The clinical applications of therapeutic enzymes are often limited due to their immunogenicity. B-cell epitope removal is an effective approach to solve this obstacle. The identification of hot spot epitopic residues is a critical step in the removal of protein B-cell epitope. Hereof, computational approaches are a suitable alternative to costly and labor-intensive experimental approaches. Arginine deiminase, a Mycoplasma arginine-catabolizing enzyme, is in the clinical trial for treating arginine auxotrophic cancers, especially hepatocellular carcinomas and melanomas through depleting plasma arginine and causing cell starvation. In this study, arginine deiminase from Mycoplasma hominis (MhADI) was computationally analyzed for recognizing and locating its immune-reactive regions. The 3D structure of the bioactive form of MhADI was modeled. The B-cell epitope mapping of protein was performed using various servers with different algorithms. Six segments: 31-40, 48-55, 131-140, 196-206, 294-314, and 331-344 were predicted to be the consensus immunogenic regions. The modification of epitopic hot spot residue was performed to reduce immune-reactiveness. The hot spot residue was selected considering a high B-cell epitope score, convexity index, surface accessibility, flexibility, and hydrophilicity. The structure stability of native and mutant proteins was evaluated through molecular dynamics simulation. The E304L mutein was suggested as a lower antigenic and stable enzyme derivative.
Keywords: 3D: 3Dimensional; ADI: arginine deiminase; B-cell epitopes removal; MD simulation: molecular dynamics simulation; MhADI: arginine deiminase from; PEG: polyethylene glycol; SVM: support vector machine; antigenic; arginine deiminase; immune-reactive; therapeutic enzymes.