Prof. Armido Studer with Dr. Hui Cao and Dr. Qiang Cheng (from left). The flipchart shows the chemical transformation of loratadine, an important histamine H1-receptor antagonist modified through a trifluoromethyl group. Credit: Münster University / Studer working group
In chemicals used in agriculture, as well as in pharmaceuticals and a variety of materials, pyridines are often found as so-called functional units that decisively determine the chemical properties of substances. Pyridines belong to the group of ring-shaped carbon-hydrogen (C-H) compounds (“heterocycles”), and they contain a nitrogen atom (N). For chemists, the direct functionalization of the carbon-hydrogen bonds (C-H bonds) of pyridines is a straightforward approach to designing and modifying complex molecules, including in the final stage of the synthesis sequence.
This latter means that active ingredients can be chemically modified without having to build them up anew. The functionalization of the pyridine in a certain position in relation to the nitrogen atom—in the difficult-to-access “meta-position”—is extremely challenging and rare. A team of researchers headed by Prof. Armido Studer at the Institute of Organic Chemistry at the University of Münster has developed a new strategy for getting various functional groups into the meta-position of pyridines. Their study has now been published in Science.
The chemists use a temporary de-aromatization of the pyridine; its electronic properties are reversed, producing a stable intermediary product—a dienamine. By means of radical and polar chemistry, the researchers are able, with a high degree of selectivity, to get a large number of fluorinated alkanes, as well as a series of “electron-poor substituents” (electrophiles), into the meta-position. These transformations also include medically and agro-chemically relevant functionalities such as trifluoromethyl and halogen groups.
“The important thing,” says Dr. Hui Cao, a postdoc in the Studer working group, “is that the functionalized dienamine intermediary products are easily re-aromatized to meta-functionalized pyridines under acidic conditions.”
His colleague Dr. Qiang Cheng adds, “The high degree of efficiency, the broad range of applications and the meta-selectivity of our approach enables twelve different types of drugs to be functionalized.”
In addition, the team developed processes in which drugs can be transformed directly into trifluoromethyl and chlorine-substituted derivatives, in so-called one-pot reactions, which involve little effort and take place in one single reaction vessel. For this purpose, the chemists use inexpensive, commercially available reagents.
“Our study supplies an answer to the unsolved problem of functionalizing pyridine in the meta-position,” says Armido Studer. “We believe that this publication will give a significant boost to the development of medicines containing pyridines and of organic functional materials.”
More information: Hui Cao et al, Radical and ionic meta -C–H functionalization of pyridines, quinolines, and isoquinolines, Science (2022). DOI: 10.1126/science.ade6029
The world’s aging population, the growing burden of chronic and infectious diseases, and the emergence of novel pathogens have made the need for new treatments more urgent than ever. Yet, discovering a new drug and bringing it to market is a long, arduous, and expensive journey marked by many failures and few successes.
Artificial intelligence has been long deemed the answer to overcoming some of these hurdles due to its ability to analyze vast reams of data, uncover patterns and relationships, and predict effects.
But despite its enormous potential, AI has yet to deliver on the promise of transforming drug discovery.
Now a multi-institutional team led by Harvard Medical School biomedical informatician Marinka Zitnik has launched a platform that aims to optimize AI-driven drug discovery by developing more realistic data sets and higher-fidelity algorithms.
The Therapeutics Data Commons, described in a recent commentary in Nature Chemical Biology, is an open-access platform that serves as a bridge between computer scientists and machine-learning researchers on one end and biomedical researchers, biochemists, clinical researchers, and drug designers on the other end—communities that traditionally have worked in isolation from one another.
The platform offers both data set curation and algorithm design and performance evaluation for multiple treatment modalities—including small-molecule drugs, antibodies, and cell and gene therapies—at all stages of drug development, from chemical compound identification to clinical trial drug performance.
Zitnik, an assistant professor of biomedical informatics in the Blavatnik Institute at HMS, conceptualized the platform and now leads the work in collaboration with researchers at MIT, Stanford University, Carnegie Mellon University, Georgia Tech, University of Illinois-Urbana Champaign, and Cornell University.
She recently discussed the Therapeutics Data Commons with Harvard Medicine News.
HMNews: What are the central challenges in drug discovery and how can AI help solve these?
Zitnik: Developing a drug from scratch that is both safe and effective is incredibly challenging. On average, it takes anywhere between 11 and 16 years and between $1 billion to $2 billion to do so. Why is that?
It’s very difficult to figure out early on whether an initially promising chemical compound would produce results in human patients consistent with the results it shows in the laboratory. The number of small molecule compounds is 10 to the power of 60—yet only a tiny fraction of this astronomically large chemical space has been canvassed for molecules with medicinal properties. Despite that, the impact of existing therapies on treating disease has been astounding. We believe that novel algorithms coupled with automation and new data sets can find many more molecules that can be translated into improving human health.
