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      In silico secretome analyses of the polyphagous root-knot nematode Meloidogyne javanica: a resource for studying M. javanica secreted proteins

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          Abstract

          Background

          Plant-parasitic nematodes (PPNs) that cause most damage include root-knot nematodes (RKNs) which are a major impediment to crop production. Root-knot nematodes, like other parasites, secrete proteins which are required for parasite proliferation and survival within the host during the infection process.

          Results

          Here, we used various computational tools to predict and identify classically and non-classically secreted proteins encoded in the Meloidogyne javanica genome. Furthermore, functional annotation analysis was performed using various integrated bioinformatic tools to determine the biological significance of the predicted secretome. In total, 7,458 proteins were identified as secreted ones. A large percentage of this secretome is comprised of small proteins of ≤ 300 aa sequence length. Functional analyses showed that M. javanica secretome comprises cell wall degrading enzymes for facilitating nematode invasion, and migration by disintegrating the complex plant cell wall components. In addition, peptidases and peptidase inhibitors are an important category of M. javanica secretome involved in compatible host-nematode interactions.

          Conclusion

          This study identifies the putative secretome encoded in the M. javanica genome. Future experimental validation analyses can greatly benefit from this global analysis of M. javanica secretome. Equally, our analyses will advance knowledge of the interaction between plants and nematodes.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-023-09366-6.

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          Most cited references74

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          The Phyre2 web portal for protein modeling, prediction and analysis.

          Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.
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            Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

            We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/. Copyright 2001 Academic Press.
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              REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

              Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.
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                Author and article information

                Contributors
                lucy.moleleki@fabi.up.ac.za
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                1 June 2023
                1 June 2023
                2023
                : 24
                : 296
                Affiliations
                GRID grid.49697.35, ISNI 0000 0001 2107 2298, Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), , University of Pretoria, ; Pretoria, South Africa
                Article
                9366
                10.1186/s12864-023-09366-6
                10236835
                37264326
                d0a682ed-8367-4913-bf6c-394090ef32b9
                © The Author(s) 2023

                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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 24 November 2022
                : 7 May 2023
                Funding
                Funded by: National Research Fund
                Award ID: 120858
                Award ID: 120858
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Genetics
                secretome prediction,plant-nematode interaction,root-knot nematode,bioinformatics,effectors.

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