عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
TLpq2235984130
انگلیسی
Engineering Deep Sequencing-Guided Platforms to Evaluate Sequence-Function Relationships between Proteins for the Development of Therapeutic Antibodies
[Thesis]
Medina-Cucurella, Angelica V.
Whitehead, Timothy A.
Michigan State University
2019
182
Ph.D.
Michigan State University
2019
Over the past two decades, monoclonal antibodies (mAbs) have been used as a major class of therapeutic treatments for cancer and autoimmune diseases given their high specificity against a given target antigen. mAbs can work as antagonists by blocking the downstream signaling pathway through receptors or as agonists by boosting the immune system response to direct tumor cell apoptosis. The understanding of the antibody-mediated recognition of pathogens reveals valuable information related to the immune-protective responses within the host organism. Such information has led scientists to develop new effective vaccines and therapeutics. Nevertheless, understanding the physical basis of affinity and specificity in these interactions is a theoretical and experimental challenge. Subsequently, researchers have developed multiple high-throughput approaches, like deep mutational scanning, to identify the relative binding contribution of individual amino acid residues towards the overall antibody:antigen complex. In this dissertation, I present the successful application of deep sequencing-guided engineering platforms to address numerous aims relevant to the protein engineering and antibody discovery field including the understanding of sequence-function relationships between proteins, antibody conformational epitope mapping, and the development of antibody therapeutics. First, we use our pipeline utilizing comprehensive mutagenesis, yeast surface display, and deep sequencing to gain insights on the interactions between interleukin-31, a cytokine involved in chronic skin inflammations, and its receptors. Identification of the binding sites on interleukin-31 by its receptors allows the development of antagonist mAbs to inhibit the downstream signaling pathway. In fact, the mapped conformational epitope of a candidate mAb shows that it inhibits the signaling pathway by binding an overlapping site shared between receptors. A significant limitation of sequence-function mapping by the above method is the requirement that the yeast surface displayed target protein be in a conformation recognizable by the antibody. For example, some proteins such as the neurotrophin family display on the yeast surface in a mostly misfolded or inactive conformation. Consequently, we developed a deep sequencing-guided protein engineering workflow to increase the production of folded canine nerve growth factor, a neurotrophin involved in multiple chronic pain conditions. We identified beneficial mutations within the pro-region of the protein that improved the display of mature, conformationally sensitive protein that enabled the determination of conformational epitopes for multiple antagonist mAbs. Two fundamental limitations in the creation of large mutagenesis libraries using current template-based mutagenesis is the overrepresentation of specific nucleobases and the difficulty of constructing user-defined libraries beyond single site comprehensive codon scanning. We improve on current methods by using unpurified oligo pools to prepare user-define single and double mutagenesis libraries from plasmid DNA. Results demonstrated a near-complete coverage of desired mutations with even representation of nucleobases and few off-target mutations. Lastly, we present a new method guided by next-generation sequencing for the selection in cell lysate of agonists mAb for OX40, a costimulatory immune receptor. This project was performed as an industrial internship during Summer 2018. Synthesized OX40 antibodies after deep sequencing selection with cell lysate showed higher therapeutic potentials compared to antibodies enriched by the traditional soluble selection method.
Bioengineering
Chemical engineering
Medina-Cucurella, Angelica V.
Whitehead, Timothy A.
مطالعه متن کتاب p
[Thesis]
276903
a
Y
