From University of Washington: “Scientists use molecular tethers and chemical ‘light sabers’ to construct platforms for tissue engineering”

From University of Washington

May 20, 2019
James Urton

Left-to-right, Cole DeForest, Gabrielle Benuska, Jared Shadish.Dennis Wise/University of Washington

Tissue engineering could transform medicine. Instead of waiting for our bodies to regrow or repair damage after an injury or disease, scientists could grow complex, fully functional tissues in a laboratory for transplantation into patients.

Proteins are key to this future. In our bodies, protein signals tell cells where to go, when to divide and what to do. In the lab, scientists use proteins for the same purpose — placing proteins at specific points on or within engineered scaffolds, and then using these protein signals to control cell migration, division and differentiation.

But proteins in these settings are also fragile. To get them to stick to the scaffolds, researchers have traditionally modified proteins using chemistries that kill off more than 90% of their function. In a paper published May 20 in the journal Nature Materials, a team of researchers from the University of Washington unveiled a new strategy to keep proteins intact and functional by modifying them at a specific point so that they can be chemically tethered to the scaffold using light. Since the tether can also be cut by laser light, this method can create evolving patterns of signal proteins throughout a biomaterial scaffold to grow tissues made up of different types of cells.

“Proteins are the ultimate communicators of biological information,” said corresponding author Cole DeForest, a UW assistant professor of chemical engineering and bioengineering, as well as an affiliate investigator with the UW Institute for Stem Cell & Regenerative Medicine. “They drive virtually all changes in cell function — differentiation, movement, growth, death.”

For that reason, scientists have long employed proteins to control cell growth and differentiation in tissue engineering.

“But the chemistries most commonly used by the community to bind proteins to materials, including scaffolds for tissue engineering, destroy the overwhelming majority of their function,” said DeForest, who is also a faculty member in the UW Molecular & Engineering Sciences Institute. “Historically, researchers have tried to compensate for this by simply overloading the scaffold with proteins, knowing that most of them will be inactive. Here, we’ve come up with a generalizable way to functionalize biomaterials reversibly with proteins while preserving their full activity.”

Their approach uses an enzyme called sortase, which is found in many bacteria, to add a short synthetic peptide to each signal protein at a specific location: the C-terminus, a site present on every protein. The team designs that peptide such that it will tether the signal protein to specific locations within a fluid-filled biomaterial scaffold common in tissue engineering, known as a hydrogel.

Targeting a single site on the signal protein is what sets the UW team’s approach apart. Other methods modify signal proteins by attaching chemical groups to random locations, which often disrupts the protein’s function. Modifying just the C-terminus of the protein is much less likely to disrupt its function, according to DeForest. The team tested the approach on more than half a dozen different types of proteins. Results show that modifying the C-terminus has no significant effect on protein function, and successfully tethers the proteins throughout the hydrogel.

Their approach is analogous to hanging a piece of framed art on a wall. Instead of hammering nails randomly through the glass, canvas and frame, they string a single wire across the back of each frame to hang it on the wall.

Photorelease of proteins from a hydrogel. Top: The mCherry red fluorescent proteins are tethered to the hydrogel. Researchers can cleave the tether with directed light (blue arrows), releasing the mCherry from the hydrogel (blue arrows). Bottom: An image of the hydrogel after mCherry release patterned in the shape of the University of Washington mascot (black). Scale bar is 100 micrometers.Shadish, Benuska and DeForest, 2019, Nature Materials.

In addition, the tethers can be cut by exposure to focused laser light, causing “photorelease” of the proteins. Using this scientific light saber allows the researchers to load a hydrogel with many different types of protein signals, and then expose the hydrogel to laser light to untether proteins from certain sections of the hydrogel. By selectively exposing only portions of the materials to the laser light, the team controlled where protein signals would stay tethered to the hydrogel.

Untethering proteins is useful in hydrogels because cells could then take up those signals, bringing them into the cell’s interior where they can affect processes like gene expression.

DeForest’s team tested the photorelease process using a hydrogel loaded with epidermal growth factor, a type of protein signal. They introduced a human cell line into the hydrogel and observed the growth factors binding to the cell membranes. The team used a beam of laser light to untether the protein signals on one side of an individual cell, but not the other side. On the tethered side of the cell, the proteins stayed on the outside of the cell since they were still stuck to the hydrogel. On the untethered side, the protein signals were internalized by the cell.

“Based on how we target the laser light, we can ensure that different cells — or even different parts of single cells — are receiving different environmental signals,” said DeForest.

Photorelease of epidermal growth factor (EGF) proteins on one side of a human cell. Left: EGF (green) is tethered to a hydrogel a single human cell (center). The cell membrane binds EGF, making its membrane green. Middle: The hydrogel after using a laser to untether and release EGF proteins on the top portion of the cell. Right: An image showing the difference in green fluorescent color between post- and pre-release images. Note the increase in color in the top portion of the cell, which indicates that the cell has started to internalize the untethered EGF proteins but only on one side. Scale bar is 10 micrometers.Shadish, Benuska and DeForest, 2019, Nature Materials.

This unique level of precision within a single cell not only helps with tissue engineering, but with basic research in cell biology, added DeForest. Researchers could use this platform to study how living cells respond to multiple combinations of protein signals, for example. This line of research would help scientists understand how protein signals work together to control cell differentiation, heal diseased tissue and promote human development.

“This platform allows us to precisely control when and where bioactive protein signals are presented to cells within materials,” said DeForest. “That opens the door to many exciting applications in tissue engineering and therapeutics research.”

Lead author on the paper is Jared Shadish, a UW doctoral student in chemical engineering. Co-author is Gabrielle Benuska, a UW undergraduate alumna who is currently an analyst for Point B Consulting. The research was funded by the National Science Foundation and the University of Washington.

See the full article here .

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From University of Oxford: “‘Impossible’ nano-sized protein cages made with the help of gold”

From University of Oxford

15 May 2019

A collaborative effort between the University of Oxford and the Malopolska Centre of Biotechnology, Jagiellonian University in Poland, has produced a super-stable artificial protein ball that apparently defies the rules of geometry and which may have applications in materials science and medicine.

Researchers are interested in making artificial protein cages in the hope that they can design them to have useful properties not found in nature. There are two challenges to achieving this goal. The first is the geometry problem: some proteins may have great potential utility but have the wrong shape to assemble into cages. The second problem is complexity: in nature the many proteins that form a protein cage are held together by a complex network of chemical bonds and these are very difficult to predict and simulate.

In new work, published in Nature, researchers found a way to solve both of these problems.

