In silico prediction of b-cell and t-cell epitopes in plasmodium falciparum merozoite invasion proteins (eba175, rh5 and ripr)

Abstract

emisinin-based combination therapy and insecticide-treated bed nets are not effective in the long term and there is a need to develop an effective malaria vaccine that will fully combat and control malaria infections. The RTS,S/AS01 malaria vaccine has achieved limited success, hence there is still need to identify novel malaria vaccine targets. This study predicted B-cell epitopes (BCEs) and T-cell epitopes (TCEs) in three merozoite invasion proteins i.e. Erythrocyte binding antigen-175 (EBA175), Reticulocyte binding-like homolog 5 (Rh5) and Ripr (Rh5 interacting protein). The approach involved comparing the prediction of TCEs from predicted BCEs and prediction of TCEs directly from full protein sequences. Circumsporozoite protein (CSP), the protein from which RTS,S/AS01 vaccine was developed, was used as a control to determine whether BCE and TCE prediction algorithms could predict experimental defined BCEs and TCEs. BCEs were predicted using the BCPreds server while TCEs were predicted using the NetMHCcons (for MHC class I binding predictions) and NetMHCIIpan 3.0 (for MHC class II binding predictions). Prediction of TCEs from full length CSP sequence outperformed the prediction of TCEs from predicted CSP BCEs, hence this approach was applied on EBA175, Rh5 and Ripr. A further step was included to identify regions of the proteins overlapping TCEs and BCEs using sequence clustering algorithms. In total, EBA175, Rh5 and Ripr yielded 5, 4 and 5 candidate cross-reactive TCEs and BCEs respectively, including both conserved and polymorphic regions across the isolates tested. Of these candidate immunogenic epitopes, (WNEFREKLWE AMLSEHKNNI, 20mers) in EBA175, 2 in Rh5 (YKNVDYKNVNFLQYHFKELSNYNIANS IDILQEKEGHLDFVIIPHYTFLDYYKHLSYNSIYHKSSTYGCIAVDAFIKKINETYDKV KSKCNDIK – 95mers and SCYNNNFCNTNGI RYHYDEYIHKLILSVKS – 30mers) and 1 in Ripr (INCQGMYISLRSVHVHTHNAILQQETLTYIKNLCDGKNNCKFDFDSIKYENKS LTHYLFFINIQYQCISPLNLQENEMC – 51mers) mapped back to experimentally verified BCEs. In silico prediction of BCEs and TCEs minimizes the resources required for laboratory analysis of pathogen gene products. An immunologist can use these computationally predicted immunogenic regions to explore the potential of developing effective drugs and vaccines. We propose that the EBA175-RII, Rh5 and Ripr BCEs and TCEs are immunogenic and recommend them for experimental lab validation and can be inclusion in the search for an effective malaria vaccine