title: Structure-guided deimmunization of therapeutic proteins.
abstract: Therapeutic proteins continue to yield revolutionary new treatments for a growing spectrum of human disease, but the development of these powerful drugs requires solving a unique set of challenges. For instance, it is increasingly apparent that mitigating potential anti-therapeutic immune responses, driven by molecular recognition of a therapeutic protein's peptide fragments, may be best accomplished early in the drug development process. One may eliminate immunogenic peptide fragments by mutating the cognate amino acid sequences, but deimmunizing mutations are constrained by the need for a folded, stable, and functional protein structure. These two concerns may be competing, as the mutations that are best at reducing immunogenicity often involve amino acids that are substantially different physicochemically. We develop a novel approach, called EpiSweep, that simultaneously optimizes both concerns. Our algorithm identifies sets of mutations making such Pareto optimal trade-offs between structure and immunogenicity, embodied by a molecular mechanics energy function and a T-cell epitope predictor, respectively. EpiSweep integrates structure-based protein design, sequence-based protein deimmunization, and algorithms for finding the Pareto frontier of a design space. While structure-based protein design is NP-hard, we employ integer programming techniques that are efficient in practice. Furthermore, EpiSweep only invokes the optimizer once per identified Pareto optimal design. We show that EpiSweep designs of regions of the therapeutics erythropoietin and staphylokinase are predicted to outperform previous experimental efforts. We also demonstrate EpiSweep's capacity for deimmunization of the entire proteins, case analyses involving dozens of predicted epitopes, and tens of thousands of unique side-chain interactions. Ultimately, Epi-Sweep is a powerful protein design tool that guides the protein engineer toward the most promising immunotolerant biotherapeutic candidates.
authors: Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.
authors: Andrew S
authors: AS
authors: Parker
authors: Yoonjoo
authors: Y
authors: Choi
authors: Karl E
authors: KE
authors: Griswold
authors: Chris
authors: C
authors: Bailey-Kellogg
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000596:Amino Acids; D000940:Antigenic Variation; D001426:Bacterial Proteins / Q000737:chemistry* / Q000235:genetics / Q000276:immunology; D017076:Computer-Aided Design; D015195:Drug Design; D018984:Epitopes, T-Lymphocyte / Q000235:genetics* / Q000276:immunology; D004921:Erythropoietin / Q000737:chemistry* / Q000235:genetics / Q000276:immunology; D006801:Humans; D008666:Metalloendopeptidases / Q000737:chemistry* / Q000235:genetics / Q000276:immunology; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D015202:Protein Engineering / Q000379:methods / Q000706:statistics & numerical data*; D013816:Thermodynamics
chemical_list: D000596:Amino Acids; D001426:Bacterial Proteins; D018984:Epitopes, T-Lymphocyte; D004921:Erythropoietin; D008666:Metalloendopeptidases; C022653:auR protein, Staphylococcus aureus
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