1.
Population-specific design of de-immunized protein biotherapeutics.
Schubert, B, Schärfe, C, Dönnes, P, Hopf, T, Marks, D, Kohlbacher, O
PLoS computational biology. 2018;(3):e1005983
Abstract
Immunogenicity is a major problem during the development of biotherapeutics since it can lead to rapid clearance of the drug and adverse reactions. The challenge for biotherapeutic design is therefore to identify mutants of the protein sequence that minimize immunogenicity in a target population whilst retaining pharmaceutical activity and protein function. Current approaches are moderately successful in designing sequences with reduced immunogenicity, but do not account for the varying frequencies of different human leucocyte antigen alleles in a specific population and in addition, since many designs are non-functional, require costly experimental post-screening. Here, we report a new method for de-immunization design using multi-objective combinatorial optimization. The method simultaneously optimizes the likelihood of a functional protein sequence at the same time as minimizing its immunogenicity tailored to a target population. We bypass the need for three-dimensional protein structure or molecular simulations to identify functional designs by automatically generating sequences using probabilistic models that have been used previously for mutation effect prediction and structure prediction. As proof-of-principle we designed sequences of the C2 domain of Factor VIII and tested them experimentally, resulting in a good correlation with the predicted immunogenicity of our model.
2.
Design and analysis of immune-evading enzymes for ADEPT therapy.
Osipovitch, DC, Parker, AS, Makokha, CD, Desrosiers, J, Kett, WC, Moise, L, Bailey-Kellogg, C, Griswold, KE
Protein engineering, design & selection : PEDS. 2012;(10):613-23
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Abstract
The unparalleled specificity and activity of therapeutic proteins has reshaped many aspects of modern clinical practice, and aggressive development of new protein drugs promises a continued revolution in disease therapy. As a result of their biological origins, however, therapeutic proteins present unique design challenges for the biomolecular engineer. For example, protein drugs are subject to immune surveillance within the patient's body; this anti-drug immune response can compromise therapeutic efficacy and even threaten patient safety. Thus, there is a growing demand for broadly applicable protein deimmunization strategies. We have recently developed optimization algorithms that integrate computational prediction of T-cell epitopes and bioinformatics-based assessment of the structural and functional consequences of epitope-deleting mutations. Here, we describe the first experimental validation of our deimmunization algorithms using Enterobacter cloacae P99 β-lactamase, a component of antibody-directed enzyme prodrug cancer therapies. Compared with wild-type or a previously deimmunized variant, our computationally optimized sequences exhibited significantly less in vitro binding to human type II major histocompatibility complex immune molecules. At the same time, our globally optimal design exhibited wild-type catalytic proficiency. We conclude that our deimmunization algorithms guide the protein engineer towards promising immunoevasive candidates and thereby have the potential to streamline biotherapeutic development.
3.
High-throughput engineering and analysis of peptide binding to class II MHC.
Jiang, W, Boder, ET
Proceedings of the National Academy of Sciences of the United States of America. 2010;(30):13258-63
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Abstract
Class II major histocompatibility complex (MHC-II) proteins govern stimulation of adaptive immunity by presenting antigenic peptides to CD4+ T lymphocytes. Many allelic variants of MHC-II exist with implications in peptide presentation and immunity; thus, high-throughput experimental tools for rapid and quantitative analysis of peptide binding to MHC-II are needed. Here, we present an expression system wherein peptide and MHC-II are codisplayed on the surface of yeast in an intracellular association-dependent manner and assayed by flow cytometry. Accordingly, the relative binding of different peptides and/or MHC-II variants can be assayed by genetically manipulating either partner, enabling the application of directed evolution approaches for high-throughput characterization or engineering. We demonstrate the application of this tool to map the side-chain preference for peptides binding to HLA-DR1 and to evolve novel HLA-DR1 mutants with altered peptide-binding specificity.