Category: Chemistry

Researchers have investigated the capability of known quantum computing algorithms for fault-tolerant quantum computing to simulate the laser-driven electron dynamics of excitation and ionization processes in small molecules. Their research is published in the Journal of Chemical Theory and Computation.

“These quantum computer algorithms were originally developed in a completely different context. We used them here for the first time to calculate electron densities of molecules, in particular their dynamic evolution after excitation by a light pulse,” says Annika Bande, who heads a group on theoretical chemistry at Helmholtz Association of German Research Centers (HZB). Bande and Fabian Langkabel, who is doing his doctorate with her, show in the study how well this works.

“We developed an algorithm for a fictitious, completely error-free quantum computer and ran it on a classical server simulating a quantum computer of ten qubits,” says Langkabel. The scientists limited their study to smaller molecules in order to be able to perform the calculations without a real quantum computer and to compare them with conventional calculations.

The quantum algorithms produced the expected results. In contrast to conventional calculations; however, the quantum algorithms are also suitable for calculating significantly larger molecules with future quantum computers.

“This has to do with the calculation times. They increase with the number of atoms that make up the molecule,” says Langkabel. While the computing time multiplies with each additional atom for conventional methods, this is not the case for quantum algorithms, which makes them much faster.

Photocatalysis, light reception and more

The study thus shows a new way to calculate electron densities and their “response” to excitations with light in advance, with very high spatial and temporal resolution. This makes it possible, for example, to simulate and understand ultrafast decay processes, which are also crucial in quantum computers made of so-called quantum dots.

Additionally, predictions about the physical or chemical behavior of molecules are possible, for example during the absorption of light and the subsequent transfer of electrical charges.

This could facilitate the development of photocatalysts for the production of green hydrogen with sunlight or help to understand processes in the light-sensitive receptor molecules in the eye.

More information: Fabian Langkabel et al, Quantum-Compute Algorithm for Exact Laser-Driven Electron Dynamics in Molecules, Journal of Chemical Theory and Computation (2022). DOI: 10.1021/acs.jctc.2c00878

Provided by Helmholtz Association of German Research Centres 

What happens in an explosion? Where do the products of that explosion go following the blast? These questions are often difficult to solve. New rugged tracer particles, developed by Pacific Northwest National Laboratory (PNNL) researchers, can provide some answers.

Beyond explosives, many industries may be interested in tracking particulates through harsh environments—which often include high pressures, high temperatures, and different chemicals.

“Lots of chemical tracers exist,” said Lance Hubbard, materials scientist supporting PNNL’s national security research. “The challenge is developing one that can survive harsh environments. It took a few years to convince anyone we could do it.”

Hubbard and his team, along with fellow PNNL researchers April Carman and Michael Foxe, created a tracer that could not only survive but thrive in extreme conditions. Their work was published in MRS Communications.

Quantum dots and water-soaked glass

Organic materials, such as fluorescent dyes, are commonly used as tracers for water leaks and tracking cells in biological experiments. While they work great in those conditions, they aren’t so good for tracing material in explosions. Their problem?

“They burn,” said Hubbard.

Instead, Hubbard and his team focused on inorganic materials to develop their rugged tracers—particularly quantum dots. Though they fared much better than organic materials in harsh conditions, the research team still needed to protect the quantum dots from the extreme conditions of a chemical explosion.

“Finding a way to protect the tracer while still maintaining its luminescent intensity proved to be difficult,” said Carman.

The tracer’s brightness—or luminescent intensity—can be greatly affected by the local environment. Some protective methods can diminish the brightness, making the tracer more difficult to detect. The team focused on using hydrated silica—”basically water-soaked glass” as Hubbard puts it—to protect the quantum dots and maintain their brightness.

Though previous silica coating methods significantly decreased tracer luminescence, the coated tracers designed by the PNNL team were almost as bright as the original quantum dots. Further testing showed that the particles could survive for long periods of time through a range of pH conditions.

