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Rino Rappuoli - One of the best experts on this subject based on the ideXlab platform.

  • comparison of open source Reverse Vaccinology programs for bacterial vaccine antigen discovery
    Frontiers in Immunology, 2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) vaccine in early 1990’s, several software programs have been developed implementing different flavours of the first RV protocol. However there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for bacterial vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson and VacSol) against a set of eleven pathogens for which a curated list of known bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman in 2011 and enhanced by Heinson in 2017. Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of bacterial proteins characterized by negative experimental results.

  • Table_1_Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery.docx
    2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for bacterial vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of bacterial proteins characterized by negative experimental results.

  • Table_2_Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery.XLSX
    2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for bacterial vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of bacterial proteins characterized by negative experimental results.

  • Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery
    Frontiers Media S.A., 2019
    Co-Authors: Mattia Dalsass, Alessandro Brozzi, Duccio Medini, Rino Rappuoli
    Abstract:

    Reverse Vaccinology (RV) is a widely used approach to identify potential vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for bacterial vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of bacterial proteins characterized by negative experimental results

  • Reverse Vaccinology: Exploiting Genomes for Vaccine Design
    Human Vaccines, 2017
    Co-Authors: E. Del Tordello, Rino Rappuoli, Isabel Delany
    Abstract:

    Abstract Reverse Vaccinology defines the process of antigen discovery starting from genome information. From its first application to Neisseria meningitidis group B, this approach has gradually evolved and is now accepted as a successful method of vaccine discovery, as it can be exploited to develop vaccines against many types of pathogens. Current Reverse Vaccinology approaches include comparative in silico analyses of multiple genome sequences in order to identify conserved antigens within a heterogeneous pathogen population and identification of antigens that are unique to pathogenic isolates but not present in commensal strains. In addition, transcriptomic and proteomic data sets are integrated into a selection process that yields a short list of candidate antigens to be tested in animal models, thus reducing the costs and time of downstream analyses. In this chapter, we will review the past and present applications of Reverse Vaccinology and describe the advantages, challenges and opportunities for this evolving discipline in the broader context of vaccine development.

Amjad Ali - One of the best experts on this subject based on the ideXlab platform.

  • Reverse Vaccinology and drug target identification through pan-genomics
    Pan-genomics: Applications Challenges and Future Prospects, 2020
    Co-Authors: Anam Naz, Kanwal Naz, Ayesha Obaid, Fatima Shahid, Hamza Arshad Dar, Nimat Ullah, Amjad Ali
    Abstract:

    Abstract The rapidly evolving sequencing techniques have made it easier for microbiologists to sequence bacterial species from various isolates, regions, and hosts. This yields a high number of genome sequences of the same pathogen. The pan-genome analysis yields accumulated gene pool of all the strains/isolates of a particular species. Getting insights into the total gene repertoire, biologists can decipher the microevolution of bacterial virulence mechanisms to combat deadly diseases caused by them. Predicted pan/core genomes can be easily exploited through Reverse Vaccinology techniques to identify novel and putative therapeutic targets. Reverse Vaccinology is actually a knowledge-based approach following several filtration steps to generate a catalog of potential antigens with their respective epitopes for vaccine development. While, procuring a portfolio of exclusively virulent, essential core genes, genes categorization, pathway annotation; understanding the interactions of these genes with drugs, presents a novel mechanism to combat pathogens by in silico drug target identification. Predicted vaccine candidates and putative drug targets can be verified in animal models henceforth minimizing the total time and cost of the drug and vaccine development processes.

