SARS-CoV-2 Predicted Epitopes
Accurate Prediction Tools for Vaccine Development
By [source]
About this dataset
This dataset provides a comprehensive look at the effectiveness of current epitope prediction tools when it comes to producing viable vaccine candidates. By evaluating the performance of 11 different HLA alleles, this data can provide insight into which models correctly identify those that are immunogenic and therefore are better suited for use in vaccine development. Furthermore, you will be able to investigate which peptides have the highest chance of becoming successful in combating SARS CoV-2 and understand how strategies such as binding affinity and molecular mechanisms influence these findings. This data can enable researchers to determine how best to proceed with identifying potential new vaccines for COVID-19 so that no stone is left unturned in the race against this global pandemic
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How to use the dataset
This dataset contains the predicted SARS-CoV-2 peptide sequences for 11 HLA alleles for vaccine candidate epitopes validation. The dataset is composed of 19 columns containing unique HLA alleles, and each row depicts the sequence of an individual SARS-CoV-2 peptide.
To use this dataset:
- Get familar with the different HLA alleles included in this dataset listed in the columns (e.g.,HLA-A0101, HLA-A0201).
- Select a specific allele from one of those columns to be used for pinpointing potential vaccine candidates that match certain criteria (e.g., length, amino acid composition) or are suggested by existing research studies and clinical data
- For each selected allele in step 2, use ‘filtering’ to find potential matching sequences within the rows by comparing each and every sequence against your criteria
4 In order to further verify whether these selected candidates are valid ones as immunogenic antigens, you can alternatively run additional in silico experiments such as network based docking analysis or machine learning analysis on those candidate peptides and their biological features
By repeating steps 1 through 4 multiple times with various combinations of criteria and filtering conditions, researchers can then confidently identify promising possible vaccine candidates amongst those peptides that pass all filtering steps through machine learning based scoring system etc which can potentially helps them move on to lab experiments with more confidence than without any computational/data mining/artificial intelligence support at all
Research Ideas
- Comparing predicted SARS-CoV-2 peptides for different HLA alleles to determine which will be most effective in creating protective immunity against the virus.
- Automate the selection of vaccine candidate epitopes to improve coverage and efficiency of SARS-CoV-2 vaccines and immunotherapies.
- Assessing which common epitope prediction tools are most accurate in identifying potential vaccine candidate peptides
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: sars-cov-2_predicted.csv
Column name |
Description |
HLA-A0101 |
Predicted peptides for HLA-A0101 allele. (String) |
HLA-A0201 |
Predicted peptides for HLA-A0201 allele. (String) |
HLA-A0301 |
Predicted peptides for HLA-A0301 allele. (String) |
HLA-A1101 |
Predicted peptides for HLA-A1101 allele. (String) |
HLA-A2402 |
Predicted peptides for HLA-A2402 allele. (String) |
HLA-B4001 |
Predicted peptides for HLA-B4001 allele. (String) |
HLA-C0401 |
Predicted peptides for HLA-C0401 allele. (String) |
HLA-C0101 |
Predicted peptides for HLA-C0101 allele. (String) |
HLA-C0701 |
Predicted peptides for HLA-C0701 allele. (String) |
HLA-C0702 |
Predicted peptides for HLA-C0702 allele. (String) |
HLA-DRB10401 |
Predicted peptides for HLA-DRB10401 allele. (String) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .