What is a Protein? A Biologist Explains

Nathan Ahlgren wrote . . . . . . . . .

What is a protein?

A protein is a basic structure that is found in all of life. It’s a molecule. And the key thing about a protein is it’s made up of smaller components, called amino acids. I like to think of them as a string of different colored beads. Each bead would represent an amino acid, which are smaller molecules containing carbon, oxygen, hydrogen and sometimes sulfur atoms. So a protein essentially is a string that’s made up of these little individual amino acids. There are 22 different amino acids that you can combine in any kind of different way.

A protein doesn’t usually exist as a string, but actually folds up into a particular shape, depending on the order and how those different amino acids interact together. That shape influences what the protein does in our body.

Where do the amino acids come from?

The amino acids in our body come from the food we eat. We also make them in our body. For example, other animals make proteins and we eat those. Our bodies take that chain and break it down into the individual amino acids. Then it can remake them into any protein that we need.

Once the proteins are broken down into amino acids in the digestive system, they are taken to our cells and kind of float around inside the cell, as those little individual beads in our analogy. And then inside the cell, your body basically connects them together to make the proteins that your body needs to make.

We can make about half of the amino acids we need on our own, but we have to get the others from our food.

What do proteins do in our body?

Scientists are not exactly sure, but most agree that there are around 20,000 different proteins in our body. Some studies suggest that there might be even more. They carry out a variety of functions from doing some metabolic conversions to holding your cells together to causing your muscles to work.

Their functions fall into a few broad categories. One is structural. Your body is made up of many different kinds of structures – think of stringlike structures, globules, anchors, etc. They form the stuff that holds your body together. Collagen is a protein that gives structure to your skin, bones and even teeth. Integrin is a protein that makes flexible linkages between your cells. Your hair and nails are made of a protein called keratin.

Another big role that they take on is biochemistry – how your body carries out particular reactions in your cell, like breaking down fat or amino acids. Remember when I said our body breaks down the protein from the food that we eat? Even that function is carried out by proteins like pepsin. Another example is hemoglobin – the protein that carries oxygen around in your blood. So they’re carrying out these special chemical reactions inside yourself.

Proteins can also process signals and information, like circadian clock proteins which keep time in our cells, but those are a few main categories of functions that proteins carry out in the cell.

Why is protein often associated with muscles and meat?

Different types of foods have different kinds of protein content. There are a lot of carbohydrates in plants like wheat and rice, but they are less rich in protein content. But meat in general has more protein content. A lot of protein is required to make the muscles in your body. That’s why protein is often associated with eating meat and building muscle, but proteins are really involved in much, much more than that.

Source: The Conversation

DeepMind’s AI Makes Gigantic Leap in Solving Protein Structures

Ewen Callaway wrote . . . . . . . . .

An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology’s grandest challenges — determining a protein’s 3D shape from its amino-acid sequence.

DeepMind’s program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference — held virtually this year — that takes stock of the exercise.

“This is a big deal,” says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. “In some sense the problem is solved.”

AI protein-folding algorithms solve structures faster than ever

The ability to accurately predict protein structures from their amino-acid sequence would be a huge boon to life sciences and medicine. It would vastly accelerate efforts to understand the building blocks of cells and enable quicker and more advanced drug discovery.

AlphaFold came top of the table at the last CASP — in 2018, the first year that London-based DeepMind participated. But, this year, the outfit’s deep-learning network was head-and-shoulders above other teams and, say scientists, performed so mind-bogglingly well that it could herald a revolution in biology.

“It’s a game changer,” says Andrei Lupas, an evolutionary biologist at the Max Planck Institute for Developmental Biology in Tübingen, Germany, who assessed the performance of different teams in CASP. AlphaFold has already helped him find the structure of a protein that has vexed his lab for a decade, and he expects it will alter how he works and the questions he tackles. “This will change medicine. It will change research. It will change bioengineering. It will change everything,” Lupas adds.

In some cases, AlphaFold’s structure predictions were indistinguishable from those determined using ‘gold standard’ experimental methods such as X-ray crystallography and, in recent years, cryo-electron microscopy (cryo-EM). AlphaFold might not obviate the need for these laborious and expensive methods — yet — say scientists, but the AI will make it possible to study living things in new ways.

The structure problem

Proteins are the building blocks of life, responsible for most of what happens inside cells. How a protein works and what it does is determined by its 3D shape — ‘structure is function’ is an axiom of molecular biology. Proteins tend to adopt their shape without help, guided only by the laws of physics.

For decades, laboratory experiments have been the main way to get good protein structures. The first complete structures of proteins were determined, starting in the 1950s, using a technique in which X-ray beams are fired at crystallized proteins and the diffracted light translated into a protein’s atomic coordinates. X-ray crystallography has produced the lion’s share of protein structures. But, over the past decade, cryo-EM has become the favoured tool of many structural-biology labs.

