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      Neuronal pentraxins as biomarkers of synaptic activity: from physiological functions to pathological changes in neurodegeneration

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          Abstract

          The diagnosis of neurodegenerative disorders is often challenging due to the lack of diagnostic tools, comorbidities and shared pathological manifestations. Synaptic dysfunction is an early pathological event in many neurodegenerative disorders, but the underpinning mechanisms are still poorly characterised. Reliable quantification of synaptic damage is crucial to understand the pathophysiology of neurodegeneration, to track disease status and to obtain prognostic information. Neuronal pentraxins (NPTXs) are extracellular scaffolding proteins emerging as potential biomarkers of synaptic dysfunction in neurodegeneration. They are a family of proteins involved in homeostatic synaptic plasticity by recruiting post-synaptic receptors into synapses. Recent research investigates the dynamic changes of NPTXs in the cerebrospinal fluid (CSF) as an expression of synaptic damage, possibly related to cognitive impairment. In this review, we summarise the available data on NPTXs structure and expression patterns as well as on their contribution in synaptic function and plasticity and other less well-characterised roles. Moreover, we propose a mechanism for their involvement in synaptic damage and neurodegeneration and assess their potential as CSF biomarkers for neurodegenerative diseases.

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          Most cited references176

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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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            SWISS-MODEL: homology modelling of protein structures and complexes

            Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.
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              UniProt: the universal protein knowledgebase in 2021

              (2020)
              Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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                Author and article information

                Contributors
                markus.otto@uni-ulm.de , markus.otto@uk-halle.de
                Journal
                J Neural Transm (Vienna)
                J Neural Transm (Vienna)
                Journal of Neural Transmission
                Springer Vienna (Vienna )
                0300-9564
                1435-1463
                30 August 2021
                30 August 2021
                2022
                : 129
                : 2
                : 207-230
                Affiliations
                [1 ]GRID grid.6582.9, ISNI 0000 0004 1936 9748, Department of Neurology, , University of Ulm, ; Ulm, Germany
                [2 ]GRID grid.5606.5, ISNI 0000 0001 2151 3065, Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), , University of Genoa, ; Genoa, Italy
                [3 ]GRID grid.424247.3, ISNI 0000 0004 0438 0426, German Center for Neurodegenerative Diseases (DZNE E.V.), ; Ulm, Germany
                [4 ]GRID grid.9018.0, ISNI 0000 0001 0679 2801, Department of Neurology, , Martin-Luther-University Halle-Wittenberg, ; Ernst-Grube-Str. 40, 06120 Halle (Saale), Germany
                Author information
                http://orcid.org/0000-0003-0319-7374
                http://orcid.org/0000-0001-5667-204X
                http://orcid.org/0000-0002-8711-5702
                http://orcid.org/0000-0002-7652-7023
                http://orcid.org/0000-0001-6697-6522
                http://orcid.org/0000-0003-4273-4267
                Article
                2411
                10.1007/s00702-021-02411-2
                8866268
                34460014
                84b32390-0075-447d-b2e0-45f641f5697d
                © The Author(s) 2021

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 June 2021
                : 17 August 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010665, h2020 marie skłodowska-curie actions;
                Award ID: 860197
                Funded by: Universität Ulm (1055)
                Categories
                Neurology and Preclinical Neurological Studies - Review Article
                Custom metadata
                © Springer-Verlag GmbH Austria, part of Springer Nature 2022

                neuronal pentraxin,biomarker,synapse,synaptic function,neurodegeneration,cerebrospinal fluid

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