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      Evolutionary activation of acidic chitinase in herbivores through the H128R mutation in ruminant livestock

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          Summary

          Placental mammals' ancestors were insectivores, suggesting that modern mammals may have inherited the ability to digest insects. Acidic chitinase (Chia) is a crucial enzyme hydrolyzing significant component of insects' exoskeleton in many species. On the other hand, herbivorous animal groups, such as cattle, have extremely low chitinase activity compared to omnivorous species, e.g., mice. The low activity of cattle Chia has been attributed to R128H mutation. The presence of either of these amino acids correlates with the feeding behavior of different bovid species with R and H determining the high and low enzymatic activity, respectively. Evolutionary analysis indicated that selective constraints were relaxed in 67 herbivorous Chia in Cetartiodactyla. Despite searching for another Chia paralog that could compensate for the reduced chitinase activity, no active paralogs were found in this order. Herbivorous animals' Chia underwent genetic alterations and evolved into a molecule with low activity due to the chitin-free diet.

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          Highlights

          • Herbivorous cattle have low chitinase activity due to R128H mutation

          • Insect-eating bovids have high chitinase activity due to the retention of R128

          • No active Chia paralogs were found in herbivorous Cetartiodactyla species

          • Selective constraints were relaxed in 67 herbivorous Chia genes in Cetartiodactyla

          Abstract

          Evolutionary biology; Molecular biology; Zoology.

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          Most cited references51

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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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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              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                03 July 2023
                18 August 2023
                03 July 2023
                : 26
                : 8
                : 107254
                Affiliations
                [1 ]Department of Chemistry and Life Science, Kogakuin University, Hachioji, Tokyo 192-0015, Japan
                [2 ]Research Fellow of Japan Society for the Promotion of Science (PD), Koujimachi, Chiyoda-ku, Tokyo 102-0083, Japan
                [3 ]Bioinova a.s., Videnska 1083, 142 00 Prague, Czech Republic
                Author notes
                []Corresponding author f-oyama@ 123456cc.kogakuin.ac.jp
                [4]

                Lead contact

                Article
                S2589-0042(23)01331-7 107254
                10.1016/j.isci.2023.107254
                10368815
                ae4033ae-48c6-477c-8049-712209b1d62e
                © 2023 The Author(s)

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

                History
                : 14 November 2022
                : 4 May 2023
                : 27 June 2023
                Categories
                Article

                evolutionary biology,molecular biology,zoology
                evolutionary biology, molecular biology, zoology

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