Tyler O'Neal, Staff Editor ENGINEERING

Warwick scientists discover future targets for Covid-19 treatments rapidly with new supercomputer simulations

Researchers have detailed a mechanism in the distinctive corona of Covid-19 that could help scientists rapidly find new treatments for the virus, and quickly test whether existing treatments are likely to work with mutated versions as they develop

  • University of Warwick scientists model movements of nearly 300 protein structures in Covid-19
  • Scientists can use the simulations to identify potential targets to test with existing drugs, and even check effectiveness with future Covid variants
  • Simulation of virus spike protein, part of the virus's corona', shows a promising mechanism that could potentially be blocked
  • Researchers have publicly released data on all protein structures to aid efforts to find potential drug targets: https://warwick.ac.uk/flex-covid19-data

Top view of spike protein structure 6vyb. Colors blue, red and yellow denote the 3 sub-parts of the homodimer.{module INSIDE STORY} Researchers have detailed a mechanism in the distinctive corona of Covid-19 that could help scientists rapidly find new treatments for the virus, and quickly test whether existing treatments are likely to work with mutated versions as they develop.

The team, led by the University of Warwick as part of the EUTOPIA community of European universities, have simulated movements in nearly 300 protein structures of the Covid-19 virus spike protein by using computational modeling techniques, to help identify promising drug targets for the virus.

In a new paper published today (19 February) in the journal Scientific Reports, the team of physicists and life scientists detail the methods they used to model the flexibility and dynamics of all 287 protein structures for the Covid-19 virus, also known as SARS-CoV-2, identified so far. Just like organisms, viruses are composed of proteins, large biomolecules that perform a variety of functions. The scientists believe that one method for treating the virus could be interfering with the mobility of those proteins.

They have made their data, movies, and structural information, detailing how the proteins move and how they deform, for all 287 protein structures for Covid-19 that were available at the time of the study, publicly accessible to allow others to investigate potential avenues for treatments.

The researchers focused particular efforts on a part of the virus known as the spike protein, also called the Covid-19 echo domain structure, which forms the extended corona that gives coronaviruses their name. This spike is what allows the virus to attach itself to the ACE2 enzyme in human cell membranes, through which it causes the symptoms of Covid-19.

The spike protein is in fact a homotrimer or three of the same type of protein combined. By modeling the movements of the proteins in the spike, the researchers identified a 'hinge' mechanism that allows the spike to hook onto a cell, and also opens up a tunnel in the virus that is a likely means of delivering the infection to the hooked cell. The scientists suggest that by finding a suitable molecule to block the mechanism - literally, by inserting a suitably sized and shaped molecule - pharmaceutical scientists will be able to quickly identify existing drugs that could be effective against the virus.

Lead author Professor Rudolf Roemer from the Department of Physics at the University of Warwick, who conducted the work while on a sabbatical at CY Cergy-Paris Université, said: "Knowing how this mechanism works is one way in which you can stop the virus, and in our study, we are the first to see the detailed movement of opening. Now that you know what the range of this movement is, you can figure out what can block it.

"All those people who are interested in checking whether the protein structures in the virus could be drug targets should be able to examine this and see if the dynamics that we compute are useful to them.

"We couldn't look closely at all the 287 proteins though in the time available. People should use the motion that we observe as a starting point for their own development of drug targets. If you find an interesting motion for a particular protein structure in our data, you can use that as the basis for further modeling or experimental studies."

To investigate the proteins' movements, the scientists used a protein flexibility modeling approach. This involves recreating the protein structure as a computer model then simulating how that structure would move by treating the protein as a material consisting of solid and elastic subunits, with the possible movement of these subunits defined by chemical bonds. The method is particularly efficient and accurate when applied to large proteins such as the coronavirus's spike protein. This can allow scientists to swiftly identify promising targets for drugs for further investigation.

