Model shows how antibodies navigate pathogen surfaces like a child at play

Antibodies aim to establish a foothold on two separate antigens, in much the same way a child might try navigating stepping stones in a stream. Credit: Ian Hoffecker

A new study shows how antibodies select the antigens that they bind to, as they navigate the surface of pathogens like coronaviruses. Researchers from KTH Royal Institute of Technology and Karolinska Institutet have created a model that suggests the migration of these pathogen hunters may be akin to the random movements of a child playing on a stream laden with stepping stones.

Ian Hoffecker, a researcher at KTH Royal Institute of Technology in Stockholm, says the model raises new ways to consider the evolution of viruses and immune systems, and that the new study yields insights that may be useful in vaccine engineering.

Antibodies are often thought of as Y-shaped proteins. But recent studies have shown that perhaps a more accurate way to envision them is to flip the picture upside down and regard as walking stick figures, stepping on antigens. Those two characteristic “Y” branches function as legs of sorts, Hoffecker says.

Paraphrasing Nancy Sinatra’s 1966 hit recording, he says: “These antibodies are made for walking.”

These stalking pathogen hunters mark their prey by planting their “feet” on antigens— scattered like stepping stones in various patterns on the surfaces of viruses. They rely on what’s called multivalence—or establishing a foothold with both “Y” branches, typically on two separate antigens—which allows them to bind as strongly as possible to their targets. Once in place, antibodies participate in a series of interactions with other signaling proteins to neutralize or kill the pathogen.

Using a nano-fabricated model of a pathogen’s antigen pattern, the researchers set out to determine how this behavior is influenced by pathogen surfaces, Hoffecker says. “What if antigens are really close together or what if they’re kind of far apart? Do the antibodies’ molecules stretch out, do they compress?”

To find out, Björn Högberg from Karolinska Institutet’s Division of Biomaterials Research says the team simulated a pathogen and antigen scenario using a method called DNA origami, in which DNA self-assembles into nanostructures with a programmable geometry that allowed them to control the distance between antigens.

“This tool has enabled us to investigate how this distance between two antigens impacts binding strength,” Högberg says. “In our new work we took this data and plugged it into a model that lets us ask interesting questions about how antibodies behave in more complex environments—without straying too far from reality.”

Hoffecker says the model reveals that antibodies behave not much differently from another well-known bipedal organism—namely, human beings.

“The process could be likened to a child playing on a river laden with stepping stones just large enough to accommodate a single foot,” Hoffecker says. “So to stand in place, the child would have to straddle two rocks or else balance on one foot.”

The antibodies in the seemed to favor antigens that are closer together and easier to stand on. And if are too far apart, they have a statistical tendency to migrate to an area where they stand closer together, he says.

Such observations raise the question of whether the flexibility and structure of antibodies is influenced by their antagonists, the pathogens. “We are asking the question, is this relevant to evolution, or co-evolution, where you have this constant arms race between the and pathogens, and this control system that basically says how antibodies move and where they go?” he says.

Hoffecker says the next steps are to observe how this property of antibodies manifests itself in , and to incorporate these findings into rationally-designed vaccines that account for the antigen spatial organization factor.

The research was published in Nature Computational Science.



More information:
Ian Hoffecker et al, Stochastic modeling of antibody binding predicts programmable migration on antigen patterns, Nature Computational Science (2022). DOI: 10.1038/s43588-022-00218-z. www.nature.com/articles/s43588-022-00218-z

Citation:
Model shows how antibodies navigate pathogen surfaces like a child at play (2022, March 24)
retrieved 24 March 2022
from https://phys.org/news/2022-03-antibodies-pathogen-surfaces-child.html

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Hexbyte Glen Cove New model of a fundamental process behind the movement of Earth’s tectonic plates

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Pacific Ring of Fire. Credit: Gringer (talk) 23:52, 10 February 2009 (UTC), Public domain, via Wikimedia Commons

A research team from University of Lisbon (Portugal) and Johannes Gutenberg University (Germany) has developed for the first time an advanced numerical model of one of the main processes behind the movement of Earth’s tectonic plates.