Professor Heddle, senior author of the research, said: ‘We were able to replace the complex interactions between proteins with a simple ‘staple’ consisting of a single gold atom. This simplifies the design problem and allows us to imbue the cages with new properties such as assembly and disassembly on demand.’

The research has also found a way to get around the geometrical problem: the building block of a protein cage is an 11-sided shape. Theoretically this should not be able to form the faces of a regular convex polyhedron. However the research has found that while this is mathematically true, some so-called ‘impossible shapes’ can assemble into cages which are so close to being regular that the errors are not noticeable.

Central to the study was the ability to characterise different cages, as well the ability to monitor and thereby understand the (dis)assembly dynamically. This work was done in the groups of Professors Justin Benesch and Philipp Kukura at Oxford, using innovative mass measurement approaches with a particular focus on biomolecular structure and assembly.

Justin Benesch, in the Department of Chemistry, said: ‘The ability to interrogate the cages using the advanced mass measurement approaches we have developed here in Oxford, both on the level of their assembly and the constituent building block, was key to not just validating their structure, but also the mechanism by which they are formed.’

The potential implications of the work are far-reaching. The researchers hope that the work can be expanded further to produce cages with new structures and new capabilities with potential applications particularly in drug delivery.

See the full article here.

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The different roles of the colleges and the University have evolved over time.

From Brookhaven National Lab: “New Approach for Solving Protein Structures from Tiny Crystals”

From Brookhaven National Lab

May 3, 2019
Karen McNulty Walsh
kmcnulty@bnl.gov
(631) 344-8350

Peter Genzer,
genzer@bnl.gov
(631) 344-3174

Technique opens door for studies of countless hard-to-crystallize proteins involved in health and disease.

Wuxian Shi, Martin Fuchs, Sean McSweeney, Babak Andi, and Qun Liu at the FMX beamline at Brookhaven Lab’s National Synchrotron Light Source II [see below], which was used to determine a protein structure from thousands of tiny crystals.

Using x-rays to reveal the atomic-scale 3-D structures of proteins has led to countless advances in understanding how these molecules work in bacteria, viruses, plants, and humans—and has guided the development of precision drugs to combat diseases such as cancer and AIDS. But many proteins can’t be grown into crystals large enough for their atomic arrangements to be deciphered. To tackle this challenge, scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and colleagues at Columbia University have developed a new approach for solving protein structures from tiny crystals.

The method relies on unique sample-handling, signal-extraction, and data-assembly approaches, and a beamline capable of focusing intense x-rays at Brookhaven’s National Synchrotron Light Source II (NSLS-II)—a DOE Office of Science user facility—to a millionth-of-a-meter spot, about one-fiftieth the width of a human hair.

“Our technique really opens the door to dealing with microcrystals that have been previously inaccessible, including difficult-to-crystallize cell-surface receptors and other membrane proteins, flexible proteins, and many complex human proteins,” said Brookhaven Lab scientist Qun Liu, the corresponding author on the study, which was published on May 3 in IUCrJ, a journal of the International Union of Crystallography.

Deciphering protein structures

Protein crystallography has been a dominant method for solving protein structures since 1958, improving over time as x-ray sources have grown more powerful, allowing more precise structure determinations. To determine a protein structure, scientists measure how x-rays like those generated at NSLS-II diffract, or bounce off, the atoms in an ordered crystalline lattice consisting of many copies of the same protein molecule all arrayed the same way. The diffraction pattern conveys information about where the atoms are located. But it’s not sufficient.

A cartoon representing the structure of a well-studied plant protein that served as a test case for the newly developed microcrystallography technique. Magenta mesh patterns surrounding sulfur atoms intrinsic to the protein (yellow spheres) indicate the anomalous signals that were extracted using low-energy x-ray diffraction of thousands of crystals measuring less than 10 millionths of a meter, the size of a bacterium.

“Only the amplitudes of diffracted x-ray ‘waves’ are recorded on the detector, but not their phases (the timing between waves),” said Liu. “Both are required to reconstruct a 3-D structure. This is the so-called crystallographic phase problem.”

Crystallographers have solved this problem by collecting phase data from a different kind of scattering, known as anomalous scattering. Anomalous scattering occurs when atoms heavier than a protein’s main components of carbon, hydrogen, and nitrogen absorb and re-emit some of the x-rays. This happens when the x-ray energy is close to the energy those heavy atoms like to absorb. Scientists sometimes artificially insert heavy atoms such as selenium or platinum into the protein for this purpose. But sulfur atoms, which appear naturally throughout protein molecules, can also produce such signals, albeit weaker. Even though these anomalous signals are weak, a big crystal usually has enough copies of the protein with enough sulfur atoms to make them measurable. That gives scientists the phase information needed to pinpoint the location of the sulfur atoms and translate the diffraction patterns into a full 3-D structure.

“Once you know the sulfur positions, you can calculate the phases for the other protein atoms because the relationship between the sulfur and the other atoms is fixed,” said Liu.

But tiny crystals, by definition, don’t have that many copies of the protein of interest. So instead of looking for diffraction and phase information from repeat copies of a protein in a single large crystal, the Brookhaven/Columbia team developed a way to take measurements from many tiny crystals, and then assemble the collective data.

Tiny crystals, big results

To handle the tiny crystals, the team developed sample grids patterned with micro-sized wells. After pouring solvent containing the microcrystals over these well-mount grids, the scientists removed the solvent and froze the crystals that were trapped on the grids.

Micro-patterned sample grids for manipulation of microcrystals.

“We still have a challenge, though, because we can’t see where the tiny crystals are on our grid,” said Liu. “To find out, we used microdiffraction at NSLS-II’s Frontier Microfocusing Macromolecular Crystallography (FMX) beamline to survey the whole grid. Scanning line by line, we can find where those crystals are hidden.”

As Martin Fuchs, the lead beamline scientist at FMX, explained, “The FMX beamline can focus the full intensity of the x-ray beam down to a size of one micron, or millionth of a meter. We can finely control the beam size to match it to the size of the crystals—five microns in the case of the current experiment. These capabilities are crucial to obtain the best signal,” he said.

Wuxian Shi, another FMX beamline scientist, noted that “the data collected in the grid survey contains information about the crystals’ location. In addition, we can also see how well each crystal diffracts, which allows us to pick only the best crystals for data collection.”

The scientists were then able to maneuver the sample holder to place each mapped out microcrystal of interest back in the center of the precision x-ray beam for data collection.

They used the lowest energy available at the beamline—tuned to approach as closely as possible sulfur atoms’ absorption energy—and collected anomalous scattering data.