The protein structures that the researchers based their modeling on are all contained in the Protein Data Bank. Anyone who publishes a biological structure has to submit it to the protein databank so that it is freely available in a standard format for others to download and study further. Since the start of the Covid-19 pandemic, scientists all over the world have already submitted thousands of protein structures of Covid-19-related proteins to the Protein Data Bank.

Professor Roemer adds: "The gold standard in modeling protein dynamics computationally is a method called molecular dynamics. Unfortunately, this method can become very time-consuming particularly for large proteins such as the Covid-19 spike, which has nearly 3000 residues - the basic building blocks of all proteins. Our method is much quicker, but naturally, we have to make more stringent simplifying assumptions. Nevertheless, we can rapidly simulate structures that are much larger than what alternative methods can do.

"At the moment, no-one has published experiments that identify protein crystal structures for the new variants of Covid-19. If new structures come out for the mutations in the virus then scientists could quickly test existing treatments and see if the new mechanics have an impact on their effectiveness using our method."

Northwestern builds new technology that enables predictive design of engineered human cells

Tyler O'Neal, Staff Editor ENGINEERING

The capability could accelerate the development of new treatments for diseases

Northwestern University synthetic biologist Joshua Leonard used to build devices when he was a child using electronic kits. Now he and his team have developed a design-driven process that uses parts from a very different kind of toolkit to build complex genetic circuits for cellular engineering.

One of the most exciting frontiers in medicine is the use of living cells as therapies. Using this approach to treat cancer, for example, many patients have been cured of previously untreatable diseases. These advances employ the approaches of synthetic biology, a growing field that blends tools and concepts from biology and engineering.

The new Northwestern technology uses computational modeling to more efficiently identify useful genetic designs before building them in the lab. Faced with myriad possibilities, modeling points researchers to designs that offer real opportunities. Synthetic biologists achieve a breakthrough in the design of living cells CREDIT Justin Muir{module INSIDE STORY}

"To engineer a cell, we first encode a desired biological function in a piece of DNA, and that DNA program is then delivered to a human cell to guide its execution of the desired function, such as activating a gene only in response to certain signals in the cell's environment," Leonard said. He led a team of researchers from Northwestern in collaboration with Neda Bagheri from the University of Washington for this study.

Leonard is an associate professor of chemical and biological engineering in the McCormick School of Engineering and a leading faculty member within Northwestern's Center for Synthetic Biology. His lab is focused on using this kind of programming capability to build therapies such as engineered cells that activate the immune system, to treat cancer.

Bagheri is an associate professor of biology and chemical engineering and a Washington Research Foundation Investigator at the University of Washington Seattle. Her lab uses computational models to better understand -- and subsequently control -- cell decisions. Leonard and Bagheri co-advised Joseph Muldoon, a recent doctoral student and the paper's first author.

"Model-guided design has been explored in cell types such as bacteria and yeast, but this approach is relatively new in mammalian cells," Muldoon said.

The study, in which dozens of genetic circuits were designed and tested, will be published today in the journal Science Advances. Like other synthetic biology technologies, a key feature of this approach is that it is intended to be readily adopted by other bioengineering groups.

To date, it remains difficult and time-consuming to develop genetic programs when relying upon trial and error. It is also challenging to implement biological functions beyond relatively simple ones. The research team used a "toolkit" of genetic parts invented in Leonard's lab and paired these parts with computational tools for simulating many potential genetic programs before conducting experiments. They found that a wide variety of genetic programs, each of which carries out a desired and useful function in a human cell, can be constructed such that each program works as predicted. Not only that, but the designs worked the first time.

"In my experience, nothing works like that in science; nothing works the first time. We usually spend a lot of time debugging and refining any new genetic design before it works as desired," Leonard said. "If each design works as expected, we are no longer limited to the building by trial and error. Instead, we can spend our time evaluating ideas that might be useful in order to hone in on the really great ideas."

"Robust representative models can have a disruptive scientific and translational impact," Bagheri added. "This development is just the tip of the iceberg."

