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

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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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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.

See the full article here .

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Rutgers, The State University of New Jersey, is a leading national research university and the state’s preeminent, comprehensive public institution of higher education. Rutgers is dedicated to teaching that meets the highest standards of excellence; to conducting research that breaks new ground; and to providing services, solutions, and clinical care that help individuals and the local, national, and global communities where they live.

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 .

Please help promote STEM in your local schools.

Stem Education Coalition

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.

From Salk: “Revealing the best-kept secrets of proteins”

Salk Institute for Biological Studies

December 14, 2017
No writer credit

From left: John Lubin, Vicki Lundblad and Tim Tucey. Credit: Salk Institute

Salk scientists develop new approach to identify important undiscovered functions of proteins.

In the bustling setting of the cell, proteins encounter each other by the thousands. Despite the hubbub, each one manages to selectively interact with just the right partners, thanks to specific contact regions on its surface that are still far more mysterious than might be expected, given decades of research into protein structure and function.

Now, Salk Institute scientists have developed a new method to discover which surface contacts on proteins are critical for these cellular interactions. The novel approach shows that essential new functions can be uncovered even for well-studied proteins, and has significant implications for therapeutic drug development, which depends heavily on how drugs physically interact with their cellular targets. The paper appeared in the early online version of Genetics in late November, and is slated for publication in the January print edition of the journal.

“This paper illustrates the power of this methodology,” says senior author Vicki Lundblad, holder of the Ralph S. and Becky O’Conner Chair. “It can not only identify previously undiscovered activities for a protein, but it can also pinpoint the exact amino acids on a protein surface that perform these new functions.”

Amino acids are the building blocks of proteins. Their specific linear arrangement determines the identity of a protein, and clusters of them on the protein’s surface serve as contacts, regulating how that protein interacts with other proteins and molecules. Lundblad and her colleagues suspected that, despite decades of work deciphering the mysteries of proteins, the extent of this regulatory landscape on the surface of proteins had remained mostly unexplored. Long ago, her group unexpectedly discovered one such regulatory amino acid cluster, while searching one-by-one through 300,000 mutant yeast cells. Although that work opened up a new area of research in the field of telomere biology, Lundblad was determined to figure out a more robust methodology that could rapidly uncover many more of these unexplored protein surfaces.

Enter John Lubin, now a PhD student in Lundblad’s lab, who began working with her as an undergraduate.

“My task was to figure out how to search through 30 mutant yeast cells, instead of 300,000, to discover new activities for a protein,” says Lubin, the paper’s co–first author. Timothy Tucey, the other co–first author, was a postdoctoral researcher in Lundblad’s group and is now at Monash University.

Together they turned to a protein called Est1, which Lundblad had discovered in yeast as a postdoctoral researcher in 1989. Est1 is a subunit of a protein (an enzyme) called telomerase, which keeps the protective caps at the ends of chromosomes (known as telomeres) from getting too short. As the first subunit of telomerase to be discovered, Est1 has been subjected to intensive study by many research groups.

The Salk team’s approach involved introducing a small, but customized, set of mutations into yeast cells that would selectively disrupt surface contacts on the cells’ Est1 protein. The team then analyzed the cells to see what effect, if any, the various mutations had. Abnormalities resulting from a specific mutation would suggest what the role of the unmutated version was. To do so, they used a genetic trick, by flooding the cells with each mutant protein, and looking for the rare mutant protein that could interfere with cell function, as their previous work had shown that this would preferentially target the protein surface.

Lundblad’s team discovered four functions for Est1 through this approach. Impairment of any of these four functions by mutations to Est1’s surface amino acids, the scientists found, resulted in cells that had critically short telomeres, indicating specific roles for the Est1 contacts in the telomerase complex.

“What has us excited about this technique is that it can be applied to numerous proteins,” says Lundblad. “In particular, many therapeutic drugs rely on being able to access a very specific location on a protein surface, which we suspect can be uncovered by this method.”

Using this approach, her team has already uncovered new functions for a set of proteins that regulate the stability of the genome, and has also applied for grants that fund research into drug targets.

The work was funded by the National Institutes of Health, the National Science Foundation, the Rose Hills Foundation and the Glenn Center for Aging Research.

See the full article here .

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Stem Education Coalition

Every cure has a starting point. Like Dr. Jonas Salk when he conquered polio, Salk scientists are dedicated to innovative biological research. Exploring the molecular basis of diseases makes curing them more likely. In an outstanding and unique environment we gather the foremost scientific minds in the world and give them the freedom to work collaboratively and think creatively. For over 50 years this wide-ranging scientific inquiry has yielded life-changing discoveries impacting human health. We are home to Nobel Laureates and members of the National Academy of Sciences who train and mentor the next generation of international scientists. We lead biological research. We prize discovery. Salk is where cures begin.

