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      Structure and function of the ecto-nucleotide pyrophosphatase/phosphodiesterase (ENPP) family: Tidying up diversity

      review-article
      1 , 2 , 1 , 2 , 2 , 1 , 2 ,
      The Journal of Biological Chemistry
      American Society for Biochemistry and Molecular Biology
      structure–function, phospholipase, phosphodiesterases, signaling, drug development, autotaxin, pyrophosphate, mineralization, cancer, AP, alkaline phosphatase, ATX, autotaxin, cGAS, cyclic GMP-AMP synthase, CPPD, calcium pyrophosphate dehydrate, ENPP, ecto-nucleotide pyrophosphatase/phosphodiesterase, GPC, glycerophosphocholine, GPCR, G protein-coupled receptor, GPI, glycosylphosphatidylinositol, LPA, lysophosphatidic acid, LPC, lysophosphatidylcholine, lysoPLD, lysophospholipase D, NAD, nicotinamide adenine dinucleotide, NUC, nuclease-like domain, PAF, platelet-activating factor, PDE, phosphodiesterase, PLC, phospholipase C, PPi, inorganic pyrophosphate, SM, sphingomyelin, STING, stimulator of interferon genes

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          Abstract

          Ecto-nucleotide pyrophosphatase/phosphodiesterase (ENPP) family members (ENPP1–7) have been implicated in key biological and pathophysiological processes, including nucleotide and phospholipid signaling, bone mineralization, fibrotic diseases, and tumor-associated immune cell infiltration. ENPPs are single-pass transmembrane ecto-enzymes, with notable exceptions of ENPP2 (Autotaxin) and ENNP6, which are secreted and glycosylphosphatidylinositol (GPI)-anchored, respectively. ENNP1 and ENNP2 are the best characterized and functionally the most interesting members. Here, we review the structural features of ENPP1–7 to understand how they evolved to accommodate specific substrates and mediate different biological activities. ENPPs are defined by a conserved phosphodiesterase (PDE) domain. In ENPP1–3, the PDE domain is flanked by two N-terminal somatomedin B-like domains and a C-terminal inactive nuclease domain that confers structural stability, whereas ENPP4–7 only possess the PDE domain. Structural differences in the substrate-binding site endow each protein with unique characteristics. Thus, ENPP1, ENPP3, ENPP4, and ENPP5 hydrolyze nucleotides, whereas ENPP2, ENPP6, and ENNP7 evolved as phospholipases through adaptions in the catalytic domain. These adaptations explain the different biological and pathophysiological functions of individual members. Understanding the ENPP members as a whole advances our insights into common mechanisms, highlights their functional diversity, and helps to explore new biological roles.

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          Most cited references88

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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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              CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

              The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.
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                Author and article information

                Contributors
                Journal
                J Biol Chem
                J Biol Chem
                The Journal of Biological Chemistry
                American Society for Biochemistry and Molecular Biology
                0021-9258
                1083-351X
                24 December 2021
                February 2022
                24 December 2021
                : 298
                : 2
                : 101526
                Affiliations
                [1 ]Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
                [2 ]Division of Biochemistry, The Netherlands Cancer Institute, Amsterdam, The Netherlands
                Author notes
                []For correspondence: Anastassis Perrakis a.perrakis@ 123456nki.nl
                Article
                S0021-9258(21)01336-3 101526
                10.1016/j.jbc.2021.101526
                8808174
                34958798
                b4e03a89-4b0f-481c-931b-62e7b466cc7b
                © 2021 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 1 October 2021
                : 15 December 2021
                Categories
                JBC Reviews

                Biochemistry
                structure–function,phospholipase,phosphodiesterases,signaling,drug development,autotaxin,pyrophosphate,mineralization,cancer,ap, alkaline phosphatase,atx, autotaxin,cgas, cyclic gmp-amp synthase,cppd, calcium pyrophosphate dehydrate,enpp, ecto-nucleotide pyrophosphatase/phosphodiesterase,gpc, glycerophosphocholine,gpcr, g protein-coupled receptor,gpi, glycosylphosphatidylinositol,lpa, lysophosphatidic acid,lpc, lysophosphatidylcholine,lysopld, lysophospholipase d,nad, nicotinamide adenine dinucleotide,nuc, nuclease-like domain,paf, platelet-activating factor,pde, phosphodiesterase,plc, phospholipase c,ppi, inorganic pyrophosphate,sm, sphingomyelin,sting, stimulator of interferon genes

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