The genetic circuits developed and implemented in this study are also more complex than the previous state of the art. This advance creates the opportunity to engineer cells to perform more sophisticated functions and to make therapies safer and more effective.

"With this new capability, we have taken a big step in being able to truly engineer biology," Leonard said.

Sussex informatics methods model the massive data generated from firing neurons

Tyler O'Neal, Staff Editor ENGINEERING

Scientists have achieved a breakthrough in predicting the behavior of neurons in large networks operating at the mysterious edge of chaos.

New research from the University of Sussex and Kyoto University outlines a new method capable of analyzing the masses of data generated by thousands of individual neurons.

The new framework outperforms previous models in predicting and assessing network properties by more accurately estimating a system's fluctuations with greater sensitivity to parameter changes.

As new technologies allow the recording of thousands of neurons from living animals, there is a pressing demand for mathematical tools to study the non-equilibrium, complex dynamics of the high-dimensional data sets they generate. In this endeavor, the researchers hope to help answer key questions about how animals process information and adapt to environmental changes. Dr Miguel Aguilera, Marie Sklodowska-Curie research fellow in the School of Engineering and Informatics at the University of Sussex.{module INSIDE STORY}

The researchers also believe their work could be effective in reducing the massive computational cost and carbon footprint of training large AI models - making such models much more widely available to smaller research labs or companies.

Dr. Miguel Aguilera, Marie Sklodowska-Curie research fellow in the School of Engineering and Informatics at the University of Sussex, said: "Only very recently have we had the technology to record thousands of individual neurons in animals while they interact with their environment, which is a tremendous stride forward from studying networks of neurons isolated in laboratory cultures or in immobilized or anesthetized animals.

"This is a very exciting advancement but we don't have the methods yet to analyze and understand the massive amount of data created by non-equilibrium behavior. Our contribution offers the possibility to advance the technology forward to find models that explain how neurons process information and generate behavior."

The paper, published today in an academic journal, develops methods to quickly approximate the complex dynamics of neural network models that capture how real neurons observed in the lab behave, how they are connected, and how they process information.

In a significant step forward, the research team has created a method that works in significantly fluctuating, non-equilibrium situations that animals operate in when interacting with their environment in the real world.

Dr. Aguilera said: "The most efficient manner of learning how large systems work is using statistical models and approximations, and the most common of these are mean-field methods, where the effect of all interactions in a network is approximated by a simplified average effect.

"But these techniques often just work in very idealized conditions. Brains are in constant change, development, and adaptation, displaying complex fluctuating patterns and interacting with rapidly changing environments. Our model aims to capture precisely the fluctuations in these non-equilibrium situations that we expect from freely behaving animals in their natural surroundings."

The statistical method captures the dynamics of large networks specifically in the region at the edge of chaos, a special region of behavior between chaotic and ordered activity, where intense fluctuations in neuronal activity, known as neuronal avalanches, take place.

As opposed to previous mathematical models, the researchers applied an information geometric approach to better capture network correlations which allowed them to create simplified maps approximating the trajectory of neural activity which in reality travel extremely complex routes that are difficult to compute directly.

Dr. S. Amin Moosavi, a research fellow in the Graduate School of Informatics at Kyoto University, said: "Information geometry provides us a clear path to systematically advance our methods and suggest novel approaches, resulting in more accurate data analysis tools."

Prof Hideaki Shimazaki, Associate Professor in the Graduate School of Informatics at Kyoto University, said: "In addition to providing advanced calculation methods for large systems, the framework unifies many existing approaches from which we can further advance neuroscience and machine learning. We are glad to offer such a unifying view that expresses a hallmark of scientific progress as a product of this intense international collaboration."

Dr. Aguilera will next apply these methods to model thousands of neurons of zebrafish in the lab interacting with a virtual reality setup as part of the EU-funded DIMENSIVE project, which aims to develop generative models of large-scale behavior and provide important insights into how behavior arises from the dynamical interaction of an organism's nervous system, body, and environment.