A combined computer-aided approach to drive the identification of potential epitopes in protein therapeutics

Background: The identification of fragment sequences, or motifs, within a therapeutic protein that may elicit an immune response when processed by T-cells can be provided by computer-aided approaches. Immunogenicity is a significant problem associated with protein therapeutics and should be investigated in the early stage of protein-based drug development to avoid treatment resistance and potentially life-threatening immune responses. Purpose: To provide a combined computer-aided protocol for investigating the immunogenic profile of a recombinant Kunitz-type inhibitor, which has been reported as promising antitumor agent by our research group. Methods: The combination of databases searching (IEDB and SYFPEITHI) and molecular docking simulations was exploited, herein. This combined protocol has allowed the identification of potential epitopes before in vitro/in vivo evaluation. Predictors of human proteasome cleavage transport and major histocompatibility complex (MHC) binding were considered as overall score assigning the corresponding intrinsic potential of being a T cell epitope to each fragment sequence. The peptides or motifs better classified in the two databases were docked into the three-dimensional (3D) structure of MHC (class I and II) complex to verify the calculated binding affinity. The binding interactions regarding the molecular recognition process by T-cells were also exploited through the MHC:ligand:T-cell complexes. Results: Regarding the Kunitz-type sequence, four motifs were identified as potentially epitopes for MHC-I and three motifs were found for MHC-II. But, those motifs were classified as moderately immunogenic. Final remarks: The combined computer-aided protocol has significantly reduced the number of potential epitopes to be considered for further analysis and could be useful to identify immunogenic fragments (high, moderate and low) in protein pharmaceutics before in vitro/in vivo experimentation.
Silva BAVG, Chudzinski-Tavassi AM, Pasqualoto KFM. A combined computer-aided approach to drive the identification of potential epitopes in protein therapeutics. J. Pharm. Pharm. Sci.. 2018;21:268-85
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