AI Solves 50-Year-Old Biology 'Grand Challenge' Decades Before Experts Predicted - ScienceAlert

A long-standing and incredibly complex scientific problem concerning the structure and behaviour of proteins has been effectively solved by a new artificial intelligence (AI) system, scientists report.

DeepMind, the UK-based AI company, has wowed us for years with its parade of ever-advancing neural networks that continually trounce humans at complex games such as chess and Go.

All those incremental advancements were about much more than mastering recreational diversions, however.

In the background, DeepMind's researchers were seeking to coax their AIs towards solving much more fundamentally important scientific puzzles – such as finding new ways to fight disease by predicting infinitesimal but vitally important aspects of human biology.

Now, with the latest version of their AlphaFold AI engine, they seem to have actually achieved this very ambitious goal – or at least gotten us closer than scientists ever have before.

For about 50 years, researchers have strived to predict how proteins achieve their three-dimensional structure, and it's not an easy problem to solve.

The astronomical number of potential configurations is so mind-bogglingly huge, in fact, that researchers postulated it would take longer than the age of the Universe to sample all the possible molecular arrangements.

Nonetheless, if we can solve this puzzle – known as the protein-folding problem – it would constitute a giant breakthrough in scientific capabilities, vastly accelerating research endeavours in things like drug discovery and modelling disease, and also leading to new applications far beyond health.

For that reason, despite the scale of the challenge, for decades researchers have been collaborating to make gains in developing solutions to the protein-folding problem.

A rigorous experiment called CASP (Critical Assessment of protein Structure Prediction) began in the 1990s, challenging scientists to devise systems capable of predicting the esoteric enigmas of protein folding.

Now, in its third decade, the CASP experiment looks to have produced its most promising solution yet – with DeepMind's AlphaFold delivering predictions of 3D protein structures with unprecedented accuracy.

"We have been stuck on this one problem – how do proteins fold up – for nearly 50 years," says CASP co-founder John Moult from the University of Maryland.

"To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts wondering if we'd ever get there, is a very special moment."

In the experiment, DeepMind used a new deep learning architecture for AlphaFold that was able to interpret and compute the 'spatial graph' of 3D proteins, predicting the molecular structure underpinning their folded configuration.

The system, which was trained up by analysing a databank of approximately 170,000 protein structures, brought its unique skillset to this year's CASP challenge, called CASP14, achieving a median score in its predictions of 92.4 GDT (Global Distance Test).

That's above the ~90 GDT threshold that's generally considered to be competitive with the same results obtained via experimental methods, and DeepMind says its predictions are only off by about 1.6 angstroms on average (about the width of an atom).

"I nearly fell off my chair when I saw these results," says genomics researcher Ewan Birney from the European Molecular Biology Laboratory.

"I know how rigorous CASP is – it basically ensures that computational modelling must perform on the challenging task of ab initio protein folding. It was humbling to see that these models could do that so accurately. There will be many aspects to understand but this is a huge advance for science."

It's worth noting that the research has not yet been peer-reviewed, nor published in a scientific journal (although DeepMind's researchers say that's on the way).

Even so, experts who are familiar with the field are already recognising and applauding the breakthrough, even if the full report and detailed results are yet to be seen.

"This computational work represents a stunning advance on the protein-folding problem, a 50-year old grand challenge in biology," says structural biologist Venki Ramakrishnan, president of the Royal Society.

"It has occurred decades before many people in the field would have predicted."

The full findings are not yet published, but you can see the abstract for the research, "High Accuracy Protein Structure Prediction Using Deep Learning", here, and find more information on CASP14 here.

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2020-12-01 02:03:06Z
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How to see a mysterious object that might be space junk fly near Earth today - CNET

This photo from 1964 shows a Centaur upper-stage rocket. Space object 2020 SO might be one of these.

NASA

The moon shouldn't feel too jealous. Earth has another satellite right now, but it's only a temporary fling. The exact identity of the object, named 2020 SO, is still a lingering question, but you can watch it on Monday, Nov. 30, when it gets close to Earth. The Virtual Telescope Project will livestream the flyby.  

The Earth's gravitational pull captured the object into our planet's orbit earlier this month, which makes 2020 SO a sort of mini-moon. 

Usually, we'd expect an object like this to be an asteroid, and there are plenty of those flying around in space. But 2020 SO may have a more Earthly identity. The orbit of 2020 SO around the sun -- which is very similar to Earth's -- has convinced researchers it's probably not a rock, but is actually a piece of space junk from a NASA mission.      

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The object's closest approach to our planet will be on Dec. 1. The Virtual Telescope Project will offer a livestream starting at 2 p.m. PT on Nov. 30. 

Virtual Telescope Project founder Gianluca Masi already managed to capture a view of the tiny object on Nov. 22. It appears as a dot against a backdrop of stars.

The Virtual Telescope Project caught sight of 2020 SO on Nov. 22. The arrow points out the object.

Gianluca Masi/Virtual Telescope Project

Scientists with NASA JPL's Center for Near-Earth Object Studies (CNEOS) analyzed 2020 SO's path and tracked it back in time.  

"One of the possible paths for 2020 SO brought the object very close to Earth and the Moon in late September 1966," CNEOS Director Paul Chodas said in a NASA statement earlier in November. "It was like a eureka moment when a quick check of launch dates for lunar missions showed a match with the Surveyor 2 mission."

NASA's ill-fated Surveyor 2 lander ended up crashing on the moon's surface, but the Centaur rocket booster escaped into space.   