“Most crystallographic beamlines could not reach the sulfur absorption edge for optimized anomalous signals,” said co-author Wayne Hendrickson of Columbia University. “Fortunately, NSLS-II is a world-leading synchrotron light source providing bright x-rays covering a broad spectrum of x-ray energy. And even though our energy level was slightly above the ideal absorption energy for sulfur, it generated the anomalous signals we needed.”

But the scientists still had some work to do to extract those important signals and assemble the data from many tiny crystals.

“We are actually getting thousands of pieces of data,” said Liu. “We used about 1400 microcrystals, each with its own data set. We have to put all the data from those microcrystals together.”

Scientists used a five-micron x-ray beam at the FMX beamline at NSLS-II to scan the entire grid and locate the tiny invisible crystals. Then a heat map (green) was used to guide the selection of positions for diffraction data acquisition.

They also had to weed out data from crystals that were damaged by the intense x-rays or had slight variations in atomic arrangements.

“A single microcrystal does not diffract x-rays sufficiently for structure solution prior to being damaged by the x-rays,” said Sean McSweeney, deputy photon division director and program manager of the Structural Biology Program at NSLS-II. “This is particularly true with crystals of only a few microns, the size of about a bacterial cell. We needed a way to account for that damage and crystal structure variability so it wouldn’t skew our results.”

They accomplished these goals with a sophisticated multi-step workflow process that sifted through the data, discarded outliers that might have been caused by radiation damage or incompatible crystals, and ultimately extracted the anomalous scattering signals.

“This is a critical step,” said Liu. “We developed a computing procedure to assure that only compatible data were merged in a way to align the individual microcrystals from diffraction patterns. That gave us the required signal-to-noise ratios for structure determination.”

Applying the technique

This technique can be used to determine the structure of any protein that has proven hard to crystallize to a large size. These include cell-surface receptors that allow cells of advanced lifeforms such as animals and plants to sense and respond to the environment around them by releasing hormones, transmitting nerve signals, or secreting compounds associated with cell growth and immunity.

“To adapt to the environment through evolution, these proteins are malleable and have lots of non-uniform modifications,” said Liu. “It’s hard to get a lot of repeat copies in a crystal because they don’t pack well.”

In humans, receptors are common targets for drugs, so having knowledge of their varied structures could help guide the development of new, more targeted pharmaceuticals.

But the technique is not restricted to just small crystals.

“The method we developed can handle small protein crystals, but it can also be used for any size protein crystals, any time you need to combine data from more than one sample,” Liu said.

This research was supported in part by Brookhaven National Laboratory’s “Laboratory Directed Research and Development” program and the National Institutes of Health (NIH) grant GM107462. The NSLS-II at Brookhaven Lab is a DOE Office of Science user facility (supported by DE-SC0012704), with beamline FMX supported primarily by the National Institute of Health, National Institute of General Medical Sciences (NIGMS) through a Biomedical Technology Research Resource P41 grant (P41GM111244), and by the DOE Office of Science.

See the full article here .

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One of ten national laboratories overseen and primarily funded by the Office of Science of the U.S. Department of Energy (DOE), Brookhaven National Laboratory conducts research in the physical, biomedical, and environmental sciences, as well as in energy technologies and national security. Brookhaven Lab also builds and operates major scientific facilities available to university, industry and government researchers. The Laboratory’s almost 3,000 scientists, engineers, and support staff are joined each year by more than 5,000 visiting researchers from around the world. Brookhaven is operated and managed for DOE’s Office of Science by Brookhaven Science Associates, a limited-liability company founded by Stony Brook University, the largest academic user of Laboratory facilities, and Battelle, a nonprofit, applied science and technology organization.

From MIT News: “Model learns how individual amino acids determine protein function”

From MIT News

March 22, 2019
Rob Matheson | MIT News Office

Technique could improve machine-learning tasks in protein design, drug testing, and other applications.

A new model developed by MIT researchers creates richer, more easily computable representations of how individual amino acids determine a protein’s function, which could be used for designing and testing new proteins.

A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein’s function, which could help researchers design and test new proteins for drug development or biological research.

Proteins are linear chains of amino acids, connected by peptide bonds, that fold into exceedingly complex three-dimensional structures, depending on the sequence and physical interactions within the chain. That structure, in turn, determines the protein’s biological function. Knowing a protein’s 3-D structure, therefore, is valuable for, say, predicting how proteins may respond to certain drugs.

However, despite decades of research and the development of multiple imaging techniques, we know only a very small fraction of possible protein structures — tens of thousands out of millions. Researchers are beginning to use machine-learning models to predict protein structures based on their amino acid sequences, which could enable the discovery of new protein structures. But this is challenging, as diverse amino acid sequences can form very similar structures. And there aren’t many structures on which to train the models.

In a paper being presented at the International Conference on Learning Representations in May, the MIT researchers develop a method for “learning” easily computable representations of each amino acid position in a protein sequence, initially using 3-D protein structure as a training guide. Researchers can then use those representations as inputs that help machine-learning models predict the functions of individual amino acid segments — without ever again needing any data on the protein’s structure.

In the future, the model could be used for improved protein engineering, by giving researchers a chance to better zero in on and modify specific amino acid segments. The model might even steer researchers away from protein structure prediction altogether.

“I want to marginalize structure,” says first author Tristan Bepler, a graduate student in the Computation and Biology group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We want to know what proteins do, and knowing structure is important for that. But can we predict the function of a protein given only its amino acid sequence? The motivation is to move away from specifically predicting structures, and move toward [finding] how amino acid sequences relate to function.”

Joining Bepler is co-author Bonnie Berger, the Simons Professor of Mathematics at MIT with a joint faculty position in the Department of Electrical Engineering and Computer Science, and head of the Computation and Biology group.

Learning from structure

Rather than predicting structure directly — as traditional models attempt — the researchers encoded predicted protein structural information directly into representations. To do so, they use known structural similarities of proteins to supervise their model, as the model learns the functions of specific amino acids.

They trained their model on about 22,000 proteins from the Structural Classification of Proteins (SCOP) database, which contains thousands of proteins organized into classes by similarities of structures and amino acid sequences. For each pair of proteins, they calculated a real similarity score, meaning how close they are in structure, based on their SCOP class.

The researchers then fed their model random pairs of protein structures and their amino acid sequences, which were converted into numerical representations called embeddings by an encoder. In natural language processing, embeddings are essentially tables of several hundred numbers combined in a way that corresponds to a letter or word in a sentence. The more similar two embeddings are, the more likely the letters or words will appear together in a sentence.

In the researchers’ work, each embedding in the pair contains information about how similar each amino acid sequence is to the other. The model aligns the two embeddings and calculates a similarity score to then predict how similar their 3-D structures will be. Then, the model compares its predicted similarity score with the real SCOP similarity score for their structure, and sends a feedback signal to the encoder.

