Click here to stream the public defence
Due to copyright issues, an electronic copy of the thesis must be ordered from the faculty. For the faculty to have time to process the order, the order must be received by the faculty at the latest 2 days before the public defence. Orders received later than 2 days before the defence will not be processed. After the public defence, please address any inquiries regarding the thesis to the candidate.
Trial Lecture – time and place
See Trial Lecture.
Adjudication committee
- First opponent: Professor Sarel-Jacob Fleishman, Weizmann Institute of Science, Israel
- Second opponent: Associate Professor Bruno Correia, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
- Third member and chair of the evaluation committee: Group Leader Charlotte Boccara, University of Oslo
Chair of the Defence
Professor Gunnveig Grødeland, University of Oslo
Principal Supervisor
Professor Victor Greiff, University of Oslo
Summary
Antibodies are highly specific biomolecules composed of two heavy and two light chains, whose complementarity-determining regions (CDRs) form the antigen-binding site. The remarkable ability of antibodies to recognize almost any target molecule has led to their central role in diagnostics and therapeutic development. Despite significant experimental advances, therapeutic antibody discovery remains a time-consuming and low-throughput process, heavily dependent on iterative laboratory screening and animal testing. Computational design promises to accelerate this process, yet its success remains limited due to the complexity and flexibility of antibody-antigen interactions. Traditional physics-based methods have provided valuable structural insights but rely heavily on empirical estimations and require significant resources. Novel data-driven methods provide practical solutions, but their black-box nature limits mechanistic understanding. Both currently achieve low predictive success rates, highlighting the need for alternative approaches. This thesis argues that this limited success stems from an incomplete understanding of the three-dimensional and biochemical rules governing antibody-antigen binding. Rather than introducing another predictive model, it focuses on uncovering the mechanistic principles that define effective binding, integrating experimental and computational approaches across three scales: structural properties of unbound antibodies, structural determinants of affinity and specificity at binding interfaces, and molecular dynamics simulations establishing quantitative structure-based rules validated against experimental data. This work produced the first quantitative set of structural rules capable of differentiating high- from low-affinity antibody binders in agreement with wet lab observations, even when applied to modeled rather than experimental structures. It contributes to advancing the next generation of computational antibody design, guided by deeper biological understanding rather than empirical approximation.
Additional information
Contact the research support staff.