NASA expects 2020 SO to stick around in an Earth orbit until March 2021 when it will wander off into a new orbit around the sun. The agency's Planetary Defense Coordination Office shared a visual of the object's journey around Earth.

The upcoming close approach should give astronomers a chance to dial in 2020 SO's composition and tell us if it is indeed a relic from the 1960s.

Even with a telescope view, 2020 SO should look like a bright spot of light traveling against the dark of space. The cool thing is getting the chance to witness a piece of space history returning to its old stomping grounds.  

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2020-12-01 00:09:00Z
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Scientists discover 'beautiful and unique' gelatinous sea animal - CNET

Researchers described the newly discovered Duobrachium sparksae based on high-definition video footage alone.

NOAA

Scientists like to get their hands on things when possible, especially when describing new animal species. A team of National Oceanic and Atmospheric Administration researchers, however, embraced a hands-off approach when it came to a surprising underwater find: a new species of comb jelly.

"It's unique because we were able to describe a new species based entirely on high-definition video," said NOAA Fisheries scientist Allen Collins in a statement Nov. 20.

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The NOAA team named the translucent animal Duobrachium sparksae. It's a ctenophore, popularly known as a comb jelly. Not to be confused with jellyfish, comb jellies are ethereal, gelatinous and carnivorous denizens of the deep. 

The new species dwells off the coast of Puerto Rico and was first spotted in video footage captured by NOAA's Deep Discoverer remotely operated vehicle in 2015. NOAA released a stunning video showing the comb jelly floating above the seafloor.

Collins described it as looking like a party balloon. The researchers used a laser system to measure the animal, which clocked in at about 2.3 inches (6 centimeters) tall, not counting the long dangly tentacles.

NOAA Fisheries scientists knew right away the creature was something unusual, but it takes time to do the legwork to declare a new species. The team published a paper describing Duobrachium sparksae this month in the journal Plankton and Benthos Research.

"It was a beautiful and unique organism," said lead author Mike Ford, a NOAA Fisheries scientist. The tentacles appeared to touch the seafloor, but it's unclear if it was somehow anchored to the bottom. 

The researchers spotted three individuals, but much remains unknown about the animals. The scientists still hope to collect an actual sample of the comb jelly to fill in some of the blanks. Until then, we can enjoy the mesmerizing video of an underwater marvel.  

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2020-11-30 22:44:14Z
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DeepMind's latest AI breakthrough can accurately predict the way proteins fold - Engadget

Alphabet-owned DeepMind may be best known for building the AI that beat a world-class Go player, but the company announced another, perhaps more vital breakthrough this morning. As part of its work for the 14th Critical Assessment of Protein Structure Prediction, or CASP, DeepMind's AlphaFold 2 AI has shown it can guess how certain proteins will fold themselves with surprising accuracy. In some cases, the results were perceived to be "competitive" with actual, experimental data.

"We have been stuck on this one problem – how do proteins fold up – for nearly 50 years," said Professor John Moult, CASP chair and co-founder, in a DeepMind blog post. "To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment."

Researchers and enthusiasts across the internet have met the news enthusiastically, with some proclaiming that AlphaFold has solved the "protein solving problem." But what does that mean, exactly? And how do we stand to benefit from it?

To start answering these questions, we need to take a closer look at the proteins themselves. As your biology teacher might have said, proteins are the building blocks of life, responsible for countless functions inside and outside the human body. Each one starts as a series of amino acids strung together into a chain, but it doesn't take long -- sometimes just milliseconds  -- before things start to get complicated. Some parts of the amino acid chain twist into helixes. Others fold back onto themselves as "sheets". Before long, these helixes and sheets coalesce and contort into a protein's final structure, and that's what gives a protein the ability to perform specific tasks, like ferrying oxygen through your body or strengthening the structure of your bones. 

In other words, shape is everything, and researchers have spent decades trying to find a way to determine a protein’s final, folded structure based solely on the amino acids that make up its backbone. That’s where CASP comes in -- since 1994, the program has served as a focal point of sorts for teams around the world working to crack the protein solving problem with computational ingenuity. The rules are fairly simple: Every other year, organizers select a series of target proteins from a bevy of submissions whose structures have been determined experimentally, but haven’t been published yet. Researchers then get a few months to tune their systems and make their predictions, which are then judged by experts in the field for about a month after submissions are closed. 

While CASP has been running for 26 years, it’s been in the past few that the scientific community has been able to bring quantum leaps in compute power and machine learning to bear on the challenge. In DeepMind’s case, that involved training AlphaFold 2’s prediction model on about 170,000 known protein structures, along with a vast number of protein sequences whose 3D structures haven’t yet been determined. This testing data, the team admits, is fairly similar to what it used in 2018, when the original AlphaFold system achieved top marks during CASP 13. (At the time, organizers hailed DeepMind’s “unprecedented progress in the ability of computational methods to predict protein structure.”) 

That said, the team made some notable changes to its machine learning approach -- they haven’t published a full paper yet, but the CASP 14 abstract book highlights some of their modifications. And beyond that, DeepMind also relied on about 128 of Google’s cloud-based TPUv3 cores, which ultimately gave AlphaFold 2 the ability to accurately determine a protein’s structure within just days, if not sooner -- the New York Times notes that, in some cases, predictions can be generated in a matter of hours. 

