Scientists at the University of Copenhagen’s department of chemistry have discovered a way to monitor enzyme workflows. Their results (“Direct observation of Thermomyces lanuginosus lipase diffusional states by Single Particle Tracking and their remodeling by Mutations and Inhibition”) appear in Scientific Reports.
“Lipases are interfacially activated enzymes that catalyze the hydrolysis of ester bonds and constitute prime candidates for industrial and biotechnological applications ranging from detergent industry, to chiral organic synthesis. As a result, there is an incentive to understand the mechanisms underlying lipase activity at the molecular level, so as to be able to design new lipase variants with tailor-made functionalities. Our understanding of lipase function primarily relies on bulk assay averaging the behavior of a high number of enzymes masking structural dynamics and functional heterogeneities.
“Recent advances in single molecule techniques based on fluorogenic substrate analogues revealed the existence of lipase functional states, and furthermore so how they are remodeled by regulatory cues. Single particle studies of lipases on the other hand directly observed diffusional heterogeneities and suggested lipases to operate in two different modes. Here to decipher how mutations in the lid region controls Thermomyces lanuginosus lipase (TLL) diffusion and function we employed a Single Particle Tracking (SPT) assay to directly observe the spatiotemporal localization of TLL and rationally designed mutants on native substrate surfaces. Parallel imaging of thousands of individual TLL enzymes and HMM analysis allowed us to observe and quantify the diffusion, abundance, and microscopic transition rates between three linearly interconverting diffusional states for each lipase.
“We proposed a model that correlates diffusion with function that allowed us to predict that lipase regulation, via mutations in lid region or product inhibition, primarily operates via biasing transitions to the active states,” the authors wrote.
“We have never been capable of witnessing what enzymes do while they work. It is the same as, not just being able to observe someone going to and from work, but having the ability to see what they are doing while at work and seeing how effective their work is,” according to Søren Schmidt-Rasmussen Bohr, whose doctoral thesis is based upon the research.
He explained that being able to monitor enzymes and map their workweek makes it possible to target the amino acid composition of enzymes, which directly controls their function.
With an understanding of how various amino acids in enzymes work, one can begin to customize enzymes and make them far more effective, according to the research team. A few of the more evident examples include the design of enzymes that more efficiently convert straw into biofuels as well as designs that reduce the concentration of enzymes in washing powders, where a few effective leftovers become tough enough to get the job done.
“Depending on the enzyme, it could be advantageous to either prolong the amount of time they work or make them more effective while at work. This will make many industrial processes both cheaper and greener,” said associate professor Nikos Hatzakis, PhD, who directs the research.
What Schmidt-Rasmussen Bohr hopes for most is that the new approach can be a step towards creating more effective enzymes for drug manufacturing that serves to reduce the toxic footprint of the pharmaceutical industry. More efficient enzymes will result in fewer waste products and manufacturing at lower temperatures, which will reduce carbon dioxide emissions.
“With better enzymes, one can simplify the chemical processes needed to manufacture pharmaceuticals, which will ultimately lead to a reduction in drug costs,” added Schmidt-Rasmussen Bohr.
The researchers have combined a method known as Single Particle Tracking, whereby the position and speed of enzymes is observed, with advanced data processing that can predict how long enzymes are at work and at pause. In practice, advanced fluorescence microscopy is used to zoom in on the nanoscale and observe the movements of individual enzymes. Thereafter, statistical models are deployed to determine what the enzymes are actually up to as they move over and interact with fats.
Until now, most targeted enzyme development has been accomplished by randomly swapping some amino acids for others, which has made it quite complicated to produce the best possible enzymes for a given purpose.