An Integrated Computational Framework for Optimally Allocating Diversity in Directed Evolution Studies

The Principal Investigator's (PI) research plan proposes an integrated computational framework that will aid in enhancing the portion of combinatorial DNA libraries, generated through directed evolution methods, that codes for proteins with the desired functionality. By linking genomic information in the form of protein family member sequence data with predictive modeling frameworks, the presence of improved protein hybrid phenotypes in the combinatorial libraries will be enhanced.

Directed evolution methods rely on the mixing of genetic material between an original small library of DNA sequences through recombination and/or mutagenesis. This diversity generating step produces a new expanded library spanning a much wider range of DNA sequence space.

Many directed evolution success stories have been reported, ranging from peptide therapeutics to industrial enzymes. It is widely accepted that screening is the most expensive, time consuming, labor intensive and thus limiting step in directed evolution studies. This project will help alleviate this bottleneck to discovery in the fertile area of combinatorial protein engineering. By boosting the percentage of functional members in the combinatorial library, the chances of identifying novel proteins will be substantially improved while simultaneously reducing the extent of screening.