The that form the Earth’s surface are like puzzle pieces that are in constant, very —on average, they move only up to around 10 centimeters a year. But these puzzle pieces don’t quite fit together: there are zones on one plate that end up plunging under another—the so-called , central to the dynamics of the planet. This movement is slow, but it can lead to moments of great energy release and, over thousands of years, large mountain ranges or marine trenches are formed in these regions.

How do these subduction zones originate, and how do they evolve over time? Geologists already knew that in these zones, on a of thousands of years, this process can stagnate and reverse itself, giving rise to new subduction zones. But it was still necessary to know how this happens, and to include in the models the various (and enormous) forces involved in this process. For the first time, it was possible to simulate in three dimensions one of the most common processes of formation of new subduction zones, ensuring that all forces are dynamically and realistically modeled, including Earth’s own gravity.

“Subduction zones are one of the main features of our planet and the main driver of plate tectonics and the global dynamics of the planet. Subduction zones are also the places where earthquakes of great magnitude occur, as is the case of the Pacific Ring of Fire, the largest system of subduction zones in the world. For this reason, it is extremely important to understand how new subduction zones start and how this process takes place”, explains Jaime Almeida, first author of this study, researcher at Instituto Dom Luiz, at Faculty of Sciences of the University of Lisbon (Ciências ULisboa).

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Hexbyte Glen Cove New model helps predict climate change-induced early spawning in fish

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Credit: Peter Prokosch, GRIDA, https://www.grida.no/resources/3510

Fisheries managers and researchers may now predict how early fish will spawn in response to warming waters due to climate change, both in the oceans and in freshwaters.

A new, simple model developed by researchers at the University of British Columbia and the Chinese Academy of Sciences, and applied to spring-spawning fish of temperate latitudes, allows predicting quite precisely shifts in the timing of maturation and spawning, given a change in mean .

Built on a description of seasonal changes represented by two sine curves, the model is based on the notion that a specific temperature threshold, when reached, is what triggers the hormonal cascade that ‘tells’ fish it is time to reproduce.

But when water temperature rises, this process goes off-balance as fish require more oxygen to survive. The problem is that their gills—which are 2D surfaces—cannot keep up with the oxygen demand of their 3D growing bodies and the new temperature-induced oxygen demand. This imbalance, thus, stresses fish and makes them mature and spawn earlier.

“When the annual mean temperature in a given area and period has increased, this leads to spring temperatures ‘arriving’ earlier. Our new model, which assumes the seasonal temperature oscillations that are observed in nature, requires as input only the difference in mean temperature between two periods, and that between summer and winter temperatures. With only these two numbers, the model predicts by how many days the spawning of is accelerated”, said Dr. Daniel Pauly, principal investigator of the Sea Around Us initiative at UBC’s Institute for the Oceans and Fisheries and lead author of the study published in Environmental Biology of Fishes.

“This simple will hopefully replace the complex hypotheses often presented to explain temporal shift of spawning in terms of adaptation to similar shifts in the emergence of prey species, which only replace one mystery by another, but don’t explain much, if anything,” said Dr. Cui Liang, co-author of the study and a researcher at CAS’ Institute of Oceanology.



More information:
Pauly, D. et al. Temperature and the maturation of fish: a simple sine-wave model for predicting accelerated spring spawning. Environ Biol Fish (2022). doi.org/10.1007/s10641-022-01212-0

Citation:
New model helps predict climate change-induced early spawning in fish (2022, February 14)
retrieved 15 February 2022
from https://phys.org/news/2022-02-climate-change-induced-early-spawning-fish.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no

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Hexbyte Glen Cove Computer model of blood enzyme may lead to new drugs for cardiovascular disease

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Hexbyte Glen Cove Model of SARS-CoV-2 dynamics reveals opportunity to prevent COVID-19 transmission thumbnail

Hexbyte Glen Cove Model of SARS-CoV-2 dynamics reveals opportunity to prevent COVID-19 transmission

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Credit: Pixabay/CC0 Public Domain

Scientists have simulated the transition of the SARS-CoV-2 spike protein structure from when it recognizes the host cell to when it gains entry, according to a study published today in eLife.

