Decreasing the immunogenicity of arginine deiminase enzyme via structure‐based computational analysis

Abstract

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.

Anti-Drug Antibodies: Emerging Approaches to Predict, Reduce or Reverse Biotherapeutic Immunogenicity

“…Prediction of B-cell epitopes on protein antigens appears to be even more challenging than prediction (without significant over-prediction) of T-cell epitopes, because B-cell epitopes are generally conformational, i.e., comprised of non-contiguous rather than linear amino acid sequences, and the three-dimensional structure of the antigen is often unknown. Significant creative effort is going into development of computational methods to identify and modify B-cell epitopes to reduce antigenicity, e.g., with recent application to recombinant arginine deiminase (a cancer biotherapeutic) [137]. Incorporation of some experimental data, e.g., peptide array or phage-display results, into epitope identification approaches greatly improves success rates, compared to ab initio methods based on amino acid sequence propensities to form beta turns, or machine-learning based algorithms [42].…”

In silico mutagenesis: decreasing the immunogenicity of botulinum toxin type A

“…Some researchers used web-server tools to evaluate the antigenicity of protein residues (Fattahian, Riahi-Madvar, Mirzaee, Asadikaram & Rahbar, 2017;Zarei et al, 2018). In total, five tools were employed to predict the linear B-cell conformation, and three types of web-server software were used to predict the conformational B-cell epitopes with various amino acid lengths.…”

“…Conserved region determined by entropy method (Supplementary data 1) usually contributed to the structural and functional properties. This region was, therefore, excluded in order to ensure the stability and the conformation of the muteins (Zarei et al, 2018). Based on the linear B-cell epitopes, conformational B-cell epitopes, surface accessibility and conserved region prediction, seven residues were obtained out of 1296 amino acids residue.…”

“…Both proposed muteins generated the two lowest VaxiJen scores which were analyzed from the total sequence. Changing the immunogenic residue into non-polar amino acids resulted in the less-immunogenicity protein (Zarei et al, 2018). One hotspot residue showed by IEDB with a good surface accessibility promoting mutability was residue 1079-1092.…”

“…B-cells can detect both linear (continuous) and conformational (discontinuous) epitopes unique to human protein although most of the B-cell epitopes are conformational or fold-dependent (Kringelum, Lundegaard, Lund, & Nielsen, 2012;Yao, Zhang, Liang, & Zhang, 2012). Modifications of these residues can alter the antigenicity (Zarei et al, 2018). Compared to time-consuming and costly experimental approaches, a wide variety of computational tools, which are publicly available, are highly recommended to predict the B-cell epitopes to reduce or minimize cost (Kolaskar & Tongaonkar, 1990;Kringelum et al, 2012;Potocnakova, Bhide, & Pulzova, 2016;Saha & Raghava, 2006;Singh, Ansari, & Raghava, 2013;Yao et al, 2012).…”