“We knew we created something special when we saw our results,” said Hubbard.

Making tracers tunable and mass-producible

Special is one thing, but useable on the commercial scale is another. Lucky for the PNNL team, their synthesis method was designed from the get-go to be completely scalable to produce mass quantities—from kilograms to potential tons per day.

Not only can they make large amounts of the tracer, but they can customize them as well. “We can tune both the tracer’s size and color to any specificity,” said Foxe. “The tracer can be fine-tuned to create a mimic of the mass or material that is being tracked. We can also use a variety of sizes with different colors to visualize how an explosion affects particles of different sizes.”

The tracers are rugged enough to be deployed in harsh environments to track mass and improve scientists’ understanding of environmental fate and transport. They can function under conditions that are too severe for traditional tracers—like in oil and gas refineries or geothermal plants. With tunable parameters and an easy-to-use system, these tracers have many potential applications for tracking material fate and transport in harsh environments.

Persistence pays off

The research has now grown from a small initial investment from the National Nuclear Security Administration (NNSA), Defense Nuclear Nonproliferation Research and Development program to encompassing several related projects.

“We are glad we could keep pursuing this project despite initial skepticism,” said Carman. “We are also thrilled to see where it leads us next.”

More information: Lance Hubbard et al, Luminescent silica microagglomerates, synthesis, and environmental testing, MRS Communications (2022). DOI: 10.1557/s43579-022-00150-3

Provided by Pacific Northwest National Laboratory 

Polychlorinated biphenyls (PCBs) have been widely used in industrial and commercial products including plastics, paints, electronic equipment and insulating fluids. Their manufacture was extensively banned from the late 1970s onwards due to their toxicity, but large amounts still remain in our environment and accumulate inside animals’ bodies.

Chiral PCBs are PCBs that have two mirror-image isomers; these isomers are identical reflections of each other with the same composition. Chiral PCBs are particularly dangerous because they have more chlorine atoms, which are hard for the body to break down, so they can accumulate in the body easily and their isomers are metabolized differently, causing isomer-specific toxicity (particularly neurodevelopmental issues). However, the process behind this selective metabolism was not previously known.

To address this, a research group has illuminated how enzymes produced by the body unevenly metabolize the mirror-image isomers. These results will make it possible to estimate PCB metabolism and detoxification pathways in animals. They will also contribute towards the development of technology to make predictions about chiral PCBs’ mirror isomers, so that we can obtain a better understanding of potential toxicity in humans and other mammals.

The research results were published in Environmental Science & Technology and Chemosphere.

Even though the manufacture and use of PCBs was banned around 50 years ago, they still remain in the environment. It has been discovered that PCBs accumulate inside the bodies of humans and other animals through food consumption. In particular, PCBs with many chlorine bonds are water-resistant and do not break down easily.

This enables high concentrations of these PCBs to accumulate inside animals’ bodies, which adversely affects their health. PCBs’ toxicity is induced by the aryl hydrocarbon receptor (AhR), causing similar adverse effects to dioxin poisoning such as cancer, teratogenesis and immune system damage. Research is being conducted on the particular types of PCB widely known to cause these effects, which are dioxin-like PCBs with one ortho chlorine substitution in the biphenyl ring of their chemical structure, or PCBs with no substitutions.

However, if a PCB has more than three chlorine substitutions at the ortho position of the biphenyl ring, it becomes a mirror-image isomer called chiral PCB. These chiral PCBs do not demonstrate dioxin-like toxicity, but are far more dangerous, binding with the ryanodine receptors (RyR) in organisms to become neurotoxic.

The two mirror-image isomers (called atropisomers) in chiral PCBs have identical physical and chemical properties and exist at a 1:1 ratio in commercial chiral PCBs, but biased ratios are often observed in the environment and in animals such as earthworms and whales, as well as humans. It is believed that this kind of unbalanced ratio is mainly caused by metabolism and that one of a chiral PCB’s atropisomers is more affected by the metabolic reaction, thus reducing its concentration.

