Computational Vaccinology and the ICoVax 2012 Workshop

Computational vaccinology or vaccine informatics is an interdisciplinary field that addresses scientific and clinical questions in vaccinology using computational and informatics approaches. Computational vaccinology overlaps with many other fields such as immunoinformatics, reverse vaccinology, postlicensure vaccine research, vaccinomics, literature mining, and systems vaccinology. The second ISV Pre-conference Computational Vaccinology Workshop (ICoVax 2012) was held on October 13, 2013 in Shanghai, China. A number of topics were presented in the workshop, including allergen predictions, prediction of linear T cell epitopes and functional conformational epitopes, prediction of protein-ligand binding regions, vaccine design using reverse vaccinology, and case studies in computational vaccinology. Although a significant progress has been made to date, a number of challenges still exist in the field. This Editorial provides a list of major challenges for the future of computational vaccinology and identifies developing themes that will expand and evolve over the next few years

Protegen: a web-based protective antigen database and analysis system

Protective antigens are specifically targeted by the acquired immune response of the host and are able to induce protection in the host against infectious and non-infectious diseases. Protective antigens play important roles in vaccine development, as biological markers for disease diagnosis, and for analysis of fundamental host immunity against diseases. Protegen is a web-based central database and analysis system that curates, stores and analyzes protective antigens. Basic antigen information and experimental evidence are curated from peer-reviewed articles. More detailed gene/protein information (e.g. DNA and protein sequences, and COG classification) are automatically extracted from existing databases using internally developed scripts. Bioinformatics programs are also applied to compute different antigen features, such as protein weight and pI, and subcellular localizations of bacterial proteins. Presently, 590 protective antigens have been curated against over 100 infectious diseases caused by pathogens and non-infectious diseases (including cancers and allergies). A user-friendly web query and visualization interface is developed for interactive protective antigen search. A customized BLAST sequence similarity search is also developed for analysis of new sequences provided by the users. To support data exchange, the information of protective antigens is stored in the Vaccine Ontology (VO) in OWL format and can also be exported to FASTA and Excel files. Protegen is publically available at http://www.violinet.org/protegen

Computational vaccinology and the ICoVax 2012 workshop

Abstract Computational vaccinology or vaccine informatics is an interdisciplinary field that addresses scientific and clinical questions in vaccinology using computational and informatics approaches. Computational vaccinology overlaps with many other fields such as immunoinformatics, reverse vaccinology, postlicensure vaccine research, vaccinomics, literature mining, and systems vaccinology. The second ISV Pre-conference Computational Vaccinology Workshop (ICoVax 2012) was held on October 13, 2013 in Shanghai, China. A number of topics were presented in the workshop, including allergen predictions, prediction of linear T cell epitopes and functional conformational epitopes, prediction of protein-ligand binding regions, vaccine design using reverse vaccinology, and case studies in computational vaccinology. Although a significant progress has been made to date, a number of challenges still exist in the field. This Editorial provides a list of major challenges for the future of computational vaccinology and identifies developing themes that will expand and evolve over the next few years.http://deepblue.lib.umich.edu/bitstream/2027.42/112516/1/12859_2013_Article_5721.pd

DNAVaxDB: the first web-based DNA vaccine database and its data analysis

Abstract Since the first DNA vaccine studies were done in the 1990s, thousands more studies have followed. Here we report the development and analysis of DNAVaxDB ( http://www.violinet.org/dnavaxdb ), the first publically available web-based DNA vaccine database that curates, stores, and analyzes experimentally verified DNA vaccines, DNA vaccine plasmid vectors, and protective antigens used in DNA vaccines. All data in DNAVaxDB are annotated from reliable resources, particularly peer-reviewed articles. Among over 140 DNA vaccine plasmids, some plasmids were more frequently used in one type of pathogen than others; for example, pCMVi-UB for G- bacterial DNA vaccines, and pCAGGS for viral DNA vaccines. Presently, over 400 DNA vaccines containing over 370 protective antigens from over 90 infectious and non-infectious diseases have been curated in DNAVaxDB. While extracellular and bacterial cell surface proteins and adhesin proteins were frequently used for DNA vaccine development, the majority of protective antigens used in Chlamydophila DNA vaccines are localized to the inner portion of the cell. The DNA vaccine priming, other vaccine boosting vaccination regimen has been widely used to induce protection against infection of different pathogens such as HIV. Parasitic and cancer DNA vaccines were also systematically analyzed. User-friendly web query and visualization interfaces are available in DNAVaxDB for interactive data search. To support data exchange, the information of DNA vaccines, plasmids, and protective antigens is stored in the Vaccine Ontology (VO). DNAVaxDB is targeted to become a timely and vital source of DNA vaccines and related data and facilitate advanced DNA vaccine research and development.http://deepblue.lib.umich.edu/bitstream/2027.42/109492/1/12859_2014_Article_6355.pd