AI algorithms can help us determine which among these molecules are most likely to be safe and effective human therapies. That’s the ultimate problem that drug discovery development is suffering from. Our vision is that machine learning models can help sift through and integrate vast amounts of biochemical data that we can more directly connect with molecular and genetic information, and ultimately to individualized patient outcomes.
HMNews: How close is AI to making this promise a reality?
Zitnik: We are not there yet. There are a number of challenges, but I’d say the biggest one is understanding how well our current algorithms work and whether their performance translates to real-world problems.
When we evaluate new AI models through computer modeling, we are testing them on benchmark data sets. Increasingly, we see in publications that those models are achieving near-perfect accuracy. If that’s the case, why aren’t we seeing widespread adoption of machine learning in drug discovery?
This is because there is a big gap between performing well on a benchmark data set and being ready to transition to real-world implementation in a biomedical or clinical setting. The data on which these models are trained and tested are not indicative of the kind of challenges these models are exposed to when they’re used in real practice, so closing this gap is really important.
HMNews: Where does the Therapeutics Data Commons platform come into this?
Zitnik: The goal of the Therapeutics Data Commons is to address precisely such challenges. It serves as a meeting point between the machine-learning community at one end and the biomedical community on the other end. It can help the machine-learning community with algorithmic innovation and make these models more translatable into real-world scenarios.
HMNews: Could you explain how it actually works?
Zitnik: First of all, keep in mind that the process of drug discovery spans the gamut from initial drug design based on data from chemistry and chemical biology, through preclinical research based on data from animal studies, and all the way to clinical research in human patients. The machine-learning models that we train and evaluate as part of the platform use different kinds of data to support the development process at all these different stages.
For example, the machine-learning models that support the design of small-molecule drugs typically rely on large data sets of molecular graphs—structures of chemical compounds and their molecular properties. These models find patterns in the known chemical space that relate parts of the chemical structure with chemical properties necessary for drug safety and efficacy.
Once an AI model is trained to identify these tell-tale patterns in the known subset of chemicals, it can be deployed and can look for the same patterns in the vast data sets of yet-untested chemicals and make predictions about how these chemicals would perform.
To design models that can help with late-stage drug discovery, we train them on data from animal studies. These models are trained to look for patterns that relate biological data to likely clinical outcomes in humans.
We can also ask whether a model can look for molecular signatures in chemical compounds that correlate with patient information to identify which subset of patients is most likely to respond to a chemical compound.
HMNews: Who are the contributors and end users of this platform?
Zitnik: We have a team of students, scientists, and expert volunteers who come from partner universities and from industry, including small start-ups in the Boston area as well as some large pharmaceutical companies in the United States and Europe. Computer scientists and biomedical researchers contribute their expertise in the form of state-of-the-art machine-learning models and pre-processed and curated data sets, which are standardized in a way that can be released and ready for use by others.
So, the platform contains both data sets ready for analysis and machine-learning algorithms, along with robust measures that tell us how well a machine-learning model performs on a specific data set.
Our end users are researchers from around the world. We organize webinars to present any new features, to receive feedback, and to answer questions. We offer tutorials. This ongoing training and feedback is really crucial.
We have 4,000 to 5,000 active users every month, most of them from the U.S., Europe, and Asia. Overall, we have seen over 65,000 downloads of our machine-learning algorithm/data set package. We have seen over 160,000 downloads of harmonized, standardized data sets. The numbers are growing, and we hope they will continue to grow.
HMNews: What are the long-range goals for the Therapeutics Data Commons?
Zitnik: Our mission is to support AI drug discovery on two fronts. First, in the design and testing of machine-learning methods across all stages of drug discovery and development, from chemical compound identification and drug design to clinical research.
Second, to support the design and validation of machine-learning algorithms across multiple therapeutic modalities, especially the newer ones, including biologic products, vaccines, antibodies, mRNA medicines, protein therapies, and gene therapies.
There is tremendous opportunity for machine learning to contribute to those novel therapies, and we have not yet seen the use of AI in those areas to the extent we have seen in small-molecule research, where much of the focus is today. This gap is mostly due to a dearth of standardized AI-ready data sets for those novel therapeutic modalities, which we hope to address with the Therapeutics Data Commons.
HMNews: What ignited your interest in this work?
Zitnik: I have always been interested in understanding and modeling interactions across complex systems, which are systems with multiple components that interact with one another in a nondependent manner. As it turns out, many problems in therapeutic science are, by definition, precisely such complex systems.
We have a protein target that is a complex three-dimensional structure, we have a small-molecule compound that is a complex graph of atoms and bonds between those atoms, and then we have a patient, whose description and health status are given in the form of a multiscale representation. This is a classic complex-system problem, and I really love to look and find ways to standardize and “tame” those complex interactions.
Therapeutic science is full of those kinds of problems that are ripe to benefit from machine learning. That’s what we’re chasing, that’s what we’re after.