Simultaneously, the model predicts a “contact map” for each embedding, which basically says how far away each amino acid is from all the others in the protein’s predicted 3-D structure — essentially, do they make contact or not? The model also compares its predicted contact map with the known contact map from SCOP, and sends a feedback signal to the encoder. This helps the model better learn where exactly amino acids fall in a protein’s structure, which further updates each amino acid’s function.

Basically, the researchers train their model by asking it to predict if paired sequence embeddings will or won’t share a similar SCOP protein structure. If the model’s predicted score is close to the real score, it knows it’s on the right track; if not, it adjusts.

Protein design

In the end, for one inputted amino acid chain, the model will produce one numerical representation, or embedding, for each amino acid position in a 3-D structure. Machine-learning models can then use those sequence embeddings to accurately predict each amino acid’s function based on its predicted 3-D structural “context” — its position and contact with other amino acids.

For instance, the researchers used the model to predict which segments, if any, pass through the cell membrane. Given only an amino acid sequence, the researchers’ model predicted all transmembrane and non-transmembrane segments more accurately than state-of-the-art models.

“The work by Bepler and Berger is a significant advance in representing the local structural properties of a protein sequence,” says Serafim Batzoglou, a professor of computer science at Stanford University. “The representation is learned using state-of-the-art deep learning methods, which have made major strides in protein structure prediction in systems such as RaptorX and AlphaFold. This work has ultimate application in human health and pharmacogenomics, as it facilitates detection of deleterious mutations that disrupt protein structures.”

Next, the researchers aim to apply the model to more prediction tasks, such as figuring out which sequence segments bind to small molecules, which is critical for drug development. They’re also working on using the model for protein design. Using their sequence embeddings, they can predict, say, at what color wavelengths a protein will fluoresce.

“Our model allows us to transfer information from known protein structures to sequences with unknown structure. Using our embeddings as features, we can better predict function and enable more efficient data-driven protein design,” Bepler says. “At a high level, that type of protein engineering is the goal.”

Berger adds: “Our machine learning models thus enable us to learn the ‘language’ of protein folding — one of the original ‘Holy Grail’ problems — from a relatively small number of known structures.”

See the full article here .

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From Rutgers University via Forbes: “A Tiny Protein Like This May Have Kick-Started Life On Earth”

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From Rutgers University

via

Forbes

Aug 31, 2018
Fiona McMillan

Ambidoxin is a synthetic small protein that wraps around a metal core composed of iron and sulfur. Vikas Nanda/Rutgers University-New Brunswick

Researchers have reverse engineered a simple protein that may have helped kick start life on Earth.

Their findings, published in the Journal of the American Chemical Society, provide strong new evidence that simple protein catalysts could have contributed to the development of life.

A few decades ago, a chemist named Günter Wächtershäuser put forward a theory that life most likely began on volcanic rocks in the ocean that were rich in iron, sulfur and a variety of other minerals and elements useful for the kind of chemistry needed for simple life forms to emerge. He and others went on to surmise that this process would have been helped along by peptides — which are short proteins — that would have been capable of functioning as catalysts.

A catalyst is anything that can speed up or increase the likelihood of a chemical reaction. Protein catalysts, or enzymes, are able to achieve this by bringing the reactants together in close proximity, and sometimes by also bringing other factors into the mix that help the reaction along, such as a metal ion, a water molecule, or some other type of molecule that gets things moving. In this way, enzymes are like really good party hosts.

Of course, modern enzymes are often big bulky things comprising hundreds of amino acids. There are 20 amino acids to choose from, so countless combinations are possible. These big, complex enzymes are able fold into a stunning variety of elaborate shapes, enabling them to capture and hold reactants, and carry out reactions. They’re absolutely critical to the function of both simple and complex cellular life; we literally couldn’t live without them.

However, such complex molecules took billions of years to evolve. Wächtershäuser and others have proposed that the earliest peptides would have had much simpler structures — perhaps just 10 or 20 amino acids — with just enough chemical complexity to enable them to carry out basic primordial chemistry.

Yet exactly what such peptides may have looked like has been a mystery.

Underwater sulfur chimneys at Northwest Eifuku volcano. Life may have begun on volcanic underwater rocks like these.Credit: Pacific Ring of Fire 2004 Expedition. NOAA Office of Ocean Exploration; Dr. Bob Embley, NOAA PMEL, Chief Scientist; Public domain image

Now Vikas Nanda and his colleagues at Rutgers University have used computer modeling to find out just how simple a peptide can get while still retaining the ability to function as a catalyst.

In so doing, they have designed a peptide only 12 amino acids long that is able to wrap around a cluster of iron and sulfur atoms, which closely resemble iron-sulfur clusters that would have been found in ancient oceans.

Interestingly, the peptide, which they named ambidoxin, doesn’t need the full variety of 20 amino acids available to modern proteins — it only requires two types of amino acid. Given its simplicity, the researchers suggest such a structure could have evolved spontaneously under the right conditions.

Importantly, ambidoxin is able to carry out simple oxidation-reduction chemistry, also known as redox catalysis. Essentially it is able to be charged and discharged without falling apart, effectively enabling it to shuttle electrons from one place to another.

“Modern proteins called ferredoxins do this, shuttling electrons around the cell to promote metabolism,” says senior author Paul G. Falkowski, who leads Rutgers’ Environmental Biophysics and Molecular Ecology Laboratory.

“A primordial peptide like the one we studied may have served a similar function in the origins of life,” he says.

By shuttling electrons around, ambidoxin (or something like it) may have contributed to early metabolic cycles, and could have served as a precursor to longer, more complex enzymes.

See the full article here .

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From SLAC National Accelerator Lab: “Catching the dance of antibiotics and ribosomes at room temperature”

From SLAC National Accelerator Lab

August 6, 2018
Ali Sundermier

Hasan DeMirci refers to ribosomes as the 3D printers of the human body because they synthesize proteins, which are essential to life. (Dawn Harmer/SLAC National Accelerator Laboratory)

Interns in DeMirci’s lab help grow ribosome crystals. Once grown and suspended in a special chemical solution called “mother liquor,” the crystals are imaged at the LCLS to uncover how they interact with antibiotics. (Dawn Harmer/SLAC National Accelerator Laboratory)

Antibiotics have been a pillar of modern medicine since the 1940s. Streptomycin, which belongs to a class of antibiotics called aminoglycosides, was the first hint of light in the millennia-long search for a treatment for tuberculosis, which remains one of the deadliest infectious diseases in human history.

