Protein deimmunization via structure‐based design enables efficient epitope deletion at high mutational loads

Abstract

Anti-drug immune responses are a unique risk factor for biotherapeutics, and undesired immunogenicity can alter pharmacokinetics, compromise drug efficacy, and in some cases even threaten patient safety. To fully capitalize on the promise of biotherapeutics, more efficient and generally applicable protein deimmunization tools are needed. Mutagenic deletion of a protein’s T cell epitopes is one powerful strategy to engineer immunotolerance, but deimmunizing mutations must maintain protein structure and function. Here, EpiSweep, a structure-based protein design and deimmunization algorithm, has been used to produce a panel of seven beta-lactamase drug candidates having 27–47% reductions in predicted epitope content. Despite bearing eight mutations each, all seven engineered enzymes maintained good stability and activity. At the same time, the variants exhibited dramatically reduced interaction with human class II major histocompatibility complex proteins, key regulators of anti-drug immune responses. When compared to 8-mutation designs generated with a sequence-based deimmunization algorithm, the structure-based designs retained greater thermostability and possessed fewer high affinity epitopes, the dominant drivers of anti-biotherapeutic immune responses. These experimental results validate the first structure-based deimmunization algorithm capable of mapping optimal biotherapeutic design space. By designing optimal mutations that reduce immunogenic potential while imparting favorable intramolecular interactions, broadly distributed epitopes may be simultaneously targeted using high mutational loads.

Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity

“…The Lib10 variants manifested a loss of 3-9.5°C in thermostability, with the six-and seven-mutation variants having greater reductions than the four-and five-mutation variants. Importantly, however, the melting temperatures (T m ) of the selected Lib10 variants were generally similar to those of fourto eight-mutation P99βL variants deimmunized in earlier studies (18)(19)(20). Thus, rigorous kinetic and thermostability analysis of the selected Lib10 variants, combined with the MHC II-binding assays described above, demonstrated that the library design algorithms achieved both objectives, reducing immunoreactivity and maintaining stability and activity.…”

“…Such high mutational loads may be important for achieving deimmunization against genetically diverse patient populations or when seeking to deimmunize proteins replete with immunogenic epitopes. In prior studies, smaller numbers of P99βL T-cell epitopes had been deleted by the introduction of two to eight mutations (3,(18)(19)(20)32). Here, deimmunized P99βL libraries produced highly active variants bearing as many as 16 mutations, and in several cases the engineered enzymes possessed equivalent stability and higher catalytic activity than the parental P99βL protein.…”

“…Pareto optimal deimmunization thus can be seen as a special case of provably optimal multistate protein design (17) in which the positive design goal seeks to maintain therapeutic function and the negative design goal seeks to reduce immune recognition. Pareto optimal designs encode combinations of epitope-deleting, functionality-preserving mutations that should "play well together" in a given variant, facilitating the rapid advance of promising candidates to experimental evaluation (18)(19)(20).…”

Poor correlation between T-cell activation assays and HLA-DR binding prediction algorithms in an immunogenic fragment of Pseudomonas exotoxin A

“…Our analysis uses a list of 15 reference alleles and treats each of them with equal importance following a prediction strategy that is considered to be state of the art (Iwai et al, 2003; Tangri et al, 2005; Koren et al, 2007; Cantor et al, 2011; Abdel-Hady et al, 2014; King et al, 2014; Li et al, 2014; Salvat et al, 2014; Salvat et al, 2015); however, it may introduce a bias because those alleles actually have different frequencies in the population. For example, if a peptide is predicted positive to DRB1*0701 (which is the most frequent in the world population), but negative to DRB1*0401 (which is much less frequent in the world population (Gonzalez-Galarza et al, 2011)), those predictions are considered equally important in the in silico analysis.…”

“…Furthermore, previous restriction assays we have done for epitope 1 (peptide 15) using 10 donors that had a positive response to that epitope showed that this epitope is restricted by DR alone (Mazor et al, 2012). Moreover, focus on DR binding prediction is considered highly common in the art of immunogenicity prediction (Iwai et al, 2003; Tangri et al, 2005; Koren et al, 2007; Cantor et al, 2011; Abdel-Hady et al, 2014; King et al, 2014; Li et al, 2014; Salvat et al, 2014; Salvat et al, 2015) and it has been recently shown that prediction of DP and DQ had very low correlation with experimental data. (Paul et al, 2015) Therefore, we chose to focus only on DR.…”