Category: Chemistry

Everyone knows someone who has had cancer. In 2020, around 19 million new cases—and around 10 million deaths—were registered worldwide. Treatments are improving all the time, but can damage healthy cells or have severe side effects that are hard on patients. In the search for new, more targeted cancer drugs, traditional medicine offers many possible candidates.

A team of Polish scientists led by Magdalena Winkiel at Adam Mickiewicz University, publishing today in Frontiers in Pharmacology, has reviewed the bioactive compounds called glycoalkaloids, found in vegetables like potatoes and tomatoes, to demonstrate their potential to treat cancer.

“Scientists around the world are still searching for the drugs which will be lethal to cancer cells but at the same time safe for healthy cells,” said Winkiel.

“It is not easy despite the advances in medicine and powerful development of modern treatment techniques. That is why it might be worth going back to medicinal plants that were used years ago with success in the treatment of various ailments. I believe that it is worth reexamining their properties and perhaps rediscovering their potential.”

Making medicine from poison

Winkiel and her colleagues focused on five glycoalkaloids—solanine, chaconine, solasonine, solamargine and tomatine—that are found in crude extracts of the Solanaceae family of plants, also known as nightshades. This family contains many popular food plants, and many that are toxic, frequently because of the alkaloids they produce as a defense against animals that eat plants. But the correct dose can turn a poison into a medicine: Once scientists have found a safe therapeutic dose for alkaloids, they can be powerful clinical tools.

Glycoalkaloids in particular inhibit cancer cell growth and may promote cancer cell death. These are key target areas for controlling cancer and improving patient prognoses, so have huge potential for future treatments. In silico studies—an important first step—suggest that the glycoalkaloids aren’t toxic and don’t risk damaging DNA or causing future tumors, although there may be some effects on the reproductive system.

“Even if we cannot replace anti-cancer drugs that are used nowadays, maybe combined therapy will increase the effectiveness of this treatment,” Winkiel suggested. “There are many questions, but without detailed knowledge of the properties of glycoalkaloids, we will not be able to find out.”

From tomatoes to treatments

One necessary step forward is using in vitro and model animal studies to determine which glycoalkaloids are safe and promising enough to test in humans. Winkiel and her colleagues highlight glycoalkaloids derived from potatoes, like solanine and chaconine—although the levels of these present in potatoes depend on the cultivar of potato and the light and temperature conditions to which the potatoes are exposed.

Solanine stops some potentially carcinogenic chemicals from transforming into carcinogens in the body and inhibits metastasis. Studies on a particular type of leukemia cells also showed that at therapeutic doses, solanine kills them. Chaconine has anti-inflammatory properties, with the potential to treat sepsis.

Meanwhile, solamargine—which is mostly found in eggplant—stops liver cancer cells from reproducing. Solamargine is one of several glycoalkaloids that could be crucial as a complementary treatment, because it targets cancer stem cells which are thought to play a significant role in cancer drug resistance.

Solasonine, which is found in several plants from the nightshade family, is also thought to attack cancer stem cells by targeting the same pathway. Even tomatoes offer potential for future medicine, with tomatine supporting the body’s regulation of the cell cycle so that it can kill cancer cells.

Further research will be needed to determine how this in vitro potential can best be turned into practical medicine, Winkiel and her team noted. There is some reason to believe that high-temperature processing improves glycoalkaloid properties, and nanoparticles have recently been found to improve transmission of glycoalkaloids to cancer cells, boosting drug delivery.

However, the glycoalkaloids’ mechanisms of action must be better understood, and all potential safety concerns must be scrutinized, before patients can benefit from cancer drugs straight out of the vegetable patch.

More information: Anticancer activity of glycoalkaloids from Solanum plants: a review, Frontiers in Pharmacology (2022). DOI: 10.3389/fphar.2022.979451

Journal information: Frontiers in Pharmacology 

Provided by Frontiers 

In search of new biomarkers for nutrition and health studies, a research team from the Leibniz Institute for Food Systems Biology at the Technical University of Munich (LSB) has identified and structurally characterized three metabolites that could be considered as specific markers for individual coffee consumption.