Until now, very little research has been carried out into differences in how these atropisomers are metabolized nor the structural arrangement of the metabolic enzymes.

To address this knowledge gap, the team conducted research focusing on the metabolic enzyme cytochrome P450 (CYP enzyme). The CYP enzyme reacts with foreign compounds that enter an animal’s body (for example, chemicals or pollutants in food or medicines). CYP can convert them into water-soluble compounds and promote their expulsion from the body. Previous research by this group has shown that CYP enzymes hydroxylate and dechlorinate dioxin-like PCBs.

This decreases a PCB’s binding with AhR and increases its water solubility, promoting expulsion from the body and therefore counteracting its toxicity. In other words, CYP is an important enzyme that determines whether or not PCBs are treated as toxic compounds by the body. To measure the metabolic action of CYP on chiral PCB, the researchers set up a CYP enzyme and PCB docking model. They used this to estimate the structure of PCB metabolites and the structure of the CYP that decides to metabolize each of the PCB atropisomers differently.

For the experiment, the group selected three types of chiral PCB, each with a different number of substituted chlorine atoms; CB45 (4 chlorine substitutions), CB91 (5 chlorine substitutions) and CB183 (7 chlorine substitutions). They separated the atropisomers for each type of chiral PCB using chromatography and let them react with a human CYP enzyme. It is believed that research on separating the atropisomers and letting them react has not been done before now.

The results revealed big differences in how each atropisomer is metabolized. Even though the two atropisomers in one PCB have the same physical and chemical composition, they are biologically different. The researchers found that one of the chiral PCB atropisomers was metabolized more than the other one, disrupting the 1:1 ratio.

In addition, it is thought that the amount of (aS)-CB183 atropisomer decreases because it is metabolized more than the other atropisomer, and this is supported by the reports of low accumulation of (aS)-CB183 in humans.

But why are these physically and chemically identical atropisomers metabolized differently by the CYP enzyme? To solve this mystery, the researchers used a computer model to investigate how easily each chiral PCB atropisomer binds to the chemical structure of CYP. They found that when an atropisomer fills up the substrate-binding cavity inside the CYP enzyme, CYP’s amino acids (that form the cavity) interfere with the binding between CYP and the atropisomer.

Therefore, the atropisomer that isn’t interfered with by CYP’s amino acids becomes easy to metabolize (atropisomer (aR)-CB45 in CB45, and (aS)-CB183 in CB183), resulting in alterations to the original 1:1 ratio of atropisomers found in chiral PCB.

The results of this research will be useful for making predictions about the atropisomers of chiral PCBs, which accumulate easily inside animals’ bodies. In other words, it will be possible to work out which atropisomer is reduced by the metabolic reaction with CYP enzymes and which atropisomer remains inside the body. A chiral PCB’s toxicity is activated by binding with RyR, though the ability to bind with RyR differs between the atropisomers. Therefore, this research will make it possible to estimate the toxicity of chiral PCBs.

More information: Hideyuki Inui et al, Differences in Enantioselective Hydroxylation of 2,2′,3,6-Tetrachlorobiphenyl (CB45) and 2,2′,3,4′,6-Pentachlorobiphenyl (CB91) by Human and Rat CYP2B Subfamilies, Environmental Science & Technology (2022). DOI: 10.1021/acs.est.2c01155

Terushi Ito et al, Enantioselective metabolism of chiral polychlorinated biphenyl 2,2′,3,4,4′,5′,6-Heptachlorobiphenyl (CB183) by human and rat CYP2B subfamilies, Chemosphere (2022). DOI: 10.1016/j.chemosphere.2022.136349

Journal information: Chemosphere  , Environmental Science & Technology 

Provided by Kobe University 

Tropane alkaloids are a particular class of plant-derived compounds that have been exploited by mankind since the domestication of medicinal plants. The distribution of these alkaloids is scattered amongst the flowering plants and the two most studied families include those from the Solanaceae (tomato, tobacco, potato relatives) and the Erythroxylaceae (coca). The WHO lists several tropane alkaloids as some of the most important medicines in the modern day pharmacopeia. However other compounds such as cocaine are more infamous for their narcotic and euphorigenic properties.

