Structure-Guided Deimmunization of Therapeutic Proteins

  • Andrew S. Parker
  • Karl E. Griswold
  • Chris Bailey-Kellogg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7262)


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. We develop a novel approach, called EpiSweep, that simultaneously optimizes both concerns. Our algorithm identifies sets of mutations making 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 global protein deimmunization, a case analysis involving 50 predicted epitopes and over 30,000 unique side-chain interactions. Ultimately, EpiSweep is a powerful protein design tool that guides the protein engineer towards the most promising immunotolerant biotherapeutic candidates.


structure-based protein design sequence-based protein design multi-objective optimization therapeutic proteints deimmunization experiment planning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrew S. Parker
    • 1
  • Karl E. Griswold
    • 2
  • Chris Bailey-Kellogg
    • 1
  1. 1.Department of Computer Science, 6211 Sudikoff LaboratoryDartmouth CollegeHanoverUSA
  2. 2.Thayer School of EngineeringDartmouth CollegeHanoverUSA

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