Developing peptide based capture agents for diagnostics and therapeutics

Iterative in situ click chemistry (IISCC) is a robust general technology for development of high throughput, inexpensive protein detection agents. In IISCC, the target protein acts as a template and catalyst, and assembles its own ligand from modular blocks of peptides. This process of ligand discovery is iterated to add peptide arms to develop a multivalent ligand with increased affinity and selectivity. The peptide based protein capture agents (PCC) should ideally have the same degree of selectivity and specificity as a monoclonal antibody, along with improved chemical stability. We had previously reported developing a PCC agent against bovine carbonic anhydrase II (bCAII) that could replace a polyclonal antibody. To further enhance the affinity or specificity of the PCC agent, I explore branching the peptide arms to develop branched PCC agents against bCAII. The developed branched capture agents have two to three fold higher affinities for the target protein. In the second part of my thesis, I describe the epitope targeting strategy, a strategy for directing the development of a peptide ligand against specific region or fragment of the protein. The strategy is successfully demonstrated by developing PCC agents with low nanomolar binding affinities that target the C-terminal hydrophobic motif of Akt2 kinase. One of the developed triligands inhibits the kinase activity of Akt. This suggests that, if targeted against the right epitope, the PCC agents can also influence the functional properties of the protein. The exquisite control of the epitope targeting strategy is further demonstrated by developing a cyclic ligand against Akt2. The cyclic ligand acts as an inhibitor by itself, without any iteration of the ligand discovery process. The epitope targeting strategy is a cornerstone of the IISCC technology and opens up new opportunities, leading to the development of protein detection agents and of modulators of protein functions.

Development of protein-catalyzed capture (PCC) agents with application to the specific targeting of the E17K Point mutation of Akt1

This thesis describes the expansion and improvement of the iterative in situ click chemistry OBOC peptide library screening technology. Previous work provided a proof-of-concept demonstration that this technique was advantageous for the production of protein-catalyzed capture (PCC) agents that could be used as drop-in replacements for antibodies in a variety of applications. Chapter 2 describes the technology development that was undertaken to optimize this screening process and make it readily available for a wide variety of targets. This optimization is what has allowed for the explosive growth of the PCC agent project over the past few years.

These technology improvements were applied to the discovery of PCC agents specific for single amino acid point mutations in proteins, which have many applications in cancer detection and treatment. Chapter 3 describes the use of a general all-chemical epitope-targeting strategy that can focus PCC agent development directly to a site of interest on a protein surface. This technique utilizes a chemically-synthesized chunk of the protein, called an epitope, substituted with a click handle in combination with the OBOC in situ click chemistry libraries in order to focus ligand development at a site of interest. Specifically, Chapter 3 discusses the use of this technique in developing a PCC agent specific for the E17K mutation of Akt1. Chapter 4 details the expansion of this ligand into a mutation-specific inhibitor, with applications in therapeutics.

Protein structure refinement algorithms

Protein structure prediction has remained a major challenge in structural biology for more than half a century. Accelerated and cost efficient sequencing technologies have allowed researchers to sequence new organisms and discover new protein sequences. Novel protein structure prediction technologies will allow researchers to study the structure of proteins and to determine their roles in the underlying biology processes and develop novel therapeutics.

Difficulty of the problem stems from two folds: (a) describing the energy landscape that corresponds to the protein structure, commonly referred to as force field problem; and (b) sampling of the energy landscape, trying to find the lowest energy configuration that is hypothesized to be the native state of the structure in solution. The two problems are interweaved and they have to be solved simultaneously. This thesis is composed of three major contributions. In the first chapter we describe a novel high-resolution protein structure refinement algorithm called GRID. In the second chapter we present REMCGRID, an algorithm for generation of low energy decoy sets. In the third chapter, we present a machine learning approach to ranking decoys by incorporating coarse-grain features of protein structures.