More information: Kexin Huang et al, Artificial intelligence foundation for therapeutic science, Nature Chemical Biology (2022). DOI: 10.1038/s41589-022-01131-2
Figure 1. Schematic representation of redox-responsive spatiotemporal segregation of oppositely chiral supramolecular assemblies within the same solution. Credit: Institute for Basic Science
In 1848, Louis Pasteur successfully segregated two types of crystals of double sodium-ammonium salt of tartaric acid, which were mirror images of each other. Back then, this was done manually through a painstaking process of observing the crystals under a microscope and utilizing a simple pair of tweezers. Following this seminal discovery, the field of stereochemistry has made great progress.
Today, advances in technology now allow chemists to explore programmable control over the helicity of chiral supramolecular polymers with molecular-level manipulation, stimuli-responsive chirality switching, etc. Unlike sub-millimeter scale stable crystals of sodium-ammonium tartrate, chiral supramolecular polymers are soft materials with dynamic structures and their size ranges from the nanometer scale to a few microns. Moreover, chiral supramolecular polymers are not uniformly present in solution due to uncontrolled diffusion.
Their distribution can be even more random and complex, especially when dealing with a mixture of supramolecular species in multicomponent self-assemblies. Manual segregation of such chiral supramolecular polymers is therefore difficult to achieve and practically impossible.
Now, a team led by Director Kim Kimoon at the Center for Self-assembly and Complexity within the Institute for Basic Science in Pohang, South Korea has successfully demonstrated that audible sound can be utilized to spatiotemporally segregate supramolecular polymers, which differ in their chirality, within separate domains within the same solution. Their study is published in Chem.
Dr. Shovan Kumar Sen, the first author of this study is very much excited about this development. He said, “Redox-responsive chiral switches are well-studied in the literature, but not all systems behave as we anticipate in the presence of audible sound. Designing the most appropriate system to demonstrate our idea, which was initiated with our previous work published in Nature Chemistry (in 2020), was one of the most important junctures of this study.”
To achieve these results, the researchers utilized out-of-equilibrium redox-responsive supramolecular systems, which form supramolecular polymers of opposite chirality in their oxidized and reduced states. Further, utilizing audible sound (40 Hz), the researchers were able to segregate supramolecular polymers of opposite helicity through spatiotemporal pattern formation.
Figure 2. Schematic representation of molecules involved in the redox-responsive PDI-LPF chiral co-assembly (M-helix) and oppositely chiral PDI2–LPF co-assembly (P-helix). SEM images of the hierarchical left and right-handed helical nanofibers are shown as insets. Credit: Institute for Basic Science
Audible sound induces surface vibrations and advection currents within the bulk solution placed in a Petri dish. This results in a faster dissolution of atmospheric oxygen at the concentric ring-shaped domains, which are located at the antinodal (maximum vibrating) regions of the surface waves, resulting in the accumulation of the oxidized polymer strands in these regions. The aforementioned rings are separated by another set of concentric domains, which are located at the nodal (minimum vibrating) regions of the surface waves and contain the oppositely chiral reduced form of the chiral supramolecular polymer (Figure 1).
The researchers designed a redox-responsive achiral system based on a perylene diimide molecule functionalized with quaternary amine groups (PDI). They then explored the co-assembly of PDI with a negatively charged (at alkaline pH) phenylalanine-derived gelator (LPF) making use of electrostatic interactions. The co-assembled PDI-LPF aggregates led to the formation of left-handed helical supramolecular aggregates (M-helix). Interestingly, when sodium dithionite (SDT) was added as a reducing agent to the PDI-LPF co-assembly, the oppositely helical supramolecular aggregates (P-helix) were formed due to the formation of PDI2--LPF aggregates. The oppositely chiral hierarchical structures (helical nanofibers) were observed from electron microscopy studies, which further confirmed the helicity inversion (Figure 2).
Dr. Rahul Dev Mukhopadhyay, who led this study believes there is more in store with this sound-based approach. He said, “As of now, we can only achieve transiently controlled segregation of two oppositely chiral supramolecular polymers within a solution. It will be very interesting and challenging to achieve permanent segregation of such oppositely helical dynamic supramolecular polymers.”
The team also explored the chiral co-assembly between PDI and adenosine triphosphate (ATP). Interestingly, the chiral co-assembly disintegrated on reduction to form achiral PDI2-, releasing ATP back into the solution. When a reduced solution of a PDI-ATP co-assembly was exposed to atmospheric oxygen in the presence of audible sound at a specific frequency (40 Hz), the solution gradually reorganized into a spatiotemporal pattern consisting of segregated domains corresponding to the achiral PDI2- aggregates and chiral PDI-ATP aggregates.