The research shows that a structure enabled by on the spike protein could be essential for cell entry and that disrupting this structure could be a strategy to halt virus transmission.

An essential aspect of SARS-CoV-2’s lifecycle is its ability to attach to host cells and transfer its genetic material. It achieves this through its spike protein, which is made up of three separate components—a transmembrane bundle that anchors the spike to the virus, and two S subunits (S1 and S2) on the exterior of the virus. To infect a , the S1 subunit binds to a molecule on the surface of human cells called ACE2, and the S2 subunit detaches and fuses the viral and human cell membranes. Although this process is known, the exact order in which it occurs is as yet undiscovered. Yet, understanding the microsecond-scale and atomic-level movements of these protein structures could reveal potential targets for COVID-19 treatment.

“Most of the current SARS-CoV-2 treatments and vaccines have focused on the ACE2 recognition step of virus invasion, but an alternative strategy is to target the structural change that allows the virus to fuse with the human host cell,” explains study co-author José N. Onuchic, Harry C & Olga K Wiess Professor of Physics at Rice University, Houston, US, and Co-Director of the Center for Theoretical Biological Physics. “But probing these intermediate, transient structures experimentally is extremely difficult, and so we used a computer simulation sufficiently simplified to investigate this large system but that maintains sufficient physical details to capture the dynamics of the S2 subunit as it transitions between pre-fusion and post-fusion shapes.”

The team was particularly interested in the role of sugar molecules on the spike protein, which are called glycans. To see whether the number, type and position of glycans play a role in the membrane fusion stage of viral cell entry by mediating these intermediate spike formations, they performed thousands of simulations using an all-atom structure-based model. Such models allow prediction of the trajectory of atoms over time, taking into account steric forces—that is, how neighboring atoms affect the movement of others.

The simulations revealed that glycans form a “cage” that traps the “head” of the S2 subunit, causing it to pause in an intermediate form between when it detaches from the S1 subunit and when the viral and cell membranes are fused. When the glycans were not there, the S2 subunit spent much less time in this conformation.

The simulations also suggest that holding the S2 head in a particular position helps the S2 subunit recruit human host and fuse with their membranes, by allowing the extension of short proteins called fusion peptides from the virus. Indeed, glycosylation of S2 significantly increased the likelihood that a fusion peptide would extend to the host cell membrane, whereas when glycans were absent, there was only a marginal possibility that this would occur.

“Our simulations indicate that glycans can induce a pause during the transition. This provides a critical opportunity for the fusion peptides to capture the ,” concludes co-author Paul C. Whitford, Associate Professor at the Center for Theoretical Biological Physics and Department of Physics, Northeastern University, Boston, US. “In the absence of glycans, the viral particle would likely fail to enter the host. Our study reveals how sugars can control infectivity, and it provides a foundation for experimentally investigating factors that influence the dynamics of this pervasive and deadly pathogen.”



More information:
Esteban Dodero-Rojas et al, Sterically confined rearrangements of SARS-CoV-2 Spike protein control cell invasion, eLife (2021). DOI: 10.7554/eLife.70362

Journal information:
eLife



Citation:
Model of SARS-CoV-2 dynamics reveals opportunity to prevent COVID-19 transmission (2021, August 31)
retrieved 31 August 2021
from https://phys.org/ne

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Hexbyte Glen Cove Machine-learning model helps determine protein structures thumbnail

Hexbyte Glen Cove Machine-learning model helps determine protein structures

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Credit: Unsplash/CC0 Public Domain

Cryo-electron microscopy (cryo-EM) allows scientists to produce high-resolution, three-dimensional images of tiny molecules such as proteins. This technique works best for imaging proteins that exist in only one conformation, but MIT researchers have now developed a machine-learning algorithm that helps them identify multiple possible structures that a protein can take.