  • Fishing for vaccines against Vibrio cholerae using in silico pan-proteomic Reverse Vaccinology approach.
    PeerJ, 2019
    Co-Authors: Muhammad Ibrahim Rashid, Amjad Ali, Sammia Rehman, Saadia Andleeb
    Abstract:

    Background Cholera, an acute enteric infection, is a serious health challenge in both the underdeveloped and the developing world. It is caused by Vibrio cholerae after ingestion of fecal contaminated food or water. Cholera outbreaks have recently been observed in regions facing natural calamities (i.e., earthquake in Haiti 2010) or war (i.e., ongoing civil war in Yemen 2016) where healthcare and sanitary setups have been disrupted as a consequence. Whole-cell oral cholera vaccines (OCVs) have been in market but their regimen efficacy has been questioned. A Reverse Vaccinology (RV) approach has been applied as a successful anti-microbial measure for many infectious diseases. Methodology With the aim of finding new protective antigens for vaccine development, the V. cholerae O1 (biovar eltr str. N16961) proteome was computationally screened in a sequential prioritization approach that focused on determining the antigenicity of potential vaccine candidates. Essential, accessible, virulent and immunogenic proteins were selected as potential candidates. The predicted epitopes were filtered for effective binding with MHC alleles and epitopes binding with greater MHC alleles were selected. Results In this study, we report lipoprotein NlpD, outer membrane protein OmpU, accessory colonization factor AcfA, Porin, putative and outer membrane protein OmpW as potential candidates qualifying all the set criteria. These predicted epitopes can offer a potential for development of a reliable peptide or subunit vaccine for V. cholerae.

  • PanRV: Pangenome-Reverse Vaccinology approach for identifications of potential vaccine candidates in microbial pangenome.
    BMC bioinformatics, 2019
    Co-Authors: Kanwal Naz, Shifa Tariq Ashraf, Muhammad Rizwan, Jamil Ahmad, Jan Baumbach, Anam Naz, Amjad Ali
    Abstract:

    A revolutionary diversion from classical Vaccinology to Reverse Vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse Vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into Reverse Vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through Reverse Vaccinology. We have designed an integrative computational pipeline term as “PanRV” that employs both the pangenome and Reverse Vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data. The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/ .

  • Reverse Vaccinology Approach to Potential Vaccine Candidates Against Acinetobacter baumannii.
    Methods in molecular biology (Clifton N.J.), 2019
    Co-Authors: Fatima Shahid, Shifa Tariq Ashraf, Amjad Ali
    Abstract:

    Acinetobacter baumannii is a rapidly evolving pathogen that largely inhabits intensive care units (ICU). This opportunistic, gram-negative organism has shown noteworthy taxonomic variations during the past three decades. A. baumannii functions as a catalase-positive, oxidase-negative obligate, aerobic, nonmotile, highly infectious, and multidrug-resistant bacterium. Therefore, the infection caused by this bacterium tends to have a fairly higher incidence rate in immune-compromised individuals ranging from 26.5% to 91%, as it colonizes in skin tissues and secretions of the respiratory tract. Recently, it has been globally labeled as a "red alert" pathogen, setting alarms throughout the medical community, arising mainly due to its widespread antibiotic resistance continuum. There is a dire need for alternative therapeutic intervention to combat A. baumannii-associated infections and the growing resistance. This chapter focuses upon the Reverse Vaccinology-based steps and strategies to identify novel potential vaccine candidates against this emerging pathogen.

  • VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive Reverse Vaccinology
    BMC Bioinformatics, 2017
    Co-Authors: Muhammad Rizwan, Jamil Ahmad, Kanwal Naz, Anam Naz, Ayesha Obaid, Tamsila Parveen, Muhammad Ahsan, Amjad Ali
    Abstract:

    Background With advances in Reverse Vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates. Reverse Vaccinology has changed the way of discovery and provides a mean to propose target identification in reduced time and labour. In this regard, high throughput genomic sequencing technologies and supporting bioinformatics tools have greatly facilitated the prompt analysis of pathogens, where various predicted candidates have been found effective against certain infections and diseases. A pipeline, VacSol, is designed here based on a similar approach to predict putative vaccine candidates both rapidly and efficiently. Results VacSol, a new pipeline introduced here, is a highly scalable, multi-mode, and configurable software designed to automate the high throughput in silico vaccine candidate prediction process for the identification of putative vaccine candidates against the proteome of bacterial pathogens. Vaccine candidates are screened using integrated, well-known and robust algorithms/tools for proteome analysis, and the results from the VacSol software are presented in five different formats by taking proteome sequence as input in FASTA file format. The utility of VacSol is tested and compared with published data and using the Helicobacter pylori 26695 reference strain as a benchmark. Conclusion VacSol rapidly and efficiently screens the whole bacterial pathogen proteome to identify a few predicted putative vaccine candidate proteins. This pipeline has the potential to save computational costs and time by efficiently reducing false positive candidate hits. VacSol results do not depend on any universal set of rules and may vary based on the provided input. It is freely available to download from: https://sourceforge.net/projects/vacsol/ .

