Ph.D Thesis

Ph.D StudentPiran Ron
SubjectBiocatalysis with Antibody Heavy Chain and Myogenesis as
a Computational Process
DepartmentDepartment of Chemistry
Supervisors PROFESSOR EMERITUS Ehud Keinan
PROF. Yoram Reiter
Full Thesis textFull thesis text - English Version


Biocatalysis with Antibody Heavy-chain

Our original goal was to improve the catalytic activity of aldolase antibodies by methods of directed evolution. While constructing libraries from several aldolase antibodies, we noticed that these antibodies and particularly 38C2 were toxic to bacteria. Attempts to express 38C2 in the form of a Fab construct as well as individual chains resulted in bacterial death. Furthermore, when expressing the heavy-chain of 38C2 in the plant Arabodpsis thaliana caused severe growth-defects. Consequently, we hypothesized that the origin of these phenomena was an aldolase catalytic activity of the heavy-chain. To examine this hypothesis we separated antibodies 38C2, 93F3, 84G3 and 24H6 by chemical methods into their individual chains and tested their catalytic activity. We found that when the active lysine residue was located on the heavy-chain its catalytic activity was retained whereas in cases where the catalytic lysine was located on the light-chain, the catalytic activity was lost. These findings define a novel minibody, which comprises a single domain heavy-chain of IgG aldolase catalytic antibody. The catalytic activity of this construct is surprisingly similar to that of the entire IgG molecule. We have demonstrated the minibody catalytic activity in-vivo with either E. coli or A. thaliana as well as in-vitro experiments. The heavy-chain monomer is the smallest construct created that possesses both ends of the antibody molecule: the variable domain as well as the constant domains, Fc. While in this work we show that the variable domain of the heavy-chain retains its substrate binding and catalysis, it still remains to be investigated whether or not the Fc part of the heavy-chain retains its biological functions.

Myogenesis as a Computational Problem

The satisfiability (SAT) formalism has been primarily applied for solving decision-making problems. Treatment of biochemical events with this formalism is highly advantageous because it provides simple conceptual tools for describing the relationship between causes and effects, with the kinetic parameters being irrelevant. Surprisingly, although such formalism has been used for describing complex genomic systems, it has not been applied to developmental biology, where all pathways can be clearly viewed as decision-making processes. Here we demonstrate the advantages of applying the Łukasiewicz logic to a diffusible protein system that leads to myogenesis. Furthermore, creating an automaton that describes the myogenesis SAT problem has led not only to a comprehensive overview of these non-trivial phenomena, but also to a hypothesis that was subsequently verified experimentally.