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      Dormant spores sense amino acids through the B subunits of their germination receptors

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          Abstract

          Bacteria from the orders Bacillales and Clostridiales differentiate into stress-resistant spores that can remain dormant for years, yet rapidly germinate upon nutrient sensing. How spores monitor nutrients is poorly understood but in most cases requires putative membrane receptors. The prototypical receptor from Bacillus subtilis consists of three proteins (GerAA, GerAB, GerAC) required for germination in response to L-alanine. GerAB belongs to the Amino Acid-Polyamine-Organocation superfamily of transporters. Using evolutionary co-variation analysis, we provide evidence that GerAB adopts a structure similar to an L-alanine transporter from this superfamily. We show that mutations in gerAB predicted to disrupt the ligand-binding pocket impair germination, while mutations predicted to function in L-alanine recognition enable spores to respond to L-leucine or L-serine. Finally, substitutions of bulkier residues at these positions cause constitutive germination. These data suggest that GerAB is the L-alanine sensor and that B subunits in this broadly conserved family function in nutrient detection.

          Abstract

          Germination of Bacillus subtilis spores in response to L-alanine requires a putative membrane receptor consisting of three proteins. Here, Artzi et al. use evolutionary co-variation analysis and functional assays of mutants to provide evidence that one of the proteins, GerAB, likely acts as the L-alanine sensor.

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          Comparative Protein Structure Modeling Using MODELLER.

          Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.
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            A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core.

            The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) is a free, one-stop web service for protein bioinformatic analysis. It currently offers 34 interconnected external and in-house tools, whose functionality covers sequence similarity searching, alignment construction, detection of sequence features, structure prediction, and sequence classification. This breadth has made the Toolkit an important resource for experimental biology and for teaching bioinformatic inquiry. Recently, we replaced the first version of the Toolkit, which was released in 2005 and had served around 2.5 million queries, with an entirely new version, focusing on improved features for the comprehensive analysis of proteins, as well as on promoting teaching. For instance, our popular remote homology detection server, HHpred, now allows pairwise comparison of two sequences or alignments and offers additional profile HMMs for several model organisms and domain databases. Here, we introduce the new version of our Toolkit and its application to the analysis of proteins.
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              Hidden Markov model speed heuristic and iterative HMM search procedure

              Background Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. Results We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K. Conclusions Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST.
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                Author and article information

                Contributors
                david_rudner@hms.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                25 November 2021
                25 November 2021
                2021
                : 12
                : 6842
                Affiliations
                [1 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Microbiology, , Harvard Medical School, ; 77 Avenue Louis Pasteur, Boston, MA 02115 USA
                [2 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biological Chemistry and Molecular Pharmacology, , Harvard Medical School, ; 250 Longwood Avenue, Boston, MA 02115 USA
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Systems Biology, , Harvard Medical School, ; 200 Longwood Avenue, Boston, MA 02115 USA
                Author information
                http://orcid.org/0000-0001-8102-5290
                http://orcid.org/0000-0002-2814-5274
                http://orcid.org/0000-0002-1467-1222
                http://orcid.org/0000-0002-0236-7143
                Article
                27235
                10.1038/s41467-021-27235-2
                8617281
                34824238
                0b2757d0-55df-4f4a-bfa6-87ca4bad88aa
                © The Author(s) 2021

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

                History
                : 26 March 2021
                : 1 November 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100009559, Life Sciences Research Foundation (LSRF);
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: GM086466
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000185, United States Department of Defense | Defense Advanced Research Projects Agency (DARPA);
                Award ID: HR001117S0029
                Award Recipient :
                Funded by: Harvard Medical School Dean's Inititative Grant
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                bacterial development,bacterial genetics,bacterial structural biology
                Uncategorized
                bacterial development, bacterial genetics, bacterial structural biology

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