Zuoshuang Xiang - One of the best experts on this subject based on the ideXlab platform.

  • Genome-wide prediction of vaccine targets for human herpes simplex viruses using Vaxign Reverse Vaccinology
    BMC Bioinformatics, 2013
    Co-Authors: Zuoshuang Xiang
    Abstract:

    Abstract Herpes simplex virus (HSV) types 1 and 2 (HSV-1 and HSV-2) are the most common infectious agents of humans. No safe and effective HSV vaccines have been licensed. Reverse Vaccinology is an emerging and revolutionary vaccine development strategy that starts with the prediction of vaccine targets by informatics analysis of genome sequences. Vaxign ( http://www.violinet.org/vaxign ) is the first web-based vaccine design program based on Reverse Vaccinology. In this study, we used Vaxign to analyze 52 herpesvirus genomes, including 3 HSV-1 genomes, one HSV-2 genome, 8 other human herpesvirus genomes, and 40 non-human herpesvirus genomes. The HSV-1 strain 17 genome that contains 77 proteins was used as the seed genome. These 77 proteins are conserved in two other HSV-1 strains (strain F and strain H129). Two envelope glycoproteins gJ and gG do not have orthologs in HSV-2 or 8 other human herpesviruses. Seven HSV-1 proteins (including gJ and gG) do not have orthologs in all 40 non-human herpesviruses. Nineteen proteins are conserved in all human herpesviruses, including capsid scaffold protein UL26.5 (NP_044628.1). As the only HSV-1 protein predicted to be an adhesin, UL26.5 is a promising vaccine target. The MHC Class I and II epitopes were predicted by the Vaxign Vaxitop prediction program and IEDB prediction programs recently installed and incorporated in Vaxign. Our comparative analysis found that the two programs identified largely the same top epitopes but also some positive results predicted from one program might not be positive from another program. Overall, our Vaxign computational prediction provides many promising candidates for rational HSV vaccine development. The method is generic and can also be used to predict other viral vaccine targets.http://deepblue.lib.umich.edu/bitstream/2027.42/112503/1/12859_2013_Article_5723.pd

  • Genome-wide prediction of vaccine targets for human herpes simplex viruses using Vaxign Reverse Vaccinology
    BMC Bioinformatics, 2013
    Co-Authors: Zuoshuang Xiang
    Abstract:

    Herpes simplex virus (HSV) types 1 and 2 (HSV-1 and HSV-2) are the most common infectious agents of humans. No safe and effective HSV vaccines have been licensed. Reverse Vaccinology is an emerging and revolutionary vaccine development strategy that starts with the prediction of vaccine targets by informatics analysis of genome sequences. Vaxign ( http://www.violinet.org/vaxign ) is the first web-based vaccine design program based on Reverse Vaccinology. In this study, we used Vaxign to analyze 52 herpesvirus genomes, including 3 HSV-1 genomes, one HSV-2 genome, 8 other human herpesvirus genomes, and 40 non-human herpesvirus genomes. The HSV-1 strain 17 genome that contains 77 proteins was used as the seed genome. These 77 proteins are conserved in two other HSV-1 strains (strain F and strain H129). Two envelope glycoproteins gJ and gG do not have orthologs in HSV-2 or 8 other human herpesviruses. Seven HSV-1 proteins (including gJ and gG) do not have orthologs in all 40 non-human herpesviruses. Nineteen proteins are conserved in all human herpesviruses, including capsid scaffold protein UL26.5 (NP_044628.1). As the only HSV-1 protein predicted to be an adhesin, UL26.5 is a promising vaccine target. The MHC Class I and II epitopes were predicted by the Vaxign Vaxitop prediction program and IEDB prediction programs recently installed and incorporated in Vaxign. Our comparative analysis found that the two programs identified largely the same top epitopes but also some positive results predicted from one program might not be positive from another program. Overall, our Vaxign computational prediction provides many promising candidates for rational HSV vaccine development. The method is generic and can also be used to predict other viral vaccine targets.