“It is critical to understand how plants produce these alkaloids in order for mankind to continue to build upon nature and develop new useful medicines,” says Dr. John D’ Auria, head of the IPK’s research group “Metabolic Diversity.”

The most studied and characterized system for tropane production has historically been within Solanaceae. There are more than ten chemical modification steps necessary to transform the beginning amino acid precursors into the final active alkaloids, and all of these steps have been identified and characterized in solanaceous plants.

The scattered distribution of tropanes among flowering plants has always hinted that different families may have developed the ability to produce these alkaloids independently from one another. In fact, several steps of tropane biosynthesis were already documented to have evolved independently within members of the Erythroxylaceae.

“We have been working on elucidating the coca-derived tropane pathway for the last 15 years and we have been successful in working on several key steps in the biosynthesis of cocaine and other related tropanes in coca,” say the researchers. “The idea that coca would share similar enzymes and genes with their distant solanaceous relatives was incorrect. While the final structure of tropanes is similar, the pathway leading to these alkaloids is different.”

In order to discover the last remaining steps of the pathway in coca, Dr. D’ Auria collaborated with the lab of Dr. Christina Smolke from Stanford University. The Smolke group are experts at manipulating yeast and microorganisms to produce important medicinal compounds via synthetic biology methods. Their combined research is published in the journal Proceedings of the National Academy of Sciences.

“With [the Smolke group’s] assistance, we used the multiplicative power of gene manipulation in yeast to test many different gene candidates for the missing steps in the coca pathway. In essence, at every unknown step, we designed and tested multiple candidate sequences,” the researchers report.

These candidate sequences originated from transcriptome studies performed in Dr. John D’ Auria’s group as well as the group of Dr. Lyndel Meinhardt from the USDA in Beltsville, Maryland (U.S.).

“Using this powerful gene discovery platform, we successfully identified all the remaining ‘missing steps’ for tropane biosynthesis in coca. This represents the culmination of more than ten graduate student projects in my group and 15 years of my research,” says Dr. D’ Auria.

The most significant portion of the findings now confirms that tropane biosynthesis has independently evolved at least twice during the evolution of flowering plants. “This is important because we also show in our study that you can mix and match the Solanaceae and Erythroxylaceae genes and produce tropanes,” say the researchers. In layman’s terms, the research provides multiple tools for synthetic biologists to begin designing the tropane alkaloid pathway in organisms that have never produced them before, and with the ability to use different enzymes for similar steps, it is possible to optimize or modify those steps for specific chemical outcomes.

“In addition, we also show that the beginning portion of the pathway in coca proceeds by an interesting ‘detour’ or alternate route that doesn’t exist in solanaceous species,” says Benjamin Chavez, the first author of the study and a Ph.D. student in the D’Auria laboratory. “This provides insights in how plant metabolism can find solutions to biochemical challenges. Namely, we can understand the interplay between early precursors and their bottlenecks.”

Lastly, the researchers discovered a specific enzyme that is responsible for the so-called “carbomethoxy group” present exclusively in coca alkaloids. Solanaceous species do not have this modification. The carbomethoxy group is partially responsible for the euphorigenic properties of cocaine.

More information: Elucidation of tropane alkaloid biosynthesis in Erythroxylum coca using a microbial pathway discovery platform, Proceedings of the National Academy of Sciences (2022). DOI: 10.1073/pnas.2215372119

Journal information: Proceedings of the National Academy of Sciences 

Provided by Leibniz Institute of Plant Genetics and Crop Plant Research

You can keep your best guesses. Engineers at Rice University’s George R. Brown School of Engineering are starting to understand exactly what goes on when doctors pump contrast agents into your body for an MRI scan.

