Tag Archives: Nicholas Kotov

Nanobiotics and a new machine learning model

A May 16, 2022 news item on phys.org announces work on a new machine learning model that could be useful in the research into engineered nanoparticles for medical purposes (Note: Links have been removed),

With antibiotic-resistant infections on the rise and a continually morphing pandemic virus, it’s easy to see why researchers want to be able to design engineered nanoparticles that can shut down these infections.

A new machine learning model that predicts interactions between nanoparticles and proteins, developed at the University of Michigan, brings us a step closer to that reality.

A May 16, 2022 University of Michigan news release by Kate McAlpine, which originated the news item, delves further into the work (Note: Links have been removed),

“We have reimagined nanoparticles to be more than mere drug delivery vehicles. We consider them to be active drugs in and of themselves,” said J. Scott VanEpps, an assistant professor of emergency medicine and an author of the study in Nature Computational Science.

Discovering drugs is a slow and unpredictable process, which is why so many antibiotics are variations on a previous drug. Drug developers would like to design medicines that can attack bacteria and viruses in ways that they choose, taking advantage of the “lock-and-key” mechanisms that dominate interactions between biological molecules. But it was unclear how to transition from the abstract idea of using nanoparticles to disrupt infections to practical implementation of the concept. 

“By applying mathematical methods to protein-protein interactions, we have streamlined the design of nanoparticles that mimic one of the proteins in these pairs,” said Nicholas Kotov, the Irving Langmuir Distinguished University Professor of Chemical Sciences and Engineering and corresponding author of the study. 

“Nanoparticles are more stable than biomolecules and can lead to entirely new classes of antibacterial and antiviral agents.”

The new machine learning algorithm compares nanoparticles to proteins using three different ways to describe them. While the first was a conventional chemical description, the two that concerned structure turned out to be most important for making predictions about whether a nanoparticle would be a lock-and-key match with a specific protein.

Between them, these two structural descriptions captured the protein’s complex surface and how it might reconfigure itself to enable lock-and-key fits. This includes pockets that a nanoparticle could fit into, along with the size such a nanoparticle would need to be. The descriptions also included chirality, a clockwise or counterclockwise twist that is important for predicting how a protein and nanoparticle will lock in.

“There are many proteins outside and inside bacteria that we can target. We can use this model as a first screening to discover which nanoparticles will bind with which proteins,” said Emine Sumeyra Turali Emre, a postdoctoral researcher in chemical engineering and co-first author of the paper, along with Minjeong Cha, a PhD student in materials science and engineering.

Emre and Cha explained that researchers could follow up on matches identified by their algorithm with more detailed simulations and experiments. One such match could stop the spread of MRSA, a common antibiotic-resistant strain, using zinc oxide nanopyramids that block metabolic enzymes in the bacteria.  

“Machine learning algorithms like ours will provide a design tool for nanoparticles that can be used in many biological processes. Inhibition of the virus that causes COVID-19 is one good example,” said Cha. “We can use this algorithm to efficiently design nanoparticles that have broad-spectrum antiviral activity against all variants.”

This breakthrough was enabled by the Blue Sky Initiative at the University of Michigan College of Engineering. It provided $1.5 million to support the interdisciplinary team carrying out the fundamental exploration of whether a machine learning approach could be effective when data on the biological activity of nanoparticles is so sparse.

“The core of the Blue Sky idea is exactly what this work covers: finding a way to represent proteins and nanoparticles in a unified approach to understand and design new classes of drugs that have multiple ways of working against bacteria,” said Angela Violi, an Arthur F. Thurnau Professor, a professor of mechanical engineering and leader of the nanobiotics Blue Sky project.

Emre led the building of a database of interactions between proteins that could help to predict nanoparticle and protein interaction. Cha then identified structural descriptors that would serve equally well for nanoparticles and proteins, working with collaborators at the University of Southern California, Los Angeles to develop a machine learning algorithm that combed through the database and used the patterns it found to predict how proteins and nanoparticles would interact with one another. Finally, the team compared these predictions for lock-and-key matches with the results from experiments and detailed simulations, finding that they closely matched.

Additional collaborators on the project include Ji-Young Kim, a postdoctoral researcher in chemical engineering at U-M, who helped calculate chirality in the proteins and nanoparticles. Paul Bogdan and Xiongye Xiao, a professor and PhD student, respectively, in electrical and computer engineering at USC [University of Southern California] contributed to the graph theory descriptors. Cha then worked with them to design and train the neural network, comparing different machine learning models. All authors helped analyze the data.

Here are links to and a citation for the research briefing and paper, respectively,

Universal descriptors to predict interactions of inorganic nanoparticles with proteins. Nature Computational Science (2022) [Research briefing] DOI: https://doi.org/10.1038/s43588-022-00230-3 Published: 28 April 2022

This paper is behind a paywall.

Unifying structural descriptors for biological and bioinspired nanoscale complexes by Minjeong Cha, Emine Sumeyra Turali Emre, Xiongye Xiao, Ji-Young Kim, Paul Bogdan, J. Scott VanEpps, Angela Violi & Nicholas A. Kotov. Nature Computational Science volume 2, pages 243–252 (2022) Issue Date: April 2022 DOI: https://doi.org/10.1038/s43588-022-00229-w Published: 28 April 2022

This paper appears to be open access.

A hedgehog particle for safer paints and coatings?

