Optimization of Recombination Methods and Expanding the Utility of Penicillin G Acylase

Protein engineering can be performed by combinatorial techniques (directed evolution) and data-driven methods using machine-learning algorithms. The main characteristic of directed evolution (DE) is the application of an effective and efficient screen or selection on a diverse mutant library. As it is important to have a diverse mutant library for the success of DE, we compared the performance of DNA-shuffling and recombination PCR on fluorescent proteins using sequence information as well as statistical methods. We found that the diversity of the libraries DNA-shuffling and recombination PCR generates were dependent on type of skew primers used and sensitive to nucleotide identity levels between genes. DNA-shuffling and recombination PCR produced libraries with different crossover tendencies, suggesting that the two protocols could be used in combination to produce better libraries. Data-driven protein engineering uses sequence, structure and function data along with analyzed empirical activity information to guide library design. Boolean Learning Support Vector Machines (BLSVM) to identify interacting residues in fluorescent proteins and the gene templates were modified to preserve interactions post recombination. By site-directed mutagenesis, recombination and expression experiments, we validated that BLSVM can be used to identify interacting residues and increase the fraction of active proteins in the library. As an extension to the above experiments, DE was applied on monomeric Red Fluorescent Proteins to improve its spectral characteristics and structure-guided protein engineering was performed on penicillin G acylase (PGA), an industrially relevant catalyst, to change its substrate specificity.Ph.D.Committee Chair: Bommarius, Andreas; Committee Member: Hu, Wei-Shou; Committee Member: Lee, Jay; Committee Member: Lutz, Stefan; Committee Member: Prausnitz, Mar

Effect of linker flexibility and length on the functionality of a cytotoxic engineered antibody fragment

10.1016/j.jbiotec.2015.02.008Journal of Biotechnology19990-9

Immersive Virtual Reality Training of Bioreactor Operations

This paper explores the application of immersive virtual reality (VR) as a training environment for biopharmaceutical engineering processes. An immersive simulation of a real-world industrial bioreactor setup was developed. This allows repeated practice in a safe and contextualized environment and mitigate limited access to equipment for hands-on training opportunities. It is hypothesized that using VR as an authentic context will assist in achieving the learning outcomes of performing operations in industry standard bioreactor setups, and understanding cellular processes. Two preliminary studies were conducted to evaluate the benefits of the VR application. Using pre- and post tests, there were comparable improvements using the VR application when compared to traditional lab lessons. Qualitatively, participants indicated benefits to learning through repeated practice and immersion in a realistic 3D lab environment. The introduction of gamification methods as motivators are areas to be further explored to better integrate students' theoretical and practical understanding compared to traditional approaches

Mammalian Systems Biotechnology Reveals Global Cellular Adaptations in a Recombinant CHO Cell Line

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