These are degradation products of a group of substances that are formed in large quantities during coffee roasting but are otherwise rarely found in other foods. This and the fact that the potential biomarkers can be detected in very small amounts of urine make them interesting for future human studies.

According to Statista, coffee is by far the most popular hot beverage in Germany. On average, around 168 liters are consumed per person per year. It is not only a stimulant, but also has positive health properties. For example, numerous observational studies indicate that moderate coffee consumption is associated with a reduced risk of type 2 diabetes or liver disease.

Biomarkers instead of self-reporting

However, with regard to the amounts of coffee drunk, such observational studies rely on participants’ self-reports, which are difficult to verify. “Complementary studies would therefore be desirable in which coffee consumption could be objectively verified using biomarkers in order to determine the health value of coffee even more reliably,” says Roman Lang, who heads the Biosystems Chemistry & Human Metabolism research group at LSB.

Although earlier studies had already pointed to biomarker candidates, research on this had stalled for years. The substances previously detected were metabolic intermediates or breakdown products (metabolites) of various coffee compounds whose urine concentrations correlated strongly with the level of coffee consumption. At the time, however, the researchers had not succeeded in clearly identifying the molecular structure of the metabolites.

Use of high-performance analytical technologies

Therefore, as part of a pilot study, Roman Lang’s team examined the urine samples of six people after they had consumed 400 ml of coffee three hours earlier. With the help of high-performance analytical technologies and self-produced reference substances, the team succeeded in identifying three candidate biomarkers in the urine and, for the first time, in clearly determining their chemical structure. These are a glucuronic acid conjugate of atractyligenin, whose glycosides are present in relatively high concentrations in coffee beverages, and two glucuronic acid derivatives of an atractyligenin oxidation product.

“Our findings help advance biomarker research,” says Roman Lang. Dose-response studies, pharmacokinetics and human studies with much larger numbers of subjects must now follow to test the biomarker suitability of the identified compounds, he adds. Veronika Somoza, director of the Freising-based Leibniz Institute adds, “Food-specific biomarkers are important tools to explore the health effects of food. Therefore, part of our scientific work at LSB is also focused on finding biomarkers for food consumption.”

More information: Roman Lang et al, Metabolites of dietary atractyligenin glucoside in coffee drinkers’ urine, Food Chemistry (2022). DOI: 10.1016/j.foodchem.2022.135026

Journal information: Food Chemistry 

Provided by Leibniz-Institut für Lebensmittel-Systembiologie

Carbon-based electrocatalysts are considered as promising alternatives to the state-of-the-art precious metal catalysts. Heteroatom doping can effectively create highly active catalytic centers, but unfortunately, result in lower electronic conductivity and thus hinder the electrocatalysis process.

To address this issue, a team from South China University of Technology developed a Janus carbon electrocatalyst with different heteroatom doping levels between the two sides, which could resolve the conflict between intrinsic activity and electronic conductivity to boost the performance in the electrocatalytic hydrazine oxidation reactions.

Electrocatalysis enables the transformation of electrical energy to chemical energy. The smooth proceeding of electrocatalytic reactions relies on the design of electrocatalysts with highly active centers and efficient electron conduction. Carbon materials represent an important class of electrocatalysts. The major barrier to performance improvement of carbon materials is the trade-off between intrinsic activity and electronic conductivity.

Now, a team led by Prof. Yingwei Li at South China University of Technology addressed this issue by developing a carbon-based catalyst with a Janus structure. The Janus carbon electrocatalyst consists of a conductive nitrogen-doped carbon block (NC) and catalytically active boron-, nitrogen co-doped carbon nanosheets (BNC).

“The design of Janus carbon nanomaterials is not an easy task. Carbon materials are usually prepared by the carbonization of carbon-containing precursors. However, conventional precursors lack the designability to synthesize carbon materials with tunable structures and compositions. Our group has been engaged in the development of efficient catalysts based on metal-organic frameworks (MOFs), a class of materials with high designability, tunable compositions, and ordered atomic distributions. The interesting properties of MOFs motivated us to design a Janus MOF as the precursor for Janus carbon nanomaterials,” explained Yingwei Li.

