At the time of Drs. Shiran Barber-Zucker entered the lab of Prof. Sarel Fleishman as a postdoctoral fellow, chose to pursue an environmental dream: breaking down rubbish with essential chemicals. Nature has a clever way of dissolving solids: For example, dead trees are regenerated by white powdery mildew, whose enzymes break down the wood into nutrients that return to the soil. So why not turn the same enzymes into man -made waste?
The Barber-Zucker problem is that these enzymes, called versatile peroxidases, are not completely stable. “These natural enzymes are natural prima donnas; they’re very difficult to work with,” said Fleishman, of the Biomolecular Science Department at the Weizmann Institute of Science. In recent years, his lab has developed statistical methods that are used by thousands of research organizations around the world to develop enzymes and other proteins with consistent and consistent enhancement. desired property. To use this method, however, the exact molecular nature of a protein must be known. Normally, the protein must be firmly fixed to form crystals, which can be fixed with X -rays to show their shape in 3D. This technique is combined with the use of lab algorithms to develop a refined protein that is not synthetic. But if the basic protein can’t be made in the lab or is too soft to form crystals, such as the versatile peroxidases, those tests can improve running a lethal effect.
Barber-Zucker, however, has had some time on prima donna enzymes, and his timing is amazing. Since the 1980s, attempts have been made to circumvent the efficiency of crystallization by predicting the 3D appearance of a protein from its DNA sequence, but for complex proteins such as peroxidases, these predictions are unreliable. However, by the end of 2020, a few weeks after the start of his project, the enzyme events predicted by Barber-Zucker were immediately apparent. It was only then that the Google DeepMind team and some other university research groups improved their AI -based predictive techniques to the point where they became more accurate. This proved to be a game changer: The path led to predictive features that were almost exactly the same as those obtained in the experiment with crystallography.
Supported with new facilities, Barber -Zucker, with colleagues – Vladimir Mindel and Jonathan J. Weinstein, research students at Fleishman’s lab, and Prof. Miguel Alcalde and Drs. Eva García Ruiz of the Institute of Catalysis in Madrid – accomplished the unexpected. There is only one enzyme in the versatile peroxidase family described by the researchers, and that project was taken to a team of experts about a decade ago. Now, in less than six months and with no prior knowledge of drug-degrading enzymes, Barber-Zucker and colleagues have developed the design, production and monitoring of different types of three versatile peroxidases whose basic powers could not, previously, be produced in the lab. Scientists have been using 3D models created by AI in their early days. They used these features in an algorithm developed in Fleishman’s lab called Protein Repair One Stop Shop, or PROSS, which designs a computer -modified protein to improve its properties. on demand.
This collaborative approach opens up a vast array of opportunities. “Millions of valuable proteins that cannot be found biochemically are currently available for research and for use in biomedicine and chemistry,” Fleishman said. He points to the fact that 3D structures have been corrected in the experiment for less than 0.05 percent of the millions of naturally occurring proteins in the DNA sequence, which cannot be accurately described and tested locally. half of all total protein in nature. “These proteins are the dark side of biology – there’s no way scientists can accurately determine their function. But now this question has become irrelevant; can we navigate with a design or not. No, that’s a real change. “
Drug development is another area that can immediately benefit from this progress. For example, antibodies produced in lab animals need to be converted to humans before they can be used in a clinical setting – a complex process that involves crystallization and conversion. Measuring many parts of the animal root. Recent advances are expected to make this and the antibody technology processes more efficient and effective.
Environmental research, which is the basis for this research, is another approach. The degrading enzymes can be converted to wood to recycle hard farm waste. Instead of burning the waste or dissolving it with polluting chemicals, as is often the case today, it may be broken down, using versatile peroxidases, into of sugar cane that can be baked into biofuel. Then farmers can re -create smaller bioreactors.
Enzymes can be designed to contaminate environmental pollutants. In fact, Barber-Zucker has previously shown that its improved enzymes can attack a complex of contaminated water. He also found that each of the three enzymes improved had been shown to perform a different function in the lab, and each specialized in degrading different pieces of wood, indicating that can work synergistically. Importantly, the three enzymes proved to be stable and resistant to heat, an important feature for their use in the industry. Barber-Zucker is currently planning to develop a “cocktail” enzyme, which will work together with different types of enzymes, with its versatile peroxidases, to break down waste wood into biofuel or biofuels. other necessities.
And what does he know about recycling hard plastics using these enzymes? “That’s still a dream, but it could become a reality in the future,” he said.
The research was published in Journal of the American Chemical Society.
Development and reflection on the design of high-function proteins: Evolution-guided atomistic design
Shiran Barber-Zucker et al, Stable and Functionally Versatile Peroxidases developed directly from the following, Journal of the American Chemical Society (2022). DOI: 10.1021 / jacs.1c12433
Presented by Weizmann Institute of Science
Directions: AI provides algorithms with ways to design biomolecules with a wide range of economic functions (2022, April 13) Retrieved 13 April 2022 from https://phys.org/news/2022-04 -ai-algorithms-biomolecules-huge-range .html
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