Identification of immunogenic candidate for new serological tests for Brucella melitensis by a proteomic approach.

Background: The diagnosis of brucellosis by serological tests is based on antigen suspensions derived from smooth lipopolysaccharide extracts, which can give false-positive results linked to cross-reactivity with other Gram-negative microorganisms, especially Yersinia enterocolitica O:9 and Escherichia coli O157:H7. Objective: The objective of the present study was the characterization by proteomic analysis of specific immunogenic proteins not associated with smooth lipopolysaccharide to improve the diagnostic tests used in the ovine brucellosis eradication programs. Methods: The serum from a sheep positive to Brucella melitensis was treated to eliminate all antibodies against such lipopolysaccharides and highlight the reaction towards the immunoreactive proteins in western blotting. Results: The immunoreactive bands were identified by nLC-MS/MS, and through bioinformatics tools, it was possible to select 12 potential candidates as protein antigens specific for Brucella melitensis. Conclusion: The detection of new antigens not subjected to cross-reactivity with other Gram-negative microorganisms can offer additional tools for the serological diagnosis of such diseases

Analisis In Silico Capsid Scaffold Protein Virus Herpes Simpleks-1 Untuk Pengembangan Vaksin Herpes

Capsid scaffold protein merupakan salah satu protein virus herpes simpleks 1 yang potensial sebagai sumber kandidat vaksin peptida untuk penyakit herpes. Hal ini dikarenakan Capsid scaffold protein bersifat lestari pada semua jenis virus herpes manusia. Penelitian ini bertujuan untuk mendapatkan urutan peptida kandidat vaksin herpes dari Capsid scaffold protein. Metode yang digunakan pada penelitian ini adalah analisis in silico melalui pendekatan vaksinologi terbalik menggunakan program Vaxign dan Autodock Vina. Hasil analisis menunjukkan bahwa urutan peptida GLSQHYPPHV dari Capsid scaffold protein merupakan kandidat yang terbaik sebagai vaksin herpes berbasis peptida

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an “Sp/Nsp cocktail vaccine” containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.http://deepblue.lib.umich.edu/bitstream/2027.42/156072/1/fimmu-11-01581.pdfSEL

Algorithms for Processing Coronavirus Genomes for the Goals and Objectives of Modern Immunoinformatics, Vaccinomics and Virology

Пандемия нового коронавируса стала причиной стимуляции научной активности вирусологии и междисциплинарных наук, таких как медицинская кибернетика и биоинформатика. Статья сфокусирована на вопросах изучения алгоритмов обработки биоинформационных данных геномной природы для целей преимущественно иммуноинформатики и вычислительной вакцинологии. Приводятся разработанные авторами схемы алгоритмов анализа биоинформационных данных. Алгоритмы, разработанные авторами на основе анализа доступной литературы и многолетнего опыта вычислительных и лабораторных экспериментов для обработки геномной информации, можно применять не только для дизайна и анализа компонентов эпитопных вакцин, но и для других задач вычислительной вирусологии и микробиологии. In silico эксперименты по анализу биоинформационных данных относительно малозатратны и многоинформативны, но требуют от ученого высокой квалификации, длительного опыта и, соответственно, широкого спектра знаний и навыков. Однако для полноценного анализа и внедрения, к примеру, эпитопных вакцин, требуется последующая валидация лабораторными и in vivo экспериментами