In Silico Tools for Predicting Peptides Binding to HLA-Class II Molecules: More Confusion than Conclusion

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Abstract

Identification of promiscuous peptides, which bind to human leukocyte antigen, is indispensable for global vaccination. However, the development of such vaccines is impaired due to the exhaustive polymorphism in human leukocyte antigens. The use of in silico tools for mining such peptides circumvents the expensive and laborious experimental screening methods. Nevertheless, the intrepid use of such tools warrants a rational assessment with respect to experimental findings. Here, we have adopted a 'bottom up' approach, where we have used experimental data to assess the reliability of existing in silico methods. We have used a data set of 179 peptides from diverse antigens and have validated six commonly used in silico methods; ProPred, MHC2PRED, RANKPEP, SVMHC, MHCPred, and MHC-BPS. We observe that the prediction efficiency of the programs is not balanced for all the HLA-DR alleles and there is extremely high level of discrepancy in the prediction efficiency apropos of the nature of the antigen. It has not escaped our notice that the in silico methods studied here are not very proficient in identifying promiscuous peptides. This puts a much constraint on the intrepid use of such programs for human leukocyte antigen class II binding peptides. We conclude from this study that the in silico methods cannot be wholly relied for selecting crucial peptides for development of vaccines.

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