Project description:Automatic single particle picking is a critical step in the data processing pipeline of cryo-electron microscopy structure reconstruction. In recent years, several deep learning-based algorithms have been developed, demonstrating their potential to solve this challenge. However, current methods highly depend on manually labeled training data, which is labor-intensive and prone to biases especially for high-noise and low-contrast micrographs, resulting in suboptimal precision and recall. To address these problems, we propose UPicker, a semi-supervised transformer-based particle-picking method with a two-stage training process: unsupervised pretraining and supervised fine-tuning. During the unsupervised pretraining, an Adaptive Laplacian of Gaussian region proposal generator is proposed to obtain pseudo-labels from unlabeled data for initial feature learning. For the supervised fine-tuning, UPicker only needs a small amount of labeled data to achieve high accuracy in particle picking. To further enhance model performance, UPicker employs a contrastive denoising training strategy to reduce redundant detections and accelerate convergence, along with a hybrid data augmentation strategy to deal with limited labeled data. Comprehensive experiments on both simulated and experimental datasets demonstrate that UPicker outperforms state-of-the-art particle-picking methods in terms of accuracy and robustness while requiring fewer labeled data than other transformer-based models. Furthermore, ablation studies demonstrate the effectiveness and necessity of each component of UPicker. The source code and data are available at https://github.com/JachyLikeCoding/UPicker.
Project description:Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise (SNR) ratios. To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labelled cryo-EM protein particle dataset - CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score and is poised to facilitate the automation of the cryo-EM protein particle picking.
Project description:The dynamic tyrosination-detyrosination cycle of α-tubulin regulates microtubule functions. Perturbation of this cycle impairs mitosis, neural physiology, and cardiomyocyte contraction. The carboxypeptidases vasohibins 1 and 2 (VASH1 and VASH2), in complex with the small vasohibin-binding protein (SVBP), mediate α-tubulin detyrosination. These enzymes detyrosinate microtubules more efficiently than soluble αβ-tubulin heterodimers. The structural basis for this substrate preference is not understood. Using cryo-electron microscopy (cryo-EM), we have determined the structure of human VASH1-SVBP bound to microtubules. The acidic C-terminal tail of α-tubulin binds to a positively charged groove near the active site of VASH1. VASH1 forms multiple additional contacts with the globular domain of α-tubulin, including contacts with a second α-tubulin in an adjacent protofilament. Simultaneous engagement of two protofilaments by VASH1 can only occur within the microtubule lattice, but not with free αβ heterodimers. These lattice-specific interactions enable preferential detyrosination of microtubules by VASH1.
Project description:The molecular motor kinesin moves along microtubules using energy from ATP hydrolysis in an initial step coupled with ADP release. In neurons, kinesin-1/KIF5C preferentially binds to the GTP-state microtubules over GDP-state microtubules to selectively enter an axon among many processes; however, because the atomic structure of nucleotide-free KIF5C is unavailable, its molecular mechanism remains unresolved. Here, the crystal structure of nucleotide-free KIF5C and the cryo-electron microscopic structure of nucleotide-free KIF5C complexed with the GTP-state microtubule are presented. The structures illustrate mutual conformational changes induced by interaction between the GTP-state microtubule and KIF5C. KIF5C acquires the 'rigor conformation', where mobile switches I and II are stabilized through L11 and the initial portion of the neck-linker, facilitating effective ADP release and the weak-to-strong transition of KIF5C microtubule affinity. Conformational changes to tubulin strengthen the longitudinal contacts of the GTP-state microtubule in a similar manner to GDP-taxol microtubules. These results and functional analyses provide the molecular mechanism of the preferential binding of KIF5C to GTP-state microtubules.
Project description:Microtubules are polymers of αβ-tubulin heterodimers essential for all eukaryotes. Despite sequence conservation, there are significant structural differences between microtubules assembled in vitro from mammalian or budding yeast tubulin. Yeast MTs were not observed to undergo compaction at the interdimer interface as seen for mammalian microtubules upon GTP hydrolysis. Lack of compaction might reflect slower GTP hydrolysis or a different degree of allosteric coupling in the lattice. The microtubule plus end-tracking protein Bim1 binds yeast microtubules both between αβ-tubulin heterodimers, as seen for other organisms, and within tubulin dimers, but binds mammalian tubulin only at interdimer contacts. At the concentrations used in cryo-electron microscopy, Bim1 causes the compaction of yeast microtubules and induces their rapid disassembly. Our studies demonstrate structural differences between yeast and mammalian microtubules that likely underlie their differing polymerization dynamics. These differences may reflect adaptations to the demands of different cell size or range of physiological growth temperatures.
Project description:Microtubules are hollow α/β-tubulin heterodimeric polymers that play critical roles in cells. In vertebrates, both α- and β-tubulins have multiple isotypes encoded by different genes, which are intrinsic factors in regulating microtubule functions. However, the structures of microtubules composed of different tubulin isotypes, especially α-tubulin isotypes, remain largely unknown. Here, we purify recombinant tubulin heterodimers composed of different mouse α-tubulin isotypes, including α1A, α1C and α4A, with the β-tubulin isotype β2A. We further assemble and determine the cryo-electron microscopy (cryo-EM) structures of α1A/β2A, α1C/β2A, and α4A/β2A microtubules. Our structural analysis demonstrates that α4A/β2A microtubules exhibit longitudinal contraction between tubulin interdimers compared with α1A/β2A and α1C/β2A microtubules. Collectively, our findings reveal that α-tubulin isotype composition can tune microtubule structures, and also provide evidence for the "tubulin code" hypothesis.