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      Immunoinformatics-aided design of a new multi-epitope vaccine adjuvanted with domain 4 of pneumolysin against Streptococcus pneumoniae strains

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          Abstract

          Background

          Streptococcus pneumoniae (Pneumococcus) has remained a leading cause of fatal infections such as pneumonia, meningitis, and sepsis. Moreover, this pathogen plays a major role in bacterial co-infection in patients with life-threatening respiratory virus diseases such as influenza and COVID-19. High morbidity and mortality in over one million cases, especially in very young children and the elderly, are the main motivations for pneumococcal vaccine development. Due to the limitations of the currently marketed polysaccharide-based vaccines, non-serotype-specific protein-based vaccines have received wide research interest in recent years. One step further is to identify high antigenic regions within multiple highly-conserved proteins in order to develop peptide vaccines that can affect various stages of pneumococcal infection, providing broader serotype coverage and more effective protection. In this study, immunoinformatics tools were used to design an effective multi-epitope vaccine in order to elicit neutralizing antibodies against multiple strains of pneumococcus.

          Results

          The B- and T-cell epitopes from highly protective antigens PspA (clades 1–5) and PhtD were predicted and immunodominant peptides were linked to each other with proper linkers. The domain 4 of Ply, as a potential TLR4 agonist adjuvant candidate, was attached to the end of the construct to enhance the immunogenicity of the epitope vaccine. The evaluation of the physicochemical and immunological properties showed that the final construct was stable, soluble, antigenic, and non-allergenic. Furthermore, the protein was found to be acidic and hydrophilic in nature. The protein 3D-structure was built and refined, and the Ramachandran plot, ProSA–web, ERRAT, and Verify3D validated the quality of the final model. Molecular docking analysis showed that the designed construct via Ply domain 4 had a strong interaction with TLR4. The structural stability of the docked complex was confirmed by molecular dynamics. Finally, codon optimization was performed for gene expression in E. coli, followed by in silico cloning in the pET28a(+) vector.

          Conclusion

          The computational analysis of the construct showed acceptable results, however, the suggested vaccine needs to be experimentally verified in laboratory to ensure its safety and immunogenicity.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12859-023-05175-6.

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          Most cited references120

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          SWISS-MODEL: homology modelling of protein structures and complexes

          Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.
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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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              Protein Identification and Analysis Tools on the ExPASy Server

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                Author and article information

                Contributors
                shabani@semums.ac.ir
                mousavi@pasteur.ac.ir
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                24 February 2023
                24 February 2023
                2023
                : 24
                : 67
                Affiliations
                [1 ]GRID grid.486769.2, ISNI 0000 0004 0384 8779, Department of Medical Biotechnology, Faculty of Medicine, , Semnan University of Medical Sciences, ; Semnan, Iran
                [2 ]GRID grid.486769.2, ISNI 0000 0004 0384 8779, Research Center of Biotechnology, , Semnan University of Medical Sciences, ; Semnan, Iran
                [3 ]GRID grid.420169.8, ISNI 0000 0000 9562 2611, Department of Bacteriology, , Pasteur Institute of Iran, ; Tehran, Iran
                [4 ]GRID grid.420169.8, ISNI 0000 0000 9562 2611, Drug Design and Bioinformatics Unit, Department of Medical Biotechnology, Biotechnology Research Center, , Pasteur Institute of Iran, ; Tehran, Iran
                [5 ]GRID grid.418970.3, Agricultural Research, Education, and Extension Organization (AREEO), , Razi Vaccine and Serum Research Institute, ; Karaj, Iran
                Article
                5175
                10.1186/s12859-023-05175-6
                9951839
                36829109
                85fff6c6-9a4d-4186-9218-57fc4926eaa2
                © The Author(s) 2023

                Open AccessThis 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
                : 26 December 2022
                : 6 February 2023
                Categories
                Research
                Custom metadata
                © The Author(s) 2023

                Bioinformatics & Computational biology
                immunoinformatics,multi-epitope vaccine,pneumococcal surface protein a (pspa),pneumococcal histidine triad protein d (phtd),domain 4 of pneumolysin (ply4),protein tlr agonist adjuvant

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