Today, aminoglycosides are the most commonly prescribed antibiotics in the world due to their low cost and high effectiveness in tackling a broad spectrum of bacterial infections. But they also bring along side effects that can have lifelong impacts. Depending on the dosage and the particular antibiotic, an estimated 10 to 20 percent of patients who take aminoglycosides suffer kidney damage and 20 to 60 percent end up with irreversible hearing loss.

Now researchers at the Department of Energy’s SLAC National Accelerator Laboratory have developed a new imaging technique to better understand the mechanisms that lead to hearing loss when aminoglycosides are introduced to the body. Using the lab’s Linac Coherent Light Source (LCLS) X-ray laser and Stanford Synchrotron Lightsource (SSRL), SLAC researchers, in collaboration with researchers at Stanford University, were able to observe interactions between the drugs and bacterial ribosomes at both extremely low and room temperatures, revealing never-before-seen details.

SLAC LCLS

SLAC/SSRL

They also demonstrated how small modifications to the antibiotics can lead to dramatic changes in ribosome shape that eliminate hearing loss. The research could lead to a better understanding of which parts of a drug molecule cause unwanted reactions in the body, which will enable the development of more effective antibiotics with fewer side effects.

The group was led by research associate and senior author Hasan DeMirci. Their results were published in Nucleic Acids Research.

3D printing proteins

Hasan DeMirci refers to ribosomes – tiny molecular machines made up of tangles of RNA and proteins clumped together and intricately wired like ramen noodles in soup – as “the 3D printers of the human body.” The ribosomes synthesize proteins using the genetic information contained in DNA, “building our bodies from the ground up.”

Ribosomes (shown here) are tiny molecular machines made up of tangles of RNA and proteins clumped together and intricately wired like ramen noodles in soup. (Hasan DeMirci/SLAC National Accelerator Laboratory)

“While one subunit of the ribosome, its brain, deciphers and translates the genetic code, the other, its hands, links together amino acids to form proteins,” DeMirci said.

Unlike viruses, which have to leech off hosts to survive, bacteria have their own ribosomes, which is where antibiotics come into play. Bacterial ribosomes are the targets of many antibiotics. So-called “cidal” antibiotics like aminoglycosides function by attacking the brains of bacterial ribosomes, causing them to make mistakes and fill the cells with protein-like garbage molecules.

“It’s like a house with a lot of hoarded junk,” DeMirci says. “There’s no going back. From that point the bacteria just die.”

The problem with this strategy is that human cells contain energy-producing factories called mitochondria that have their very own ribosomes – and since those ribosomes are dangerously similar to those found in bacteria, they’re also vulnerable to antibiotic attack.

“We’re killing the bacteria, but the same drug gets into our mitochondria and destroys the ribosomes there,” DeMirci says. “Now we cannot produce those enzymes that power us. You take an antibiotic and you start losing your hearing, your kidney fails.”

Insights into molecular machinery

DeMirci has a strong interest in aminoglycosides because he can use them to gain insight into the molecular machinery of the ribosome.

“What I really want to know is what those drugs can teach us about how ribosomes decipher the genetic code,” DeMirci said. “Drugs give us an opportunity to stop that process at different stages to understand how each and every step is catalyzed by the ribosome.”

To better understand this process, he struck up a collaboration with Anthony Ricci, a biophysicist and professor of medicine at Stanford who focuses on the inner ear. In previous research, Ricci found that aminoglycosides infiltrate specialized channels to target the sensory cells essential to hearing.

“You can think of it as a roach motel,” Ricci says. “The drugs can get in but they can’t get out. They start to build up, binding to the ribosomes and altering protein synthesis. This puts a huge metabolic load on the sensory cells, which eventually leads to their deaths.”

A major goal of Ricci’s lab has been to design and develop new aminoglycosides that kill bacteria but cannot squeeze through the channel. In order to do this, the researchers need to understand exactly how the aminoglycosides interact with the ribosomes so they can modify parts of the drug without weakening its bacteria-killing properties.

Defrosting interactions

The best way to reach this understanding, researchers have found, is through a technique called X-ray crystallography. In X-ray crystallography, researchers use the patterns formed when a beam of X-rays scatters off a crystal sample to form a 3D model of how its atoms and molecules are arranged. This technique allows researchers to observe how a drug binds to a ribosome.

While the key interactions in these processes happen at body temperature, around 37 degrees Celsius, X-ray crystallography usually has to be done at extremely low, or cryogenic, temperatures, around minus 180 degrees Celsius. This leads to gaps in the data, obscuring tiny details that could greatly inform future experiments.

“Our bodies are warm, so the important biology is happening at body temperature,” DeMirci said, “but in crystallography everything is frozen. When you cool these processes down, you miss out on thermal fluctuations, tiny movements that could change your understanding of how the drugs and ribosomes are behaving.”

In order to design better antibiotics, they need to get as close a view as they can of this interaction happening under physiological conditions. At the LCLS, using a technique called serial femtosecond crystallography, DeMirci is able to catch the intricate waltz of the drugs and ribosomes at room temperature. Rather than freeze the ribosome crystals, the researchers suspend them in ‘mother liquor,’ a special chemical solution they were grown in that keeps them stable, so they are “swimming happily, still wiggling and fluctuating,” he says.

The crystals travel from a reservoir to the interaction region through a single capillary, like a garden hose. Once in the interaction region, the crystals are zapped with a beam of X-rays from the LCLS, which scatters off of them into a detector and provides the researchers with patterns they can use to build detailed 3D models of the ribosome before and after they’ve bound with the drugs. They then use these models to piece together a simulation of the interaction.

At LCLS, crystallized ribosomes travel through a capillary into the interaction region, where they are zapped with a beam of X-rays. The X-rays scatter off the crystals into a detector, providing the researchers with patterns they can use to build detailed 3D models of interactions between the drug and ribosome. (Greg Stewart/SLAC National Accelerator Laboratory)

Uncovering hidden wiggles

To demonstrate their technique, the researchers imaged modified and unmodified drugs binding to ribosomes at both cryogenic and room temperatures to see if they could catch any differences. They found that the drug molecules were less flexible at cryogenic temperatures: Tiny wiggles essential to a better understanding of their interactions with ribosomes were frozen in place.

“Despite the fact that we’ve recorded hundreds of thousands of structures of ribosomal interactions, less than a handful of new-generation drugs have been designed based on these cryogenic structures,” DeMirci said. “That’s because every small interaction makes a huge difference, even a single hydrogen bond.”

With the images taken at room temperature, Ricci’s group identified a site where the drug could be modified without altering its effectiveness.

“We now have some idea that when the drug binds with the ribosome, a global change occurs in the ribosome that might actually be important for the function of the antibiotic and the sensitivity of the ribosome,” Ricci said.

