Tyler O'Neal, Staff Editor LATEST

Penn State's molecular dynamics simulation of proteins unveils clues on origins of Parkinson's disease

Parkinson's disease is the second most common neurodegenerative disease and affects more than 10 million people around the world. To better understand the origins of the disease, researchers from Penn State College of Medicine and The Hebrew University of Jerusalem have developed an integrative approach, combining experimental and computational methods, to understand how individual proteins may form harmful aggregates, or groupings, that are known to contribute to the development of the disease. They said their findings could guide the development of new therapeutics to delay or even halt the progression of neurodegenerative diseases.

Alpha-synuclein is a protein that helps regulate the release of neurotransmitters in the brain and is found in neurons. It exists as a single unit but commonly joins together with other units to perform cellular functions. When too many units combine, it can lead to the formation of Lewy bodies, which are associated with neurodegenerative diseases like Parkinson's Disease and dementia.

Although researchers know that aggregates of this protein cause disease, how they form is not well understood. Alpha-synuclein is highly disordered, meaning it exists as an ensemble of different conformations, or shapes, rather than a well-folded 3D structure. This characteristic makes the protein difficult to study using standard laboratory techniques -- but the research team used computers together with leading-edge experiments to predict and study the different conformations it may fold into.

"Computational biology allows us to study how forces within and outside of a protein may act on it," said Nikolay Dokholyan, professor of pharmacology at the College of Medicine and Penn State Cancer Institute researcher. "Using experiments performed in professor Eitan Lerner's laboratory at the Biological Chemistry Department at The Hebrew University of Jerusalem, a series of algorithms accounts for effective forces acting in and upon a specific protein and can identify the various conformations it will take based on those forces. This allows us to study the conformations of alpha-synuclein in a way that is otherwise difficult to identify in experimental studies alone."

In the paper published today (May 19) in the journal Structure, the researchers detailed their methodology for studying the different conformations of alpha-synuclein. They used data from previous experiments to program the molecular dynamics of the protein into their calculations. Their experiments revealed the conformational ensemble of alpha-synuclein, which is a series of different shapes the protein can assume.

Using leading-edge experiments, the researchers found that some shapes of alpha-synuclein are surprisingly stable and last longer than milliseconds. They said this is much slower than estimates of a disordered protein that constantly changes conformations.

"Prior knowledge showed this spaghetti-like protein would undergo structural changes in microseconds," Lerner said. "Our results indicate that alpha-synuclein is stable in some conformations for milliseconds -- slower than previously estimated."

"We believe that we've identified stable forms of alpha-synuclein that allow it to form complexes with itself and other biomolecules," said Jiaxing Chen, a graduate student at the College of Medicine. "This opens up possibilities for the development of drugs that can regulate the function of this protein."

Chen's lead co-author, Sofia Zaer, alongside colleagues at Hebrew University, used a series of experimental techniques to verify that alpha-synuclein could fold into the stable forms the simulation predicted. The research team continues to study these stable conformations as well as the whole process of alpha-synuclein aggregation in the context of Parkinson's disease.

"The information from our study could be used to develop small molecule regulators of alpha-synuclein activity," Lerner said. "Drugs that prevent protein aggregation and enhance its normal neurophysiological function may interfere with the development and progression of neurodegenerative diseases."

NYU modeling predicts mutation hotspots, antibody escapers in SARS-CoV-2 spike protein

Tyler O'Neal, Staff Editor LATEST

The study identifies the structural basis of spike protein mutations with stronger binding and antibody resistance, which may explain the transmissibility of new COVID-19 variants

SARS-CoV-2 has evolved to acquire mutations on the spike protein--the part of the virus that protrudes from its surface and latches onto cells to infect them--that enhance the coronavirus's ability to bind to human cells or evade antibodies. A new study from the Centers for Genomics and Systems Biology at New York University and NYU Abu Dhabi uses computational modeling to assess the biological significance of spike protein mutations, uncovering versions of the virus that bind more tightly or resist antibodies and offering a promising public health surveillance tool.

The study, which appears in the Journal of Molecular Biology, also suggests that these mutations on the spike protein are a key reason for the virus's rapid spread in parts of the world.

