Optimization of Therapeutic Protein PEGylation by Integrating Coarse-grain Simulation and Cell-free Protein Synthesis

ABSTRACT The development of protein-based therapeutics has ushered in a new era of precision medicine, delivering hope and healing to millions facing severe and life-threatening conditions. Unfortunately, as natural biopolymers, protein therapeutics have fundamental limitations compared to small molecule therapeutics. Specifically, most proteins found outside their native location in the body are degraded, recognized by the immune system, lose stability/function, or are naturally cleared-a fatal flaw for a therapeutic. To overcome these limitations, therapeutic proteins may be functionalized with chemical groups to obscure their identity or increase stability. In 1990, the FDA first approved the covalent attachment of the polymer polyethylene glycol (PEG) to therapeutic proteins towards this end. When done properly, PEGylation improves the pharmacokinetic half-life, provides some masking from the immune system, and reduces side effects due to lower dosage frequency. The problem is that PEG attachment can only be done with certain amino acids (e.g. lysine, N-terminus), so control is very limited. Additionally, covalently attaching a PEG molecule to a protein is complex because this chemical modification alters the interactions and positions of the amino acid side chains, disrupting protein function. This disruption commonly outweighs the benefit, such that only 3% of FDA approved therapeutic proteins are PEGylated. This work seeks to greatly expand the benefit of PEGylating protein therapeutics by combining state of the art, computationally efficient coarse-grain molecular simulations with rapid cell-free protein synthesis and site- specific functionalization. Preliminary results have demonstrated that this simulation approach can predict with significant accuracy the optimal amino acid among the hundreds in a protein to target for PEGylation. Additionally, the cell-free protein synthesis approach allows for site-specific incorporation of a uniquely functionalized unnatural amino acid at any desired position-a capability which enables site-specific optimization of PEGylation and is crucial to broad application of the method. Using a design-build-test-learn strategy, parameterization of PEG-amino acid interactions will be incorporated into the simulation for the first time, which is hypothesized to greatly increase its accuracy. This approach will be validated with two protein therapeutics which have not yet been optimally PEGylated (Onconase for Mesothelioma/Ebola Virus Disease treatment and Crisantaspase for Acute Lymphoblastic Leukemia treatment) to improve their efficacy by increasing their activity, stability and retention.