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      Thermophiles and carbohydrate-active enzymes (CAZymes) in biofilm microbial consortia that decompose lignocellulosic plant litters at high temperatures

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          Abstract

          The SKY hot spring is a unique site filled with a thick layer of plant litter. With the advancement of next-generation sequencing, it is now possible to mine many new biocatalyst sequences. In this study, we aimed to (i) identify the metataxonomic of prokaryotes and eukaryotes in microbial mats using 16S and 18S rRNA markers, (ii) and explore carbohydrate degrading enzymes (CAZymes) that have a high potential for future applications. Green microbial mat, predominantly photosynthetic bacteria, was attached to submerged or floating leaves litter. At the spring head, the sediment mixture consisted of plant debris, predominantly brownish-reddish gelatinous microbial mat, pale tan biofilm, and grey-white filament biofilm. The population in the spring head had a higher percentage of archaea and hyperthermophiles than the green mat. Concurrently, we cataloged nearly 10,000 sequences of CAZymes in both green and brown biofilms using the shotgun metagenomic sequencing approach. These sequences include β-glucosidase, cellulase, xylanase, α-N-arabinofuranosidase, α- l-arabinofuranosidase, and other CAZymes. In conclusion, this work elucidated that SKY is a unique hot spring due to its rich lignocellulosic material, often absent in other hot springs. The data collected from this study serves as a repository of new thermostable macromolecules, in particular families of glycoside hydrolases.

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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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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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              Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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                Author and article information

                Contributors
                gohkianmau@utm.my
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 February 2022
                18 February 2022
                2022
                : 12
                : 2850
                Affiliations
                [1 ]GRID grid.410877.d, ISNI 0000 0001 2296 1505, Faculty of Science, , Universiti Teknologi Malaysia, ; 81310 Skudai, Johor Malaysia
                [2 ]GRID grid.11875.3a, ISNI 0000 0001 2294 3534, School of Biological Sciences, , Universiti Sains Malaysia, ; 11800, Gelugor, Pulau Pinang Malaysia
                [3 ]GRID grid.10347.31, ISNI 0000 0001 2308 5949, Division of Genetics and Molecular Biology, Institute of Biological Sciences, Faculty of Science, , University of Malaya, ; 50603 Kuala Lumpur, Malaysia
                [4 ]GRID grid.444483.b, ISNI 0000 0001 0694 3091, Faculty of Applied Sciences and Technology, , Universiti Tun Hussein Onn Malaysia, ; 84600 Pagoh, Johor Malaysia
                Article
                6943
                10.1038/s41598-022-06943-9
                8857248
                35181739
                fa9a6498-89ca-42b6-9448-54d4c9f5e169
                © The Author(s) 2022

                Open Access This 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
                : 24 October 2021
                : 9 February 2022
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                microbial ecology,biofilms,bioinformatics,water microbiology
                Uncategorized
                microbial ecology, biofilms, bioinformatics, water microbiology

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