Designing Enzymes from Scratch: A Breakthrough in Chemistry

Researchers at UC Santa Barbara, UCSF, and the University of Pittsburgh have made a groundbreaking discovery in chemistry, enabling the design of enzymes from scratch. This breakthrough has far-reaching implications for various fields, including drug development, materials science, and biotechnology.

According to Professor Yang Yang, a senior author on the paper, “If people could design very efficient enzymes from scratch, you could solve many important problems.” De novo enzyme design can overcome limitations in function and stability found in natural catalysts without losing their inherent selectivity and efficiency.

Catalysts, both biological and synthetic, are the backbone of chemistry. They accelerate reactions that change the structures of target molecules. Enzymes, in particular, are “nature’s privileged catalysts” due to their high level of selectivity and efficiency. However, natural enzymes tend to function under narrow conditions, favoring specific molecules and environments.

To address this limitation, scientists have turned to de novo protein design – a bottom-up approach that uses amino acid building blocks to create proteins with specific structures and functions. De novo proteins are relatively small, which provides favorable efficiency relative to most enzymes. They also exhibit excellent thermal and organic solvent stability, allowing for wider temperature ranges and up to 60% of organic solvents.

The researchers demonstrated their proof-of-concept by using de novo protein design to create enzymes that can form carbon-carbon or carbon-silicon bonds – a challenging transformation that requires efficient natural enzymes. They used a helical bundle protein as a framework, which they then modified using state-of-the-art artificial intelligence methods to design sequences of amino acids with the desired functionalities and properties.

The initial results showed reasonable catalysts but not the best due to modest efficiency and selectivity. However, after a second round of design using a loop searching algorithm, four out of 10 designs exhibited high activity and excellent stereoselectivity.

This breakthrough demonstrates that de novo protein design can be a powerful tool in catalysis, offering chemists more efficient and selective reactions as well as products that aren’t easily reached with natural enzymes or small-molecule synthetic catalysts. Further work will involve exploring ways to mimic natural enzyme function with simpler, smaller but equally active de novo enzymes and generating de novo enzymes that operate via mechanisms not previously known in nature.

Research in this paper was conducted by Kaipeng Hou, Wei Huang, Miao Qui, Thomas H. Tugwell, Turki Alturaifi, Yuda Chen, Xingjie Zhang, Lei Lu, and Samuel I. Mann.

New Computer Language Unlocks Hidden Pollutants in Environmental Data

In a breakthrough for environmental science, researchers at UC Riverside have developed a programming language called Mass Query Language (MassQL) that enables biologists and chemists to quickly identify previously unknown pollutants in massive chemical datasets. This innovative tool has already helped scientists discover toxic compounds hidden in plain sight.

The power of MassQL lies in its ability to function like a search engine for mass spectrometry data, which is akin to a chemical fingerprint. By making it easier to search these vast datasets, the language allows researchers to find patterns that would otherwise require advanced programming skills. This has significant implications for environmental science, as scientists can now quickly identify pollutants in water, air, and other samples.

Developed by Mingxun Wang, an assistant professor of computer science at UC Riverside, MassQL was created to empower chemists and biologists without extensive coding experience to mine their data exactly how they want. This user-friendly approach has the potential to revolutionize environmental research, enabling scientists to quickly identify pollutants and develop strategies for removal.

One notable example of MassQL’s effectiveness is its use by Nina Zhao, a UCR postdoctoral student now at UC San Diego. She employed the language to sift through the entire world’s mass spectrometry data on water samples, searching for organophosphate esters – compounds commonly found in flame retardants. The results were staggering: MassQL pulled out thousands of measurements, including some chemicals that have not been previously described or catalogued.

These findings highlight the importance of MassQL in environmental science. By providing a powerful tool for identifying pollutants, researchers can now develop strategies to address these toxic compounds and protect human and animal health.

MassQL’s development was made possible by a collaborative effort involving over 70 scientists from various fields. This consensus-driven approach ensured that the language would be useful across multiple disciplines and real-life situations.

The potential applications of MassQL are vast, ranging from detecting fatty acids as markers of alcohol poisoning to identifying new drugs to combat antibiotic resistance. The research team has demonstrated the effectiveness of the language in a variety of scenarios, including finding forever chemicals on playgrounds.

As Wang notes, “I wanted to create one language that could handle multiple kinds of queries. And now we have. I’m excited to hear about the discoveries that could come from this.”

With MassQL, researchers can now quickly identify pollutants and develop strategies for removal, paving the way for a cleaner, healthier environment for all.

The threat of future pandemics has been heightened by the emergence of new influenza viruses. In recent years, researchers from the Helmholtz Centre for Infection Research (HZI) and the Medical Center — University of Freiburg have made significant progress in understanding how these viruses interact with host cells.

Led by Professor Christian Sieben’s team at HZI, scientists have developed a novel method to study the initial contact between influenza viruses and host cells. This breakthrough allows researchers to investigate the complex process of viral entry in unprecedented detail.

The researchers immobilized individual viruses on microscopy glass surfaces and then seeded cells on top. This innovative “upside-down” experimental setup enables scientists to analyze the critical moment when viruses interact with cells but do not enter them, stabilizing the initial cell contact for further investigation.

Using high-resolution and super-resolution microscopy, the team demonstrated that contact between the virus and the cell surface triggers a cascade of cellular reactions. The accumulation of local receptors at the binding site, the recruitment of specific proteins, and the dynamic reorganization of the actin cytoskeleton are just some of the processes observed in this study.

What’s more remarkable is that researchers applied their method not only to an established influenza A model but also to a novel strain found in bats. The H18N11 virus, which targets MHC class II complexes rather than glycans on the cell surface, was shown to cluster specific MHCII molecules upon contact with the cell.

This groundbreaking research has significant implications for understanding alternative receptors used by new and emerging influenza viruses. The findings provide a critical basis for investigating potential pandemic pathogens in a more targeted manner, identifying new targets for antiviral therapies, and ultimately developing effective treatments against future pandemics.

The EU project COMBINE, launched in 2025 and coordinated by Professor Sieben’s team at HZI, aims to investigate the virus entry process of newly emerging viruses. This research has far-reaching implications for understanding and combating infectious diseases, making it a significant contribution to the global fight against pandemics.