A biohybrid, leaf-spring design of DNA origami functions as a pulsating nanoengine that exploits the DNA-templated RNA transcription mechanism while consuming nucleoside triphosphates as fuel. The nanoengine also drives a nanomechanical follower structure.
When it comes to delivering drugs to the body, a major challenge is ensuring that they remain in the area they’re treating and continuing to deliver their payload accurately. While major strides have been made in delivering drugs, monitoring them is a challenge that often requires invasive procedures like biopsies.
Researchers at NYU Tandon led by Jin Kim Montclare, Professor of Chemical and Biomolecular Engineering, have developed proteins that can assemble themselves into fibers to be used as therapeutic agents for the potential treatments of multiple diseases.
These biomaterials can encapsulate and deliver therapeutics for a host of diseases. But while Montclare’s lab has long worked on producing these materials, there was once a challenge that was hard to overcome—how to make sure that these proteins continued to deliver their therapeutics at the correct location in the body for the necessary amount of time.
A non-organic intelligent system has for the first time designed, planned and executed a chemistry experiment, Carnegie Mellon University researchers report in the journal Nature (“Autonomous chemical research with large language models”).
The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to limited understanding of how nanoparticle (NP) properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by using various predictive models based on collection of large data sets of 403 data points incorporating both numerical and categorical features. Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain. Furthermore, the physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve. The analysis employed various linear models, with a particular emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional accuracy. The model was validated via the preparation and administration of two distinct NP formulations via the intranasal and intravenous routes. Among the various modeling approaches, LMEMs exhibited superior performance in capturing underlying patterns. Factors such as the release rate and molecular weight had a negative impact on brain targeting. The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate.
Researchers at the University of Sussex have discovered the transformative potential of Martian nanomaterials, potentially opening the door to sustainable habitation on the red planet.
Using resources and techniques currently applied on the International Space Station and by NASA, Dr. Conor Boland, a Lecturer in Materials Physics at the University of Sussex, led a research group that investigated the potential of nanomaterials—incredibly tiny components thousands of times smaller than a human hair —for clean energy production and building materials on Mars.
Taking what was considered a waste product by NASA and applying only sustainable production methods, including water-based chemistry and low-energy processes, the researchers have successfully identified electrical properties within gypsum nanomaterials—opening the door to potential clean energy and sustainable technology production on Mars.
For instance, the pegRNA molecules used in prime editing are difficult and expensive to chemically synthesise or laborious to clone, which hampers the crucial optimisation of prime-editing efficiency. Additionally, the reverse transcriptase (RT) enzymes used in prime editing are relatively error-prone and have low processivity, which may limit the precision and size of edits that can be introduced. Furthermore, RTs have a low affinity for dNTPs, which can impact prime-editing efficiency in non-dividing and differentiated cells.
To address these issues, two research groups led by Dr. Ben Kleinstiver at Mass General Hospital (MGH) & Harvard Medical School, and Dr. Erik Sontheimer at the RNA Therapeutics Institute (UMass Chan Medical School) have independently developed new approaches that build upon prime editing by replacing RT with another type of enzyme, namely a DNA-dependent DNA polymerase. This change permits the use of DNA instead of RNA as a template for editing, potentially addressing some of the main limitations of prime editing by allowing higher efficiency and adaptability.
A “chaperone” molecule that slows the formation of certain proteins reversed disease signs, including memory impairment, in a mouse model of Alzheimer’s disease, according to a study from researchers at the Perelman School of Medicine at the University of Pennsylvania.
In the study, published in Aging Biology, researchers examined the effects of a compound called 4-phenylbutyrate (PBA), a fatty-acid molecule known to work as a “chemical chaperone” that inhibits protein accumulation. In mice that model Alzheimer’s disease, injections of PBA helped to restore signs of normal proteostasis (the protein regulation process) in the animals’ brains while also dramatically improving their performance on a standard memory test, even when administered late in the disease course.
“By generally improving neuronal and cellular health, we can mitigate or delay disease progression,” said study senior author Nirinjini Naidoo, Ph.D., a research associate professor of Sleep Medicine. “In addition, reducing proteotoxicity— irreparable damage to the cell that is caused by an accumulation of impaired and misfolded proteins—can help improve some previously lost brain functions.”
Neurons communicate through chemical signals known as neurotransmitters. Researchers at St. Jude Children’s Research Hospital, leveraging their expertise in structural biology, have successfully elucidated the structures of the vesicular monoamine transporter 2 (VMAT2), a key component of neuronal communication.
By visualizing VMAT2 in different states, scientists now better understand how it functions and how the different shapes the protein takes influence drug binding — critical information for drug development to treat hyperkinetic (excess movement) disorders such as Tourette syndrome. The work was recently published in the journal Nature.
Groundbreaking research reveals new details about water vapor’s interaction with metals at an atomic level, with implications for corrosion management and clean-energy development.
When water vapor meets metal, the resulting corrosion can lead to mechanical problems that harm a machine’s performance. Through a process called passivation, it also can form a thin inert layer that acts as a barrier against further deterioration.
Either way, the exact chemical reaction is not well understood on an atomic level, but that is changing thanks to a technique called environmental transmission electron microscopy (TEM), which allows researchers to directly view molecules interacting on the tiniest possible scale.
A non-organic intelligent system has for the first time designed, planned and executed a chemistry experiment, Carnegie Mellon University researchers report in the Dec. 21 issue of the journal Nature.
“We anticipate that intelligent agent systems for autonomous scientific experimentation will bring tremendous discoveries, unforeseen therapies and new materials. While we cannot predict what those discoveries will be, we hope to see a new way of conducting research given by the synergetic partnership between humans and machines,” the Carnegie Mellon research team wrote in their paper.
The system, called Coscientist, was designed by Assistant Professor of Chemistry and Chemical Engineering Gabe Gomes and chemical engineering doctoral students Daniil Boiko and Robert MacKnight. It uses large language models (LLMs), including OpenAI’s GPT-4 and Anthropic’s Claude, to execute the full range of the experimental process with a simple, plain language prompt.