Refining the jigsaw pieces

In the next phase of experiments, DeMirci hopes to design a setup in which the antibiotics aren’t introduced until the last second before the ribosome is imaged so that they can watch as it binds to the ribosome, rather than just taking images before and after.

Up to this point, Ricci said, his group had been doing drug synthesis with very little information or insight into how the antibiotic interacts with the ribosome.

“What this paper and overall collaboration allow is a direct investigation of the drug-ribosome interaction,” he said. “It’s like having more defined pieces to the jigsaw puzzle. You don’t have to guess about what’s happening.”

Developing antibiotics that can fight off drug-resistant bacteria with minimal side effects is essential because the rise of antibiotic resistant strains is currently the biggest threat to modern medicine, DeMirci said.

“Every year more than a million people die from tuberculosis and nearly half a million are HIV positive,” he said. “People don’t usually die from HIV or cancer, they die because their immune system is suppressed and they can’t fight off bacterial infections. That’s when you need antibiotics. But what if you don’t have one that’s effective against the resistant strains? That’s exactly what’s happening right now. This research can help us make informed decisions when designing the next generation of drugs.”

The research team included scientists from LCLS; SSRL; SLAC’s Biosciences Division; the Stanford PULSE Institute; and the Stanford School of Medicine.

See the full article here .

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SLAC is a multi-program laboratory exploring frontier questions in photon science, astrophysics, particle physics and accelerator research. Located in Menlo Park, California, SLAC is operated by Stanford University for the DOE’s Office of Science.

From University of Washington: “UW, PNNL to host energy research center focusing on bio-inspired design and assembly”

From University of Washington

August 3, 2018
James Urton

The United States Department of Energy has awarded an expected $10.75 million, four-year grant to the University of Washington, the Pacific Northwest National Laboratory and other partner institutions for a new interdisciplinary research center to define the enigmatic rules that govern how molecular-scale building blocks assemble into ordered structures — and give rise to complex hierarchical materials.

PNNL

The Center for the Science of Synthesis Across Scales, or CSSAS, will bring together researchers from biology, engineering and the physical sciences to uncover new insights into how molecular interactions control assembly and apply these principles toward creating new materials with novel and revolutionary properties for applications in energy technology.

“This center seeks to understand the fundamental rules of how order emerges from the interaction of simple building blocks,” said CSSAS Director François Baneyx, the Matthaei Professor and Chair of the UW Department of Chemical Engineering. “What are the energetics, rates and pathways involved, and what properties emerge when simple components come together in increasingly complex layers? Those are some of our driving questions.”

The UW-based CSSAS is among the newest members of the Energy Frontier Research Centers announced June 29 by the Department of Energy. These centers, operated out of universities and national labs, are funded by the Department of Energy and devoted to specific goals in energy science. The work at the CSSAS will focus on understanding the principles of “hierarchical synthesis” — the process by which molecules come together, bind, interact and create layer upon layer of higher-ordered structures.

The initial stage of the assembly of protein building blocks (left) and a self-assembled peptoid sheet (right). Scale bars indicate length in nanometers.Jim De Yoreo/Chun-Long Chen

CSSAS experiments will focus on protein-based building blocks, but will also probe protein-like synthetic compounds called peptoids as well as inorganic nanoparticles. Studying the biologically inspired assembly of these systems individually and in combination will shed new light on how living organisms, through billions of years of adaptation and evolution, have created complex hierarchical systems to solve a host of challenges, said Baneyx.

Understanding hierarchical synthesis would allow engineers to design and build new materials with unique properties for innovative technological advancements that can come about only when scientists exert precise control over a material. For example, controlling how charges move precisely through a material — or how a substrate is shuttled between the active sites of a series of enzymes positioned with nanoscale precision — could be key to creating new materials for energy storage, transmission and generation. The precision control that scientists envision could also yield functional materials that are self-healing or self-repairing, and have other custom physical properties designed within them.

“Scientists have been trying to create these types of innovative materials largely through ‘top-down’ approaches, and often by reverse engineering an interesting biological material,” said Baneyx. “We will begin with the blocks themselves, exploring how order evolves in the synthesis process when the blocks are put together and interact.”

CSSAS research will focus on three major areas:

Investigating the emergence of order from the interactions of individual building blocks, be they peptoids, inorganic nanoparticles or protein-based particles
Probing how hierarchy unfolds as these building blocks are combined to construct lattices, active structures and hybrid materials
Using machine learning, computational simulations and big data analytics to learn new ways to control the assembly dynamics of hierarchical structures

No image caption or credit

These investigations will build upon work conducted at the UW Institute for Protein Design, led by UW biochemistry professor and Howard Hughes Medical Institute investigator David Baker, and harness the expertise of researchers at the University of Chicago, the Oak Ridge National Laboratory and the University of California, San Diego.

Dr. David Baker, Baker Lab, U Washington

David Baker’s Rosetta@home project, a project running on BOINC software from UC Berkeley

Rosetta@home BOINC project

The CSSAS effort was enabled by the Northwest Institute for Materials Physics, Chemistry, and Technology, or NW IMPACT, which was formally launched earlier this year by UW President Ana Mari Cauce and PNNL Director Steven Ashby to fertilize cross-disciplinary collaborations between UW and PNNL researchers. NW IMPACT co-director Jim De Yoreo, who is the PNNL chief scientist for materials synthesis and simulation across scales and also holds a joint appointment at the UW in both chemistry and materials science and engineering, will serve as the deputy director of the CSSAS.

“This center’s focus is ultimately on unlocking potential,” said Baneyx. “Once we understand the fundamental rules governing the assembly of bioinspired building blocks, we will be able to design new materials to meet a broad range of technological needs.”

For more information, contact Baneyx at 206-685-7659 or baneyx@uw.edu and De Yoreo at 509-375-6494 or james.deyoreo@pnnl.gov.

See the full article here .

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The University of Washington is one of the world’s preeminent public universities. Our impact on individuals, on our region, and on the world is profound — whether we are launching young people into a boundless future or confronting the grand challenges of our time through undaunted research and scholarship. Ranked number 10 in the world in Shanghai Jiao Tong University rankings and educating more than 54,000 students annually, our students and faculty work together to turn ideas into impact and in the process transform lives and our world. For more about our impact on the world, every day.
So what defines us —the students, faculty and community members at the University of Washington? Above all, it’s our belief in possibility and our unshakable optimism. It’s a connection to others, both near and far. It’s a hunger that pushes us to tackle challenges and pursue progress. It’s the conviction that together we can create a world of good. Join us on the journey.

From Rosetta@home via Bloomberg View: “Protein Engineering May Be the Future of Science”

Rosetta@home

Bloomberg View

March 27, 2018
Faye Flam

Some scientists think designing new proteins could become as significant as tweaking DNA.

