Tuning enzymatic activity by combining Virtual Docking and Residue Interaction Networks

• Main Authors: de los Santos, Yossef Lopez, Fajardo, Ying Lian Chew, Brault, Guillaume, Doucet, Nicolas
• Format: Other/Unknown Material
• Language: unknown
• Published: 2017
• Subjects: -; Antarc*; Antarctica
• Online Access: https://espace.inrs.ca/id/eprint/6715/; https://onlinelibrary.wiley.com/toc/1469896x/2017/26/S1

Within the evolvability landscape of protein engineering approaches, the vast majority of mutations yield neutral, deleterious, or destabilizing effects. It has been demonstrated that stabilizing mutations are usually achieved by random mutagenesis, making the identification of improved protein variants an exhaustive and inefficient process. Herein we present a semi-rational combinatorial approach supported by docking simulations and Residue Interaction Networks (RINs) to design smaller and smarter libraries of mutants. Lipase B from Pseudozyma antarctica (CalB) was selected as an industrially relevant model system. Since CalB displays very low activity towards bulky substrates, the main goal of this project was to develop CalB variants with enhanced synthetic activity towards aromatic substrates like cinnamic and salicylic acid. We used this combined approach to uncover the importance of residues in the CalB active-site cavity and their contribution to the synthetic reaction (Docking), in addition to calculating the energetic contributions upon site-directed mutagenesis (RINs). As a result, we improved the synthetic activity of CalB from 2% to more than 70% of the total substrate-product conversion ratio. This strategy allowed us to obtain more than 5 CalB variants with enhanced activity toward two bulky substrates after only two rounds of directed evolution. The sequential incorporation of favorable mutations increased our chances of selecting improved CalB variants and reduced screening effort. The use of a ‘bottom-up’ strategy such as the RINs allowed us to further understand the effects of mutations throughout the protein structure, a powerful tool for protein engineering purposes.