Protein Engineering

Zymeworks protein engineering process uses a unique two-expert approach. Proprietary biophysical insight into the protein system and targets is generated by the ZymeCAD™ platform. In-house protein engineers then use the detailed insight gained from the ZymeCAD™ platform to make knowledge-based decisions regarding structural modifications. Zymeworks’ rigorous analysis of structural data coupled with ZymeCAD™ based computational modeling of protein dynamics, energetics and thermodynamics effectively expands the scope of traditional structure guided protein optimization.

The protein engineering process initially involves an extensive system-related structure and data gathering effort that provides the groundwork for knowledge driven model building and refinement. Different components in the ZymeCAD™ platform are applied to the resultant molecular model to calibrate a variety of biophysical characteristics compared to known experimental data about the system. The technique iteratively refines the quality of structural models using a tight integration of data and simulation driven insights, providing a greater degree of realism for rational protein engineering.

Detailed structure, dynamics and energetic characterization help to profile critical hotspot positions in the protein. These extensive conformational dynamics studies are able to provide insight into the impact of distal residues on protein characteristics of interest. These details are invaluable in classifying amino acid position as either congruous or incongruous to mutations for achieving the target activity of interest.

Building on this characterization, Zymeworks’ protein engineers are able to use other components in the ZymeCAD™ platform to extensively model numerous single point and multisite mutations involving these hotspot positions. Apart from remodeling and refining the structure with the altered side chain compositions and coupled backbone conformational changes, extensive scoring techniques are employed to evaluate the stability and other functionally relevant characteristics of the protein. A limited number of the promising protein candidates are expressed and assayed using a variety of in vitro techniques such as chromatography, differential scanning calorimetry, surface plasmon resonance or cell based methods where appropriate. This data is used to drive subsequent iterative optimization cycles of the lead candidates.