New and more transmissible COVID-19 variants have emerged in recent months, fueling surges of cases in countries like India and Brazil. As a public health measure, rapid surveillance methods are needed to determine the biological effects of variants and to help anticipate emerging infectious viral strains. But monitoring new variants is no small task; genome sequencing shows that the SARS-CoV-2 spike protein alone, for example, has about 5,000 possible variants. Computational modeling shows that mutations on SARS-CoV-2's spike protein that enhance the virus' ability to bind to the ACE2 receptor occur in two clusters or mutation "hotspots." CREDIT Image courtesy of Hin Hark Gan and Kristin Gunsalus, NYU's Department of Biology

"Screening such a large set of variants poses a tremendous challenge for conventional experimental methods," said Hin Hark Gan, a senior research scientist at NYU's Center for Genomics and Systems Biology and the study's lead author. "An advantage of computer-based modeling is that a hundred mutations can be readily assessed in a few days."

Gan and his colleagues turned to a computational method that models how the SARS-CoV-2 spike protein recognizes the ACE2 receptor--a protein on the surface of many types of cells--to gain entry into host cells. Studies of coronaviruses indicate that spike-ACE2 recognition is the basis for infection.

The researchers focused on screening the mutations located where the spike protein and ACE2 receptor meet. They assessed 1,003 mutation combinations in the spike and ACE2 proteins, including those resulting in the fast-spreading spike variants that have originated in Brazil, South Africa, the U.K., and India.

Their systematic assessment of variants uncovered that spike mutations that bind tightly to the ACE2 receptor occur in two clusters or mutation "hotspots" on the binding interface. These hotspots are located in structurally flexible regions, indicating that mutations that increase binding effectively reprogrammed the spike conformation to enhance its recognition of the ACE2 receptor.

The researchers also looked at single, double, and triple mutations in the critical spike interface region, which make up some of the recently emerged infectious variants. Their modeling analysis suggests the spike variants S477N, N501Y, and S477N + E484K and E484K + N501Y--fast-spreading double mutants found in Brazil, South Africa, the U.S., and the U.K.--have increased binding to the ACE2 receptor relative to the original coronavirus that emerged in Wuhan.

Gan and colleagues observed that the E484K and E484Q mutations found in some recent fast-spreading variants are not only predicted to bind more strongly to ACE2 but have also been shown to confer antibody resistance. Neutralizing antibodies are produced in response to viral infection and target different sites on the spike protein to prevent the virus from invading host cells. This prompted the researchers to look at another factor contributing to viral transmission: antibody resistance of individual spike mutations.

In particular, the variant circulating in India has two mutations in the spike interface region: L452R and E484Q. This variant is not predicted to bind to the ACE2 receptor more tightly than the virus that originated in Wuhan, likely because the individual mutations have opposing effects (the L452R mutation binds less easily while the E484Q mutation binds more easily). Strikingly, however, both of these mutations are strong antibody evaders, a scenario not found in other recent variants.

"As more of antibody target sites become resistant to antibodies due to viral mutations, the efficacy of existing antibodies and vaccines may diminish," added Gan. "This scenario is a likely cause for the rapid spread of the variant in India."

The study not only provides explanations for the coronavirus's rapid spread--both mutations that enhance binding to human cells and help evade antibodies--but also points to a promising predictive tool in the ongoing public health fight against SARS-CoV-2.

"Our computational modeling method can be used as a real-time surveillance tool to screen for emerging infectious COVID-19 variants. It allows for a more timely response to emerging outbreaks and could be used to guide the development of new vaccines," said Kristin C. Gunsalus, professor of biology at NYU, faculty director of bioinformatics at NYU Abu Dhabi, and the study's senior author.

Cornell wins grant to accelerate AI materials discovery for emissions-free driving

Tyler O'Neal, Staff Editor LATEST

Cornell University is partnering in a $36 million grant from the Toyota Research Institute (TRI) for its Accelerated Materials Design and Discovery (AMDD) collaborative university research program, which seeks to use artificial intelligence to discover new materials that could help achieve emissions-free driving.

Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science, is among the lead researchers on the four-year, multi-institution grant.

"AI for scientific discovery is among the most promising but challenging areas of AI," said Gomes, who is also director of Cornell's Institute for Computational Sustainability. "TRI's multidisciplinary approach enables our team to develop new AI approaches for materials discovery. We combine data-driven deep learning with knowledge-driven reasoning and optimization techniques. This approach allows us to inject scientific background knowledge into the discovery and data-analysis process."

AMDD launched in 2017 with the aim of using AI to discover new materials for emissions-free mobility. The total scope of the initial investment was $35 million over four years, across multiple university partners. The new round of funding adds to the initial investment and seeks ongoing collaboration with program partners, including Cornell.

"Our focus on collaboration is what makes our research program unique," said Brian Storey, director of TRI's AMDD program. "Rather than acting strictly as a funding source, TRI has formed deep collaborations with researchers which have led to joint publications as well as co-developed open-source data and software. This collaborative approach is critical to accelerate the development of new materials for battery and fuel cell vehicles, as no single entity can do this alone."