DeepMind

This all sounds impressive -- and it is, certainly -- but there’s still plenty of work to be done. On the whole, AlphaFold’s results represented a dramatic improvement in accuracy compared to past years, and as mentioned, some of DeepMind’s predictions were accurate enough to rival experimental results at an atomic level. Others, however, fell short of that threshold. The company notes that “for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT” -- that’s just shy of the 90 GDT metric CASP co-founder Moult uses as the barrier for calling results “competitive” with real data. Put another way, DeepMind hasn’t fully solved the protein solving problem, but it’s getting closer than many had thought possible. 

As DeepMind’s work continues, we’ll start to see the full extent of accurate protein prediction take shape -- for now, the jury still seems out on what practical benefits we could expect to see in the short term. The company points to potential advances in sustainability and drug design as a result of its protein folding research, though it didn’t elaborate on specifics. Meanwhile, Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute, told Nature that she hopes this leap in accuracy could shed light on the functions of “thousands” of unsolved proteins at work in the human body. If nothing else, though, researchers could be looking at a glut of new protein structure data to investigate, test against, and work backward from -- that’s worth celebrating, even if we don’t know how it’ll be used yet.

In this article: science, ai, machine learning, deep learning, deepmind, alphafold, alphafold 2, protein, protein folding, genomics, news, tomorrow

All products recommended by Engadget are selected by our editorial team, independent of our parent company. Some of our stories include affiliate links. If you buy something through one of these links, we may earn an affiliate commission.

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2020-11-30 20:33:33Z
52781217477573

DeepMind's latest AI breakthrough can accurately predict the way proteins fold - Engadget

Alphabet-owned DeepMind may be best known for building the AI that beat a world-class Go player, but the company announced another, perhaps more vital breakthrough this morning. As part of its work for the 14th Critical Assessment of Protein Structure Prediction, or CASP, DeepMind's AlphaFold 2 AI has shown it can guess how certain proteins will fold themselves with surprising accuracy. In some cases, the results were perceived to be "competitive" with actual, experimental data.

"We have been stuck on this one problem – how do proteins fold up – for nearly 50 years," said Professor John Moult, CASP chair and co-founder, in a DeepMind blog post. "To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment."

Researchers and enthusiasts across the internet have met the news enthusiastically, with some proclaiming that AlphaFold has solved the "protein solving problem." But what does that mean, exactly? And how do we stand to benefit from it?

To start answering these questions, we need to take a closer look at the proteins themselves. As your biology teacher might have said, proteins are the building blocks of life, responsible for countless functions inside and outside the human body. Each one starts as a series of amino acids strung together into a chain, but it doesn't take long -- sometimes just milliseconds  -- before things start to get complicated. Some parts of the amino acid chain twist into helixes. Others fold back onto themselves as "sheets". Before long, these helixes and sheets coalesce and contort into a protein's final structure, and that's what gives a protein the ability to perform specific tasks, like ferrying oxygen through your body or strengthening the structure of your bones. 

In other words, shape is everything, and researchers have spent decades trying to find a way to determine a protein’s final, folded structure based solely on the amino acids that make up its backbone. That’s where CASP comes in -- since 1994, the program has served as a focal point of sorts for teams around the world working to crack the protein solving problem with computational ingenuity. The rules are fairly simple: Every other year, organizers select a series of target proteins from a bevy of submissions whose structures have been determined experimentally, but haven’t been published yet. Researchers then get a few months to tune their systems and make their predictions, which are then judged by experts in the field for about a month after submissions are closed. 

While CASP has been running for 26 years, it’s been in the past few that the scientific community has been able to bring quantum leaps in compute power and machine learning to bear on the challenge. In DeepMind’s case, that involved training AlphaFold 2’s prediction model on about 170,000 known protein structures, along with a vast number of protein sequences whose 3D structures haven’t yet been determined. This testing data, the team admits, is fairly similar to what it used in 2018, when the original AlphaFold system achieved top marks during CASP 13. (At the time, organizers hailed DeepMind’s “unprecedented progress in the ability of computational methods to predict protein structure.”) 

That said, the team made some notable changes to its machine learning approach -- they haven’t published a full paper yet, but the CASP 14 abstract book highlights some of their modifications. And beyond that, DeepMind also relied on about 128 of Google’s cloud-based TPUv3 cores, which ultimately gave AlphaFold 2 the ability to accurately determine a protein’s structure within just days, if not sooner -- the New York Times notes that, in some cases, predictions can be generated in a matter of hours. 

DeepMind

This all sounds impressive -- and it is, certainly -- but there’s still plenty of work to be done. On the whole, AlphaFold’s results represented a dramatic improvement in accuracy compared to past years, and as mentioned, some of DeepMind’s predictions were accurate enough to rival experimental results at an atomic level. Others, however, fell short of that threshold. The company notes that “for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT” -- that’s just shy of the 90 GDT metric CASP co-founder Moult uses as the barrier for calling results “competitive” with real data. Put another way, DeepMind hasn’t fully solved the protein solving problem, but it’s getting closer than many had thought possible. 

As DeepMind’s work continues, we’ll start to see the full extent of accurate protein prediction take shape -- for now, the jury still seems out on what practical benefits we could expect to see in the short term. The company points to potential advances in sustainability and drug design as a result of its protein folding research, though it didn’t elaborate on specifics. Meanwhile, Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute, told Nature that she hopes this leap in accuracy could shed light on the functions of “thousands” of unsolved proteins at work in the human body. If nothing else, though, researchers could be looking at a glut of new protein structure data to investigate, test against, and work backward from -- that’s worth celebrating, even if we don’t know how it’ll be used yet.

