An overview of bioinformatics tools for epitope prediction: Implications on vaccine development

AbstractExploitation of recombinant DNA and sequencing technologies has led to a new concept in vaccination in which isolated epitopes, capable of stimulating a specific immune response, have been identified and used to achieve advanced vaccine formulations; replacing those constituted by whole pathogen-formulations. In this context, bioinformatics approaches play a critical role on analyzing multiple genomes to select the protective epitopes in silico. It is conceived that cocktails of defined epitopes or chimeric protein arrangements, including the target epitopes, may provide a rationale design capable to elicit convenient humoral or cellular immune responses. This review presents a comprehensive compilation of the most advantageous online immunological software and searchable, in order to facilitate the design and development of vaccines. An outlook on how these tools are supporting vaccine development is presented. HIV and influenza have been taken as examples of promising developments on vaccination against hypervariable viruses. Perspectives in this field are also envisioned

Predictive modeling of influenza in New England using a recurrent deep neural network

Predicting seasonal variation in influenza epidemics is an ongoing challenge. To better predict seasonal influenza and provide early warning of pandemics, a novel approach to Influenza-Like-Illness (ILI) prediction was developed. This approach combined a deep neural network with ILI, climate, and population data. A predictive model was created using a deep neural network based on TensorFlow 2.0 Beta. The model used Long-Short Term Memory (LSTM) nodes. Data was collected from the Center for Disease Control, the National Center for Environmental Information (NCEI) and the United States Census Bureau. These parameters were temperature, precipitation, wind speed, population size, vaccination rate and vaccination efficacy. Temperature was confirmed as the greatest predictor for ILI rates, with precipitation providing a small increase in predictive power. After training, the model was able to predict ILI rates 10 weeks out. As a result of this thesis, a framework was developed that may be applied to weekly ILI tracking as well as early prediction of outlier pandemic years

Concept and application of a computational vaccinology workflow

BACKGROUND : The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders. RESULTS : We introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage. CONCLUSION : Based on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges

Emerging Vaccine Informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning

Rational engineering of microRNA-regulated viruses for cancer gene therapy

MicroRNAs (miRNAs) are small noncoding RNA molecules that have important regulatory roles in a wide range of biological processes. miRNAs are often expressed in a tissue- and/or differentiation state-specific patterns, and it is estimated that miRNAs can regulate the expression of more than 50% of all human genes. We have exploited these tissue-specific miRNA expression patterns in the modification of viral replicative tropism. In order to engineer the replicative tropism of oncolytic adenoviruses, we developed a recombinant adenovirus that in the 3 UTR of the critical E1A gene contains sequences complementary to the liver-specific miRNA miR122. This allowed us to generate a novel recombinant adenovirus that was severely attenuated in human liver, but replicated to high titres in colorectal cancer. Systemic injection of miR122-targeted adenovirus into mice did not induce liver toxicity. In a human lung cancer xenograft mouse model this miR122-targeted adenovirus showed potent antitumour activity. We also studied the possibility to exploit neuron-specific miRNA expression patterns in the modification of tissue tropism of an alphavirus Semliki Forest virus (SFV). We engineered SFV genome to contain sequences complementary to the neuron-specific miRNA miR124. In vitro characterization of this novel virus showed that the modification of the SFV genome per se did not affect polyprotein processing or oncolytic potency. Intraperitoneally administered miR124-targeted SFV displayed an attenuated spread into the central nervous system (CNS) and increased survival of infected mice. Also, mice pre-infected with miR124-targeted SFV elicited strong protective immunity against otherwise lethal challenge with a highly virulent wild-type SFV strain. In conclusion, these results show that miRNA-targeting is a potent new strategy to engineer viral tropism in development of safer and more efficient reagents for virotherapy applications.MikroRNA:t (miRNA) ovat pieniä ei-koodaavia RNA molekyylejä joilla on tärkeä tehtävä useiden erilaisten biologisten prosessien säätelyssä. MiRNA:t ekpressoituvat usein kudos- ja/tai kehitysvaihespesifisesti sekä säätelevät jopa yli 50 prosenttia kaikista ihmisen geeneistä. Tässä väitöskirjatutkimuksessa pyrimme käyttämään hyväksi miRNA:iden kudosspesifistä ekpressiota virusten kudostropismin muokkaamisessa vähentääksemme virusvektoreiden haitallista kudostoksisuutta. Muokataksemme adenovirusvektoreiden kudostropismia, kehitimme uudentyyppisen adenoviruksen jonka E1A-geenin 3 ei-koodaavalle alueelle lisäsimme ihmisen maksaspesifisen miRNA miR122:n tunnistussekvenssejä. Tunnistussekvenssien lisäyksellä saimme aikaan adenoviruksen (miR122-targetoitu adenovirus) jonka replikaatiokyky oli huomattavasti heikentynyt ihmisen maksassa, mutta pystyi replikoitumaan voimakkaasti perä- ja paksusuolisyöpäkudoksessa. Hiireen systeemisesti injisoitu miR122-targetoitu adenovirus ei aiheuttanut maksatoksisuutta. Ihmisen keuhkosyöpähiirimallissa miR122-targetoitu virus tappoi tehokkaasti syöpäsoluja. Tässä väitöskirjatutkimuksessa tutkimme myös hermosoluspesifisen miRNA miR124:n hyväksikäyttöä Semliki Forest-viruksen (SFV) kudostropismin muokkauksessa. Kehitimme SFV:n jonka genomiin oli sisällytetty miR124:n tunnistussekvenssejä. In vitro-kokeilla osoitimme tämän miR124-targetoidun SFV:n proteiinien prosessoituvan normaalisti sekä onkolyyttisen tehon säilyneen villityypin viruksen kaltaisena. Vatsaonteloon injisoitu miR124-targetoitu SFV levisi hyvin heikosti keskushermostossa joka johti vähentyneeseen neurotoksisuuteen. Osoitimme myös miR124-targetoidun viruksen toimivan tehokkaana rokotteena erittäin patogeeniselle L10 SFV-kannalle. Tässä väitöskirjatutkimuksessa pystyimme osoittamaan miRNA-targetoinnin olevan tehokas uusi tapa muokata virusten kudostropismia ja parantaa virusvektoreiden turvallisuutta

