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      A versatile Halo- and SNAP-tagged BMP/TGFβ receptor library for quantification of cell surface ligand binding

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          Abstract

          TGFβs, BMPs and Activins regulate numerous developmental and homeostatic processes and signal through hetero-tetrameric receptor complexes composed of two types of serine/threonine kinase receptors. Each of the 33 different ligands possesses unique affinities towards specific receptor types. However, the lack of specific tools hampered simultaneous testing of ligand binding towards all BMP/TGFβ receptors. Here we present a N-terminally Halo- and SNAP-tagged TGFβ/BMP receptor library to visualize receptor complexes in dual color. In combination with fluorescently labeled ligands, we established a Ligand Surface Binding Assay (LSBA) for optical quantification of receptor-dependent ligand binding in a cellular context. We highlight that LSBA is generally applicable to test (i) binding of different ligands such as Activin A, TGFβ1 and BMP9, (ii) for mutant screens and (iii) evolutionary comparisons. This experimental set-up opens opportunities for visualizing ligand-receptor binding dynamics, essential to determine signaling specificity and is easily adaptable for other receptor signaling pathways.

          Abstract

          A TGFβ/BMP receptor library with Halo- and SNAP-tags is generated and with the application of labeled growth factor ligands, a ligand surface binding assay is developed, allowing assessment of preferential ligand-receptor binding in cells.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

            The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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              HaloTag: a novel protein labeling technology for cell imaging and protein analysis.

              We have designed a modular protein tagging system that allows different functionalities to be linked onto a single genetic fusion, either in solution, in living cells, or in chemically fixed cells. The protein tag (HaloTag) is a modified haloalkane dehalogenase designed to covalently bind to synthetic ligands (HaloTag ligands). The synthetic ligands comprise a chloroalkane linker attached to a variety of useful molecules, such as fluorescent dyes, affinity handles, or solid surfaces. Covalent bond formation between the protein tag and the chloroalkane linker is highly specific, occurs rapidly under physiological conditions, and is essentially irreversible. We demonstrate the utility of this system for cellular imaging and protein immobilization by analyzing multiple molecular processes associated with NF-kappaB-mediated cellular physiology, including imaging of subcellular protein translocation and capture of protein--protein and protein--DNA complexes.
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                Author and article information

                Contributors
                petra.knaus@fu-berlin.de
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                12 January 2023
                12 January 2023
                2023
                : 6
                : 34
                Affiliations
                [1 ]Institute of Chemistry and Biochemistry - Biochemistry, Berlin, Germany
                [2 ]GRID grid.14095.39, ISNI 0000 0000 9116 4836, Berlin-Brandenburg School for Regenerative Therapies (BSRT), ; Berlin, Germany
                [3 ]GRID grid.418832.4, ISNI 0000 0001 0610 524X, Leibniz-Forschungsinstitut für Molekulare Pharmakologie, ; Berlin, Germany
                [4 ]GRID grid.5335.0, ISNI 0000000121885934, Department of Biochemistry, , University of Cambridge, ; Cambridge, UK
                Author information
                http://orcid.org/0000-0002-1757-0267
                http://orcid.org/0000-0002-4495-5779
                http://orcid.org/0000-0002-6151-9151
                http://orcid.org/0000-0001-8683-4070
                http://orcid.org/0000-0003-3084-6595
                http://orcid.org/0000-0003-2492-1667
                Article
                4388
                10.1038/s42003-022-04388-4
                9837045
                36635368
                ea73b4a1-3af4-4102-a607-6d19adcd7db1
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 May 2022
                : 20 December 2022
                Funding
                Funded by: Morbus Osler Society Germany Einstein Center for Regenerative Therapies, Berlin
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                cell signalling,membrane proteins,cellular imaging
                cell signalling, membrane proteins, cellular imaging

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