AllerCatPro—prediction of protein allergenicity potential from the protein sequence

Maurer‐Stroh, Sebastian; Krutz, Nora L; Kern, Petra; Gunalan, Vithiagaran; Nguyen, Minh N. H.; Limviphuvadh, Vachiranee; Eisenhaber, Frank; Gerberick, G. Frank

doi:10.1093/bioinformatics/btz029

Cited by 119 publications

(92 citation statements)

References 42 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Global population HLA allele coverage for this peptide subset was separately calculated for class I and class II molecules using the Population Coverage tool from IEDB 52 and the predicted HLA alleles identified in our analyses. The potential toxicity and allergenicity of each peptide were calculated using the ToxinPred 53 and AllerCatPro 54 web tools, respectively. Default parameters were used for all sequence inputs.…”

Section: Prediction Of Sars-cov-2 T Cell Epitopes Prediction Of Hla mentioning

confidence: 99%

Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome

Crooke

Ovsyannikova

Kennedy

et al. 2020

Sci Rep

View full text Add to dashboard Cite

A novel coronavirus (SARS-CoV-2) emerged from China in late 2019 and rapidly spread across the globe, infecting millions of people and generating societal disruption on a level not seen since the 1918 influenza pandemic. A safe and effective vaccine is desperately needed to prevent the continued spread of SARS-CoV-2; yet, rational vaccine design efforts are currently hampered by the lack of knowledge regarding viral epitopes targeted during an immune response, and the need for more in-depth knowledge on betacoronavirus immunology. to that end, we developed a computational workflow using a series of open-source algorithms and webtools to analyze the proteome of SARS-CoV-2 and identify putative T cell and B cell epitopes. Utilizing a set of stringent selection criteria to filter peptide epitopes, we identified 41 T cell epitopes (5 HLA class I, 36 HLA class II) and 6 B cell epitopes that could serve as promising targets for peptide-based vaccine development against this emerging global pathogen. To our knowledge, this is the first study to comprehensively analyze all 10 (structural, non-structural and accessory) proteins from SARS-CoV-2 using predictive algorithms to identify potential targets for vaccine development. In December 2019, public health officials in Wuhan, China, reported the first case of severe respiratory disease attributed to infection with the novel coronavirus SARS-CoV-2 1. Since its emergence, SARS-CoV-2 has spread rapidly via human-to-human transmission 2 , threatening to overwhelm healthcare systems around the world and resulting in the declaration of a pandemic by the World Health Organization 3. The disease caused by the virus (COVID-19) is characterized by fever, pneumonia, and other respiratory and inflammatory symptoms that can result in severe inflammation of lung tissue and ultimately death-particularly among older adults or individuals with underlying comorbidities 4-6. As of this writing, the SARS-CoV-2 pandemic has resulted in 4 million confirmed cases of COVID-19 and over 280,000 deaths worldwide 7. SARS-CoV-2 is the third pathogenic coronavirus to cross the species barrier into humans in the past two decades, preceded by severe acute respiratory syndrome coronavirus (SARS-CoV) 8,9 and Middle-East respiratory syndrome coronavirus (MERS-CoV) 10. All three of these viruses belong to the β-coronavirus genus and have either been confirmed (SARS-CoV) or suggested (MERS-CoV, SARS-CoV-2) to originate in bats, with transmission to humans occurring through intermediary animal hosts 11-14. While previous zoonotic spillovers of coronaviruses have been marked by high case fatality rates (~ 10% for SARS-CoV; ~ 34% for MERS-CoV), widespread transmission of disease has been relatively limited (8,098 cases of SARS; 2,494 cases of MERS) 15. In contrast, SARS-CoV-2 is estimated to have a lower case fatality rate (~ 2 to 4%) but is far more infectious and has achieved worldwide spread in a matter of months 16. As the number of COVID-19 cases continues to grow, there is an urgent need for a safe and ef...