The researchers developed a “molecular clipping and re-suturing” strategy for the construction of the Janus MOF. ZIF-8 crystals were heated in a methanol solution of boric acid. ZIF-8 was slowly etched by boric acid to release metal ions and ligands, followed by nucleation and growth of B-MOF on etched ZIF-8. ZIF-8/B-MOF was then employed as precursors for the synthesis of Janus NC/BNC.

The NC side displayed a lower doping level and thus a higher electronic conductivity compared with the BNC side. However, the BNC side possessed catalytically active BO₃ sites with higher intrinsic activity. The integration of NC with BNC could not only ensure high electronic conductivity of the hybrid, but also induce further charge delocalization of active sites on the BNC side with enhanced catalytic activity.

In the electrocatalytic hydrazine oxidation reaction, NC/BNC exhibited significantly improved activity than the single counterparts and simple physical mixtures.

In view of the big family of MOFs, the team believes that the proposed MOF-templated strategy can be extended to the synthesis of various Janus carbon materials with tunable compositions and structures. This will hopefully enrich the toolbox of tailorable chemistry and nanotechnology for potential applications in interfacial stabilizers, drug delivery, and phase-transfer catalysis.

The research is published in the journal National Science Review.

More information: Jieting Ding et al, A Janus heteroatom-doped carbon electrocatalyst for hydrazine oxidation, National Science Review (2022). DOI: 10.1093/nsr/nwac231

Provided by Science China Press 

When winter chill strikes, people stay indoors more often, giving airborne pathogens—such as SARS-CoV-2 and influenza—prime opportunities to spread. Germicidal ultraviolet (GUV) lamps can help disinfect circulating air, but their UVC wavelengths could also transform airborne compounds into potentially harmful substances.

Now, researchers reporting in Environmental Science & Technology Letters have modeled the reactions initiated by UVC sanitizing light and find that there’s a trade-off between removing viruses and producing air pollutants.

Disinfecting UVC, also called germicidal UV, lamp systems have long been a cost-effective way to rapidly inactivate airborne pathogens indoors. One design uses lamps that shine at 254 nm, a wavelength that’s damaging to humans’ skin and eyes, requiring the devices to be mounted near the ceiling or inside ventilation ducts. Recently, light at 222 nm has been suggested for whole-room disinfection because the wavelength is reported to be safer for humans.

However, UVC light can set off many reactions. For example, this type of light is known to break apart molecules in the air, forming strong oxidants, such as hydroxyl radicals and ozone. Then these oxidants can convert volatile organic compounds (VOCs) already in the air into peroxides and carbonyl compounds, which can be further broken up by UVC light into organic radicals.

The strong oxidants and organic radicals are known to undergo secondary reactions to generate additional VOCs and particulate matter, some of which could negatively impact people’s health. But the levels of compounds potentially generated from these secondary reactions from GUV systems hadn’t been studied. So, Zhe Peng, Shelly Miller and Jose Jimenez wanted to use computer models to evaluate the possible impact that the two types of UVC air cleaning systems could have on disinfection and air quality in typical indoor conditions.

With computer simulations, the researchers estimated the SARS-CoV-2 virus removal rate and the amount of secondary VOCs that would be generated in three indoor scenarios in conjunction with different room ventilation rates. Initial results indicated that both UVC wavelengths would significantly decrease the risk of infection by SARS-CoV-2 compared to ventilation alone.

The models also projected that the systems would initiate secondary reactions with VOCs expected to be in indoor air. Although only small amounts of secondary VOCs, ozone and particulate matter would likely be produced, the estimated levels weren’t negligible.

Based on the results, the team recommends the use of GUV systems in environments at high risk of airborne pathogen transmission—those in which the benefit of removing these microbes outweighs the impact of the added air pollutants. The researchers point out, however, that this study’s findings are limited to the conditions chosen for the computer models, which could be different in real-world locations.

