Application of In-Silico Protein Engineering and Optimization Methods to Design Target-Specific Antibody Mimetics

Abstract

Antibodies (Abs) are proteins that bind target antigens (Ags) and interact with immune receptors. Over the years they have been developed into therapeutics with tremendous potential to treat various diseases. Their global market share is projected to cross $500 Billion by 2030. As excellent as therapeutic antibodies are when administered to patients, they exhibit limitations associated with solubility, aggregation propensity, and degradation tendency when working outside the body. Therefore, it is important to move beyond conventional Abs and develop alternative protein binders that can mimic their functions while being able to address these limitations at the same time. Previously, several works have developed in-silico algorithms to design functioning proteins to bind their targets. However, they typically generate many predictions, which are practically unfeasible for experimental testing due to high cost and time constraints. This dissertation demonstrates the application of complex scientific protocols to shortlist the most promising computational designs from a large dataset of predictions most worthy of experimental testing. Chapter Two will use a novel PETEI algorithm to design thousands of 10th fibronectin type III domains to bind 3 distinct tag epitope peptides, FLAG, HA, and MYC. Since it is unfeasible to test all the designs, they will be energy minimized using CHARMM and Rosetta forcefields based on three or fewer positive CDR residues. The Rosetta Interface Analyzer will calculate and analyze their binding energy per buried surface area. Similar binding metrics will be calculated for an energy minimized, non-redundant 231 Ab-protein database and compared to the designs. The designs will be rank ordered and the top 30 will be selected for experiments. This chapter is part of a bigger project, submitted for publication as of July 2024. 3 Chapter Three will use another novel algorithm called MutDock to design 2145 DARPin – α-Cobratoxin complexes. They will be down-selected to the top 39 using the scientific protocols mentioned previously, and affinity matured using specific RosettaDesign protocols to improve binding. 3900 affinity matured designs will be narrowed down to 73 by applying similar protocols and visually inspected in UCSF Chimera. Short 5 ns NAMD MD simulations will be conducted to assess their structural integrity over time and conformational binding metric trajectory will be plotted against time to analyze their characteristics. Their standard deviations will be calculated and this analysis will assist in down-selecting the top 51 designs for experiments. Chapter Four will use tools like CamSol, HoTMuSiC, SCooP, IEDB De-immunization, and RosettaDesign to fine-tune the physicochemical properties of 5 α-helical bundles to arrive at the desired protein. These top 5 were shortlisted from a list of 75 similar structures after discarding the ones with Cys residues, de novo designs, extra end loop, and the position of an end loop to cause steric hindrance. RosettaFold predicted 1 out of 5 re-designed sequences to fold to their native state. This binder will be the starting point for the next 2 more iterative rounds of fine-tuning to generate 16 different ‘ideal’ binding proteins. Therefore, observably, the level of complexity of the development and applied scientific protocols implemented in the subsequent chapters keeps increasing progressively which leads to a more sophisticated down-selection process for designs worthy of experiments. Chapter Five will elucidate the usage of I-Tasser, Robetta, and trRosetta to model 3 anti-CTLA4 nanobodies and canine CTLA4 protein. ZDOCK was used to dock them, which resulted in 85 of the 90 predictions showing the nanobodies binding to the CTLA4 conserved epitope. This was a modeling of experimental results, and it was published in Scientific Reports. Chapter Six will explain the future directions of the current projects.