Synthetic Biology Approach to Systematic Optimization of Protein Function

Current gene synthesis technologies allow precise control of all sequence features critical for biological function. Variables at the levels of amino acid substitutions, vector elements and host genome mutations can all be critical for achieving the desired output. Modern machine learning tools and algorithms are well equipped to identify and quantify the relative contributions of each variable. We can address questions related to additivity and multi-dimensional effects of substitutions on various properties and activities that can be measured accurately under commercially viable condition.

We present data and models from several projects illustrating the advantages of this approach.

References:

ACS Synth Biol 2014. Mapping of Amino Acid Substitutions Conferring Herbicide Resistance in Wheat Glutathione Transferase. Govindarajan et al.

J Am Chem Soc 2013. Improved biocatalysts from a synthetic circular permutation library of the flavin-dependent oxidoreductase Old Yellow Enzyme. Daugherty et al.

Protein Eng Des Sel 2013 26(1):25-33. Redesigning and characterizing the substrate specificity and activity of Vibrio fluvialis aminotransferase for the synthesis of imagabalin. Midelfort, KS. et al.