More information: Zhe Peng et al, Model Evaluation of Secondary Chemistry due to Disinfection of Indoor Air with Germicidal Ultraviolet Lamps, Environmental Science & Technology Letters (2022). DOI: 10.1021/acs.estlett.2c00599

Journal information: Environmental Science & Technology Letters 

Provided by American Chemical Society 

Numerous plastics are principally biodegradable, but are only degraded very slowly in the open air, wastewater, or composting plants. Known enzymes with the ability to degrade plastics could solve this problem.

To do so, however, they must be able to withstand high temperatures. An interdisciplinary team from the Collaborative Research Center “Microplastics” at the University of Bayreuth has now presented new methods in the journal Biomacromolecules that are a crucial prerequisite for protecting enzymes from high heat. If enzymes are thermally stable, they can be added to biodegradable plastics during production and later accelerate natural degradation.

In principle, natural plastic degradation in the environment could be accelerated with the help of enzymes. For example, the enzyme proteinase K is capable of attacking and breaking down PLLA molecules. This ability of certain enzymes to degrade plastics could be optimally exploited if it were possible to equip biodegradable plastics with these enzymes during their production.

The enzymes would then later become active in the environment, in wastewater, or composting plants. However, precisely this attractive solution to the problem has so far been prevented by the fact that melt extrusion is used in the industrial production of aliphatic polyesters and other biodegradable plastics.

This is an indispensable production step that takes place at very high temperatures of well over 100 degrees Celsius. Until now, no way has been found to protect enzymes well enough to keep them stable at high heat and thereby preserve essential functions such as the ability to degrade plastics. There was a lack of scientific methods that could be used to obtain precise data on the heat resistance of enzymes.

At this point, the interdisciplinary team of the Bayreuth Collaborative Research Center 1537 “Microplastics” has now made decisive progress. In collaboration with the Federal Institute for Materials Research and Testing (BAM), the scientists have developed quantitative methods using proteinase K as an example, which allow the thermal stability of enzymes to be determined with a previously unattained level of detail—up to a temperature of 200 degrees Celsius.

“With the methods we present in our new study, it will be possible to preserve enzymes from thermal decomposition much better than before. We now have a reliable tool in hand to evaluate technical measures developed and proposed to protect enzymes in terms of their effectiveness,” says the study’s first author Chengzhang Xu, a doctoral student at the Chair of Macromolecular Chemistry II at the University of Bayreuth.

She already has her sights set on further research steps: “In Bayreuth, we intend to explore new methods for heat-resistant encapsulation of proteinase K. Encapsulation seems to be a promising way to introduce enzymes into the production of biodegradable plastics.”

“The research results we have achieved using proteinase K as an example are potentially transferable to other proteins. They thus strengthen a still young research direction that is developing new hybrid materials based on enzymatically degradable plastics that can be deformed under heat. These materials not only serve to combat microplastic waste, but can also support the development of new drugs or the regeneration of diseased or damaged tissue, for example,” says Prof. Dr. Andreas Greiner, holder of the Chair of Macromolecular Chemistry II, who coordinated the research work.

More information: Chengzhang Xu et al, Investigation of the Thermal Stability of Proteinase K for the Melt Processing of Poly(l-lactide), Biomacromolecules (2022). DOI: 10.1021/acs.biomac.2c01008

Journal information: Biomacromolecules 

Provided by University of Bayreuth 

In recent years, research and development on quantum computers has made considerable progress. Quantum chemical calculations for electronic structures of atoms and molecules are attracting great attention as one of the most promising applications for quantum computers. In order to utilize quantum chemical calculations for chemistry and related fields, it is essential to develop geometry optimization methods to find the most stable structure for molecules. Geometry optimization requires calculations of energy derivatives with respect to nuclear coordinates of molecules.

The finite difference method is one approach for energy derivative calculations. On a classical computer, calculations based on this method for one-dimensional systems require at least two evaluations of the energy. Previous research has shown that a quantum computer, in contrast, requires only a single query to calculate the energy derivatives based on the finite difference method, regardless of the number of degrees of freedom. However, quantum circuits relevant to quantum algorithms capable of performing energy derivative calculations have not been implemented.