In this article: science, ai, machine learning, deep learning, deepmind, alphafold, alphafold 2, protein, protein folding, genomics, news, tomorrow

All products recommended by Engadget are selected by our editorial team, independent of our parent company. Some of our stories include affiliate links. If you buy something through one of these links, we may earn an affiliate commission.

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2020-11-30 20:33:03Z
52781217477573

DeepMind's latest AI breakthrough can accurately predict the way proteins fold - Engadget

Alphabet-owned DeepMind may be best known for building the AI that beat a world-class Go player, but the company announced another, perhaps more vital breakthrough this morning. As part of its work for the 14th Critical Assessment of Protein Structure Prediction, or CASP, DeepMind's AlphaFold 2 AI has shown it can guess how certain proteins will fold themselves with surprising accuracy. In some cases, the results were perceived to be "competitive" with actual, experimental data.

"We have been stuck on this one problem – how do proteins fold up – for nearly 50 years," said Professor John Moult, CASP chair and co-founder, in a DeepMind blog post. "To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment."

Researchers and enthusiasts across the internet have met the news enthusiastically, with some proclaiming that AlphaFold has solved the "protein solving problem." But what does that mean, exactly? And how do we stand to benefit from it?

To start answering these questions, we need to take a closer look at the proteins themselves. As your biology teacher might have said, proteins are the building blocks of life, responsible for countless functions inside and outside the human body. Each one starts as a series of amino acids strung together into a chain, but it doesn't take long -- sometimes just milliseconds  -- before things start to get complicated. Some parts of the amino acid chain twist into helixes. Others fold back onto themselves as "sheets". Before long, these helixes and sheets coalesce and contort into a protein's final structure, and that's what gives a protein the ability to perform specific tasks, like ferrying oxygen through your body or strengthening the structure of your bones. 

In other words, shape is everything, and researchers have spent decades trying to find a way to determine a protein’s final, folded structure based solely on the amino acids that make up its backbone. That’s where CASP comes in -- since 1994, the program has served as a focal point of sorts for teams around the world working to crack the protein solving problem with computational ingenuity. The rules are fairly simple: Every other year, organizers select a series of target proteins from a bevy of submissions whose structures have been determined experimentally, but haven’t been published yet. Researchers then get a few months to tune their systems and make their predictions, which are then judged by experts in the field for about a month after submissions are closed. 

While CASP has been running for 26 years, it’s been in the past few that the scientific community has been able to bring quantum leaps in compute power and machine learning to bear on the challenge. In DeepMind’s case, that involved training AlphaFold 2’s prediction model on about 170,000 known protein structures, along with a vast number of protein sequences whose 3D structures haven’t yet been determined. This testing data, the team admits, is fairly similar to what it used in 2018, when the original AlphaFold system achieved top marks during CASP 13. (At the time, organizers hailed DeepMind’s “unprecedented progress in the ability of computational methods to predict protein structure.”) 

That said, the team made some notable changes to its machine learning approach -- they haven’t published a full paper yet, but the CASP 14 abstract book highlights some of their modifications. And beyond that, DeepMind also relied on about 128 of Google’s cloud-based TPUv3 cores, which ultimately gave AlphaFold 2 the ability to accurately determine a protein’s structure within just days, if not sooner -- the New York Times notes that, in some cases, predictions can be generated in a matter of hours. 

DeepMind

This all sounds impressive -- and it is, certainly -- but there’s still plenty of work to be done. On the whole, AlphaFold’s results represented a dramatic improvement in accuracy compared to past years, and as mentioned, some of DeepMind’s predictions were accurate enough to rival experimental results at an atomic level. Others, however, fell short of that threshold. The company notes that “for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT” -- that’s just shy of the 90 GDT metric CASP co-founder Moult uses as the barrier for calling results “competitive” with real data. Put another way, DeepMind hasn’t fully solved the protein solving problem, but it’s getting closer than many had thought possible. 

As DeepMind’s work continues, we’ll start to see the full extent of accurate protein prediction take shape -- for now, the jury still seems out on what practical benefits we could expect to see in the short term. The company points to potential advances in sustainability and drug design as a result of its protein folding research, though it didn’t elaborate on specifics. Meanwhile, Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute, told Nature that she hopes this leap in accuracy could shed light on the functions of “thousands” of unsolved proteins at work in the human body. If nothing else, though, researchers could be looking at a glut of new protein structure data to investigate, test against, and work backward from -- that’s worth celebrating, even if we don’t know how it’ll be used yet.

In this article: science, ai, machine learning, deep learning, deepmind, alphafold, alphafold 2, protein, protein folding, genomics, news, tomorrow

All products recommended by Engadget are selected by our editorial team, independent of our parent company. Some of our stories include affiliate links. If you buy something through one of these links, we may earn an affiliate commission.

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2020-11-30 20:30:21Z
52781217477573

DeepMind's latest AI breakthrough can accurately predict the way proteins fold - Engadget

Alphabet-owned DeepMind may be best known for building the AI that beat a world-class Go player, but the company announced another, perhaps more vital breakthrough this morning. As part of its work for the 14th Critical Assessment of Protein Structure Prediction, or CASP, DeepMind's AlphaFold 2 AI has shown it can guess how certain proteins will fold themselves with surprising accuracy. In some cases, the results were perceived to be "competitive" with actual, experimental data.

"We have been stuck on this one problem – how do proteins fold up – for nearly 50 years," said Professor John Moult, CASP chair and co-founder, in a DeepMind blog post. "To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment."

Researchers and enthusiasts across the internet have met the news enthusiastically, with some proclaiming that AlphaFold has solved the "protein solving problem." But what does that mean, exactly? And how do we stand to benefit from it?

