Computer Model Predicts Outcome Of DNA Shuffling.(Brief Article)

2001 MAR 28 - (NewsRx.com & NewsRx.net) --

Industries using DNA shuffling to improve enzymes, therapeutic proteins, vaccines, and viral vectors may soon have a computational method for predicting the number and likely locations of crossovers, according to a Pennsylvania State University research team.

"To date, the application of these methods has been based on experience and empirical methods and there was no model to understand the process which can be time consuming, expensive and of uncertain outcome," says Costas D. Maranas, assistant professor of chemical engineering. "We used thermodynamics and reaction engineering to evaluate and model this complex reaction network so we can now predict where the DNA from different parent genes will recombine."

DNA shuffling uses related genes from different species or genes with related function, fragments them, and reassembles then through recombination. Researchers then place recombined genes into the Escherichia coli bacterium to identify which new genes produce usable or potentially interesting products. Those genes that express a potentially interesting protein or enzyme are again fragmented and reassembled to form new recombinant genes. The process continues until a protein with the desired qualities is found. "Beginning with genes that produce enzymes that are moderately good detergents, for example, the process iteratively searches for enzymes that are better detergents," says Maranas.

The important factors in creating recombined genes with DNA shuffling include the temperature at which annealing - joining of single-stranded DNA induced by cooling - occurs, the similarity of the genes, and the size of the DNA fragments. The computer program developed by Maranas; Gregory L. Moore, graduate student in chemical engineering; Stefan Lutz, postdoctoral fellow in chemistry; and Stephen J. Benkovic, the Evan Pugh Professor of Chemistry and holder of the Eberly Chair in Chemistry, was described in the March 13, 2001, issue of the Proceedings of the National Academy of Sciences.

The mathematical model, which provides a predictive framework for DNA shuffling, looked into how fragment length, annealing temperature, sequence identity, and the number of shuffled parent sequences affect the number, type, and distribution of crossovers along ...