show abstract

Section: Prediction Of Sars-cov-2 T Cell Epitopes Prediction Of Hla mentioning

confidence: 99%

Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome

Crooke

Ovsyannikova

Kennedy

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The selected T-cell epitopes were predicted for allergenicity by using the AllerCatPro server [31] and toxicity prediction using a toxpred tool.…”

Section: Non-allegenicity and Non-toxicity Prediction Of Selected T Cmentioning

confidence: 99%

Drug and Vaccine Design against Novel Coronavirus (2019-nCoV) Spike Protein through Computational Approach

Kumar¹

2020

Preprint

View full text Add to dashboard Cite

The recent outbreak of the new virus in Wuhan city, China from the sea food market has led to the identification of a new strain called the corona virus and named as novel corona virus (2019-nCoV) belonging to Coronaviridae family. This has created major havoc and concern due to the mortality of 250 persons and affecting more than 10,000 people. This virus causes sudden fever, pneumonia and also kidney failure. In this study a computational approach is proposed for drug and vaccine design. The spike protein sequences were collected from a protein database and analysed with various bioinformatics tools to identify suitable natural inhibitors for the N-terminal receptor binding domain of spike protein. Also, it is attempted to identify suitable vaccine candidates by identifying B-Cell and T-cell epitopes. In the drug design, the tanshinone Iia and methyl Tanshinonate were identified as natural inhibitors based on the docking score. In the vaccine design, B-cell epitope VLLPLVSSQCVNLTTRTQLPPAYTN was found to have the highest antigenicity. FVFLVLLPL of MHC class-I allele and FVFLVLLPL of MHC class-II allele were identified as best peptides based on a number of alleles and antigencity scores. The present study identifies natural inhibitors and putative antigenic epitopes which may be useful as effective drug and vaccine candidates for the eradication of novel corona virus.

show abstract

“…To predict the peptides' allergicity, the AllergenFP v.1.0 [22] and AllerCatPro v. 1.7 [23] servers were used. In contrast, to predict the peptides' toxicity, the ToxinPred server [24] was used.…”

Section: Allergicity and Toxicity Predictionmentioning

confidence: 99%

“…Besides the binding with the MHC molecules, the predicted peptide must be non-toxic and non-allergen, hence, their safety was predicted using the AllergenFP v.1.0 [22], AllerCatPro [23] v. 1.7, ToxinPred [24] servers. The result showed that all the peptides were non-toxic.…”

Section: Thementioning

confidence: 99%

See 1 more Smart Citation

A Multiple Peptides Vaccine against nCOVID-19 Designed from the Nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the Immunoinformatics Approach

Soa

Al-Nour

Elhag

et al. 2020

Preprint

View full text Add to dashboard Cite

Due to the current COVID-19 pandemic, the rapid discovery of a safe and effective vaccine is an essential issue, consequently, this study aims to predict potential COVID-19 peptide-based vaccine utilizing the Nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the Immunoinformatics approach. To achieve this goal, several Immune Epitope Database (IEDB) tools, molecular docking, and safety prediction servers were used. According to the results, The Spike peptide peptides SQCVNLTTRTQLPPAYTNSFTRGVY is predicted to have the highest binding affinity to the B-Cells. The Spike peptide FTISVTTEI has the highest binding affinity to the MHC I HLA-B1503 allele. The Nucleocapsid peptides KTFPPTEPK and RWYFYYLGTGPEAGL have the highest binding affinity to the MHC I HLA-A0202 allele and the three MHC II alleles HLA-DPA1*01:03/DPB1*02:01, HLA-DQA1*01:02/DQB1-*06:02, HLA-DRB1, respectively. Furthermore, those peptides were predicted as non-toxic and non-allergen. Therefore, the combination of those peptides is predicted to stimulate better immunological responses with respectable safety.

show abstract

AllerCatPro—prediction of protein allergenicity potential from the protein sequence

Cited by 119 publications

References 42 publications

Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome

Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome

Drug and Vaccine Design against Novel Coronavirus (2019-nCoV) Spike Protein through Computational Approach

A Multiple Peptides Vaccine against nCOVID-19 Designed from the Nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the Immunoinformatics Approach

Contact Info

Product

Resources

About