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      Biophysical characterization of the homodimers of HomA and HomB, outer membrane proteins of Helicobacter pylori

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          Abstract

          Helicobacter pylori is a Gram-negative bacterium that causes chronic inflammations in the stomach area and is involved in ulcers, which can develop into gastric malignancies. H. pylori attaches and colonizes to the human epithelium using some of their outer membrane proteins (OMPs). HomB and HomA are the most studied OMPs from H. pylori as they play a crucial role in adherence, hyper biofilm formation, antibiotic resistance and are also associated with severe gastric malignancies. The role of HomA and HomB in pathogenesis concerning their structure and function has not been evaluated yet. In the present study, we explored the structural aspect of HomA and HomB proteins using various computational, biophysical and small-angle X-ray scattering (SAXS) techniques. Interestingly, the in-silico analysis revealed that HomA/B consists of 8 discontinuous N and C terminal β-strands forming a small β-barrel, along with a large surface-exposed globular domain. Further, biophysical experiments suggested that HomA and HomB are dimeric and most likely the cysteine residues present on surface-exposed loops participate in protein–protein interactions. Our study provides essential structural information of unexplored proteins of the Hom family that can help in a better understanding of H. pylori pathogenesis.

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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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            SignalP 5.0 improves signal peptide predictions using deep neural networks

            Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
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              The I-TASSER Suite: protein structure and function prediction.

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                Author and article information

                Contributors
                pkodgire@iiti.ac.in
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                28 December 2021
                28 December 2021
                2021
                : 11
                Affiliations
                [1 ]GRID grid.450280.b, ISNI 0000 0004 1769 7721, Discipline of Biosciences and Biomedical Engineering, , Indian Institute of Technology Indore, ; Indore, Madhya Pradesh 453 552 India
                [2 ]GRID grid.418304.a, ISNI 0000 0001 0674 4228, High Pressure and Synchrotron Radiation Physics Division, , Bhabha Atomic Research Center, ; Trombay, Mumbai, India
                [3 ]GRID grid.417641.1, ISNI 0000 0004 0504 3165, Protein Science and Engineering Division, , CSIR-Institute of Microbial Technology, ; Chandigarh, India
                Article
                4039
                10.1038/s41598-021-04039-4
                8714817
                34963695
                dec90323-de0c-46d0-aa77-e44029e89698
                © 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 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/.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001411, Indian Council of Medical Research;
                Award ID: OMI/17/2020-ECD-1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001843, Science and Engineering Research Board;
                Award ID: CRG/2020/358
                Award Recipient :
                Categories
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                © The Author(s) 2021

                Uncategorized

                chemical biology, proteins, membrane proteins

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