Let’s build a better sperm whale. Photograph: SSPL/Getty Images

Scientists are increasingly betting their time and effort that the way to control the world is through proteins. Proteins are what makes life animated. They take information encoded in DNA and turn it into intricate three-dimensional structures, many of which act as tiny machines. Proteins work to ferry oxygen through the bloodstream, extract energy from food, fire neurons, and attack invaders. One can think of DNA as working in the service of the proteins, carrying the information on how, when and in what quantities to make them.

Living things make thousands of different proteins, but soon there could be many more, as scientists are starting to learn to design new ones from scratch with specific purposes in mind. Some are looking to design new proteins for drugs and vaccines, while others are seeking cleaner catalysts for the chemical industry and new materials.

David Baker, director for the Institute for Protein Design at the University of Washington, compares protein design to the advent of custom tool-making. At some point, proto-humans went beyond merely finding uses for pieces of wood, rock or bone, and started designing tools to suit specific needs — from screwdrivers to sports cars.

See the full article here.

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Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s (See our Disease Related Research for more information). Please join us in our efforts! Rosetta@home is not for profit.

About Rosetta

One of the major goals of Rosetta is to predict the shapes that proteins fold up into in nature. Proteins are linear polymer molecules made up of amino acid monomers and are often refered to as “chains.” Amino acids can be considered as the “links” in a protein “chain”. Here is a simple analogy. When considering a metal chain, it can have many different shapes depending on the forces exerted upon it. For example, if you pull its ends, the chain will extend to a straight line and if you drop it on the floor, it will take on a unique shape. Unlike metal chains that are made of identical links, proteins are made of 20 different amino acids that each have their own unique properties (different shapes, and attractive and repulsive forces, for example), and in combination, the amino acids exert forces on the chain to make it take on a specific shape, which we call a “fold.” The order in which the amino acids are linked determines the protein’s fold. There are many kinds of proteins that vary in the number and order of their amino acids.

To predict the shape that a particular protein adopts in nature, what we are really trying to do is find the fold with the lowest energy. The energy is determined by a number of factors. For example, some amino acids are attracted to each other so when they are close in space, their interaction provides a favorable contribution to the energy. Rosetta’s strategy for finding low energy shapes looks like this:

Start with a fully unfolded chain (like a metal chain with its ends pulled).
Move a part of the chain to create a new shape.
Calculate the energy of the new shape.
Accept or reject the move depending on the change in energy.
Repeat 2 through 4 until every part of the chain has been moved a lot of times.

We call this a trajectory. The end result of a trajectory is a predicted structure. Rosetta keeps track of the lowest energy shape found in each trajectory. Each trajectory is unique, because the attempted moves are determined by a random number. They do not always find the same low energy shape because there are so many possibilities.

A trajectory may consist of two stages. The first stage uses a simplified representation of amino acids which allows us to try many different possible shapes rapidly. This stage is regarded as a low resolution search and on the screen saver you will see the protein chain jumping around a lot. In the second stage, Rosetta uses a full representation of amino acids. This stage is refered to as “relaxation.” Instead of moving around a lot, the protein tries smaller changes in an attempt to move the amino acids to their correct arrangment. This stage is regarded as a high resolution search and on the screen saver, you will see the protein chain jiggle around a little. Rosetta can do the first stage in a few minutes on a modern computer. The second stage takes longer because of the increased complexity when considering the full representation (all atoms) of amino acids.

Your computer typically generates 5-20 of these trajectories (per work unit) and then sends us back the lowest energy shape seen in each one. We then look at all of the low energy shapes, generated by all of your computers, to find the very lowest ones. This becomes our prediction for the fold of that protein.

To join this project, download and install the BOINC software on which it runs. Then attach to the project. While you are at BOINC, look at some of the other projects to see what else might be of interest to you.

U Washington Dr. David Baker

My BOINC

From Rutgers: “Rutgers Scientists Discover ‘Legos of Life'”

Rutgers University

THIS POST IS DEDICATED TO J.L. FROM HP AND RUTGERS. I HOPE HE SEES IT.

January 21, 2018
Todd B. Bates

Deep dive into the 3D structures of proteins reveals key building blocks.

Rutgers researchers identified a small set of simple protein building blocks (left) that likely existed at the earliest stages of life’s history. Over billions of years, these “Legos of life” were assembled and repurposed by evolution into complex proteins (right) that are at the core of modern metabolism.
Image: Vikas Nanda/Rutgers Robert Wood Johnson Medical School.

Rutgers scientists have found the “Legos of life” – four core chemical structures that can be stacked together to build the myriad proteins inside every organism – after smashing and dissecting nearly 10,000 proteins to understand their component parts.

The four building blocks make energy available for humans and all other living organisms, according to a study published online today in the Proceedings of the National Academy of Sciences.

The study’s findings could lead to applications of these stackable, organic building blocks for biomedical engineering and therapeutic proteins and the development of safer, more efficient industrial and energy catalysts – proteins and enzymes that, like tireless robots, can repeatedly carry out chemical reactions and transfer energy to perform tasks.

“Understanding these parts and how they are connected to each other within the existing proteins could help us understand how to design new catalysts that could potentially split water, fix nitrogen or do other things that are really important for society,” said Paul G. Falkowski, study co-author and a distinguished professor who leads the Environmental Biophysics and Molecular Ecology Laboratory at Rutgers University–New Brunswick.

The scientists’ research was done on computers, using data on the 3D atomic structures of 9,500 proteins in the RCSB Protein Data Bank based at Rutgers, a rich source of information about how proteins work and evolve.

“We don’t have a fossil record of what proteins looked like 4 billion years ago, so we have to take what we have today and start walking backwards, trying to imagine what these proteins looked like,” said Vikas Nanda, senior author of the study and an associate professor in the Department of Biochemistry and Molecular Biology at Rutgers’ Robert Wood Johnson Medical School, within Rutgers Biomedical and Health Sciences. “The study is the first time we’ve been able to take something with thousands of amino acids and break it down into reasonable chunks that could have had primordial origins.”

The identification of four fundamental building blocks for all proteins is just a beginning. Nanda said future research may discover five or 10 more building blocks that serve as biological Legos.

“Now we need to understand how to put these parts together to make more interesting functional molecules,” he said. “That’s the next grand challenge.”

The study’s lead author is Hagai Raanana, a post-doctoral associate in the Environmental Biophysics and Molecular Ecology Program. Co-authors include Douglas H. Pike, a doctoral student at the Rutgers Institute for Quantitative Biomedicine, and Eli K. Moore, a post-doctoral associate in the Environmental Biophysics and Molecular Ecology Program.

