Amino acid folding game

How much retention of participants are there? It might not matter in this case, but good citizen science projects often have a strong community of participants and those participants spend a good amount of time collecting the data in this case, folding proteins and getting high scores! Which leads me to how the citizen scientists benefit from this game: How much competition, enjoyment, and reward exist from this game?

I watched a Youtube Video , albeit outdated, that showed that there is a score board showing the players and score as a follow-up to this story.

But if I were, I would consider using this in a classroom laboratory setting to teach students how protein synthesis works from the chain of animo acids to, where it shines best, the 3D modelling of those amino acid chains. Really cool! In regards to recruitment for the game, based on my understanding from this publication and previous ones, rather than going out and actively recruiting players, the team is more focused on making the game accessible for anyone who comes by interested in playing.

He mentions that players can be motivated by a sense of purpose of contributing to science this work is one example! The game is also designed to encourage competition through their scoring system, but also teamwork and community through social interactions on forums, which I can imagine also helps to retain players.

This is so cool! I wish I had known about this game when I was learning about amino acids and protein folding. Reading about the one gamer-created fold that had not been observed in nature, makes me wonder if that fold is out there and not yet discovered. If so, this game is even a potential predictor of new protein folds.

I also wonder if players of this game could help researchers using directed evolution to more quickly develop better proteins for various purposes. If so, I hope those researchers know of this game as a potential resource. Cori's work determined glycogen storage "disease" had several subtypes, each with a unique molecular cause. Maggie Chen , Harvard University. June 1, Marnie Willman , University of Manitoba Bannatyne.

July 9, Elise Cutts , Massachusetts Institute of Technology. July 30, Shi En Kim , University of Chicago. May 19, Cassie Freund , Wake Forest University. January 26, Lauren Gandy , Rensselaer Polytechnic Institute. January 9, September 9, Akshata R. Naik , Wayne State University. March 4, May 23, Abdullah Asad Iqbal , University of Leeds. February 25, Ankita Arora , University of Colorado. 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.

Proteins are the building blocks of life, responsible for most of what happens inside cells. 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. But, over the past decade, cryo-EM has become the favoured tool of many structural-biology labs. Early attempts to use computers to predict protein structures in the s and s 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.

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.

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. CASP takes place over several months. Target proteins or portions of proteins called domains — about 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 computational protein designers. Some predictions were better than others, but nearly two-thirds were comparable in quality to experimental structures. The network also struggles to model individual structures in protein complexes, or groups, whereby interactions with other proteins distort their shapes. There are 20 different kinds of amino acids, which differ from one another based on what atoms are in their sidechains.

These 20 amino acids fall into different groups based on their chemical properties: acidic or alkaline, hydrophilic water-loving or hydrophobic greasy. What shape will a protein fold into? Even though proteins are just a long chain of amino acids, they don't like to stay stretched out in a straight line. The protein folds up to make a compact blob, but as it does, it keeps some amino acids near the center of the blob, and others outside; and it keeps some pairs of amino acids close together and others far apart.

Every kind of protein folds up into a very specific shape -- the same shape every time. Most proteins do this all by themselves, although some need extra help to fold into the right shape. The unique shape of a particular protein is the most stable state it can adopt. Picture a ball at the top of a hill -- the ball will always roll down to the bottom. If you try to put the ball back on top it will still roll down to the bottom of the hill because that is where it is most stable. Why is shape important?

This structure specifies the function of the protein. For example, a protein that breaks down glucose so the cell can use the energy stored in the sugar will have a shape that recognizes the glucose and binds to it like a lock and key and chemically reactive amino acids that will react with the glucose and break it down to release the energy.

What do proteins do? Proteins are involved in almost all of the processes going on inside your body: they break down food to power your muscles, send signals through your brain that control the body, and transport nutrients through your blood.

Many proteins act as enzymes, meaning they catalyze speed up chemical reactions that wouldn't take place otherwise. But other proteins power muscle contractions, or act as chemical messages inside the body, or hundreds of other things. Here's a small sample of what proteins do:. Proteins are present in all living things, even plants, bacteria, and viruses.

Some organisms have proteins that give them their special characteristics:. You can find more information on the rules of protein folding in our FAQ. With all the things proteins do to keep our bodies functioning and healthy, they can be involved in disease in many different ways.

The more we know about how certain proteins fold, the better new proteins we can design to combat the disease-related proteins and cure the diseases. Below, we list three diseases that represent different ways that proteins can be involved in disease. Proteins are found in all living things, including plants. Certain types of plants are grown and converted to biofuel, but the conversion process is not as fast and efficient as it could be.

A critical step in turning plants into fuel is breaking down the plant material, which is currently done by microbial enzymes proteins called "cellulases". Perhaps we can find new proteins to do it better. If this turns out to be true, we can then teach human strategies to computers and fold proteins faster than ever! You can find more information about the goals of the project in our FAQ. Josh A. Christoffer Norn, Basile I.

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