A research group including Dr. Kenji Sugisaki, Professor Kazunobu Sato, and Professor Emeritus Takeji Takui from the Graduate School of Science at Osaka Metropolitan University has successfully extended the quantum phase difference estimation algorithm, a general quantum algorithm for the direct calculations of energy gaps, to enable the direct calculation of energy differences between two different molecular geometries. This allows for the computation, based on the finite difference method, of energy derivatives with respect to nuclear coordinates in a single calculation.

Furthermore, the research group has applied the developed energy derivative calculations to execute geometry optimizations of H₂, LiH, BeH₂, and N₂ molecules without calculating the total energies, demonstrating the usefulness of the developed method. The group also discussed how quantum circuits can be assembled according to different degrees of freedom of the molecules.

This research is the latest in a series of the researchers’ articles on quantum chemical calculations on quantum computers. “Our latest findings bring us one step closer to applying quantum chemical calculations on a quantum computer to real-world problems,” said Dr. Sugisaki.

“Since energy derivative calculations are used for not only molecular geometry optimizations but also various calculations for molecular properties, the application of our method is expected to play a very important role in a wide range of related fields, such as in silico drug discovery/design and materials development.”

The study is published in The Journal of Physical Chemistry Letters.

More information: Quantum Algorithm for Numerical Energy Gradient Calculations at the Full Configuration Interaction Level of Theory, The Journal of Physical Chemistry Letters (2022). DOI: 10.1021/acs.jpclett.2c02737

Journal information: Journal of Physical Chemistry Letters 

Provided by Osaka Metropolitan University

A new open-source toolkit automates the process of computing molecular properties in the solution phase, clearing new pathways for artificial-intelligence design and discovery in chemistry and beyond. The Journal of Chemical Physics published the free, open-source toolkit developed by theoretical chemists at Emory University.

Known as AutoSolvate, the toolkit can speed the creation of large, high-quality datasets needed to make advances in everything from renewable energy to human health.

“By using our automated workflow, researchers can quickly generate 10, or even 100 times, more data compared to the traditional approach,” says Fang Liu, Emory assistant professor of chemistry and corresponding author of the paper. “We hope that many researchers will access our toolkit to perform high-throughput simulation and data curation for molecules in solution.”

Such datasets, Liu adds, will provide a foundation for applying state-of-the-art machine-learning techniques to drive innovation in a broad range of scientific endeavors.

First author of the paper is Eugen Hruska, a postdoctoral fellow in the Liu lab. Co-authors include Emory Ph.D. candidate Ariel Gale and Xiao Huang, who worked on the paper as an Emory undergraduate and is now a graduate student of chemistry at Duke University.

Exploring the quantum world

A theoretical chemist, Liu leads a team specializing in computational quantum chemistry, including modeling and deciphering molecular properties and reactions in the solution phase.

The world becomes much more complex as it shrinks down to the scale of atoms and small molecules, where quantum mechanics describes the wave-particle duality of energy and matter.

Theoretical chemists use supercomputers to simulate the structures of molecules and the vast array of interactions that can occur during a reaction so that they can make predictions about how a molecule will behave under certain conditions. Understanding these dynamics is key to identifying promising molecules for various applications and for driving reactions efficiently.

Researchers have already generated datasets for the properties of many molecules in the gas phase. Molecular properties in the solution phase, however, remain relatively unexplored in the context of big data and machine learning, despite the fact that most reactions occur in solution.

The problem is that studying a molecule in solution requires much more time and effort.

A complicated process

“In the gas phase, molecules are far from each other,” Liu explains, “so when we study a molecule of interest, we don’t have to consider its neighbors.”

In the solution phase, however, a molecule is closely immersed with many other molecules, making the system much larger. “Imagine a solvent molecule surrounded by layers and layers of water molecules,” Liu says. “Depending on its size and structure, a molecule may be covered by tens, or even up to hundreds, of water molecules. In systems of such large size, the computation will be slow and may not even be feasible.”

Before running a quantum chemistry program for a molecule in the solution phase it’s necessary to first determine the geometry of the molecule and the location and orientation of the surrounding solvent molecules.

“This process is difficult to do,” Liu says. “It takes so much time and effort, and it’s so complicated, that a researcher can only perform this calculation for a few systems that they care about in one paper,” Liu says.