Scientists have long wondered how a protein’s constituent parts — a string of different amino acids — map out the many twists and folds of its eventual shape. Early attempts to use computers to predict protein structures in the 1980s and 1990s performed poorly, say researchers. Lofty claims for methods in published papers tended to disintegrate when other scientists applied them to other proteins.

Moult started CASP to bring more rigour to these efforts. The event challenges teams to predict the structures of proteins that have been solved using experimental methods, but for which the structures have not been made public. Moult credits the experiment — he doesn’t call it a competition — with vastly improving the field, by calling time on overhyped claims. “You’re really finding out what looks promising, what works, and what you should walk away from,” he says.

DeepMind’s 2018 performance at CASP13 startled many scientists in the field, which has long been the bastion of small academic groups. But its approach was broadly similar to those of other teams that were applying AI, says Jinbo Xu, a computational biologist at the University of Chicago, Illinois.

The first iteration of AlphaFold applied the AI method known as deep learning to structural and genetic data to predict the distance between pairs of amino acids in a protein. In a second step that does not invoke AI, AlphaFold uses this information to come up with a ‘consensus’ model of what the protein should look like, says John Jumper at DeepMind, who is leading the project.

The team tried to build on that approach but eventually hit the wall. So it changed tack, says Jumper, and developed an AI network that incorporated additional information about the physical and geometric constraints that determine how a protein folds. They also set it a more difficult, task: instead of predicting relationships between amino acids, the network predicts the final structure of a target protein sequence. “It’s a more complex system by quite a bit,” Jumper says.

Startling accuracy

CASP takes place over several months. Target proteins or portions of proteins called domains — about 100 in total — are released on a regular basis and teams have several weeks to submit their structure predictions. A team of independent scientists then assesses the predictions using metrics that gauge how similar a predicted protein is to the experimentally determined structure. The assessors don’t know who is making a prediction.

AlphaFold’s predictions arrived under the name ‘group 427’, but the startling accuracy of many of its entries made them stand out, says Lupas. “I had guessed it was AlphaFold. Most people had,” he says.

Some predictions were better than others, but nearly two-thirds were comparable in quality to experimental structures. In some cases, says Moult, it was not clear whether the discrepancy between AlphaFold’s predictions and the experimental result was a prediction error or an artefact of the experiment.

AlphaFold’s predictions were poor matches to experimental structures determined by a technique called nuclear magnetic resonance spectroscopy, but this could be down to how the raw data is converted into a model, says Moult. The network also struggles to model individual structures in protein complexes, or groups, whereby interactions with other proteins distort their shapes.

Overall, teams predicted structures more accurately this year, compared with the last CASP, but much of the progress can be attributed to AlphaFold, says Moult. On protein targets considered to be moderately difficult, the best performances of other teams typically scored 75 on a 100-point scale of prediction accuracy, whereas AlphaFold scored around 90 on the same targets, says Moult.

About half of the teams mentioned ‘deep learning’ in the abstract summarizing their approach, Moult says, suggesting that AI is making a broad impact on the field. Most of these were from academic teams, but Microsoft and the Chinese technology company Tencent also entered CASP14.

Mohammed AlQuraishi, a computational biologist at Columbia University in New York City and a CASP participant, is eager to dig into the details of AlphaFold’s performance at the contest, and learn more about how the system works when the DeepMind team presents its approach on 1 December. It’s possible — but unlikely, he says — that an easier-than-usual crop of protein targets contributed to the performance. AlQuraishi’s strong hunch is that AlphaFold will be transformational.

“I think it’s fair to say this will be very disruptive to the protein-structure-prediction field. I suspect many will leave the field as the core problem has arguably been solved,” he says. “It’s a breakthrough of the first order, certainly one of the most significant scientific results of my lifetime.”

Demis Hassabis, DeepMind’s chief executive, says that the company is learning what biologists want from AlphaFold.Credit: OLI SCARFF/AFP/Getty

Faster structures

An AlphaFold prediction helped to determine the structure of a bacterial protein that Lupas’s lab has been trying to crack for years. Lupas’s team had previously collected raw X-ray diffraction data, but transforming these Rorschach-like patterns into a structure requires some information about the shape of the protein. Tricks for getting this information, as well as other prediction tools, had failed. “The model from group 427 gave us our structure in half an hour, after we had spent a decade trying everything,” Lupas says.

Demis Hassabis, DeepMind’s co-founder and chief executive, says that the company plans to make AlphaFold useful so other scientists can employ it. (It previously published enough details about the first version of AlphaFold for other scientists to replicate the approach.) It can take AlphaFold days to come up with a predicted structure, which includes estimates on the reliability of different regions of the protein. “We’re just starting to understand what biologists would want,” adds Hassabis, who sees drug discovery and protein design as potential applications.

