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Row-wise Sum
Row-wise Sum
In another alternative approach based upon inclusion (strategy 4), we use all possible tools (without elimination/filtering) to perform comprehensive evaluation of query protein sequences/proteome.
In another alternative approach based upon inclusion (strategy 4), we use all possible tools (without elimination/filtering) to perform comprehensive evaluation of query protein sequences/proteome.
First, we convert the resultant values from different tools (N) into binaries (1/0) using threshold values. Notably, two different types of threshold values were taken into account (shown below):
First, we convert the resultant values from different tools (N) into binaries (1/0) using threshold values. Notably, two different types of threshold values were taken into account (shown below):
Custom cut-off values for RV tools
Literature-based cut-off values for RV tools
Second, a row-wise sum corresponding to all the properties [i.e., Si] was computed. This is followed by computation of probability value (Pi= Si/N).
Second, a row-wise sum corresponding to all the properties [i.e., Si] was computed. This is followed by computation of probability value (Pi= Si/N).
A higher Pi indicates the propensity of a given protein molecule to possess desirable properties in order to be a good vaccine candidate.
A higher Pi indicates the propensity of a given protein molecule to possess desirable properties in order to be a good vaccine candidate.
1.1 Computation of Custom cut-off values
1.1 Computation of Custom cut-off values
Strategy for the computation of custom cut-off values
dataset-100pos-neg-for-cutoff.xlsx100 Positive and 100 Negative Protein Dataset used for the computation of cut-off values
100 Positive and 100 Negative Protein Dataset used for the computation of cut-off values
Custom cut-off values for Vax-ELAN pipeline tools
1.2 Literature-based cut-off values
1.2 Literature-based cut-off values
Methodology - Features and Thresholds.xlsxReferences
Monterrubio-López, G. P., & Ribas-Aparicio, R. M. (2015). Identification of novel potential vaccine candidates against tuberculosis based on reverse vaccinology. BioMed research international, 2015.
Naz, K., Naz, A., Ashraf, S. T., Rizwan, M., Ahmad, J., Baumbach, J., & Ali, A. (2019). PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome. BMC bioinformatics, 20(1), 1-10.
Muruato, L. A., Tapia, D., Hatcher, C. L., Kalita, M., Brett, P. J., Gregory, A. E., ... & Torres, A. G. (2017). Use of reverse vaccinology in the design and construction of nanoglycoconjugate vaccines against Burkholderia pseudomallei. Clinical and Vaccine Immunology, 24(11).
Solanki, V., & Tiwari, V. (2018). Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii. Scientific reports, 8(1), 1-19.
Goodswen, S. J., Kennedy, P. J., & Ellis, J. T. (2014). Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology. Bioinformatics, 30(16), 2381-2383.
Schroeder, J., & Aebischer, T. (2011). Vaccines for leishmaniasis: from proteome to vaccine candidates. Human vaccines, 7(sup1), 10-15.
Dhanda, S.K., Usmani, S.S., Agrawal, P., Nagpal, G., Gautam, A. and Raghava, G.P., 2017. Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Briefings in Bioinformatics, 18(3), pp.467-478
Liebenberg, J., Pretorius, A., Faber, F. E., Collins, N. E., Allsopp, B. A., & Van Kleef, M. (2012). Identification of Ehrlichia ruminantium proteins that activate cellular immune responses using a reverse vaccinology strategy. Veterinary immunology and immunopathology, 145(1-2), 340-349.
Pearson, W. R. (2013). An introduction to sequence similarity (“homology”) searching. Current Protocols in Bioinformatics, Chapter 3(SUPPL.42).
2. Row-wise Sum (Si) & Probability Value (Pi)
2. Row-wise Sum (Si) & Probability Value (Pi)
Sorting data frame of binary values based on Si and Pi
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