In a new study that could lead to better scans, a Rice-led team digs deeper via molecular simulations that, unlike earlier models, make absolutely no assumptions about the basic mechanisms at play when gadolinium agents are used to highlight soft tissues.

The study led by Rice chemical and biomolecular engineer Philip Singer, former associate research professor Dilip Asthagiri, now of Oak Ridge National Laboratory, and graduate student Thiago Pinheiro dos Santos appears in Physical Chemistry Chemical Physics.

It employs the sophisticated models first developed at Rice for oil and gas studies to conclusively analyze how hydrogen nuclei at body temperatures “relax” under nuclear magnetic resonance (NMR), the technology used by magnetic resonance imaging, aka MRI.

Doctors use MRI to “see” the state of soft tissues, including the brain, in a patient by inducing magnetic moments in the hydrogen nuclei of water molecules to align with the magnetic field, a process that can be manipulated when gadolinium agents are in the vicinity. The device detects bright spots when the aligned nuclei relax back to thermal equilibrium following an excitation. The faster they relax, the brighter the contrast.

Gadolinium molecules are naturally paramagnetic and sensitive to magnetic excitation. Because they’re toxic, they are usually chelated when part of a contrast agent. “A chelate basically hugs the gadolinium and protects your body from directly interacting with the metal,” Pinheiro dos Santos said. “We’re asking, exactly how do these molecules behave?”

Though gadolinium-based contrast agents are injected by the ton into patients each year, how they work on a molecular level has never been fully understood.

“Going back 40 years, in the NMR field people assumed liquid water is just a collection of marbles moving about, and the dipoles in the marbles randomly reorient,” Asthagiri said.

But such assumptions are limiting, he said. “What Thiago does with his explicit simulation is show how the water network evolves in time,” Asthagiri said. “These are complicated, computationally intensive calculations.”

The Rice simulations make use of highly refined, polarizable force fields to study the phenomenon in detail, and that required intensive GPU-accelerated computing.

The team validated its molecular dynamics approach with experimental data by co-author Steven Greenbaum, a professor of physics at Hunter College in the City University of New York, whose lab specializes in NMR measurements of ionic and molecular transport processes in condensed matter.

The simulations revealed distinct differences in how the inner and outer shells of water molecules around gadolinium respond to thermal excitation. “The inner shell is the group of eight or nine water molecules around gadolinium,” Pinheiro dos Santos said. “They’re strongly attached to the gadolinium and they stay there for a long time, a few nanoseconds. The outer shell encompasses all of the remaining water molecules.”

The researchers found that while the structure of the inner shell does not change between 41 and 98.6 degrees Fahrenheit, its dynamics are very susceptible to thermal effects. They also discovered that temperature greatly affects the self-diffusivity of molecules in the gadolinium-water simulations in a way that affects outer-shell relaxation.

“Overall, these discoveries open a new way to elucidate how contrast agents respond at human body conditions during an MRI scan,” Singer said. “By better understanding this, one can develop new, safer and more sensitive contrast agents, as well as use simulations to enhance the interpretation of MRI data.”

He said future studies will examine chelated gadolinium complexes in fluids that are more representative of cellular interiors.

More information: Thiago J. Pinheiro dos Santos et al, Thermal and concentration effects on 1H NMR relaxation of Gd3+-aqua using MD simulations and measurements, Physical Chemistry Chemical Physics (2022). DOI: 10.1039/D2CP04390D

Journal information: Physical Chemistry Chemical Physics 

Provided by Rice University 

Benzobactins are bacterial natural products that have special biological activity due to a compound consisting of two ring structures. The bacterial genes responsible for the formation of the compound were previously unclear. Now, scientists at the Max Planck Institute for Terrestrial Microbiology have been able to decipher its biosynthesis through extensive genomic research. Their research facilitates the discovery of numerous previously unknown natural compounds for medical drug therapy.