In early 2020, the company released predictions of the structures of a handful of SARS-CoV-2 proteins that hadn’t yet been determined experimentally. DeepMind’s predictions for a protein called Orf3a ended up being very similar to one later determined through cryo-EM, says Stephen Brohawn, a molecular neurobiologist at the University of California, Berkeley, whose team released the structure in June. “What they have been able to do is very impressive,” he adds.

Real-world impact

AlphaFold is unlikely to shutter labs, such as Brohawn’s, that use experimental methods to solve protein structures. But it could mean that lower-quality and easier-to-collect experimental data would be all that’s needed to get a good structure. Some applications, such as the evolutionary analysis of proteins, are set to flourish because the tsunami of available genomic data might now be reliably translated into structures. “This is going to empower a new generation of molecular biologists to ask more advanced questions,” says Lupas. “It’s going to require more thinking and less pipetting.”

“This is a problem that I was beginning to think would not get solved in my lifetime,” says Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute in Hinxton, UK, and a past CASP assessor. She hopes the approach could help to illuminate the function of the thousands of unsolved proteins in the human genome, and make sense of disease-causing gene variations that differ between people.

AlphaFold’s performance also marks a turning point for DeepMind. The company is best known for wielding AI to master games such Go, but its long-term goal is to develop programs capable of achieving broad, human-like intelligence. Tackling grand scientific challenges, such as protein-structure prediction, is one of the most important applications its AI can make, Hassabis says. “I do think it’s the most significant thing we’ve done, in terms of real-world impact.”

Source : Nature

Researchers Identify a New Source of Protein for Humans

Rapeseed has the potential to replace soy as the best plant-based source of protein for humans. In a current study, nutrition scientists at the Martin Luther University Halle-Wittenberg (MLU), found that rapeseed protein consumption has comparable beneficial effects on human metabolism as soy protein. The glucose metabolism and satiety were even better. Another advantage: The proteins can be obtained from the by-products of rapeseed oil production. The study was published in the journal “Nutrients”.

For a balanced and healthy diet, humans need protein. “It contains essential amino acids which can not be synthesized in the body,” says Professor Gabriele Stangl from the Institute of Agricultural and Nutritional Sciences at MLU. Meat and fish are important sources of high-quality proteins. However, certain plants can also provide valuable proteins. “Soy is generally considered the best source of plant protein as it contains a particularly beneficial composition of amino acids,” says Stangl.

Her team investigated whether rapeseed, which has a comparably beneficial composition of amino acids, could be an alternative to soy. Rapeseed also contains phytochemicals – chemical compounds produced by plants – which could have beneficial effects on health, says Stangl. “So far, only a few data on the effect of rapeseed protein intake in humans had been available,” adds the scientist. In comparison to soy rapeseed has several other advantages: It is already being cultivated in Europe and the protein-rich by-products of the rapeseed oil production could be used as ingredients for new food products. These by-products are currently used exclusively for animal feed.

In a study with 20 participants, the team investigated the effect of ingested rapeseed and soy proteins on human metabolism. Before the interventions the participants were asked to document their diets for a few days. Then they were invited to eat a specifically prepared meal on three separate days: noodles with tomato sauce, that either contained no additional protein, or was enriched with soy or rapeseed protein. After the meal, blood was regularly drawn from the participants over a six-hour period. “By using this study design, we were able to assess the acute metabolic response of each study participants to the dietary treatments.” says Stangl.

The study showed: “The rapeseed protein induced comparable effects on metabolic parameters and cardiovascular risk factors as soy protein. Rapeseed even produced a slightly more beneficial insulin response in the body,” says nutritionist Christin Volk from MLU. Another benefit was that the participants had a longer feeling of satiety after eating the rapeseed protein. “To conclude, rapeseed appears to be a valuable alternative to soy in the human diet,” says Volk.

The only drawback: “Rapeseed protein, in contrast to soy protein, has a mustard flavour,” says Volk. Therefore, rapeseed is more suitable for the production of savoury foods rather than sweet foods, explains the researcher.

Source: Martin Luther University Halle-Wittenberg

When It Comes to Healthy Protein, Fish is the Dish

When it comes to healthy sources of protein, fish is the dish. An entrée like grilled white fish with avocado relish is tasty, easy to cook and good for your heart.

“We recommend people to eat fish at least two times a week. It makes for a satisfying entrée that’s relatively low in saturated fat compared to something like a hamburger or quiche,” said Alice H. Lichtenstein, senior scientist and director of the Cardiovascular Nutrition Laboratory at Tufts University in Boston.

