prediction.

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