Towards The Rational Design Of A Target-Specific Antibody
Mahajan, Sai Pooja
Antibodies and antibody-fragments have emerged as promising tools for many therapeutic and biotechnological applications. Antibody fragments (e.g., scFvs, Fabs, VHHs) derive functionality via their variable domains, which bind to a target (antigen) of interest. Antibody fragments obtained from conventional antibodies (i.e., human or mice IgGs) comprise two chains: variable heavy and variable light. Nanobodies (hereafter VHHs) are unique antibodies found in camelids. VHHs are the smallest naturally occurring binding domains and derive functionality via a single variable domain on a heavy chain. Only 3 hypervariable loops (H1, H2, H3) form the antigen-binding surface as opposed to 6 loops in conventional antibody fragments (3 from heavy and 3 from light chain). Due to their small size and surprising ability to bind a wide range of antigens with high specificity and affinity, VHHs are excellent candidates for antibody engineering. Despite their recent discovery, many engineered VHHs have already entered into clinical trials for treatment of a range of human diseases. It is our aim to rationally engineer VHHs with specificity for a target antigen by tailoring the hypervariable loops. As a first step toward such a goal, the design of loops with a desired conformation was considered. As proof-of-concept and to build our understanding of the binding loops of VHH antibodies, the study focused on the H1 loop of the anti-human Chorionic Gonadotropin (hCG) llama VHH that exhibits a noncanonical conformation. This loop was redesigned to "tilt" the stability of the loop structure from a noncanonical conformation to a (humanized) type 1 canonical conformation by studying the effect of selected mutations to the amino acid sequence of the H1, H2, and proximal residues. To test and extend our understanding of antigen-binding by VHHs, a dual modelingexperimental approach was pursued for designing a VHH specific to Alpha-Synuclein (AS). AS is the main pathological marker and perhaps the causative agent of Parkinson's disease. Starting from an immunized Camelid library against the Non-amyloid component (NAC) region of A53T mutant of AS (A53T), a bacteria-based selection technique was used to obtain a NAC-specific VHH, followed by computational modeling of the VHH and the VHH-NAC binding. The use of FLITRAP (an E. Coli based high-throughput screening technique) allows us to select for a soluble and intracellularly stable VHH (intrabody). Furthermore, using computational modeling the following tasks were completed: 1) Propose possible conformations of the VHH binding region, 2) Postulate viable modes of VHH binding to the NAC region, 3) Propose mutations that would enhance binding and ultimately, 4) Validate the proposed predictions through experiments. Counterintuitively, it was found that while mutations targeting the central hydrophobic NAC region only led to weak binding affinities, mutations, at the periphery of the binding site, that target the charged flanking hydrophilic residues of NAC are key to substantially increase binding affinity. The main goal of this research was hence to demonstrate the possibility of developing a model of binding in-silico starting from the amino acid sequence of the Antibody and the antigen and using it to predict affinity-enhancing mutations. This work differs from many other structure-based design studies in that the crystal structure of neither the Antibody, the antigen, or the complex is known; it hence tackles a much more challenging (but common) situation. This work also differs from high-throughput screening techniques based on multiple rounds of screening to obtain a high-affinity binder. Our dual experimental-modeling approach can be considered as an important step towards developing rational design strategies based on ab-initio modeling and bottom-up design approaches, which would ultimately enable us to gain a deeper understanding of protein surfaces and interactions.
Rational Design; Antibody; Parkinsons Disease
Loring,Roger F; Delisa,Matthew
Ph. D., Chemical Engineering
Doctor of Philosophy
dissertation or thesis