Bor1p is a secondary transporter in yeast that is responsible for boron transport. Bor1p belongs to the SLC4 family which controls bicarbonate exchange and pH regulation in animals as well as borate uptake in plants. The SLC4 family is more distantly related to members of the Amino acid‐Polyamine‐organoCation (APC) superfamily, which includes well studied transporters such as LeuT, Mhp1, AdiC, vSGLT, UraA, SLC26Dg. Their mechanism generally involves relative movements of two domains: a core domain that binds substrate and a gate domain that in many cases mediates dimerization. To shed light on conformational changes governing transport by the SLC4 family, we grew helical membrane crystals of Bor1p from
Exocyst is an evolutionarily conserved hetero‐octameric tethering complex that plays a variety of roles in membrane trafficking, including exocytosis, endocytosis, autophagy, cell polarization, cytokinesis, pathogen invasion, and metastasis. Exocyst serves as a platform for interactions between the Rab, Rho, and Ral small GTPases, SNARE proteins, and Sec1/Munc18 regulators that coordinate spatial and temporal fidelity of membrane fusion. However, its mechanism is poorly described at the molecular level. Here, we determine the molecular architecture of the yeast exocyst complex by an integrative approach, based on a 3D density map from negative‐stain electron microscopy (EM) at ~16 Å resolution, 434 disuccinimidyl suberate and 1‐ethyl‐3‐(3‐dimethylaminopropyl)carbodiimide hydrochloride cross‐links from chemical‐crosslinking mass spectrometry, and partial atomic models of the eight subunits. The integrative structure is validated by a previously determined cryo‐EM structure, cross‐links, and distances from in vivo fluorescence microscopy. Our subunit configuration is consistent with the cryo‐EM structure, except for Sec5. While not observed in the cryo‐EM map, the integrative model localizes the N‐terminal half of Sec3 near the Sec6 subunit. Limited proteolysis experiments suggest that the conformation of Exo70 is dynamic, which may have functional implications for SNARE and membrane interactions. This study illustrates how integrative modeling based on varied low‐resolution structural data can inform biologically relevant hypotheses, even in the absence of high‐resolution data.
more » « less- NSF-PAR ID:
- 10458064
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Protein Science
- Volume:
- 29
- Issue:
- 6
- ISSN:
- 0961-8368
- Page Range / eLocation ID:
- p. 1486-1501
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Saccharomyces mikatae and determined a structure at ∼6 Å resolution using cryo‐electron microscopy. To evaluate the conformation of Bor1p in these crystals, a homology model was built based on the related anion exchanger from red blood cells (AE1). This homology model was fitted to the cryo‐EM density map using the Molecular Dynamics (MD) Flexible Fitting method and then relaxed by all‐atom MD simulation in explicit solvent and membrane. Mapping of water accessibility indicates that the resulting structure represents an inward‐facing conformation. Comparisons of the resulting Bor1p model with the X‐ray structure of AE1 in an outward‐facing conformation, together with MD simulations of inward‐facing and outward‐facing Bor1p models, suggest rigid body movements of the core domain relative to the gate domain. These movements are consistent with the rocking‐bundle transport mechanism described for other members of the APC superfamily. -
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Abstract Motivation The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3–4.5 Å), improvement in the map quality facilitates structure modeling.
Results We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3–6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools.
Availability and implementation https://github.com/kiharalab/EM-GAN, Google Colab: https://tinyurl.com/3ccxpttx.
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Abstract Sample purity is central to in vitro studies of protein function and regulation, and to the efficiency and success of structural studies using techniques such as x-ray crystallography and cryo-electron microscopy (cryo-EM). Here, we show that mass photometry (MP) can accurately characterize the heterogeneity of a sample using minimal material with high resolution within a matter of minutes. To benchmark our approach, we use negative stain electron microscopy (nsEM), a popular method for EM sample screening. We include typical workflows developed for structure determination that involve multi-step purification of a multi-subunit ubiquitin ligase and chemical cross-linking steps. When assessing the integrity and stability of large molecular complexes such as the proteasome, we detect and quantify assemblies invisible to nsEM. Our results illustrate the unique advantages of MP over current methods for rapid sample characterization, prioritization and workflow optimization.
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Abstract Background Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps.
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Abstract An increasing number of protein structures are determined by cryo‐electron microscopy (cryo‐EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo‐EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is a
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