The researchers did not extract particles from hedgehogs for this work but they are attempting to provide a description for a class of particles, which could make paints and coatings more environmentally friendly. From a Jan. 28, 2015 news item on phys.org,

A new process that can sprout microscopic spikes on nearly any type of particle may lead to more environmentally friendly paints and a variety of other innovations. Made by a team of University of Michigan engineers, the “hedgehog particles” are named for their bushy appearance under the microscope. …

A Jan. 28, 2015 University of Michigan news release (also EurekAlert), which originated the news item, describes the research,

The new process modifies oily, or hydrophobic, particles, enabling them to disperse easily in water. It can also modify water-soluble, or hydrophilic, particles, enabling them to dissolve in oil or other oily chemicals.

The unusual behavior of the hedgehog particles came as something of a surprise to the research team, says Nicholas Kotov, the Joseph B. and Florence V. Cejka Professor of Engineering.

“We thought we’d made a mistake,” Kotov said. “We saw these particles that are supposed to hate water dispersing in it and we thought maybe the particles weren’t hydrophobic, or maybe there was a chemical layer that was enabling them to disperse. But we double-checked everything and found that, in fact, these particles defy the conventional chemical wisdom that we all learned in high school.”

The team found that the tiny spikes made the particles repel each other more and attract each other less. The spikes also dramatically reduce the particles’ surface area, helping them to diffuse more easily.

One of the first applications for the particles is likely to be in paints and coatings, where toxic volatile organic compounds (VOCs) like toluene are now used to dissolve pigment. Pigments made from hedgehog particles could potentially be dissolved in nontoxic carriers like water, the researchers say.

This would result in fewer VOC emissions from paints and coatings, which the EPA [US Environmental Protection Agency] estimates at over eight million tons per year in the United States alone. VOCs can cause a variety of respiratory and other ailments and also contribute to smog and climate change. Reducing their use has become a priority for the Environmental Protection Agency and other regulatory bodies worldwide.

“VOC solvents are toxic, they’re flammable, they’re expensive to handle and dispose of safely,” Kotov said. “So if you can avoid using them, there’s a significant cost savings in addition to environmental benefits.”

While some low- and no-VOC coatings are already available, Kotov says hedgehog particles could provide a simpler, more versatile and less expensive way to manufacture them.

For the study, the team created hedgehog particles by growing zinc oxide spikes on polystyrene microbeads. The researchers say that a key advantage of the process is its flexibility; it can be performed on virtually any type of particle, and makers can vary the number and size of the spikes by adjusting the amount of time the particles sit in various solutions while the protrusions are growing. They can also make the spikes out of materials other than zinc oxide.

“I think one thing that’s really exciting about this is that we’re able to make such a wide variety of hedgehog particles,” said Joong Hwan Bahng, a chemical engineering doctoral student. “It’s very controllable and very versatile.”

The researchers say the process is also easily scalable, enabling hedgehog particles to be created “by the bucketful,” according to Kotov. Further down the road, Kotov envisions a variety of other applications, including better oil dispersants that could aid in the cleanup of oil spills and better ways to deliver non-water-soluble prescription medications.

As is becoming more common in news releases, there’s a reference to commercial partners, suggesting (to me) they might be open to offers,

“Anytime you need to dissolve an oily particle in water, there’s a potential application for hedgehog particles,” he said. “It’s really just a matter of finding the right commercial partners. We’re only just beginning to explore the uses for these particles, and I think we’re going to see a lot of applications in the future.”

Here’s a link to and a citation for the paper,

Anomalous dispersions of ‘hedgehog’ particles by Joong Hwan Bahng, Bongjun Yeom, Yichun Wang, Siu On Tung, J. Damon Hoff, & Nicholas Kotov. Nature 517, 596–599 (29 January 2015) doi:10.1038/nature14092 Published online 28 January 2015

This paper is behind a paywall.

Supraparticles, self-assembly, and uniformity and Futurity

I’m not sure what I find more interesting the research  or the website. First the research, from the August 25, 2011 news item on Futurity,

In another instance of forces behaving in unexpected ways at the nanoscale, scientists [at the University of Michigan] discovered that if you start with small nanoscale building blocks that are varied enough in size, the electrostatic repulsion force and van der Waals attraction force will balance each other and limit the growth of the clusters, enabling formations that are uniform in size. The findings are published in the Nature Nanotechnology.

Researchers created the inorganic superclusters—technically called “supraparticles”—out of red, powdery cadmium selenide In many ways the structures are similar to viruses. They share many attributes with the simplest forms of life, including size, shape, core-shell structure, and the abilities to both assemble and dissemble, says co-author Nicholas Kotov.

Here’s a graphic that accompanies the news item,

Under the right circumstances, basic atomic forces can be exploited to enable nanoparticles to assemble into superclusters that are uniform in size and share attributes with viruses. (Credit: T.D.Nguyen)

I’m particularly interested in that comment about the resemblance to viruses. Now on to Futurity, a science news aggregator (from the About Futurity page)

Futurity features the latest discoveries in all fields from scientists at the top universities in the US, UK, and Canada. The site, which is hosted at the University of Rochester, launched in 2009 as a way to share research news with the public.

Who is Futurity?
A consortium of participating universities manages and funds the project. The university partners are members of the Association of American Universities (AAU) and of the Russell Group. Futurity aggregates the very best research news from these top universities.

There are two universities from Canada involved, University of Toronto and McGill University.