To start answering these questions, we need to take a closer look at the proteins themselves. As your biology teacher might have said, proteins are the building blocks of life, responsible for countless functions inside and outside the human body. Each one starts as a series of amino acids strung together into a chain, but it doesn't take long -- sometimes just milliseconds  -- before things start to get complicated. Some parts of the amino acid chain twist into helixes. Others fold back onto themselves as "sheets". Before long, these helixes and sheets coalesce and contort into a protein's final structure, and that's what gives a protein the ability to perform specific tasks, like ferrying oxygen through your body or strengthening the structure of your bones. 

In other words, shape is everything, and researchers have spent decades trying to find a way to determine a protein’s final, folded structure based solely on the amino acids that make up its backbone. That’s where CASP comes in -- since 1994, the program has served as a focal point of sorts for teams around the world working to crack the protein solving problem with computational ingenuity. The rules are fairly simple: Every other year, organizers select a series of target proteins from a bevy of submissions whose structures have been determined experimentally, but haven’t been published yet. Researchers then get a few months to tune their systems and make their predictions, which are then judged by experts in the field for about a month after submissions are closed. 

While CASP has been running for 26 years, it’s been in the past few that the scientific community has been able to bring quantum leaps in compute power and machine learning to bear on the challenge. In DeepMind’s case, that involved training AlphaFold 2’s prediction model on about 170,000 known protein structures, along with a vast number of protein sequences whose 3D structures haven’t yet been determined. This testing data, the team admits, is fairly similar to what it used in 2018, when the original AlphaFold system achieved top marks during CASP 13. (At the time, organizers hailed DeepMind’s “unprecedented progress in the ability of computational methods to predict protein structure.”) 

That said, the team made some notable changes to its machine learning approach -- they haven’t published a full paper yet, but the CASP 14 abstract book highlights some of their modifications. And beyond that, DeepMind also relied on about 128 of Google’s cloud-based TPUv3 cores, which ultimately gave AlphaFold 2 the ability to accurately determine a protein’s structure within just days, if not sooner -- the New York Times notes that, in some cases, predictions can be generated in a matter of hours. 

DeepMind

This all sounds impressive -- and it is, certainly -- but there’s still plenty of work to be done. On the whole, AlphaFold’s results represented a dramatic improvement in accuracy compared to past years, and as mentioned, some of DeepMind’s predictions were accurate enough to rival experimental results at an atomic level. Others, however, fell short of that threshold. The company notes that “for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT” -- that’s just shy of the 90 GDT metric CASP co-founder Moult uses as the barrier for calling results “competitive” with real data. Put another way, DeepMind hasn’t fully solved the protein solving problem, but it’s getting closer than many had thought possible. 

As DeepMind’s work continues, we’ll start to see the full extent of accurate protein prediction take shape -- for now, the jury still seems out on what practical benefits we could expect to see in the short term. The company points to potential advances in sustainability and drug design as a result of its protein folding research, though it didn’t elaborate on specifics. Meanwhile, Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute, told Nature that she hopes this leap in accuracy could shed light on the functions of “thousands” of unsolved proteins at work in the human body. If nothing else, though, researchers could be looking at a glut of new protein structure data to investigate, test against, and work backward from -- that’s worth celebrating, even if we don’t know how it’ll be used yet.

In this article: science, ai, machine learning, deep learning, deepmind, alphafold, alphafold 2, protein, protein folding, genomics, news, tomorrow

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2020-11-30 20:12:15Z
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DeepMind's latest AI breakthrough can accurately predict the way proteins fold - Engadget

Alphabet-owned DeepMind may be best known for building the AI that beat a world-class Go player, but the company announced another, perhaps more vital breakthrough this morning. As part of its work for the 14th Critical Assessment of Protein Structure Prediction, or CASP, DeepMind's AlphaFold 2 AI has shown it can guess how certain proteins will fold themselves with surprising accuracy. In some cases, the results were perceived to be "competitive" with actual, experimental data.

"We have been stuck on this one problem – how do proteins fold up – for nearly 50 years," said Professor John Moult, CASP chair and co-founder, in a DeepMind blog post. "To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment."

Researchers and enthusiasts across the internet have met the news enthusiastically, with some proclaiming that AlphaFold has solved the "protein solving problem." But what does that mean, exactly? And how do we stand to benefit from it?

To start answering these questions, we need to take a closer look at the proteins themselves. As your biology teacher might have said, proteins are the building blocks of life, responsible for countless functions inside and outside the human body. Each one starts as a series of amino acids strung together into a chain, but it doesn't take long -- sometimes just milliseconds  -- before things start to get complicated. Some parts of the amino acid chain twist into helixes. Others fold back onto themselves as "sheets". Before long, these helixes and sheets coalesce and contort into a protein's final structure, and that's what gives a protein the ability to perform specific tasks, like ferrying oxygen through your body or strengthening the structure of your bones. 

In other words, shape is everything, and researchers have spent decades trying to find a way to determine a protein’s final, folded structure based solely on the amino acids that make up its backbone. That’s where CASP comes in -- since 1994, the program has served as a focal point of sorts for teams around the world working to crack the protein solving problem with computational ingenuity. The rules are fairly simple: Every other year, organizers select a series of target proteins from a bevy of submissions whose structures have been determined experimentally, but haven’t been published yet. Researchers then get a few months to tune their systems and make their predictions, which are then judged by experts in the field for about a month after submissions are closed. 