Deconvolving mutational patterns of poliovirus outbreaks reveals its intrinsic fitness landscape.

Vaccination has essentially eradicated poliovirus. Yet, its mutation rate is higher than that of viruses like HIV, for which no effective vaccine exists. To investigate this, we infer a fitness model for the poliovirus viral protein 1 (vp1), which successfully predicts in vitro fitness measurements. This is achieved by first developing a probabilistic model for the prevalence of vp1 sequences that enables us to isolate and remove data that are subject to strong vaccine-derived biases. The intrinsic fitness constraints derived for vp1, a capsid protein subject to antibody responses, are compared with those of analogous HIV proteins. We find that vp1 evolution is subject to tighter constraints, limiting its ability to evade vaccine-induced immune responses. Our analysis also indicates that circulating poliovirus strains in unimmunized populations serve as a reservoir that can seed outbreaks in spatio-temporally localized sub-optimally immunized populations

An update on novel approaches for diagnosis and treatment of SARS-CoV-2 infection

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health and economic crisis worldwide which united global efforts to develop rapid, precise, and cost-efficient diagnostics, vaccines, and therapeutics. Numerous multi-disciplinary studies and techniques have been designed to investigate and develop various approaches to help frontline health workers, policymakers, and populations to overcome the disease. While these techniques have been reviewed within individual disciplines, it is now timely to provide a cross-disciplinary overview of novel diagnostic and therapeutic approaches summarizing complementary efforts across multiple fields of research and technology. Accordingly, we reviewed and summarized various advanced novel approaches used for diagnosis and treatment of COVID-19 to help researchers across diverse disciplines on their prioritization of resources for research and development and to give them better a picture of the latest techniques. These include artificial intelligence, nano-based, CRISPR-based, and mass spectrometry technologies as well as neutralizing factors and traditional medicines. We also reviewed new approaches for vaccine development and developed a dashboard to provide frequent updates on the current and future approved vaccines

The Impact of Bioinformatics on Vaccine Design and Development

Vaccines are the pharmaceutical products that offer the best cost‐benefit ratio in the prevention or treatment of diseases. In that a vaccine is a pharmaceutical product, vaccine development and production are costly and it takes years for this to be accomplished. Several approaches have been applied to reduce the times and costs of vaccine development, mainly focusing on the selection of appropriate antigens or antigenic structures, carriers, and adjuvants. One of these approaches is the incorporation of bioinformatics methods and analyses into vaccine development. This chapter provides an overview of the application of bioinformatics strategies in vaccine design and development, supplying some successful examples of vaccines in which bioinformatics has furnished a cutting edge in their development. Reverse vaccinology, immunoinformatics, and structural vaccinology are described and addressed in the design and development of specific vaccines against infectious diseases caused by bacteria, viruses, and parasites. These include some emerging or re‐emerging infectious diseases, as well as therapeutic vaccines to fight cancer, allergies, and substance abuse, which have been facilitated and improved by using bioinformatics tools or which are under development based on bioinformatics strategies