Each serving of this grilled white fish recipe boasts 21 grams of protein. Fish also is a good source of omega-3 fatty acids, which promotes heart health. If you want to up your omega-3 intake, substitute salmon for white fish in this recipe, Lichtenstein said.

The mix of avocado, pineapple, red onion and cilantro gives the dish a dash of color and flavor, and provides dietary fiber. “It looks good from a visual perspective as well as a health perspective,” she said.

One more attractive thing is fish fillets are fairly easy and quick to cook.

“Fish doesn’t require long cooking times, and there are a lot of different ways to prepare it,” Lichtenstein said. “Fish is very flexible when it comes to preparation techniques and combination with other ingredients.”

Source: American Heart Association


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How Protein Protects Against Fatty Liver

Non-alcoholic fatty liver disease is the most common chronic liver disease in the world, with sometimes life-threatening consequences. A high-protein, calorie-reduced diet can cause the harmful liver fat to melt away – more effectively than a low-protein diet. A new study by DIfE/DZD researchers published in the journal ‘Liver International’ shows which molecular and physiological processes are potentially involved.

Microscopic images of liver biopsies. Left: healthy liver with normal accumulation of fat, right: non-alcoholic fatty liver disease with increased accumulation of fat. Source: DIfE

Causes and consequences of a non-alcoholic fatty liver

Non-alcoholic fatty liver disease is characterized by a build-up of fat in the liver and is often associated with obesity, type 2 diabetes, high blood pressure and lipid disorders. If left untreated, fatty liver can lead to cirrhosis with life-threatening consequences. The causes of the disease range from an unhealthy lifestyle – that is, eating too many high-fat, high-sugar foods and lack of exercise – to genetic components. Already in previous studies, the research team led by PD Dr. Olga Ramich and Professor Andreas Pfeiffer from the German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) observed a positive effect of a high-protein diet on liver fat content. “The new results now give us deeper insights into how the high-protein diet works,” said Ramich, head of the research group Molecular Nutritional Medicine at DIfE.

High-protein diet is more effective than low-protein diet

For the current study, the research team led by Ramich and Pfeiffer investigated how the protein content of food influences the amount of liver fat in obese people with a non-alcoholic fatty liver. For this, the 19 participants were to follow either a diet with a high or low protein content for three weeks. Subsequently, surgery to treat obesity (bariatric surgery) was carried out and liver samples were collected.

Analysis of the samples showed that a calorie-reduced, high-protein diet decreased liver fat more effectively than a calorie-reduced, low-protein diet: while the liver-fat content in the high-protein group decreased by around 40 percent, the amount of fat in the liver samples of the low-protein group remained unchanged. The study participants in both groups lost a total of around five kilograms. “If the results continue to be confirmed in larger studies, the recommendation for an increased intake of protein together with a healthy low-fat diet as part of an effective fatty liver therapy could find its way into medical practice,” said Andreas Pfeiffer, head of the Research Group Clinical Nutrition/DZD at DIfE and the Clinic for Endocrinology in the Charité ─ Universitätsmedizin Berlin, Campus Benjamin Franklin.

Molecular fat absorption mechanisms

The researchers assume that the positive effect of the high-protein diet is mainly due to the fact that the uptake, storage and synthesis of fat is suppressed. This is indicated by extensive genetic analyses of the liver samples that Professor Stephan Herzig and his team at Helmholtz Zentrum München conducted. According to these analyses, numerous genes that are responsible for the absorption, storage and synthesis of fat in the liver were less active after the high-protein diet than after the low-protein diet.

Unexpected results

In addition, Olga Ramich’s research group, together with the Department of Physiology of Energy Metabolism at DIfE, also investigated the functions of the mitochondria. “Mitochondrial activity was very similar in both groups. That surprised us. We originally assumed that the high-protein diet would increase mitochondrial activity and thus contribute to the degradation of liver fat,” said Department Head Professor Susanne Klaus. The researchers were also surprised that the serum levels of Fibroblast Growth Factor 21 (FGF21) were lower after the high-protein diet which reduced liver fat than after the low-protein diet. “FGF21 is known to have beneficial effects on metabolic regulation. Further studies will be necessary to show why the factor was reduced in the actually positively acting high-protein diet,” said Ramich. Furthermore, autophagy activity was lower in liver tissue after the high-protein diet compared to the low-protein diet. “Lipid degradation via ‘lipophagy’, as a special form of autophagy, therefore does not appear to be involved in the breakdown of liver fat in the high-protein diet.”

As a next step, Ramich and Pfeiffer intend to follow up their findings about the mechanisms involved and thus gain new insights into the mode of action of targeted dietary intervention strategies.

Source: DZD


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