  • vaxign the first web based vaccine design program for Reverse Vaccinology and applications for vaccine development
    BioMed Research International, 2010
    Co-Authors: Yongqun He, Zuoshuang Xiang, Harry L T Mobley
    Abstract:

    Vaxign is the first web-based vaccine design system that predicts vaccine targets based on genome sequences using the strategy of Reverse Vaccinology. Predicted features in the Vaxign pipeline include protein subcellular location, transmembrane helices, adhesin probability, conservation to human and/or mouse proteins, sequence exclusion from genome(s) of nonpathogenic strain(s), and epitope binding to MHC class I and class II. The precomputed Vaxign database contains prediction of vaccine targets for >70 genomes. Vaxign also performs dynamic vaccine target prediction based on input sequences. To demonstrate the utility of this program, the vaccine candidates against uropathogenic Escherichia coli (UPEC) were predicted using Vaxign and compared with various experimental studies. Our results indicate that Vaxign is an accurate and efficient vaccine design program.

  • vaxign a web based vaccine target design program for Reverse Vaccinology
    Procedia in Vaccinology, 2009
    Co-Authors: Zuoshuang Xiang
    Abstract:

    Abstract In the post-genomic era, strategies of vaccine development have progressed dramatically from traditional Pasteur's principles of isolating, inactivating and injecting the causative agent of an infectious disease, to Reverse Vaccinology that starts from bioinformatics analysis of the genome information. Based on the Reverse Vaccinology strategy, we have developed a web-based vaccine design system called Vaxign ( http://www.violinet.org/vaxign/ ). Vaxign predicts possible vaccine targets based on various vaccine design criteria using microbial genomic and protein sequences as input data. Major predicted features in the Vaxign pipeline include subcellular location of a protein, transmembrane domain, adhesion probability, sequence conservation among genomes, sequence similarity to host (human or mouse) proteome, and epitope binding to MHC class I and class II. This pipeline integrates both existing open source tools and internally developed programs with user-friendly web interfaces. A user can either query pre-computed Vaxign results for one protein sequence(s) from one genome(s) or perform dynamic analysis on input protein sequence(s). Vaxign has stored pre-computed results from more than 40 genomes from various pathogens (e.g., Brucella ). The analysis results indicate that Vaxign specifically and sensitively predicts known vaccine targets and also provides new vaccine target candidates. Vaxign is part of the web-based system called Vaccine Investigation and Online Information Network (VIOLIN, http://www.violinet.org ). Vaxign is a freely available program that facilitates vaccine researchers to efficiently design vaccine targets and develop vaccines using rationale Reverse Vaccinology.

M. Rodriguez Valle - One of the best experts on this subject based on the ideXlab platform.

  • Erratum to "A review of Reverse Vaccinology approaches for the development of vaccines against ticks and tick borne diseases" [Ticks Tick-borne Dis. 7 (4) (2016) 573-585].
    Ticks and tick-borne diseases, 2016
    Co-Authors: Ala E. Lew-tabor, M. Rodriguez Valle
    Abstract:

    The publisher regrets Incorrect formatting of Table 2. Table 2. A summary of web servers and tools for used for in silico vaccine candidate identification including Reverse Vaccinology pipelines (modified from Tomar and De, 2014). The publisher would like to apologise for any inconvenience caused.