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Founded in 1766, Rutgers teaches across the full educational spectrum: preschool to precollege; undergraduate to graduate; postdoctoral fellowships to residencies; and continuing education for professional and personal advancement.

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From UCLA Newsroom: “Imaging technique could be ‘new ballgame’ in drug development”

UCLA Newsroom

January 02, 2018
Tami Dennis

UCLA researcher Tamir Gonen explores the potential of MicroED in neurological diseases.

Courtesy of Tamir Gonen
Tamir Gonen published his proof of principle paper [eLIFE] on MicroED in 2013.

Biochemistry and structural biology are surprisingly — at least to the uninitiated — visual fields. This is especially true in the study of proteins. Scientists like to see the structure of proteins within cells to help them truly understand how they work, how they don’t work or how they can be modified to work as they should. That is, how they can be targeted with drugs to cure disease.

Current methods, however, have their downsides. Many widely used techniques require large amounts of protein for analysis, even though many diseases are caused by proteins that are far from abundant or that are difficult to amass in large quantities. A new method pioneered by a professor who recently joined UCLA overcomes this challenge, offering untold potential in the exploration of disease and treatment.

Called “MicroED,” for micro-electron diffraction, the technique uses high-powered electron microscopy to determine the structure of proteins with atomic precision, using samples that are only one-billionth the size required by other imaging methods.

Tamir Gonen, a new professor of physiology and biological chemistry at the David Geffen School of Medicine at UCLA, is the developer of MicroED. For the past five years, Gonen has been spearheading the exploration of MicroED in his lab at the Janelia Research Campus of the Howard Hughes Medical Institute near Washington D.C.

Now, in joining UCLA’s faculty, Gonen’s goal is to set up a lab centered on this new imaging tool. Already the university is using the technique in the labs of Jose Rodriguez, assistant professor of biochemistry, and David Eisenberg, a professor of chemistry and biochemistry and of biological chemistry. Both use MicroED to view the structures of proteins involved in neurodegeneration.

“With MicroED, the way we think about disease is going to be different,” Gonen said. “Because it uses minute samples and the resolution we get is very high, problems that were beyond our reach are suddenly attainable. We can see individual atoms and even peer deeper into subatomic space and see things that have not been seen before.”

Samples used in MicroED resemble jewels with one exception, they are made out of biological material rather than precious mineral. Gonen lab.

Gonen began working on the technique at the Howard Hughes Medical Institute Janelia Research Campus, where he was a group leader. Upon moving to UCLA Gonen is now an investigator of the Howard Hughes Medical Institute and professor of physiology and biological chemistry. Prior to that, he was an assistant professor, then a tenured associate professor, at the University of Washington School of Medicine, as well as a Howard Hughes Medical Institute Early Career Scientist.

Since 2013, which marked his publication of a proof of principle paper on MicroED [eLIFE], Gonen has been an advocate for the technique. As other researchers have come to understand the long-sought imaging potential MicroED offers, about 20 institutions worldwide have begun setting up MicroED labs, many with Gonen’s help.

“I have fantastic folks working in my lab, and they are extremely collegial and want to help others get their science done,” Gonen said.

That focus on getting “science done” — and MicroED itself — has enormous ramifications for the treatment of HIV, Parkinson’s, Alzheimer’s and other neurodegenerative diseases. “When you’re talking about drug discovery, it’s a whole new ballgame,” Gonen said.

Gonen’s development of MicroED stemmed from his study of cell membranes, specifically the protein gateways within those membranes.

These gateways can help cells maintain healthy homeostasis in which everything works as it should — think of it as a biological “peace.” When things go wrong, however, as happens with disease, these gateways might allow too much of one substance, such as water or sugar, in or out.

This illustration of a protein shows an example of a structure that could only be determined by the capabilities of micro-electron diffraction. Gonen lab.

Gonen knew that targeting these gateways — or targeting their function — could lead to innovative ways to control disease and help patients. But he and other scientists needed good images to facilitate the design of better drugs.

“More than 90 percent of all medicines sold these days target G-protein coupled receptors,” Gonen said. “When you feel pain, when you see light, when you taste, when you have any neurological sensation, all of this is occurring because of these receptors.”

Another potential, and timely, target of this type: opioid receptors. “Opioids are an increasingly challenging problem, and not surprisingly there exists an opioid receptor,” Gonen said.

MicroED makes it possible to image these G-protein coupled receptors, which move around a lot. This movement has made it difficult for traditional methods to capture images of them.

MicroED’s potential goes far beyond such receptors, however.

Larry Zipursky, a distinguished professor biological chemistry and the leader of the neuroscience research theme at the David Geffen School of Medicine at UCLA, called the approach “revolutionary.”

“This technique uses a rational approach to disease,” Zipursky said. “It allows researchers to assess the structure of abnormal proteins that give rise to disease; from this structural determination, they can assess the disease in a more strategic way.”

Gonen is enthusiastic about the larger potential as well. “For a medical school, this is going to be quite a resource for pushing research forward. I’m hoping to collaborate with a lot of people.”

In fact, he’s already working on several projects, including one that points to a way to make more efficient HIV medicines. This is in addition to the projects with Eisenberg and Rodriguez, who’s studying the conversion of proteins from a normal state into an abnormal, clumped — or aggregated — state, as seen in Alzheimer’s and other diseases of the brain and nervous system.

“We need new and better treatments for neurodegenerative diseases, one way to achieve this is to understand how atomic scale changes in the brain lead to disease. What do the structures look like before and after, and, quite simply, how are they so toxic?” Rodriguez said. “MicroED may finally open the door to that understanding.”

The potential of such collaborations is what brought Gonen — and his emphasis on MicroED — to UCLA.

“What I like about UCLA is it’s a top-rate institution — you can find some expert in every field,” Gonen said. “They’re a world leader, but they come without the ego you may find at other institutions.”

That collaboration, he’s convinced, will lead to new approaches, new discoveries and new cures.

Further papers:
>Structure of catalase determined by MicroED
Taking the measure of MicroED
Protein structure determination by MicroED

See the full article here .

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For nearly 100 years, UCLA has been a pioneer, persevering through impossibility, turning the futile into the attainable.

We doubt the critics, reject the status quo and see opportunity in dissatisfaction. Our campus, faculty and students are driven by optimism. It is not naïve; it is essential. And it has fueled every accomplishment, allowing us to redefine what’s possible, time after time.

This can-do perspective has brought us 12 Nobel Prizes, 12 Rhodes Scholarships, more NCAA titles than any university and more Olympic medals than most nations. Our faculty and alumni helped create the Internet and pioneered reverse osmosis. And more than 100 companies have been created based on technology developed at UCLA.