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      Type 1 secretion necessitates a tight interplay between all domains of the ABC transporter

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          Abstract

          Type I secretion systems (T1SS) facilitate the secretion of substrates in one step across both membranes of Gram-negative bacteria. A prime example is the hemolysin T1SS which secretes the toxin HlyA. Secretion is energized by the ABC transporter HlyB, which forms a complex together with the membrane fusion protein HlyD and the outer membrane protein TolC. HlyB features three domains: an N-terminal C39 peptidase-like domain (CLD), a transmembrane domain (TMD) and a C-terminal nucleotide binding domain (NBD). Here, we created chimeric transporters by swapping one or more domains of HlyB with the respective domain(s) of RtxB, a HlyB homolog from Kingella kingae. We tested all chimeric transporters for their ability to secrete pro-HlyA when co-expressed with HlyD. The CLD proved to be most critical, as a substitution abolished secretion. Swapping only the TMD or NBD reduced the secretion efficiency, while a simultaneous exchange abolished secretion. These results indicate that the CLD is the most critical secretion determinant, while TMD and NBD might possess additional recognition or interaction sites. This mode of recognition represents a hierarchical and extreme unusual case of substrate recognition for ABC transporters and optimal secretion requires a tight interplay between all domains.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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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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              Basic local alignment search tool.

              A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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                Author and article information

                Contributors
                lutz.schmitt@hhu.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 April 2024
                18 April 2024
                2024
                : 14
                : 8994
                Affiliations
                [1 ]Institute of Biochemistry, Heinrich Heine University Düsseldorf, ( https://ror.org/024z2rq82) Universitätsstraße 1, 40225 Düsseldorf, Germany
                [2 ]Center for Structural Studies, Heinrich Heine University Düsseldorf, ( https://ror.org/024z2rq82) Universitätsstraße 1, 40225 Düsseldorf, Germany
                [3 ]Present Address: INCONSULT, ( https://ror.org/005b83279) Duisburg, Germany
                Article
                59759
                10.1038/s41598-024-59759-0
                11026475
                38637678
                f973b575-51c1-4e2d-81e7-aed92fdd2944
                © The Author(s) 2024

                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
                : 27 November 2023
                : 15 April 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: Grant number 417919780
                Award ID: INST 208/761-1 FUGG
                Award ID: CRC 1208 project A01
                Award Recipient :
                Funded by: Heinrich-Heine-Universität Düsseldorf (3102)
                Categories
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                © Springer Nature Limited 2024

                Uncategorized
                type i secretion system,abc transporter,hemolysin,substrate recognition,proteins,membrane proteins

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