Prof. Kim Kimoon, who was responsible for the overall supervision of the study, strongly believes that the present strategy provides a new tool for researchers in the research field of systems chemistry to control supramolecular systems. Currently, the approach helps us segregate only two types of polymers within the same solution. In the future, Prof. Kim hopes we will have a chance to control multiple aggregates using audible sound. He added, “Always remember what Pasteur said: ‘Chance favors the prepared mind.'”
Strategy for the development of macrocyclic peptides against the Lys63-linked Ub chain. a The general discovery process for macrocyclic peptides for Di-Ub chains using chemical protein synthesis and RaPID approach to affect specific biological functions. b Schematic presentation of RaPID method for the identification of potent binder 1 (CP1) for Lys63-linked Di-Ub. Credit: Nature Communications (2022). DOI: 10.1038/s41467-022-33808-6
A new paper published in Nature Communications presents research on unique peptides with anti-cancer potential.
The research was led by Professor Ashraf Brik and post-doctoral fellows Dr. Ganga B. Vamisetti and Dr. Abbishek Saha from the Schulich Faculty of Chemistry at the Technion—Israel Institute of Technology in Haifa, along with Professor Nabieh Ayoub from the Technion’s Faculty of Biology and Professor Hiroaki Suga from the University of Tokyo.
Peptides are short chains of amino acids linked by peptide bonds, the name given to chemical bonds formed between two molecules when the carboxyl group of one molecule reacts with the amino group of the other molecule.
Unlike proteins that usually contain hundreds of amino acids, peptides contain—at most—dozens of such acids. The cyclic peptides the researchers discovered bind specifically to chains of ubiquitin proteins—proteins that are usually used as a “death tag” for damaged proteins. The labeling of the damaged proteins leads to their being broken down in the proteasome, or the cell’s “garbage can.”
The discovery of the ubiquitin system led to the awarding of the 2004 Nobel Prize in Chemistry to three researchers, including Distinguished Professors Aharon Ciechanover and Avraham Hershko of the Technion’s Ruth and Bruce Rappaport Faculty of Medicine.
Over the years, it became clear that the activity of the ubiquitin system depends in part on the point where the ubiquitin molecules are linked to each other in the chain. For example, linking the ubiquitin in the chain at position 48 (K48) leads to the removal of proteins to the proteasome, while linking the ubiquitin at position 63 (K63) leads to the repair of damaged DNA.
In recent years, Technion researchers have developed a new approach to influencing the ubiquitin mechanisms. Instead of interfering with the activity of enzymes that affect these mechanisms, they decided to try to directly intervene in the ubiquitin chain itself.
Based on this approach, the researchers in a previous work developed cyclic peptides that bind the K48-linked ubiquitin chains, preventing it from leading to the breakdown of the damaged proteins. This disruption gradually leads to programmed death of cells. In the same study, they hypothesized and then proved that when such an event formed in a malignant tumor, it kills the cancer cells, potentially protecting the patient.
This discovery, published in 2019 in the journal Nature Chemistry, led to the establishment of a new startup that is advancing the discovery towards clinical use.
In the current study, cyclic peptides that bind the chains linked to position 63 in ubiquitin and that are involved in repairing damaged DNA were discovered. The researchers found that when attached to these ubiquitin chains, such peptides disrupt the aforementioned repair mechanism.
This leads to the accumulation of damaged DNA, and to cell death. Here too, when this binding occurs in cancer cells, it destroys these cells. The researchers believe this therapeutic strategy could be more effective than existing anti-cancer drugs, against which patients gradually develop a resistance.
More information: Ganga B. Vamisetti et al, Selective macrocyclic peptide modulators of Lys63-linked ubiquitin chains disrupt DNA damage repair, Nature Communications (2022). DOI: 10.1038/s41467-022-33808-6
Mickal Nawatha et al, De novo macrocyclic peptides that specifically modulate Lys48-linked ubiquitin chains, Nature Chemistry (2019). DOI: 10.1038/s41557-019-0278-x
Top) Aster koraiensis and Bottom) Codonopsis lanceolata, which are native plants in South Korea. Credit: Institute for Basic Science
Codonopsis lanceolata, more commonly referred to as “deodeok,” is used as a medicinal herb in South Korea. It is cultivated in large quantities and has been an integral part of Korean cuisine across history. Aster koraiensis, or Korean starwort, is a common flower that resembles a daisy, which is only found in the Korean peninsula.
A team of researchers led by Director C. Justin LEE from the Life Science Institute (Center for Cognition and Sociality) within the Institute for Basic Science (IBS), South Korea, recently announced the discovery of new antiviral compounds derived from these two Korean native plants.
The researchers discovered that the saponins found within these plants were particularly effective at inhibiting SARS-CoV-2 infection by blocking membrane fusion, which allows the viruses to invade the host cells. These findings were published in Antiviral Research in October 2022 and Antimicrobial Agents and Chemotherapy in November 2022.