  • A review of Reverse Vaccinology approaches for the development of vaccines against ticks and tick borne diseases.
    Ticks and tick-borne diseases, 2015
    Co-Authors: Ala E. Lew-tabor, M. Rodriguez Valle
    Abstract:

    The field of Reverse Vaccinology developed as an outcome of the genome sequence revolution. Following the introduction of live vaccinations in the western world by Edward Jenner in 1798 and the coining of the phrase ‘vaccine’, in 1881 Pasteur developed a rational design for vaccines. Pasteur proposed that in order to make a vaccine that one should ‘isolate, inactivate and inject the microorganism’ and these basic rules of Vaccinology were largely followed for the next 100 years leading to the elimination of several highly infectious diseases. However, new technologies were needed to conquer many pathogens which could not be eliminated using these traditional technologies. Thus increasingly, computers were used to mine genome sequences to rationally design recombinant vaccines. Several vaccines for bacterial and viral diseases (i.e. meningococcus and HIV) have been developed, however the on-going challenge for parasite vaccines has been due to their comparatively larger genomes. Understanding the immune response is important in Reverse Vaccinology studies as this knowledge will influence how the genome mining is to be conducted. Vaccine candidates for anaplasmosis, cowdriosis, theileriosis, leishmaniasis, malaria, schistosomiasis, and the cattle tick have been identified using Reverse Vaccinology approaches. Some challenges for parasite vaccine development include the ability to address antigenic variability as well the understanding of the complex interplay between antibody, mucosal and/or T cell immune responses. To understand the complex parasite interactions with the livestock host, there is the limitation where algorithms for epitope mining using the human genome cannot directly be adapted for bovine, for example the prediction of peptide binding to major histocompatibility complex motifs. As the number of genomes for both hosts and parasites increase, the development of new algorithms for pan-genomic mining will continue to impact the future of parasite and ricketsial (and other tick borne pathogens) disease vaccine development.

  • Reverse Vaccinology approach for the identification of novel Rhipicephalus (boophilus) microplus vaccine candidates
    2008
    Co-Authors: A. Lew, M. Rodriguez Valle, L.a. Jackson, Emily K. Piper, Sebastian Kurscheid, Paula Moolhuijzen, Constantin Constantinoiu, Megan E Jones, William Jorgensen, Cedric Gondro
    Abstract:

    Ticks and tick borne diseases cost Australian cattle enterprises $US170m per annum with global losses estimated at $US2.5bn. Rising acaricide resistance and market failure of TickGARD PLUS (Bm86) vaccine in Australia has led to an investment to identify new vaccine candidates with longer lasting immunity. Capitalizing on 13,643 available R. microplus ESTs (BmiGeneIndex2), Ixodes scapularis draft tick genome contigs and gene discovery tools such as suppressive subtractive hybridization, R. microplus microarrays (NimbleGen), proteomics and bioinformatics, we are applying a Reverse Vaccinology approach to identify putative R. microplus vaccine candidates. In parallel, we have undertaken comprehensive analyses of host responses pre- and post- R.microplus infestation by measuring peripheral cellular and antibody responses, skin histology and immunohistochemistry, and bovine microarray (Affymetrix) analysis of skin and blood from rsistant (Brahman and Santa-Gertrudis) and susceptible (Holstein-Fricsian and Santa-Gertrudis) cattle. Our trials demonstrated that the resistant host mounts a Th1 protective response to ticks whereas the immune response of susceptible cattle appears to become 'confused' as cattle respond vigorously to a wide variety of tick extracts. We have developed novel in vitro screening tools (utilizing cells and sera from the above trials) for pre-in vivo high-throughput screening of expressed candidates. Bioinformatics and gene discovery studies identified 250 vaccine candidates including lipocalins, lipoprotein receptors, proteases/metalloproteases, extracellular matrix proteins, membrane proteins, cuticle enzymes, chitin binding and ~170 proteins of unknown function mostly specific to tick species. These candidates are under further scrutiny using criteria such as hydropathy/epitope prediction, relative abundance of similar epitopes in other tick species and host proteins, abundance in multiple tick stages, in vitro functional analyses and immune recognition (proteomics) to select a total of 50 genes for expression for in vitro screening prior to selection for in vivo 'proof of concept' trials.

Muhammad Rizwan - One of the best experts on this subject based on the ideXlab platform.

  • PanRV: Pangenome-Reverse Vaccinology approach for identifications of potential vaccine candidates in microbial pangenome
    BMC Bioinformatics, 2019
    Co-Authors: Shifa Tariq Ashraf, Muhammad Rizwan, Jamil Ahmad, Jan Baumbach
    Abstract:

    Background A revolutionary diversion from classical Vaccinology to Reverse Vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse Vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into Reverse Vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through Reverse Vaccinology. Results We have designed an integrative computational pipeline term as “PanRV” that employs both the pangenome and Reverse Vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data. Conclusion The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/ .