Technical issues can also arise during each step in the process, she adds, leading to errors in the results.

A streamlined solution

Liu and her colleagues replaced the complicated steps required to perform these calculations with their automated system AutoSolvate.

Previously, a computational chemist might have to type hundreds of lines of code into a supercomputer to run a simulation. The command-line interface for AutoSolvate, however, requires just a few lines of code to conduct hundreds of calculations automatically.

“The time for running the simulations may be long, but that’s a job for the computer,” Liu says. “We’ve freed the researchers from most of the tedious, manual tasks of data input so that they can focus on analyzing their results and other creative work.”

In addition to the command-line interface geared toward more experienced theoretical chemists, AutoSolvate includes an intuitive graphical interface that is suitable for graduate students who are learning to run simulations.

Labs can now efficiently generate many data points for solvated molecules and then use the dataset to build machine-learning models for chemical design and discovery. AutoSolvate also makes it easier to build and share datasets across different research groups.

Setting the stage for machine learning

“During the past 10 years, machine learning has become a popular tool for chemistry but the lack of computational datasets has been a bottleneck,” Liu says. “AutoSolvate will allow the research community to curate a huge number of datasets for molecular properties in the solution phase.”

Determining the redox potential of a solvent molecule, or the likelihood for an oxidation to occur, is just one example of a key research area that AutoSolvate could help advance. Redox-active molecules hold potential for applications in the development of anticancer drugs and chemical batteries for renewable-energy storage.

“Building up redox-potential datasets will then allow us to use machine learning to look at millions of different compounds to rapidly find the ones with redox potential within the desired range,” Liu says.

Instead of a black-box result, such analyses of large datasets can yield interpretable artificial intelligence, or basic rules for molecular models.

“The ultimate goal is to identify rules that can then be applied to solve a broad range of fundamental science problems,” Liu says.

More information: Eugen Hruska et al, AutoSolvate: A toolkit for automating quantum chemistry design and discovery of solvated molecules, The Journal of Chemical Physics (2022). DOI: 10.1063/5.0084833

Journal information: Journal of Chemical Physics 

Provided by Emory University 

A team of researchers writing in the journal Angewandte Chemie has developed a bioorthogonal molecular system for the targeted introduction of nitrite ions into cells. Their system releases nitrite ions in cancer cells using a “click-to-release” strategy and these ions, along with other active ingredients, help to initiate cell death. The system could improve the synergistic effects of various cancer therapy drugs.

Cells rapidly convert nitrite ions into nitrogen monoxide (NO), which is involved in many cell processes. For example, it can enhance the effect of various cancer drugs by forming reactive oxygen species. However, the targeted introduction of nitrite to a specific location is complicated.

The research groups of Fude Feng at Nanjing University and Shu Wang at the Chinese Academy of Sciences, Beijing, China, have now developed a bioorthogonal system that selectively transports nitrite ions along with other active ingredients to the endoplasmic reticulum, where they are then released.

Bioorthogonal systems facilitate useful chemical reactions (“click reactions”) in cells, without the risk of the reaction partners having adverse effects on the body on their journey to the target site. They have paved the way for an exciting array of novel disease treatment approaches. A testament to this is the fact that the 2022 Nobel prize in chemistry was awarded for the development of click chemistry and bioorthogonal chemistry.

To transport reaction partners to a target site without them participating in unwanted reactions, nitrite ions have to be bound to a carrier molecule as a nitro group. However, the conditions needed to release nitrite again when they reach their target are usually much harsher than those found in living cells. For this reason, the researchers designed two bioorthogonal precursors: one to transport the nitro group and other active ingredients, and another to carry out the click-to-release reaction by reacting with the first precursor.

The first of the two precursors, ER-Non, performed a number of roles. Firstly, it is readily taken in by the endoplasmic reticulum. Not only do many important cell processes take place in this cell organelle, but it is also the site of action of a number of drugs. Secondly, alongside the nitro group, ER-Non transported the active substance novidamide, which triggers cellular stress responses at high doses and can thus cause cancer cells to initiate cell death.