From IST Austria “Researchers at IST Austria define function of an enigmatic synaptic protein”

IST Austria

November 21, 2017

Synaptotagmin 7 ensures efficiency of inhibitory signal transmission – Study published in Cell Reports

Photo credit: Chen et al, Cell Reports

Communication is often mired in contradiction – also in the brain. Neuroscientists at IST Austria were now able to resolve one such contradiction.

In our brains, neurons communicate by sending chemical signals across their connections, the synapses. The molecular machinery required to send a signal involves not only the signal itself, the neurotransmitter, but a large variety of other proteins that act as sensors, effectors, modulators, and scaffolds. Synaptotagmins are part of this complex machinery. Synaptotagmin proteins come in many flavours: humans and other mammals have 17 different varieties. For most of these proteins, however, scientists do not yet understand the function. A group of neuroscientists led by Peter Jonas, Professor at the Institute of Science and Technology Austria (IST Austria), have now resolved the role of Synaptotagmin 7 during signal transmission at an inhibitory synapse. Writing in Cell Reports, the team, including first author and PhD student Chong Chen as well as researchers at the Max Planck Florida Institute for Neuroscience, shows that Synaptotagmin 7 ensures the efficiency of high-frequency inhibitory synaptic transmission. “The role of Synaptotagmin 7 has been controversial. For the first time, we defined its functional contribution at an inhibitory GABAergic synapse”, explains lead author Peter Jonas.

Conflicting role in signal transmission

Only in January of this year, Jonas and Chen showed that Synaptotagmin 2 is the calcium sensor that makes certain synapses fast – those that use the transmitter GABA. In the current study, the researchers turned their attention to another member of the Synaptotagmin family, Synaptotagmin 7. The brain contains a large amount of Synaptotagmin 7, but so far, scientists were not able to pinpoint the protein’s function. Partly, the reason is a contradiction between the function Synaptotagmin 7 seemed to have, and the characteristics of signal transmission observed.

Synaptotagmin 7 appears to function as a calcium sensor that mediates the release of a variable barrage of neurotransmitter into the synaptic cleft, a phenomenon called asynchronous transmitter release. There are also indications that Synaptotagmin 7 plays a role in facilitation, an increase in neurotransmission at the synapse. But on the other hand Synaptotagmin 7 is also found in large amounts in a class of neurons called fast-spiking, parvalbumin-expressing GABAergic interneurons. These neurons form synapses which appear to contradict the suggested functions of Synaptotagmin 7: the synapses release neurotransmitter in a tightly synchronized manner, rather than asynchronously, and show a reduction in neurotransmission, rather than facilitation during repetitive stimulation. In their study, Chen et al. now resolve this apparent contradiction.

Synaptotagmin 7 regulates information flow in cerebellum

The researchers investigated how signal transmission changes when they delete Synaptotagmin 7 in an inhibitory synapse, the GABAergic synapse between the basket cells (BCs) and Purkinje cells (PCs) in the cerebellum (a brain region required for motor control). They show that, indeed, the function of Synaptotagmin 7 is to contribute to asynchronous transmitter release, replenishment of vesicles containing neurotransmitter, and facilitation. But rather than being mutually exclusive, these three functions appear to coexist at BC-PC synapses. Elucidating the effects of Synaptotagmin 7 required careful analysis, because asynchronous release is small and facilitation is overlaid by depression. However the authors demonstrate a substantial difference in the extent of depression. Little depression occurred in the presence of Synaptotagmin 7, but a lot of depression was found in the absence of Synaptotagmin 7. Thus, Synaptotagmin 7 ensures the efficient and frequency-independent signal transmission at the BC-PC synapse, one of its fundamental properties.

When the researchers looked at the level of neuronal networks, they found that Synaptotagmin 7 allows single basket cells to control the activity of a Purkinje cell. These neurons are the only ones that send information out of the cerebellum. Synaptotagmin 7 therefore has a strategic position to regulate the flow of information in this motor circuit. The researchers also found that Synaptotagmin 7 plays a similar (albeit quantitatively smaller) role at BC synapses in the hippocampus, a seahorse-shaped brain region involved in memory and spatial coding. Peter Jonas summarizes: “We have identified a critical role for Synaptotagmin 7 in maintaining the efficacy of transmission at GABAergic synapses in the cerebellum and hippocampus.”

