PLoS BiologyCell BiologyPeer-reviewed journal article
Artificial Intelligence-Enabled AlphaFold II Pipeline Guides Functional Fluorescence Labeling of Tubulin Across Species
Kaiming XuiD, Zhiyuan LiiD, Linfan Mao, Zhengyang GuoiD, Zhe CheniD, Yongping ChaiiD, Chao XieiD, Xuerui YangiD, Jie NaiD, Wei LiiD, Guangshuo OuiD
Abstract
Dynamic properties are essential for microtubule (MT) physiology. Current techniques for in vivo imaging of MTs present intrinsic limitations in elucidating the isotype-specific nuances of tubulins, which contribute to their versatile functions. Harnessing the power of AlphaFold II pipeline, we engineered a strategy for the minimally invasive fluorescence labeling of endogenous tubulin isotypes or those harboring missense mutations. We demonstrated that a specifically designed 16-amino acid linker, coupled with sfGFP11 from the split-sfGFP system and integration into the H1-S2 loop of tubulin, facilitated tubulin labeling without compromising MT dynamics, embryonic development, or ciliogenesis in C. elegans. Extending this technique to human cells and murine oocytes, we visualized MTs with the minimal background fluorescence and a pathogenic tubulin isoform with fidelity. The utility of our approach across biological contexts and species set an additional paradigm for studying tubulin dynamics and functional specificity, with implications for understanding tubulin-related diseases known as tubulinopathies.
- organism: C. elegans
- organism: human
- organism: mouse
- assay: fluorescence / live-cell microscopy
- research focus: cell type annotation
- research focus: protein folding & dynamics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: experimental assay
- Citations
- 24
- FWCI
- 3.50
- Field percentile
- top 6%
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