  • PanRV: Pangenome-Reverse Vaccinology approach for identifications of potential vaccine candidates in microbial pangenome.
    BMC bioinformatics, 2019
    Co-Authors: Kanwal Naz, Shifa Tariq Ashraf, Muhammad Rizwan, Jamil Ahmad, Jan Baumbach, Anam Naz, Amjad Ali
    Abstract:

    A revolutionary diversion from classical Vaccinology to Reverse Vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse Vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into Reverse Vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through Reverse Vaccinology. We have designed an integrative computational pipeline term as “PanRV” that employs both the pangenome and Reverse Vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data. The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/ .

  • VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive Reverse Vaccinology
    BMC Bioinformatics, 2017
    Co-Authors: Muhammad Rizwan, Jamil Ahmad, Kanwal Naz, Anam Naz, Ayesha Obaid, Tamsila Parveen, Muhammad Ahsan, Amjad Ali
    Abstract:

    Background With advances in Reverse Vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates. Reverse Vaccinology has changed the way of discovery and provides a mean to propose target identification in reduced time and labour. In this regard, high throughput genomic sequencing technologies and supporting bioinformatics tools have greatly facilitated the prompt analysis of pathogens, where various predicted candidates have been found effective against certain infections and diseases. A pipeline, VacSol, is designed here based on a similar approach to predict putative vaccine candidates both rapidly and efficiently. Results VacSol, a new pipeline introduced here, is a highly scalable, multi-mode, and configurable software designed to automate the high throughput in silico vaccine candidate prediction process for the identification of putative vaccine candidates against the proteome of bacterial pathogens. Vaccine candidates are screened using integrated, well-known and robust algorithms/tools for proteome analysis, and the results from the VacSol software are presented in five different formats by taking proteome sequence as input in FASTA file format. The utility of VacSol is tested and compared with published data and using the Helicobacter pylori 26695 reference strain as a benchmark. Conclusion VacSol rapidly and efficiently screens the whole bacterial pathogen proteome to identify a few predicted putative vaccine candidate proteins. This pipeline has the potential to save computational costs and time by efficiently reducing false positive candidate hits. VacSol results do not depend on any universal set of rules and may vary based on the provided input. It is freely available to download from: https://sourceforge.net/projects/vacsol/ .

  • VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive Reverse Vaccinology
    BMC bioinformatics, 2017
    Co-Authors: Muhammad Rizwan, Jamil Ahmad, Kanwal Naz, Anam Naz, Ayesha Obaid, Tamsila Parveen, Muhammad Ahsan, Amjad Ali
    Abstract:

    With advances in Reverse Vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates. Reverse Vaccinology has changed the way of discovery and provides a mean to propose target identification in reduced time and labour. In this regard, high throughput genomic sequencing technologies and supporting bioinformatics tools have greatly facilitated the prompt analysis of pathogens, where various predicted candidates have been found effective against certain infections and diseases. A pipeline, VacSol, is designed here based on a similar approach to predict putative vaccine candidates both rapidly and efficiently. VacSol, a new pipeline introduced here, is a highly scalable, multi-mode, and configurable software designed to automate the high throughput in silico vaccine candidate prediction process for the identification of putative vaccine candidates against the proteome of bacterial pathogens. Vaccine candidates are screened using integrated, well-known and robust algorithms/tools for proteome analysis, and the results from the VacSol software are presented in five different formats by taking proteome sequence as input in FASTA file format. The utility of VacSol is tested and compared with published data and using the Helicobacter pylori 26695 reference strain as a benchmark. VacSol rapidly and efficiently screens the whole bacterial pathogen proteome to identify a few predicted putative vaccine candidate proteins. This pipeline has the potential to save computational costs and time by efficiently reducing false positive candidate hits. VacSol results do not depend on any universal set of rules and may vary based on the provided input. It is freely available to download from: https://sourceforge.net/projects/vacsol/ .