See the full article here.

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The Institute of Science and Technology Austria (IST Austria) is a young international institute dedicated to basic research and graduate education in the natural and mathematical sciences, located in Klosterneuburg on the outskirts of Vienna. Established jointly by the federal government of Austria and the provincial government of Lower Austria, the Institute was inaugurated in 2009 and will grow to about 90 research groups by 2026.

The governance and management structures of IST Austria guarantee its independence and freedom from political and commercial influences. The Institute is headed by the President, who is appointed by the Board of Trustees and advised by the Scientific Board. The first President of IST Austria is Thomas A. Henzinger, a leading computer scientist and former professor of the University of California at Berkeley and the EPFL Lausanne in Switzerland.

From UCLA Newsroom: “UCLA bioengineers discover mechanism that regulates cells’ ‘powerhouses’”

UCLA Newsroom

November 27, 2017
Matthew Chin

In this artist’s rendering, Dnm1 proteins surrounding a mitochondrion are breaking it up into two. Jaime de Anda/ACS Central Science.

UCLA bioengineers and their colleagues have discovered a new perspective on how cells regulate the sizes of mitochondria, the parts of cells that provide energy, by cutting them into smaller units.

The researchers wrote that this finding, demonstrated with yeast proteins, could eventually be used to help address human diseases associated with an imbalanced regulation of mitochondria size — for example, Alzheimer’s or Parkinson’s diseases. In addition, since having mitochondria that are too small or too large can potentially lead to incurable diseases, it is conceivable that the proteins responsible for this process could be potential targets for future therapies.

The study was published in ACS Central Science and was led by UCLA bioengineering professor Gerard Wong.

Inside the cell, mitochondria resemble the long balloons used to create balloon animals. If the mitochondria are too long, they can get tangled. Their sizes are known to be primarily regulated by two proteins, one of which breaks up longer mitochondria into smaller sizes. They are known as cells’ “powerhouses” as they convert chemical energy from food into a form useful for cells to perform all their functions.

Keeping mitochondria at optimal sizes is important to cells’ health. An insufficient amount of the regulating protein, known as Dnm1, results in the mitochondria getting too long and tangled. Too much Dnm1 results in too many short mitochondria. In both cases, the mitochondria are rendered essentially ineffective as power providers for the cell. This situation could lead to neurodevelopmental disorders or neurodegenerative diseases, such as Alzheimer’s or Parkinson’s.

To better understand this mechanism, the researchers used a machine-learning approach they developed in 2016 to figure out exactly how the proteins break up one mitrochondrion into two smaller ones. They also used a powerful technique called “synchrotron small-angle X-ray scattering” at the Stanford Synchrotron Radiation Lightsource, a U.S. Department of Energy research facility, to see how these proteins deform mitochondrial membranes during this process.

SLAC/SSRL

Before this study, it was thought that these proteins encircled the mitochondria, then cut it in two by simply squeezing tightly. The process, the team discovered, is more subtle.

“When Dnm1 wraps around mitochondria, it has been previously shown that the protein physically tightens and pinches,” said Michelle Lee, a recent UCLA bioengineering doctoral graduate who was advised by Wong and is one of two lead authors of the study. “What we found is that when Dnm1 contacts the mitochondrial surface, it also makes that area of the mitochondrion itself more moldable and easier to undergo cleavage. These two effects work hand in hand to make the process of mitochondrial division efficient.”

The other lead author is Ernest Lee, a graduate student in the UCLA-Caltech Medical Scientist Training Program and a bioengineering graduate student also advised by Wong. He carried out the computational analyses for the experiment.

“Using our machine-learning tool, we were able to discover hidden membrane-remodeling activity in Dnm1, consistent with our X-ray studies,” Lee said. “Interestingly, by analyzing distant relatives of Dnm1, we found that the protein gradually evolved this ability over time.”

“This is a very unexpected result — no one thought these molecules would have a split personality, with both personalities necessary for the biological function,” said Wong, who is also a UCLA professor of chemistry and biochemistry and is a member of the California NanoSystems Institute. “The multifunctional behavior we identified may be the rule rather than the exception for proteins.”

Other authors include Andy Ferguson from the University of Illinois at Urbana-Champaign and Blake Hill from the Medical College of Wisconsin.

The research was supported by the National Science Foundation and the National Institutes of Health, with additional support from the Department of Energy for imaging experiments.

See the full article here .

Please help promote STEM in your local schools.

Stem Education Coalition

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.