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      PTX3 structure determination using a hybrid cryoelectron microscopy and AlphaFold approach offers insights into ligand binding and complement activation

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          Significance

          Long pentraxins are soluble pattern-recognition molecules. There is a wealth of functional data linking them to processes as varied as innate immune defense, female fertility, and neurobiology. However, structural information is missing. Here, we present a complete high-resolution model of a long pentraxin, PTX3, using a hybrid structural biology approach combining cryoelectron microscopy, mass spectrometry, and AlphaFold-based artificial intelligence structure prediction, which was able to model flexible regions of the complex. This gives insights into the functions of PTX3, such as immune defense, as well as exemplifying a method to resolving flexible domains of protein complexes.

          Abstract

          Pattern recognition molecules (PRMs) form an important part of innate immunity, where they facilitate the response to infections and damage by triggering processes such as inflammation. The pentraxin family of soluble PRMs comprises long and short pentraxins, with the former containing unique N-terminal regions unrelated to other proteins or each other. No complete high-resolution structural information exists about long pentraxins, unlike the short pentraxins, where there is an abundance of both X-ray and cryoelectron microscopy (cryo-EM)-derived structures. This study presents a high-resolution structure of the prototypical long pentraxin, PTX3. Cryo-EM yielded a 2.5-Å map of the C-terminal pentraxin domains that revealed a radically different quaternary structure compared to other pentraxins, comprising a glycosylated D4 symmetrical octameric complex stabilized by an extensive disulfide network. The cryo-EM map indicated α-helices that extended N terminal of the pentraxin domains that were not fully resolved. AlphaFold was used to predict the remaining N-terminal structure of the octameric PTX3 complex, revealing two long tetrameric coiled coils with two hinge regions, which was validated using classification of cryo-EM two-dimensional averages. The resulting hybrid cryo-EM/AlphaFold structure allowed mapping of ligand binding sites, such as C1q and fibroblast growth factor-2, as well as rationalization of previous biochemical data. Given the relevance of PTX3 in conditions ranging from COVID-19 prognosis, cancer progression, and female infertility, this structure could be used to inform the understanding and rational design of therapies for these disorders and processes.

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          Most cited references54

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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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            UCSF ChimeraX : Structure visualization for researchers, educators, and developers

            UCSF ChimeraX is the next-generation interactive visualization program from the Resource for Biocomputing, Visualization, and Informatics (RBVI), following UCSF Chimera. ChimeraX brings (a) significant performance and graphics enhancements; (b) new implementations of Chimera's most highly used tools, many with further improvements; (c) several entirely new analysis features; (d) support for new areas such as virtual reality, light-sheet microscopy, and medical imaging data; (e) major ease-of-use advances, including toolbars with icons to perform actions with a single click, basic "undo" capabilities, and more logical and consistent commands; and (f) an app store for researchers to contribute new tools. ChimeraX includes full user documentation and is free for noncommercial use, with downloads available for Windows, Linux, and macOS from https://www.rbvi.ucsf.edu/chimerax.
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              ColabFold: making protein folding accessible to all

              ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                8 August 2022
                16 August 2022
                8 August 2022
                : 119
                : 33
                : e2208144119
                Affiliations
                [1] aDepartment of Cell and Chemical Biology, Leiden University Medical Center , 2300 RC Leiden, The Netherlands;
                [2] bDepartment of Immunology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands;
                [3] cCenter for Proteomics and Metabolomics, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands
                Author notes
                1To whom correspondence may be addressed. Email: t.sharp@ 123456lumc.nl .

                Edited by Hao Wu, Harvard Medical School, Boston, MA; received May 16, 2022; accepted July 6, 2022

                Author contributions: D.P.N. and T.H.S. designed research; D.P.N., D.J.D., T.T.v.d.K., P.A.v.V., A.H.d.R., and P.J.H. performed research; D.P.N., D.J.D., and L.A.T. contributed new reagents/analytic tools; D.P.N., P.A.v.V., A.H.d.R., P.J.H., and T.H.S. analyzed data; and D.P.N. and T.H.S. wrote the paper.

                Author information
                https://orcid.org/0000-0003-3349-5591
                https://orcid.org/0000-0002-3193-5445
                https://orcid.org/0000-0002-1990-2333
                Article
                202208144
                10.1073/pnas.2208144119
                9388099
                35939690
                2cd039a1-1e72-484d-8865-3c2dba013ee6
                Copyright © 2022 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 06 July 2022
                Page count
                Pages: 10
                Funding
                Funded by: EC | ERC | HORIZON EUROPE European Research Council (ERC) 100019180
                Award ID: 759517
                Award Recipient : Thomas H Sharp
                Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) 501100003246
                Award ID: OCENW.KLEIN.291
                Award Recipient : Thomas H Sharp
                Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) 501100003246
                Award ID: VI.Vidi.193.014
                Award Recipient : Thomas H Sharp
                Categories
                420
                Biological Sciences
                Immunology and Inflammation

                long pentraxin,complement,alphafold,cryoem,covid19
                long pentraxin, complement, alphafold, cryoem, covid19

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