Coronaviruses are known to enter human cells via endosomes or fusion at the plasma membranes. In both of these two pathways, a process known as “membrane fusion” must occur between the coronavirus envelope and the cell membrane. The research team revealed that two saponins (astersaponin I and lansemaside A) found within the two beforementioned plants are capable of blocking this fusion of the membrane between the coronavirus and human cells, thereby effectively blocking all the ways that the virus can infect its host.
Astersaponin I prevents COVID-19 infection in a dose-dependent manner, with an IC50 value of 2 μM. The saponin worked equally as well against all variants of SARS-CoV-2, due to its ability to block membrane fusion. Credit: Institute for Basic Science
The research team first made a SARS-CoV-2 infection model using human lung cells overexpressing ACE2 receptor protein and a pseudovirus that expresses the viral spike protein on its surface, which can be used in the relatively less restrictive biosafety level 2 research facility. The cells were treated with astersaponin I and lansemaside A to test the compounds’ inhibitory effect on virus infection.
Both saponins were found to have an IC50 value (half maximal inhibitory concentration) of 2 μM, indicating that they were highly effective at stopping the coronavirus from entering the cell. The same results were confirmed in subsequent experiments using actual authentic coronaviruses, and infection was suppressed with almost the same efficiency. More importantly, the inhibitory effect was identical for all SARS-CoV-2 variants, such as omicron.
Astersaponin I and lansemaside A are triterpenoid saponins. They both have central ringed hydrocarbon (or core) structures very similar to that of cholesterol, which is the main component of cell membranes. in addition to a polysaccharide chain attached to one side. The central part of these saponins readily binds to the cell membrane thanks to their similarity to cholesterol. When the molecule penetrates into the cell membrane, the long sugar chain on protrudes out of the cell membrane. It is believed that this protruding sugar is what blocks the cell membrane from fusing with the coronavirus envelope.
SARS-CoV-2 variants such as omicron are more infectious than original one due to the mutations in the spike protein, which enhances their binding affinity with the ACE2 cell receptor. However, no matter how much the SARS-CoV-2 variants to increase its affinity, it will be unable to enter the cell if the whole membrane fusion process, which occurs after viral binding to the receptor, is blocked. That is, the membrane fusion inhibitor can effectively prevent the infection of SARS-CoV-2 variants regardless of the their affinity to human cell receptor.
Left) 12 different synthetic saponins were synthesized using Platycodin D as the base. Right) One of the synthetic saponins showed twice higher ability to inhibit SARS-CoV-2 infection. Credit: Institute for Basic Science
Coronaviruses enter cells through membrane fusion between the virus envelope and cell membrane. When cells expressing coronavirus spike protein (green) are cultivated with human lung cells (red), membrane fusion followed by fusion between the two cells can be observed. Lansemaside A inhibits this membrane fusion, thereby confirming that its mechanism is based on blocking membrane fusion. Credit: Institute for Basic Science
Astersaponin I, lancemaside A, and platycodin D are triterpenoid saponins with central ringed hydrocarbon structures similar to that of cholesterol. This allows one side of the saponin to become readily embedded within the cell membrane. It is believed that the polysaccharide chain protruding from the cell membrane is what prevents membrane fusion from occurring. Credit: Institute for Basic Science
Left) 12 different synthetic saponins were synthesized using Platycodin D as the base. Right) One of the synthetic saponins showed twice higher ability to inhibit SARS-CoV-2 infection. Credit: Institute for Basic Science
Coronaviruses enter cells through membrane fusion between the virus envelope and cell membrane. When cells expressing coronavirus spike protein (green) are cultivated with human lung cells (red), membrane fusion followed by fusion between the two cells can be observed. Lansemaside A inhibits this membrane fusion, thereby confirming that its mechanism is based on blocking membrane fusion. Credit: Institute for Basic Science
In the past, the IBS team worked jointly with Dr. Kim Seungtaek from Korea Pasteur Institute and discovered another natural triterpenoid saponin called platycotin D from the balloon flower. This saponin was also found to be effective against SARS-CoV-2 infection. This research was published in the journal Experimental & Molecular Medicine in May 2021.
Armed with this knowledge, the IBS researchers in collaboration with Prof. Han Sunkyu’s team from Korea Advanced Institute for Science and Technology (KAIST) explored the creation of synthetic saponins with potentially even more powerful effects. The joint team made and tested a dozen synthetic saponins possessing different polysaccharide chains with varying lengths and types of sugars. One of these saponins was found to have up to twice higher activity as that of platycodin D. This research was published in the 2022 October issue of the journal Bioorganic Chemistry.
Director C. Justin Lee stated, “Natural saponins contained in these plants are major constituents in many foods and herbal medicines that are readily accessible in everyday life. When ingested, it can be delivered at high concentrations to the epithelial cells of the upper respiratory tract, which means it can be effective in an asymptomatic or early stage of COVID-19 infection.” He added, “While their effects have been confirmed only in vitro at the moment, clinical trials may be possible in the future if positive results are obtained in animal tests.”