While CASP has been running for 26 years, it’s been in the past few that the scientific community has been able to bring quantum leaps in compute power and machine learning to bear on the challenge. In DeepMind’s case, that involved training AlphaFold 2’s prediction model on about 170,000 known protein structures, along with a vast number of protein sequences whose 3D structures haven’t yet been determined. This testing data, the team admits, is fairly similar to what it used in 2018, when the original AlphaFold system achieved top marks during CASP 13. (At the time, organizers hailed DeepMind’s “unprecedented progress in the ability of computational methods to predict protein structure.”) 

That said, the team made some notable changes to its machine learning approach -- they haven’t published a full paper yet, but the CASP 14 abstract book highlights some of their modifications. And beyond that, DeepMind also relied on about 128 of Google’s cloud-based TPUv3 cores, which ultimately gave AlphaFold 2 the ability to accurately determine a protein’s structure within just days, if not sooner -- the New York Times notes that, in some cases, predictions can be generated in a matter of hours. 

DeepMind

This all sounds impressive -- and it is, certainly -- but there’s still plenty of work to be done. On the whole, AlphaFold’s results represented a dramatic improvement in accuracy compared to past years, and as mentioned, some of DeepMind’s predictions were accurate enough to rival experimental results at an atomic level. Others, however, fell short of that threshold. The company notes that “for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT” -- that’s just shy of the 90 GDT metric CASP co-founder Moult uses as the barrier for calling results “competitive” with real data. Put another way, DeepMind hasn’t fully solved the protein solving problem, but it’s getting closer than many had thought possible. 

As DeepMind’s work continues, we’ll start to see the full extent of accurate protein prediction take shape -- for now, the jury still seems out on what practical benefits we could expect to see in the short term. The company points to potential advances in sustainability and drug design as a result of its protein folding research, though it didn’t elaborate on specifics. Meanwhile, Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute, told Nature that she hopes this leap in accuracy could shed light on the functions of “thousands” of unsolved proteins at work in the human body. If nothing else, though, researchers could be looking at a glut of new protein structure data to investigate, test against, and work backward from -- that’s worth celebrating, even if we don’t know how it’ll be used yet.

In this article: science, ai, machine learning, deep learning, deepmind, alphafold, alphafold 2, protein, protein folding, genomics, news, tomorrow

All products recommended by Engadget are selected by our editorial team, independent of our parent company. Some of our stories include affiliate links. If you buy something through one of these links, we may earn an affiliate commission.

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2020-11-30 19:50:02Z
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DeepMind Breakthrough Helps to Solve How Diseases Invade Cells - Financial Post

Article content continued

Read More: AI Drug Hunters Could Give Big Pharma a Run for Its Money

“These algorithms are now becoming strong enough and powerful enough to be applicable to scientific problems,” DeepMind Chief Executive Officer Demis Hassabis said in a call with reporters. After four years of development “we have a system that’s accurate enough to actually have biological significance and relevance for biological researchers.”

DeepMind is now looking into ways of offering scientists access to the AlphaFold system in a “scalable way,” Hassabis said.

“Citizen Science”

CASP scientists analyzed the shape of amino acid sequences for a set of about 100 proteins. Competitors were given the sequences, and charged with predicting their shape. AlphaFold’s assessment lined up almost perfectly with the CASP analysis for two-thirds of the proteins, compared to about 10% from the other teams, and better than what DeepMind’s tool achieved two years ago

Hassabis said his inspiration for AlphaFold came from “citizen science” attempts to find unknown protein structures, like Foldit, which presented amateur volunteers with the problem in the form of a puzzle. In its first two years, the human gamers proved to be surprisingly good at solving the riddles, and ended up discovering a structure that had baffled scientists and designing a new enzyme that was later confirmed in the lab.

“Determining a single protein structure often required years of experimental effort,” said Janet Thornton, director emeritus of the European Bioinformatics Institute and one of the pioneers of using computational approaches to understanding protein structure. “A better understanding of protein structures and the ability to predict them using a computer means a better understanding of life, evolution and, of course, human health and disease.”

©2020 Bloomberg L.P.

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2020-11-30 18:33:45Z
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DeepMind's AlphaFold Crosses Threshold in Solving Protein Riddle - Financial Post

Article content continued

“These algorithms are now becoming strong enough and powerful enough to be applicable to scientific problems,” DeepMind Chief Executive Officer Demis Hassabis said in a call with reporters. After four years of development “we have a system that’s accurate enough to actually have biological significance and relevance for biological researchers.”

DeepMind is now looking into ways of offering scientists access to the AlphaFold system in a “scalable way,” Hassabis said.

“Citizen Science”

CASP scientists analyzed the shape of amino acid sequences for a set of about 100 proteins. Competitors were given the sequences, and charged with predicting their shape. AlphaFold’s assessment lined up almost perfectly with the CASP analysis for two-thirds of the proteins, compared to about 10% from the other teams, and better than what DeepMind’s tool achieved two years ago

Hassabis said his inspiration for AlphaFold came from “citizen science” attempts to find unknown protein structures, like Foldit, which presented amateur volunteers with the problem in the form of a puzzle. In its first two years, the human gamers proved to be surprisingly good at solving the riddles, and ended up discovering a structure that had baffled scientists and designing a new enzyme that was later confirmed in the lab.

“Determining a single protein structure often required years of experimental effort,” said Janet Thornton, director emeritus of the European Bioinformatics Institute and one of the pioneers of using computational approaches to understanding protein structure. “A better understanding of protein structures and the ability to predict them using a computer means a better understanding of life, evolution and, of course, human health and disease.”

