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      Structure of fish Toll-like receptors (TLR) and NOD-like receptors (NLR)

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          Abstract

          Innate immunity driven by pattern recognition receptor (PRR) protects the host from invading pathogens. Aquatic animals like fish where the adaptive immunity is poorly developed majorly rely on their innate immunity modulated by PRRs like toll-like receptors (TLR) and NOD-like receptors (NLR). However, current development to improve the fish immunity via TLR/NLR signaling is affected by a poor understanding of its mechanistic and structural features. This review discusses the structure of fish TLRs/NLRs and its interaction with pathogen associated molecular patterns (PAMPs) and downstream signaling molecules. Over the past one decade, significant progress has been done in studying the structure of TLRs/NLRs in higher eukaryotes; however, structural studies on fish innate immune receptors are undermined. Several novel TLR genes are identified in fish that are absent in higher eukaryotes, but the function is still poorly understood. Unlike the fundamental progress achieved in developing antagonist/agonist to modulate human innate immunity, analogous studies in fish are nearly lacking due to structural inadequacy. This underlies the importance of exploring the structural and mechanistic details of fish TLRs/NLRs at an atomic and molecular level. This review outlined the mechanistic and structural basis of fish TLR and NLR activation.

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          Improved protein-ligand docking using GOLD.

          The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol. Copyright 2003 Wiley-Liss, Inc.
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            Molecular Dynamics Simulation for All

            The impact of molecular dynamics (MD) simulations in molecular biology and drug discovery has expanded dramatically in recent years. These simulations capture the behavior of proteins and other biomolecules in full atomic detail and at very fine temporal resolution. Major improvements in simulation speed, accuracy, and accessibility, together with the proliferation of experimental structural data, have increased the appeal of biomolecular simulation to experimentalists—a trend particularly noticeable in , though certainly not limited to, neuroscience. Simulations have proven valuable in deciphering functional mechanisms of proteins and other biomolecules, in uncovering the structural basis for disease, and in the design and optimization of small molecules, peptides, and proteins. Here we describe in practical terms the types of information MD simulations can provide and the ways in which they typically motivate further experimental work.
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              A fast flexible docking method using an incremental construction algorithm.

              We present an automatic method for docking organic ligands into protein binding sites. The method can be used in the design process of specific protein ligands. It combines an appropriate model of the physico-chemical properties of the docked molecules with efficient methods for sampling the conformational space of the ligand. If the ligand is flexible, it can adopt a large variety of different conformations. Each such minimum in conformational space presents a potential candidate for the conformation of the ligand in the complexed state. Our docking method samples the conformation space of the ligand on the basis of a discrete model and uses a tree-search technique for placing the ligand incrementally into the active site. For placing the first fragment of the ligand into the protein, we use hashing techniques adapted from computer vision. The incremental construction algorithm is based on a greedy strategy combined with efficient methods for overlap detection and for the search of new interactions. We present results on 19 complexes of which the binding geometry has been crystallographically determined. All considered ligands are docked in at most three minutes on a current workstation. The experimentally observed binding mode of the ligand is reproduced with 0.5 to 1.2 A rms deviation. It is almost always found among the highest-ranking conformations computed.
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                Author and article information

                Contributors
                Journal
                Int J Biol Macromol
                Int. J. Biol. Macromol
                International Journal of Biological Macromolecules
                Published by Elsevier B.V.
                0141-8130
                1879-0003
                2 August 2020
                2 August 2020
                Affiliations
                Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
                Article
                S0141-8130(20)34052-6
                10.1016/j.ijbiomac.2020.07.293
                7396143
                32755705
                9b491367-62b2-4798-b56f-5e45987ca46f
                © 2020 Published by Elsevier B.V.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 19 June 2020
                : 26 July 2020
                : 27 July 2020
                Categories
                Article

                Biochemistry
                toll-like receptor,nod-like receptor,innate immunity
                Biochemistry
                toll-like receptor, nod-like receptor, innate immunity

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