Senior Researcher Kim Taeyoung from the IBS said, “Historically, many important drugs such as penicillin, aspirin, or the antimalarial drug artemisinin have been derived from natural organisms. As these saponins’ mechanism of action relies on inhibiting membrane fusion, it may even be possible to develop broad-spectrum antiviral drugs based on this principle.”
More information: Tai Young Kim et al, Astersaponin I from Aster koraiensis is a natural viral fusion blocker that inhibits the infection of SARS-CoV-2 variants and syncytium formation, Antiviral Research (2022). DOI: 10.1016/j.antiviral.2022.105428
Tai Young Kim et al, Lancemaside A from Codonopsis lanceolata: studies on antiviral activity and mechanism of action against SARS-CoV-2 and its variants of concern, Antimicrobial Agents and Chemotherapy (2022). Accepted for publication. d197for5662m48.cloudfront.net/ … 84348128900f0fd5.pdf
Tai Young Kim et al, Platycodin D, a natural component of Platycodon grandiflorum, prevents both lysosome- and TMPRSS2-driven SARS-CoV-2 infection by hindering membrane fusion, Experimental & Molecular Medicine (2021). DOI: 10.1038/s12276-021-00624-9
Youngho Jang et al, Synthesis and structure–activity relationship study of saponin-based membrane fusion inhibitors against SARS-CoV-2, Bioorganic Chemistry (2022). DOI: 10.1016/j.bioorg.2022.105985
Oak Ridge National Laboratory scientists designed a recyclable polymer for carbon-fiber composites to enable circular manufacturing of parts that boost energy efficiency in automotive, wind power and aerospace applications.
Carbon-fiber composites, or fiber-reinforced polymers, are strong, lightweight materials that can help lower fuel consumption and reduce emissions in critical areas such as transportation. However, unlike metal competitors, carbon-fiber composites are not typically recyclable, meaning wider adoption could present waste challenges.
“Our goal is to extend the lifecycle of these materials by making reuse possible without sacrificing performance,” said ORNL’s Md Anisur Rahman.
The team’s approach incorporates dynamic covalent bonds that are reversible, enabling both carbon fiber and polymer recycling. The new polymer maintained mechanical strength in six reprocessing cycles, a sharp contrast to previously reported polymers.
“ORNL’s carbon-fiber composites enable fast processing and can be repaired or reprocessed multiple times, opening pathways to circular, low-carbon manufacturing,” said ORNL’s Tomonori Saito.
The research was published in Cell Reports Physical Science.
More information: Zhengping Zhou et al, Unraveling a path for multi-cycle recycling of tailored fiber-reinforced vitrimer composites, Cell Reports Physical Science (2022). DOI: 10.1016/j.xcrp.2022.101036
A study from six chemists at the University of Chicago shows an innovative new system for artificial photosynthesis that is more productive than previous artificial systems by an order of magnitude. Above, an artistic illustration of the process. Credit: Peter Allen
For the past two centuries, humans have relied on fossil fuels for concentrated energy; hundreds of millions of years of photosynthesis packed into a convenient, energy-dense substance. But that supply is finite, and fossil fuel consumption has tremendous negative impact on Earth’s climate.
“The biggest challenge many people don’t realize is that even nature has no solution for the amount of energy we use,” said University of Chicago chemist Wenbin Lin. Not even photosynthesis is that good, he said: “We will have to do better than nature, and that’s scary.”
One possible option scientists are exploring is “artificial photosynthesis“—reworking a plant’s system to make our own kinds of fuels. However, the chemical equipment in a single leaf is incredibly complex, and not so easy to turn to our own purposes.
A Nature Catalysis study from six chemists at the University of Chicago shows an innovative new system for artificial photosynthesis that is more productive than previous artificial systems by an order of magnitude. Unlike regular photosynthesis, which produces carbohydrates from carbon dioxide and water, artificial photosynthesis could produce ethanol, methane, or other fuels.
Though it has a long way to go before it can become a way for you to fuel your car every day, the method gives scientists a new direction to explore—and may be useful in the shorter term for production of other chemicals.
“This is a huge improvement on existing systems, but just as importantly, we were able to lay out a very clear understanding of how this artificial system works at the molecular level, which has not been accomplished before,” said Lin, who is the James Franck Professor of Chemistry at the University of Chicago and senior author of the study.
‘We will need something else’
“Without natural photosynthesis, we would not be here. It made the oxygen we breathe on Earth and it makes the food we eat,” said Lin. “But it will never be efficient enough to supply fuel for us to drive cars; so we will need something else.”
The trouble is that photosynthesis is built to create carbohydrates, which are great for fueling us, but not our cars, which need much more concentrated energy. So researchers looking to create alternates to fossil fuels have to re-engineer the process to create more energy-dense fuels, such as ethanol or methane.