In their natural habitat, microorganisms are often exposed to changing environmental conditions that require numerous survival responses. The most efficient one is their capability to produce a wide array of natural products with diverse chemical structures and functions.

Benzobactines—powerful, but rare

Benzoxazolinate is a rare natural compound that confers extraordinary bioactivities on natural products. It is, for example, the essential part of lidamycin, an antitumor antibiotic that is one of the most cytotoxic compounds so far. The reason for this capacity is the fact that benzoxazolinate consists of two rings, a structure that allows it to interact with protein as well as with DNA. However, tracking down the producers of this rare substance in nature resembles the proverbial search for a needle in a haystack.

In order to exploit new pharmaceutically valuable natural compounds, like antibiotics, tumor suppressants or immunosuppressants, it is necessary to know the responsible genes, or more precisely, their biosynthetic gene clusters (BGCs). BGCs are locally clustered groups of two or more genes that together encode the production of a certain set of enzymes—and thus the corresponding natural products produced by these enzymes.

So far, the biosynthetic gene cassette of benzoxazolinate remained elusive, hindering to expand the repertoire of bioactive benzoxazolinate-containing compounds. More specifically, the last formation step of benzoxazolinate was unclear. Now a team of Max-Planck scientists led by Dr. Yi-Ming Shi and Prof. Dr. Helge Bode succeeded in the biosynthetic characterization of the benzoxazolinate pathway.

During the biosynthesis, the pathway obviously “borrows” an intermediate from the so-called phenazine pathway, responsible for the production of another natural product. Most importantly, the researchers identified the enzyme that is responsible for the last step, the cyclization towards benzoxazolinate.

Using an enzyme as a probe for natural substances

Ph.D. student Jan Crames, co-first author of the study, explains, “Knowing the enzyme’s identity, we used it as a probe. With genome-mining we were able to detect many closely related biosynthetic pathways for benzoxazolinate-containing natural products, so-called benzobactins.”

According to the scientists, the most striking aspect was the wide distribution of these genes in other bacteria. “These pathways were found in taxonomically and ecologically remarkably diverse bacteria ranging from land to ocean, as well as plant pathogens and biocontrol microbes. Their wide distribution indicates that these molecules have a significant ecological function on the producers,” as Yi-Ming Shi, first author of the study indicates.

Prof. Helge Bode, leader of the department “Natural Products in Organismic Interactions” at the Max-Planck Institute for Terrestrial Microbiology in Marburg, adds, “Our findings reveal the immense biosynthetic potential of a widespread biosynthetic gene cluster for benzobactin. Now, we have to find out their ecological function and if we can apply them as antibiotics or other drugs.”

The research was published in Angewandte Chemie International Edition.

More information: Yi‐Ming Shi et al, Genome Mining Enabled by Biosynthetic Characterization Uncovers a Class of Benzoxazolinate‐Containing Natural Products in Diverse Bacteria, Angewandte Chemie International Edition (2022). DOI: 10.1002/anie.202206106

Journal information: Angewandte Chemie International Edition 

Provided by Max Planck Society 

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

Provided by University of Münster 

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

Journal information: Nature Chemical Biology 

Provided by Harvard Medical School 

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.

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 PDI^2--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 PDI^2-, 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 PDI^2- 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.'”

More information: Kimoon Kim, Spatiotemporal segregation of chiral supramolecular polymers, Chem (2022). DOI: 10.1016/j.chempr.2022.10.022. www.cell.com/chem/fulltext/S2451-9294(22)00564-2

Journal information: Nature Chemistry  , Chem 

Provided by Institute for Basic Science 

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

Journal information: Nature Communications  , Nature Chemistry 

Provided by Technion – Israel Institute of Technology