As part of this initiative, Gomes will collaborate closely with John Gregoire, a staff scientist at the California Institute of Technology. The research focuses on using AI for accelerating high-throughput experimentation for materials discovery, and in particular the discovery of new clean energy materials.

"Our research has led to fundamentally new ways of using AI and machine learning methods to explore the vastness of material space," Gomes said. "For example, you can create all kinds of materials by combining elements, such as the ones we find in the periodic table. Part of the issue is which elements you should combine for obtaining certain properties, like semiconductors or catalysts. This exploration involves discovering the combinations of elements and what the synthesis conditions should be."

With the grant, Gomes and collaborators in Cornell Bowers CIS and the College of Engineering will explore material space from hypothesis formulation to the planning, design, and execution of experiments, using semi-autonomous and eventually autonomous systems. If the researchers are successful, Gomes said, at some point, AI could make decisions on the discovery of new materials that will contribute to the efficient and beneficial use of Earth's resources.

German astrophysicists succeed in gaining new insight into the origin of the elements

Tyler O'Neal, Staff Editor LATEST

Europium is the key for understanding the formation of the heavy elements by the fast neutron capture process, the so-called r-process. This is crucial both for the formation of half of the elements heavier than iron and for the total abundance of thorium and uranium in the universe. The EUROPIUM group has combined theoretical astrophysical simulations with observations of the oldest stars in our Galaxy and in dwarf galaxies. The latter are small, dark-matter-dominated galaxies orbiting our Galaxy. Dwarf galaxies are excellent test objects for studying the r-process, as some of the oldest metal-poor stars, those that have existed for 10 to 13 billion years, have exhibited an overabundance of r-process elements. Studies have even postulated that only a single neutron-rich event could be responsible for this enrichment in the smallest dwarf galaxies. The EUROPIUM group has combined theoretical astrophysical simulations with observations of the oldest stars in our galaxy and in dwarf galaxies.

With their discovery, the researchers in Darmstadt and Heidelberg have succeeded in determining the highest europium content ever observed – and they have created a new name for these stars: "europium stars". These stars belong to the dwarf galaxy Fornax – a dwarf spheroidal galaxy with high stellar content. In their publication, the group also reports the first-ever observation of lutetium in a dwarf galaxy and the largest sample of observed zirconium.

The "europium stars" in Fornax were born shortly after an explosive production of heavy elements. Based on the high stellar metal abundance, the extreme r-process event must have occurred as recently as four to five billion years ago. This is a very rare finding, as most europium-rich stars are much older. Therefore, europium stars provide insight into the origin of elements in the universe at a very specific and late time.

Heavy elements are formed by the r-process in the merger of two neutron stars or in the explosive end of massive stars with strong magnetic fields. The EUROPIUM group has analyzed these two high-energy events and performed detailed studies of element production in these environments. However, due to the still large uncertainties in the nuclear physics data, it is not possible to unambiguously assign the heavy elements in the "europium stars" to one of these astrophysical environments. Future experiments in the new accelerator center FAIR at the GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt will significantly reduce these uncertainties.

In addition, the new Hessian cluster project ELEMENTS, in which Professor Arcones is a principal investigator, will uniquely combine supercomputer simulations of neutron star fusion, nucleosynthesis calculations with the latest experimental information and observations to investigate the long-standing question: Where and how are heavy elements produced in the universe?

Chinese researchers achieve quantum information masking experimentally

Tyler O'Neal, Staff Editor LATEST

The research team, led by Academician GUO Guangcan from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, collaborating with LI Bo from Shangrao Normal University and CHEN Jingling from Nankai University, achieved the masking of optical quantum information. The researchers concealed quantum information into non-local quantum entangled states. The study was published in the journal Physical Review Letters.

Quantum information masking as one of the new information processing protocols transfers quantum information from a single quantum carrier to the quantum entangled state between multiple carriers avoiding the information decode from a single quantum carrier. Not all the kind of quantum states can achieve masking, but the variety of that helps people to select.

Quantum information masking can be used in a wide situation, not only in actual quantum information tasks such as quantum secret sharing but also in the further understanding of the conservation of quantum information.

In this research, the team realized quantum information masking for the first time based on the linear optics research platform.

Compared with the theoretical value, the fidelity of the entangled state can be 97.7%, meaning that the secure transmission of simple images can be complete for the three-party quantum secret sharing based on quantum information masking.

This study has great significance for theoretical research and the practical application of secure quantum communication. Based on it, the feasibility of quantum information masking as a brand-new quantum information processing protocol is improved.