In nature, photosynthesis is performed by several very complex assemblies of proteins and pigments. They take in water and carbon dioxide, break the molecules apart, and rearrange the atoms to make carbohydrates—a long string of hydrogen-oxygen-carbon compounds. Scientists, however, need to rework the reactions to instead produce a different arrangement with just hydrogen surrounding a juicy carbon core—CH4, also known as methane.
This re-engineering is much trickier than it sounds; people have been tinkering with it for decades, trying to get closer to the efficiency of nature.
Lin and his lab team thought that they might try adding something that artificial photosynthesis systems to date haven’t included: amino acids.
The team started with a type of material called a metal-organic framework or MOF, a class of compounds made up of metal ions held together by an organic linking molecules. Then they designed the MOFs as a single layer, in order to provide the maximum surface area for chemical reactions, and submerged everything in a solution that included a cobalt compound to ferry electrons around. Finally, they added amino acids to the MOFs, and experimented to find out which worked best.
They were able to make improvements to both halves of the reaction: the process that breaks apart water and the one that adds electrons and protons to carbon dioxide. In both cases, the amino acids helped the reaction go more efficiently.
Even with the significantly improved performance, however, artificial photosynthesis has a long way to go before it can produce enough fuel to be relevant for widespread use. “Where we are now, it would need to scale up by many orders of magnitude to make an sufficient amount of methane for our consumption,” Lin said.
The breakthrough could also be applied widely to other chemical reactions; you need to make a lot of fuel for it to have an impact, but much smaller quantities of some molecules, such as the starting materials to make pharmaceutical drugs and nylons, among others, could be very useful.
“So many of these fundamental processes are the same,” said Lin. “If you develop good chemistries, they can be plugged into many systems.”
More information: Guangxu Lan et al, Biomimetic active sites on monolayered metal–organic frameworks for artificial photosynthesis, Nature Catalysis (2022). DOI: 10.1038/s41929-022-00865-5
CO2 Adsorption/desorption cycles of NiRuNa, NiRuK and NiRuCa at (A) 350 °C, (B) 450 °C, (C) 550 °C and (D) 650 °C. Credit: Nanoscale (2022). DOI: 10.1039/D2NR02688K
It is possible to capture carbon dioxide (CO2) from the surrounding atmosphere and repurpose it into useful chemicals usually made from fossil fuels, according to a study from the University of Surrey.
The technology could allow scientists to both capture CO2 and transform it into useful chemicals such as carbon monoxide and synthetic natural gas in one circular process.
Dr. Melis Duyar, senior lecturer of chemical engineering at the University of Surrey explained: “Capturing CO2 from the surrounding air and directly converting it into useful products is exactly what we need to approach carbon neutrality in the chemicals sector. This could very well be a milestone in the steps needed for the U.K. to reach its 2050 net-zero goals.
“We need to get away from our current thinking on how we produce chemicals, as current practices rely on fossil fuels which are not sustainable. With this technology we can supply chemicals with a much lower carbon footprint and look at replacing fossil fuels with carbon dioxide and renewable hydrogen as the building blocks of other important chemicals.”
The technology uses patent-pending switchable Dual Function Materials (DFMs) that capture carbon dioxide on their surface and catalyze the conversion of captured CO2 directly into chemicals. The “switchable” nature of the DFMs comes from their ability to produce multiple chemicals depending on the operating conditions or the composition of the added reactant. This makes the technology responsive to variations in demand for chemicals as well as availability of renewable hydrogen as a reactant.
Loukia-Pantzechroula Merkouri, Postgraduate student leading this research at the University of Surrey added, “Not only does this research demonstrate a viable solution to the production of carbon neutral fuels and chemicals, but it also offers an innovative approach to combat the ever-increasing CO2 emissions contributing to global warming.”
The research is published in Nanoscale.
More information: Loukia-Pantzechroula Merkouri et al, Feasibility of switchable dual function materials as a flexible technology for CO2 capture and utilisation and evidence of passive direct air capture, Nanoscale (2022). DOI: 10.1039/D2NR02688K
Incorporating dragonfly-shaped gate molecules into PCP/MOFs makes it a hundred times more efficient than before to separate water from heavy water. The two have been difficult to separate due to their similar properties. Credit: Mindy Takamiya/Kyoto University iCeMS
A research group led by Susumu Kitagawa of Kyoto University’s Institute for Cell-Material Sciences (iCeMS), Japan and Cheng Gu of South China University of Technology, China have made a material that can effectively separate heavy water from normal water at room temperature.
Until now, this process has been very difficult and energy intensive. The findings have implications for industrial—and even biological—processes that involve using different forms of the same molecule. The scientists reported their results in the journal Nature.