The other molecular precursor, a dithiol, is activated by enzymes typical for cancer cells. In a click-to-release reaction, the activated molecule releases both the nitrite and the novidamide from ER-Non. The chemicals are not simply released; the reaction causes the new substance to fluoresce and, in so doing, to become a photosensitizer.

Under the action of light, it enhances the ability of the nitrite ion and the novidamide to generate reactive oxygen species and to thereby trigger cellular stress. This phenomenon of photosensitizing is used in photodynamic cancer therapy.

The researchers tested their bioorthogonal system on liver cancer cells and observed arrested growth of these cells. They also observed a notable increase in reactive oxygen species after adding both bioorthogonal components. Since none of the components alone would exert this effect, the team concluded that synergistic effects occur. This opens up new possibilities for more effective cancer therapies.

More information: Jian Sun et al, Dithiol‐Activated Bioorthogonal Chemistry for Endoplasmic Reticulum‐Targeted Synergistic Chemophototherapy, Angewandte Chemie International Edition (2022). DOI: 10.1002/anie.202213765

Journal information: Angewandte Chemie International Edition  , Angewandte Chemie 

Provided by Wiley 

Nucleobase-containing coenzymes are believed to be the relics of an ancient RNA world and can provide information on the origin and evolution of proteins. However, coenzyme-protein interactions largely remain unclear.

Recently, researchers from the Okinawa Institute of Science and Technology Graduate University looked at Rossmann enzymes for answers, discovering that insertions and deletions essentially mold coenzyme specificity in these proteins. Their findings, while evolutionarily significant, also potentially provide a novel strategy to engineer coenzyme specificity.

Coenzymes are molecules that assist proteins in nearly half of all the reactions they catalyze. These small, organic molecules contain nucleotides just like in the building blocks of our DNA and RNA. While coenzymes play an extremely crucial role in the catalysis of proteins, their importance is not limited to this alone.

Nucleobase-containing coenzymes are considered the fossil remnants of an ancient RNA-based world, which has been hypothesized to exist even before the very first proteins came into existence. They can, hypothetically, offer a closer look at how proteins emerged and evolved. Unfortunately, not much is known about the evolution of coenzyme-protein interactions.

The structure and function of any protein is coded in its amino acid sequence. Certain structures are evolutionarily conserved across all kingdoms of life. One such recurring structure—the “Rossmann fold”—was discovered by Dr. Michael Rossmann in 1970.

The Rossmann fold is the most catalytically diverse and abundant protein architecture in nature and is an excellent target to study coenzyme-protein interactions. Rossmann proteins display diverse catalytic functions owing to minuscule differences in their structure. These differences, affect the binding specificities of their co-acting catalysts, coenzymes.

Researchers from Okinawa Institute of Science and Technology (OIST) recently modified the coenzyme pocket of a Rossmann protein that naturally performs redox reactions to bind a methylating agent instead. While the natural protein binds to the coenzyme NAD (nicotinamide adenine dinucleotide), the mutant lost the ability to bind NAD and acquired binding for SAM (S-adenosyl methionine). Their findings have been published in Proceedings of the National Academy of Sciences.

Dr. Paola Laurino, assistant professor who leads OIST Protein Engineering and Evolution Unit, says, “As proof of principle, we engineered an oxidoreductase protein to accept a methylating coenzyme SAM instead of its natural coenzyme NAD. This involves the redesign of the ancient and highly conserved glycine rich loop. This task is not trivial because of the complexity of the intramolecular H-bonding and it has attracted the attention of many protein engineers in the past.”

Historically, protein interactions have been studied using site-directed mutagenesis—a process that gives rise to modified proteins where one or more amino acids are substituted with others. However, until now, researchers have not fully unleashed the potential of “insertions” and “deletions” (collectively referred to as “InDels”). In contrast to amino acid substitutions, an “InDel” significantly modifies protein structure because of the addition or removal of one or more amino acids from the corresponding protein sequence.

