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 weeks 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 produce forms of dihydrofolate reductases or DHFR that are 44 percent identical at the protein level. ITCHY or incremental truncation for the creation of hybrid enzymes was used to splice these DHFR enzymes together. Libraries of new and potentially interesting enzymes were created, but these new proteins do not necessarily have any enzymatic activity and therefore many of them were tested in the laboratory for activity. Working on the computer, rather than in the laboratory, Manish C. Saraf, graduate student in chemical engineering and Costas D. Maranas, associate professor of chemical engineering developed FamClash to understand and predict which combinations of pieces from the original enzymes would cause clashes and diminish activity and which will form active hybrid enzymes.
“First we have the computer program generate all the hybrids that could form using ITCHY,” says Saraf. “Then we look at every residue combination in each hybrid for pair clashes.”
To function properly, protein strands need to fold in a specific way so that certain domains are next to or aligned with other domains. Both forms of enzymes studies here have similar structure and function, however, clashes occur in hybrids when they retain fragments from original enzymes that are not compatible with each other.
“Pairs of residues that are too big, or too small, or have the wrong electrical charge can cause these clashes that prevent these hybrids from folding correctly,” says Saraf. “We hypothesize that the greater the number of clashes that exist in the hybrids, the less likely it is to fold correctly and therefore lower activity will be present.”
The hybrid combinations are then ranked for predicted enzyme activity based on the number of clashes present. “It is very helpful to experimentalists to know where introduced crossovers will produce high activity,” says Horswill. “The long-term goal is to engineer enzymes for specific functions.”
This engineering might come about by altering the residues so that clashes no longer exist. At this point, the researchers consider all clashes equal in reducing activity, but this is not necessarily true. Some clashes may be much more damaging than others.
“Now we assume that more clashes are worse, but we do not really know that,” says Saraf. “We want to see what happens if we eliminate all clashes. Will it have equal activity? We are hoping that will tell us which predictions are right and which are wrong. ”
The researchers have also tried the approach on other enzyme systems and observed similar trends in prediction.
The National Science Foundation and the National Institutes of Health supported this research.
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