Isotopologues are molecules that have the same chemical formula and whose atoms bond in similar arrangements, but at least one of their atoms has a different number of neutrons than the parent molecule. For example, a water molecule (H2O) is formed of one oxygen and two hydrogen atoms.
The nucleus of each of the hydrogen atoms contains one proton and no neutrons. In heavy water (D2O), on the other hand, the deuterium (D) atoms are hydrogen isotopes with nuclei containing one proton and one neutron. Heavy water has applications in nuclear reactors, medical imaging and in biological investigations.
“Water isotopologues are among the most difficult to separate because their properties are so similar,” explains materials scientist Cheng Gu. “Our work provided an unprecedented mechanism for separating water isotopologues using an adsorption-separation method.”
Gu and chemist Susumu Kitagawa, together with colleagues, based their separation technique on a copper-based porous coordination polymer (PCP). PCPs are porous crystalline materials formed of metal nodes connected by organic linkers. The team tested two PCPs made with different types of linkers.
What makes their PCPs especially important for isotopologue separation is that the linkers flip when moderately heated. This flipping action acts like a gate, allowing molecules to pass from one ‘cage’ in the PCP to another. Movement is blocked when the material is cooled.
When the scientists exposed their “flip-flop dynamic crystals” to vapor containing a mixture of normal, heavy and semi-heavy water and then slightly warmed it, they adsorbed normal water much faster than they did the other two isotopologues. Crucially, this process happened within room temperature ranges.
“The adsorptive separation of water isotopologues in our work is substantially superior to conventional methods due to very high selectivity at room temperature operation,” says Kitagawa. “We are optimistic that new materials guided by our work will be developed to separate other isotopologues.”
To convert heat into electricity, easily accessible materials from harmless raw materials open up new perspectives in the development of safe and inexpensive so-called “thermoelectric materials.” A synthetic copper mineral acquires a complex structure and microstructure through simple changes in its composition, thereby laying the foundation for the desired properties, according to a study published in the journal Angewandte Chemie.
The novel synthetic material is composed of copper, manganese, germanium, and sulfur, and it is produced in a rather simple process, explains materials scientist Emmanuel Guilmeau, CNRS researcher at CRISMAT laboratory, Caen, France, who is the corresponding author of the study. “The powders are simply mechanically alloyed by ball-milling to form a precrystallized phase, which is then densified by 600 degrees Celsius. This process can be easily scaled up,” he says.
Thermoelectric materials convert heat to electricity. This is especially useful in industrial processes where waste heat is reused as valuable electric power. The converse approach is the cooling of electronic parts, for example, in smartphones or cars. Materials used in this kind of applications have to be not only efficient, but also inexpensive and, above all, safe for health.
However, thermoelectric devices used to date make use of expensive and toxic elements such as lead and tellurium, which offer the best conversion efficiency. To find safer alternatives, Emmanuel Guilmeau and his team have turned to derivatives of natural copper-based sulfide minerals. These mineral derivatives are mainly composed of nontoxic and abundant elements, and some of them have thermoelectric properties.
Now, the team has succeeded in producing a series of thermoelectric materials showing two crystal structures within the same material. “We were very surprised at the result. Usually, slightly changing the composition has little effect on the structure in this class of materials,” says Emmanuel Guilmeau, describing their discovery.
The team found that replacing a small fraction of the manganese with copper produced complex microstructures with interconnected nanodomains, defects, and coherent interfaces, which affected the material’s transport properties for electrons and heat.
Emmanuel Guilmeau says that the novel material produced is stable up to 400 degrees Celsius, a range well within the waste heat temperature range of most industries. He is convinced that, based on this discovery, cheaper novel and nontoxic thermoelectric materials could be designed to replace more problematic materials.
More information: V. Pavan Kumar et al, Engineering Transport Properties in Interconnected Enargite‐Stannite Type Cu 2+ x Mn 1− x GeS 4 Nanocomposites, Angewandte Chemie International Edition (2022). DOI: 10.1002/anie.202210600
Journal information: Angewandte Chemie International Edition , Angewandte Chemie
Left) 12 different synthetic saponins were synthesized using Platycodin D as the base. Right) One of the synthetic saponins showed twice higher ability to inhibit SARS-CoV-2 infection. Credit: Institute for Basic Science
Coronaviruses enter cells through membrane fusion between the virus envelope and cell membrane. When cells expressing coronavirus spike protein (green) are cultivated with human lung cells (red), membrane fusion followed by fusion between the two cells can be observed. Lansemaside A inhibits this membrane fusion, thereby confirming that its mechanism is based on blocking membrane fusion. Credit: Institute for Basic Science
Astersaponin I, lancemaside A, and platycodin D are triterpenoid saponins with central ringed hydrocarbon structures similar to that of cholesterol. This allows one side of the saponin to become readily embedded within the cell membrane. It is believed that the polysaccharide chain protruding from the cell membrane is what prevents membrane fusion from occurring. Credit: Institute for Basic Science