Unlike AI techniques that aim to predict from sequence data alone, structure can also be experimentally determined using cryo-EM, which produces hundreds of thousands, or even millions, of two-dimensional images of protein samples frozen in a thin layer of ice. Computer algorithms then piece together these images, taken from different angles, into a three-dimensional representation of the protein in a process termed reconstruction.

In a Nature Methods paper, the MIT researchers report a new AI-based software for reconstructing multiple structures and motions of the imaged protein—a major goal in the protein science community. Instead of using the traditional representation of protein structure as electron-scattering intensities on a 3-D lattice, which is impractical for modeling multiple structures, the researchers introduced a new neural network architecture that can efficiently generate the full ensemble of structures in a single model.

“With the broad representation power of neural networks, we can extract structural information from noisy images and visualize detailed movements of macromolecular machines,” says Ellen Zhong, an MIT graduate student and the lead author of the paper.

With their software, they discovered protein motions from imaging datasets where only a single static 3-D structure was originally identified. They also visualized large-scale flexible motions of the spliceosome—a protein complex that coordinates the splicing of the protein coding sequences of transcribed RNA.

“Our idea was to try to use machine-learning techniques to better capture the underlying structural heterogeneity, and to allow us to inspect the variety of structural states that are present in a sample,” says Joseph Davis, the Whitehead Career Development Assistant Professor in MIT’s Department of Biology.

Davis and Bonnie Berger, the Simons Professor of Mathematics at MIT and head of the Computation and Biology group at the Computer Science and Artificial Intelligence Laboratory, are the senior authors of the study, which appears today in Nature Methods. MIT postdoc Tristan Bepler is also an author of the paper.

Visualizing a multistep process

The researchers demonstrated the utility of their new approach by analyzing structures that form during the process of assembling ribosomes—the cell organelles responsible for reading messenger RNA and translating it into proteins. Davis began studying the structure of ribosomes while a postdoc at the Scripps Research Institute. Ribosomes have two major subunits, each of which contains many individual proteins that are assembled in a multistep process.

To study the steps of ribosome assembly in detail, Davis stalled the process at different points and then took electron microscope images of the resulting structures. At some points, blocking assembly resulted in accumulation of just a single structure, suggesting that there is only one way for that step to occur. However, blocking other points resulted in many different structures, suggesting that the assembly could occur in a variety of ways.

Because some of these experiments generated so many different protein structures, traditional cryo-EM reconstruction tools did not work well to determine what those structures were.

“In general, it’s an extremely challenging problem to try to figure out how many states you have when you have a mixture of particles,” Davis says.

After starting his lab at MIT in 2017, he teamed up with Berger to use machine learning to develop a model that can use the two-dimensional images produced by cryo-EM to generate all of the three-dimensional structures found in the original sample.

In the new Nature Methods study, the researchers demonstrated the power of the technique by using it to identify a new ribosomal state that hadn’t been seen before. Previous studies had suggested that as a ribosome is assembled, large structural elements, which are akin to the foundation for a building, form first. Only after this foundation is formed are the “active sites” of the ribosome, which read messenger RNA and synthesize proteins, added to the structure.

In the new study, however, the researchers found that in a very small subset of ribosomes, about 1 percent, a that is normally added at the end actually appears before assembly of the foundation. To account for that, Davis hypothesizes that it might be too energetically expensive for cells to ensure that every single ribosome is assembled in the correct order.

“The cells are likely evolved to find a balance between what they can tolerate, which is maybe a small percentage of these types of potentially deleterious structures, and what it would cost to completely remove them from the assembly pathway,” he says.

Viral proteins

The researchers are now using this technique to study the coronavirus spike protein, which is the viral protein that binds to receptors on human cells and allows them to enter cells. The receptor binding domain (RBD) of the spike protein has three subunits, each of which can point either up or down.

“For me, watching the pandemic unfold over the past year has emphasized how important front-line antiviral drugs will be in battling similar viruses, which are likely to emerge in the future. As we start to think about how one might develop small molecule compounds to force all of the RBDs into the ‘down’ state so that they can’t interact with human cells, understanding exactly what the ‘up’ state looks like and how much conformational flexibility there is will be informative for drug design. We hope our new technique can reveal these sorts of structural details,” Davis says.