©2020 Bloomberg L.P.

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2020-11-30 15:49:19Z
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London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery - The New York Times

Some scientists spend their lives trying to pinpoint the shape of tiny proteins in the human body.

Proteins are the microscopic mechanisms that drive the behavior of viruses, bacteria, the human body and all living things. They begin as strings of chemical compounds, before twisting and folding into three-dimensional shapes that define what they can do — and what they cannot.

For biologists, identifying the precise shape of a protein often requires months, years or even decades of experimentation. It requires skill, intelligence and more than a little elbow grease. Sometimes they never succeed.

Now, an artificial intelligence lab in London has built a computer system that can do the job in a few hours — perhaps even a few minutes.

DeepMind, a lab owned by the same parent company as Google, said on Monday that its system, called AlphaFold, had solved what is known as “the protein folding problem.” Given the string of amino acids that make up a protein, the system can rapidly and reliably predict its three-dimensional shape.

This long-sought breakthrough could accelerate the ability to understand diseases, develop new medicines and unlock mysteries of the human body.

Computer scientists have struggled to build such a system for more than 50 years. For the last 25, they have measured and compared their efforts through a global competition called the Critical Assessment of Structure Prediction, or C.A.S.P. Until now, no contestant had even come close to solving the problem.

DeepMind solved the problem with a wide range of proteins, reaching an accuracy level that rivaled physical experiments. Many scientists had assumed that moment was still years, if not decades, away.

“I always hoped I would live to see this day,” said John Moult, a professor at the University of Maryland who helped create C.A.S.P. in 1994 and continues to oversee the biennial contest. “But it wasn’t always obvious I was going to make it.”

As part of this year’s C.A.S.P., DeepMind’s technology was reviewed by Dr. Moult and other researchers who oversee the contest.

If DeepMind’s methods can be refined, he and other researchers said, they could speed the development of new drugs as well as efforts to apply existing medications to new viruses and diseases.

The breakthrough arrives too late to make a significant impact on the coronavirus. But researchers believe DeepMind’s methods could accelerate the response to future pandemics. Some believe it could also help scientists gain a better understanding of genetic diseases along the lines of Alzheimer’s or cystic fibrosis.

Still, experts cautioned that this technology would affect only a small part of the long process by which scientists identify new medicines and analyze disease. It was also unclear when or how DeepMind would share its technology with other researchers.

DeepMind is one of the key players in a sweeping change that has spread across academia, the tech industry and the medical community over the past 10 years. Thanks to an artificial intelligence technology called a neural network, machines can now learn to perform many tasks that were once beyond their reach — and sometimes beyond the reach of humans.

A neural network is a mathematical system loosely modeled on the network of neurons in the human brain. It learns skills by analyzing vast amounts of data. By pinpointing patterns in thousands of cat photos, for instance, it can learn to recognize a cat.

This is the technology that recognizes faces in the photos you post to Facebook, identifies the commands you bark into your smartphone and translates one language into another on Skype and other services. DeepMind is using this technology to predict the shape of proteins.

If scientists can predict the shape of a protein in the human body, they can determine how other molecules will bind or physically attach to it. This is one way drugs are developed: A drug binds to particular proteins in your body and alters their behavior.

John Jumper, a lead scientist on the DeepMind team.Credit...DeepMind

By analyzing thousands of known proteins and their physical shapes, a neural network can learn to predict the shapes of others. In 2018, using this method, DeepMind entered the C.A.S.P. contest for the first time and its system outperformed all other competitors, signaling a significant shift. But its team of biologists, physicists and computer scientists, led by a researcher named John Jumper, were nowhere close to solving the ultimate problem.

In the two years since, Dr. Jumper and his team designed an entirely new kind of neural network specifically for protein folding, and this drove an enormous leap in accuracy. Their latest version provides a powerful, if imperfect, solution to the protein folding problem, said the DeepMind research scientist Kathryn Tunyasuvunakool.

The system can accurately predict the shape of a protein about two-thirds of the time, according to the results of the C.A.S.P. contest. And its mistakes with these proteins are smaller than the width of an atom — an error rate that rivals physical experiments.

“Most atoms are within an atom diameter of where they are in the experimental structure,” said Dr. Moult, the contest organizer. “And with those that aren’t, there are other possible explanations of the differences.”

Andrei Lupas, director of the department of protein evolution at the Max Planck Institute for Developmental Biology in Germany, is among those who worked with AlphaFold. He is part of a team that spent a decade trying to determine the physical shape of a particular protein in a tiny bacteria-like organism called an archaeon.

This protein straddles the membrane of individual cells — part is inside the cell, part is outside — and that makes it difficult for scientists like Dr. Lupas to determine the shape of the protein in the lab. Even after a decade, he could not pinpoint the shape.

With AlphaFold, he cracked the problem in half an hour.

If these methods continue to improve, he said, they could be a particularly useful way of determining whether a new virus could be treated with a cocktail of existing drugs.

“We could start screening every compound that is licensed for use in humans,” Dr. Lupas said. “We could face the next pandemic with the drugs we already have.”

During the current pandemic, a simpler form of artificial intelligence proved helpful in some cases. A system built by another London company, BenevolentAI, helped pinpoint an existing drug, baricitinib, that could be used to treat seriously ill Covid-19 patients. Researchers have now completed a clinical trial, though the results have not yet been released.

As researchers continue to improve the technology, AlphaFold could further accelerate this kind of drug repurposing, as well as the development of entirely new vaccines, especially if we encounter a virus that is even less understood than Covid-19.

