@article {70054,
	title = {Design of Orthogonal Far-Red, Orange and Green Fluorophore-binding Proteins for Multiplex Imaging},
	journal = {bioRxiv},
	year = {2025},
	month = {2025 Aug 03},
	abstract = {<p>Fluorescent proteins have transformed biological imaging, yet their limited photostability and brightness restrict their applications. We used deep learning-based de novo protein design methods to design binders to three bright, stable and cell-permeable dyes spanning the imaging spectrum: JF657 (far red), JF596 (orange-red) and JF494 (green). We obtain highly specific dye-binding proteins with low nanomolar affinities for the intended target; a crystal structure of one binder confirms close resemblance to the design model. Simultaneous labeling of mammalian cells expressing three dye-specific binders at different subcellular compartments demonstrates the utility in multiplex imaging. We further expand the functionality of the binder by incorporating an active site that carries out nucleophilic aromatic substitution to form a covalent linkage with the dye, and develop split versions which reconstitute fluorescence at subcellular locations where both halves are present towards monitoring in-cell protein interactions and chemically induced dimerization. Our designed high affinity and specificity dye binders open up new opportunities for multiplexed biological imaging.</p>
},
	doi = {10.1101/2025.08.03.668343},
	url = {https://www.biorxiv.org/content/early/2025/08/03/2025.08.03.668343},
	author = {Tran, Long and Sharma, Shajesh and Klein, Steffen and Jurgens, David and Decarreau, Justin and Liu, Bingxu and Wang, Yujia and Bera, Asim K. and Kang, Alex and Woods, Jon and Joyce, Emily and Vafeados, Dionne K and Roullier, Nicole and Chen, Wei and Lee, Gyu Rie and Mahamid, Julia and Lavis, Luke D. and An, Linna and Baker, David}
}
@article {67313,
	title = {Locating Macromolecular Assemblies in Cells by 2D Template Matching with TEM.},
	journal = {eLife},
	volume = {10},
	year = {2021},
	month = {2021 Jun 11},
	abstract = {<p>For a more complete understanding of molecular mechanisms, it is important to study macromolecules and their assemblies in the broader context of the cell. This context can be visualized at nanometer resolution in three dimensions (3D) using electron cryo-tomography, which requires tilt series to be recorded and computationally aligned, currently limiting throughput. Additionally, the high-resolution signal preserved in the raw tomograms is currently limited by a number of technical difficulties, leading to an increased false-positive detection rate when using 3D template matching to find molecular complexes in tomograms. We have recently described a 2D template matching approach that addresses these issues by including high-resolution signal preserved in single-tilt images. A current limitation of this approach is the high computational cost that limits throughput. We describe here a GPU-accelerated implementation of 2D template matching in the image processing software TEM that allows for easy scaling and improves the accessibility of this approach. We apply 2D template matching to identify ribosomes in images of frozen-hydrated cells with high precision and sensitivity, demonstrating that this is a versatile tool for visual proteomics and structure determination. We benchmark the results with 3D template matching of tomograms acquired on identical sample locations and identify strengths and weaknesses of both techniques, which offer complementary information about target localization and identity.</p>
},
	issn = {2050-084X},
	doi = {10.7554/eLife.68946},
	author = {Lucas, Bronwyn A and Himes, Benjamin A and Xue, Liang and Grant, Tim and Mahamid, Julia and Grigorieff, Nikolaus}
}