First, the researchers performed extensive analyses and reshaped the ancient coenzyme-binding motif of NAD into the SAM-binding one. To achieve this, they removed an InDel of three amino acids from the NAD coenzyme pocket and solved the structure of the resulting mutant. As expected, the mutant exhibited the characteristic structural features of a SAM-binding pocket.

Next, the team decided to validate their finding by studying the interactions of the generated mutant with SAM. To this end, the team performed isothermal titration calorimetry measurements—a biophysical technique that determines binding affinities—and validated the successful coenzyme switch when they observed that the mutant was indeed binding. The results were further corroborated through computerized simulations.

Lead author Dr. Saacnicteh Toledo-Patiño, a postdoctoral researcher at OIST Protein Engineering and Evolution Unit concludes, “It is amazing that the sequence combinations possible for a small protein, about 100 amino acids in length, exceeds the number of atoms in the known universe. For this reason, nature has only explored an infinitesimal fragment of these possibilities, and yet, is able to drive the vast number of reactions that sustain life.”

More information: Saacnicteh Toledo-Patiño et al, Insertions and deletions mediated functional divergence of Rossmann fold enzymes, Proceedings of the National Academy of Sciences (2022). DOI: 10.1073/pnas.2207965119

Journal information: Proceedings of the National Academy of Sciences 

Provided by Okinawa Institute of Science and Technology 

The Plant Physiology and Biochemistry research team at the University of Konstanz has discovered previously unknown molecular mechanisms by which plants adapt to their environment—important basic knowledge in times of climate change

Plants are exposed to constant environmental changes, and their survival depends on their ability to sense and adapt to environmental stimuli. Protein molecules in the cell membrane play a crucial role in coordinating extracellular signals and intracellular reactions. The Plant Physiology and Biochemistry research team (University of Konstanz) now succeeded in identifying two deubiquitinating enzymes involved in the molecular mechanism of this adaptation process. The study has been published in the current issue of Nature Communications.

The amount of protein molecules is crucial

In its adaptation process, the cell needs to sense, for example, nutrients or pathogens in its environment. That is the job of special protein molecules—the transporters and receptors, located on the cell membrane separating the inside of the cell from the outside world. They are produced as well as degraded in the cell. Decisive for the plant’s signal perception is the amount of these molecules.

The small signal protein ubiquitin hooks on other proteins and thus ensures that they are degraded. At the same time, there are deubiquitylating enzymes that can reverse this effect by removing ubiquitins.

Biology professor Erika Isono’s team analyzed 18 of these deubiquitylating enzymes in the model plant Arabidopsis thaliana. In cooperation with Karin Hauser, Michael Kovermann and Christine Peter from the Department of Chemistry, the team found two of these enzymes, designated OTU11 and OTU12, which are localized at the cell membrane and are actually involved in regulating the amount of cell membrane proteins.

Lead author Karin Vogel explains the biochemical mechanism: “OTU11 and OTU12 can shorten certain ubiquitin chains. This influences the degradation of the modified proteins.” The biologist also discovered how they are activated: by binding to negatively charged lipids on the cell membrane.

Previously unknown form of activity adjustment

This means that the activity of these enzymes is tightly controlled. Several activation mechanisms for deubiquitylating enzymes are already known. The mechanism reported here is a previously unknown form of activity adaptation. This regulation is so crucial because the effect of deubiquitylating enzymes can have major consequences for the function of the cell.

“Our discovery shows that the deubiquitylating enzymes do not become active until they arrive at the membrane, where the lipids are located. This fits perfectly with the intracellular localization and function of the two enzymes,” says Erika Isono.

Model plants are used in basic biological research to investigate fundamental biochemical and molecular biological mechanisms. The long-term goal is to optimize agricultural yields, which is particularly important in times of climate change, as plant growing conditions can change as a consequence. Erika Isono says, “It’s important that we understand at the molecular level how plants respond to the environment. The ubiquitin-dependent signaling pathway probably plays an important role in this process.”

More information: Karin Vogel et al, Lipid-mediated activation of plasma membrane-localized deubiquitylating enzymes modulate endosomal trafficking, Nature Communications (2022). DOI: 10.1038/s41467-022-34637-3

Journal information: Nature Communications 

Provided by University of Konstanz