David Baker, the director of the Institute for Protein Design at the University of Washington, who has been using similar computer technology to design anti-coronavirus drugs, said DeepMind’s methods could accelerate that work.

“We were able to design coronavirus-neutralizing proteins in several months,” he said. “But our goal is to do this kind of thing in a couple of weeks.”

Still, development speed must contend with other issues, like massive clinical trials, said Dr. Vincent Marconi, a researcher at Emory University in Atlanta who helped lead the baricitinib trial. “That takes time,” he said.

But DeepMind’s methods could be a way of determining whether a clinical trial will fail because of toxic reactions or other problems, at least in some cases.

Demis Hassabis, DeepMind’s chief executive and co-founder, said the company planned to publish details describing its work, but that was unlikely to happen until sometime next year. He also said the company was exploring ways of sharing the technology itself with other scientists.

DeepMind is a research lab. It does not sell products directly to other labs or businesses. But it could work with other companies to share access to its technology over the internet.

The lab’s biggest breakthroughs in the past have involved games. It built systems that surpassed human performance on the ancient strategy game Go and the popular video game StarCraft — enormously technical achievements with no practical application. Now, the DeepMind team are eager to push their artificial intelligence technology into the real world.

“We don’t want to be a leader board company,” Dr. Jumper said. “We want real biological relevance.”

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2020-11-30 15:36:00Z
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Iron hydroxide forms more easily on mineral surfaces than previously thought - MINING.com

Knowing the exact moment when iron hydroxide forms on quartz substrates can support water quality processes at acid mine drainage sites

Nucleation and growth together are known as precipitation — and their sum has been used to predict iron hydroxide’s formation behaviour. But these predictions have largely omitted separate consideration of nucleation.

In Jun’s view, this means that previous results were not accurate enough.

“Our work provides an empirical, quantitative description of nucleation, not a computation, so we can provide scientific evidence about this missing link,” the researcher said.

By using X-rays and a novel experimental cell she developed to study environmentally relevant complex systems with plenty of water, ions and substrate material, Jun was able to observe nucleation in real-time.

The work consisted of employing an X-ray scattering technique called “grazing-incidence small-angle X-ray scattering.” By shining X-rays onto a substrate with a very shallow angle, close to the critical angle that allows total reflection of light, this technique can detect the first appearance of nanometer size particles on a surface.

The empirical measuring of the initial point of nucleation revealed that the general estimates scientists have been using overstate the amount of energy needed for this process.

“Iron hydroxide forms much more easily on mineral surfaces than scientists thought because less energy is needed for nucleation of highly hydrated solids on surfaces,” Jun said.

According to the scientist, her findings can help better understand processes related to water quality at acid mine drainage sites, the reduction of membrane fouling and pipeline scale formation, and the developing of more environmentally friendly superconductor materials.

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2020-11-30 14:22:29Z
CBMiZGh0dHBzOi8vd3d3Lm1pbmluZy5jb20vaXJvbi1oeWRyb3hpZGUtZm9ybXMtbW9yZS1lYXNpbHktb24tbWluZXJhbC1zdXJmYWNlcy10aGFuLXByZXZpb3VzbHktdGhvdWdodC_SAQA

Brightly burning meteor seen across wide areas of Japan - northeastNOW

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2020-11-30 11:18:47Z
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Brightly burning meteor seen across wide areas of Japan - EverythingGP

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2020-11-30 10:48:34Z
52781214226231

Brightly burning meteor seen across wide areas of Japan - paNOW

By Canadian Press

Nov 30, 2020 4:39 AM

TOKYO — A brightly burning meteor was seen plunging from the sky in wide areas of Japan, capturing attention on television and social media.

The meteor glowed strongly as it rapidly descended through the Earth’s atmosphere on Sunday.

Many people in western Japan reported on social media seeing the rare sight.

NHK public television said its cameras in the central prefectures of Aichi, Mie and elsewhere captured the fireball in the southern sky.

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2020-11-30 10:39:59Z
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Brightly burning meteor seen across wide areas of Japan - Vancouver Is Awesome

TOKYO — A brightly burning meteor was seen plunging from the sky in wide areas of Japan, capturing attention on television and social media.

The meteor glowed strongly as it rapidly descended through the Earth's atmosphere on Sunday.

Many people in western Japan reported on social media seeing the rare sight.

NHK public television said its cameras in the central prefectures of Aichi, Mie and elsewhere captured the fireball in the southern sky.

A camera at Nagoya port showed the meteor shining as brightly as a full moon as it neared the Earth, the Asahi newspaper reported.

Some experts said small fragments of the meteorite might have reached the ground.

The Associated Press

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2020-11-30 09:44:42Z
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Renewed hopes for humanity in space - The Hill Times

The successful launch of NASA’s SpaceX Crew-1 mission to the International Space Station on Nov. 15 is a symbolic milestone that should be celebrated. Onboard the commercial launch vehicle were American and Japanese astronauts, who joined the other Russian and American crew already residing in the International Space Station, itself a remarkable example of the power of cooperation in space among many countries around the world. As the Falcon 9 soared into space, the collaborative, cooperative and commercial nature of space was once again clear for all to see. 

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2020-11-30 05:00:05Z
CBMiT2h0dHBzOi8vd3d3LmhpbGx0aW1lcy5jb20vMjAyMC8xMS8zMC9yZW5ld2VkLWhvcGVzLWZvci1odW1hbml0eS1pbi1zcGFjZS8yNzMxNTXSAQA