Chemists in the US have modeled and expressed proteins in bacteria that bind to porphyrin iron complexes and used them to catalyze the cyclopropanation of double bonds and the insertion of diazo compounds into silicon-carbon bonds. In both cases, the chemists write in Science, the de novo-modeled catalysts were effective without the use of directed evolution.
Cyclopropanation and element-hydrogen insertion reactions can be carried out using organometallic catalysts. Typically, in such a catalyst, the metal is bound to a chiral ligand, due to which the reaction product is formed as a single optical isomer. With this approach, choosing a suitable ligand can be difficult, and chemists have to try many different catalysts in search of the most effective one.
Another approach to asymmetric catalysis is to bind organometallic catalysts to proteins. In this case, instead of chiral ligands, the protein structure is responsible for selectivity. If the structure of the active center is chosen correctly, the amino acid residues will "direct" the reaction in the active center so that only one optical isomer of the product is obtained. However, if such a protein catalyst does not work well enough, its structure can be optimized using directed evolution. We talked about how it works in the text "Playing God".
It is this latter approach that chemists led by William F. DeGrado of the University of California, San Francisco, have recently taken. Their idea was to design protein catalysts for the reactions of cyclopropanation of alkenes and insertion into silicon-hydrogen bonds.
First, the chemists took a known catalytically active protein with a diphenylporphyrin cofactor (it forms a complex with an iron ion) and tested it in a reaction of cyclopropanation of styrene with ethyl diazoacetate. The product was obtained with a yield and enantiomeric excess of 40 percent. Then the scientists assumed that the structure of the protein does not allow reagents to easily penetrate the active center, and because of this, the yield is low.
To model more efficient catalysts based on this protein, the scientists used several machine learning algorithms, including one previously developed in their lab. As a result, they obtained a set of several dozen protein structures, from which they selected ten with the most defined active center structure, and expressed them in bacteria. Most of the resulting proteins turned out to be efficient catalysts for styrene cyclopropanation. The best result obtained was a quantitative yield of the product and an enantiomeric excess of about 99 percent.
The chemists then used the same method to model protein structures to catalyze the reaction of insertion of diazo compounds into the silicon-hydrogen bond. But this time, instead of the diphenylporphyrin cofactor, they chose protoporphyrin IX, which acts as a precursor to heme in cells. The chemists again expressed the selected proteins in bacteria, and they turned out to be effective catalysts. In addition, the scientists managed to further increase the enantioselectivity of insertion using the directed evolution of the obtained catalysts in living cells. This worked because the scientists chose the cellular metabolite protoporphyrin IX as a cofactor.
Thus, the chemists managed to obtain a set of protein catalysts for two reactions. But despite the high efficiency of the obtained catalysts, small changes in the structure of the starting materials often led to a drop in yield and enantioselectivity.
Previously, we reported on how chemists used yeast lipase in combination with a ruthenium catalyst for the enantioselective synthesis of macrocycles.