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Hexbyte Glen Cove Model predicts where ticks, Lyme disease will appear next in Midwest states thumbnail

Hexbyte Glen Cove Model predicts where ticks, Lyme disease will appear next in Midwest states

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Entomology professor Brian Allan and his colleagues built a model that can accurately predict future occurrences of black-legged ticks in the Midwest. Credit: L. Brian Stauffer

By drawing from decades of studies, scientists created a timeline marking the arrival of black-legged ticks, also known as deer ticks, in hundreds of counties across 10 Midwestern states. They used these data—along with an analysis of county-level landscape features associated with the spread of ticks—to build a model that can predict where ticks are likely to appear in future years.

Black-legged ticks can carry the bacterium that causes Lyme disease, an infection that can affect the nervous system, heart and joints. The new will help prepare for the onset of Lyme disease in their counties before the first cases appear, researchers say. They report their findings in the Proceedings of the Royal Society B.

Black-legged ticks were first found in the Midwest in the 1960s in a few counties in Wisconsin and Minnesota. The first known case of what was later named Lyme disease occurred in the Midwest in 1969. Since then, black-legged ticks have expanded into numerous counties across those states and into Illinois, Indiana, Iowa, Michigan, Ohio, Nebraska, North Dakota and South Dakota. The first Lyme disease cases in those counties track closely with the first reports of ticks.

Understanding that local health departments report new Lyme disease cases in their counties to federal officials and that the National Land Cover Database includes information about landscape features of each county, the researchers chose to use county-level data in their model. Their goal was to identify factors associated with the spread of ticks and Lyme disease to new counties.

“We used historical information to build a model that forecasts the future spread of Lyme disease in the Midwest,” said Brian Allan, an entomology professor at the University of Illinois Urbana-Champaign who led the research with former doctoral student Allison Gardner, a professor of biology and ecology at the University of Maine.

“Our model was based on a few landscape factors that were highly predictive of the spread of ticks and Lyme disease and could be used as an early warning system to forecast areas likely to undergo invasion next,” Gardner said.

The researchers observed “a wavelike pattern of spread, where counties that get invaded with black-legged ticks tend to be adjacent to a county that has already been invaded,” Allan said. “And in some Midwestern states, we see that areas adjacent to major rivers are invaded in sequence. In Illinois, for example, the ticks first arrived along the Illinois River and then spread up and down the river quite quickly.”

The percentage of forest cover in a county also was important in predicting whether black-legged ticks would occur there. These three factors—proximity to a county where ticks had been detected, the presence of a river and the percentage of —together can predict the future occurrence of ticks in counties where none had been previously reported, the researchers found.

To test their model, Gardner used data gathered before 2012 to determine how ticks would spread into new areas in the Midwest from 2012 to 2016. The model predicted the appearance of ticks in new counties with greater than 90% accuracy.

“It was a little surprising to me that so few parameters could make these predictions with such high accuracy,” Gardner said.

Looking forward, the researchers identified 42 additional counties in the Midwest where black-legged ticks are likely to be detected by the end of 2021. The evidence suggests those ticks will carry the Lyme disease bacterium.

Understanding where ticks may be present before they have been reported may prompt public health officials and clinicians to include Lyme disease as a possible diagnosis for patients appearing with symptoms consistent with the infection, Allan said.

“If they don’t think the occurs in their area, doctors may be reluctant to diagnose a patient with Lyme disease,” he said.



More information:
Landscape features predict the current and forecast the future geographic spread of Lyme disease, Proceedings of the Royal Society B, rspb.royalsocietypublishing.or … .1098/rspb.2020.2278

Citation:
Model predicts where ticks, Lyme disease will appear next in Midwest states (2020, December 22)
retrieved 23 December 2020
from https://phys.org/news/2020-12-lyme-disease-midwest-states.html

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