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      Bioinformatics analysis of calcium-dependent protein kinase 4 (CDPK4) as Toxoplasma gondii vaccine target

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          Abstract

          Objectives

          Toxoplasma gondii ( T. gondii), an obligate intracellular apicomplexan parasite, could affect numerous warm-blooded animals, such as humans. Calcium-dependent protein kinases (CDPKs) are essential Ca 2+ signaling mediators and participate in parasite host cell egress, outer membrane motility, invasion, and cell division.

          Results

          Several bioinformatics online servers were employed to analyze and predict the important properties of CDPK4 protein. The findings revealed that CDPK4 peptide has 1158 amino acid residues with average molecular weight (MW) of 126.331 KDa. The aliphatic index and GRAVY for this protein were estimated at 66.82 and – 0.650, respectively. The findings revealed that the CDPK4 protein comprised 30.14% and 34.97% alpha-helix, 59.84% and 53.54% random coils, and 10.02% and 11.49% extended strand with SOPMA and GOR4 tools, respectively. Ramachandran plot output showed 87.87%, 8.40%, and 3.73% of amino acid residues in the favored, allowed, and outlier regions, respectively. Also, several potential B and T-cell epitopes were predicted for CDPK4 protein through different bioinformatics tools. Also, antigenicity and allergenicity evaluation demonstrated that this protein has immunogenic and non-allergenic nature. This paper presents a basis for further studies, thereby provides a fundamental basis for the development of an effective vaccine against T. gondii infection.

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          Most cited references51

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          Protein Identification and Analysis Tools on the ExPASy Server

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            ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins

            A major problem in structural biology is the recognition of errors in experimental and theoretical models of protein structures. The ProSA program (Protein Structure Analysis) is an established tool which has a large user base and is frequently employed in the refinement and validation of experimental protein structures and in structure prediction and modeling. The analysis of protein structures is generally a difficult and cumbersome exercise. The new service presented here is a straightforward and easy to use extension of the classic ProSA program which exploits the advantages of interactive web-based applications for the display of scores and energy plots that highlight potential problems spotted in protein structures. In particular, the quality scores of a protein are displayed in the context of all known protein structures and problematic parts of a structure are shown and highlighted in a 3D molecule viewer. The service specifically addresses the needs encountered in the validation of protein structures obtained from X-ray analysis, NMR spectroscopy and theoretical calculations. ProSA-web is accessible at https://prosa.services.came.sbg.ac.at
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              VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines

              Background Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. Results Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. Conclusion VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods. It is freely-available online at the URL: .
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                Author and article information

                Contributors
                masoud_foroutan_rad@yahoo.com
                Ali.dalirghafari@yahoo.com
                shahrzadsoltani225@yahoo.com
                hamidreza.majidiani@gmail.com
                alitaghipor71@yahoo.com
                sabaghan.m@ajums.ac.ir
                Journal
                BMC Res Notes
                BMC Res Notes
                BMC Research Notes
                BioMed Central (London )
                1756-0500
                6 February 2021
                6 February 2021
                2021
                : 14
                : 50
                Affiliations
                [1 ]USERN Office, Abadan Faculty of Medical Sciences, Abadan, Iran
                [2 ]GRID grid.412266.5, ISNI 0000 0001 1781 3962, Department of Parasitology, Faculty of Medical Sciences, , Tarbiat Modares University, ; P. O. Box 14115-111, Tehran, Iran
                [3 ]GRID grid.411528.b, ISNI 0000 0004 0611 9352, Zoonotic Diseases Research Center, , Ilam University of Medical Sciences, ; Ilam, Iran
                [4 ]Behbahan Faculty of Medical Sciences, Behbahan, Iran
                Author information
                http://orcid.org/0000-0002-8661-7217
                http://orcid.org/0000-0001-9635-2876
                http://orcid.org/0000-0001-9898-1297
                http://orcid.org/0000-0001-5568-1366
                http://orcid.org/0000-0002-6876-5902
                http://orcid.org/0000-0003-4670-767X
                Article
                5467
                10.1186/s13104-021-05467-1
                7865105
                0baef405-cc25-4d42-9499-bedcbedbf58c
                © The Author(s) 2021

                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
                : 11 October 2020
                : 28 January 2021
                Funding
                Funded by: Behbahan Faculty of Medical Sciences, Behbahan, Iran
                Award ID: Grant No. 99013
                Award Recipient :
                Categories
                Research Note
                Custom metadata
                © The Author(s) 2021

                Medicine
                toxoplasma gondii,calcium-dependent protein kinase 4,bioinformatics analysis,in silico,vaccine

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