Computer method identifies potentially active enzymes

Better drugs, improved industrial applications and even cleaner laundry may be possible with a new computer method to predict which hybrid enzymes are likely to have high activity, according to a team of Penn State chemists and chemical engineers.

"FamClash is quite successful at qualitatively predicting the pattern of the specific activity of the hybrids," the researchers report in this week's online issue of the Proceedings of the National Academy of Sciences. "By identifying incompatible residue pairs in the hybrids, this method provides valuable insights for protein engineering interventions to remedy these clashes," the researchers say. FamClash is a computer method used to predict which hybrid enzymes are likely to have activity and which are not. Hybrid enzymes form when researchers combine similar enzymes from two or more different organisms. The variant enzymes are broken and recombined with parts from the original enzymes creating the new one.

"We have worked out ways to make libraries of novel enzymes by splicing proteins together," says Alexander R. Horswill, postdoctoral fellow in chemistry. "We wanted to know how active the new enzymes would be compared to the wild type."

Industrial processes use enzymes when reactions are too slow or too expensive to carry out without a catalytic boost. The most familiar use of enzymes is in laundry detergents where dirt-removing enzymes can gobble up stains even in cold water.

" It is hard to create an enzyme that is better than what occurs in nature," says Horswill. "But the FamClash approach will aid in engineering enzymes to work better in unnatural conditions, such as low or high temperatures, basic or acidic environments or organic solvents."

Horswill and Stephen J. Benkovic, the University professor, the Evan Pugh Professor of Chemistry and holder of the Eberly Chair in Chemistry, used enzymes from Escherichia coli and Bacillus subtilis, two common bacteria. Both p

Contact: A'ndrea Elyse Messer
Penn State

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