Breaking: Google’s DeepMind solves the ‘protein folding problem,’ one of biology’s biggest challenges

[F-22Raptor]

Alphabet Inc.’s artificial intelligence research lab DeepMind Technologies said today it has solved a 50-year-old “grand challenge” in biology by creating software that can predict the atomic structure that proteins will fold into in a matter of days.

The breakthrough could pave the way for a better understanding of diseases and new drug discoveries.

DeepMind’s software, known as “AlphaFold,” has solved what’s known as the “protein folding problem,” which refers to attempts to understand how a protein’s amino acid sequence shapes its 3D atomic structure. The form it folds into is dictated by interatomic forces and thermodynamics, and it’s extremely difficult to predict.

It’s an important discovery because every living cell contains thousands of different proteins that keep it alive and functioning. By predicting the shape a protein folds into, scientists can determine its function, and nearly all diseases, including cancer and dementia, are related to how proteins function.

The American biologist Christian Boehmer Anfinsen Jr. won the 1972 Nobel Prize in chemistry for theorizing that a protein’s amino acid sequence should fully determine its structure, and that hypothesis sparked a five-decade search for a computational simulation that could predict a protein’s structure based on its 1D amino acid sequence.

“A major challenge, however, is that the number of ways a protein could theoretically fold before settling into its final 3D structure is astronomical,” DeepMind’s AlphaFold team wrote in a blog post. “In 1969 Cyrus Levinthal noted that it would take longer than the age of the known universe to enumerate all possible configurations of a typical protein by brute force calculation – Levinthal estimated 10^300 possible conformations for a typical protein.”


The Critical Assessment for Structure Prediction, or CASP, was established in 1994 as a biannual challenge that aimed to catalyze research into the problem. CASP’s measure of success is the Global Distance Test, which is based on the percentage of amino acid residues that are predicted within a threshold distance of their correct position. The possible scores range from 0-100, with the unofficial benchmark being anything above 90 GDT.

DeepMind said finally cracked the problem in its 14th attempt, CASP14, scoring 92.4 GDT, which means its predictions have an average error of just 1.6 Angstroms, comparable to the width of an atom, or just 0.1 nanometer. That’s a significant improvement over its last effort, in 2018, which scored less than 60 GDT.

“For the latest version of AlphaFold, used at CASP14, we created an attention-based neural network system, trained end-to-end, that attempts to interpret the structure of this graph, while reasoning over the implicit graph that it’s building,” DeepMind explained. “It uses evolutionarily related sequences, multiple sequence alignment (MSA), and a representation of amino acid residue pairs to refine this graph.”


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. “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.”

DeepMind said the AlphaFold model was created using a neural network that runs on 128 of Google LLC’s latest TPU neural processing cores, crunching data from 170,000 protein from public and other databases. It took “a few weeks” to train the model, which is able to successfully predict the final structure of new proteins in just a few days time.

Analyst Charles King of Pund-IT Inc. told SiliconANGLE that DeepMind has scored an impressive achievement that could fundamentally alter the way scientists and clinicians understand and address protein structures.

“The DeepMind team and Alphabet should be over the moon with surpassing this milestone,” King said. “While solving the protein folding challenge certainly highlights the potential and potential value of AI, it also underscores the continuing vital role that computational technologies have in modern scientific inquiry. This is the latest peak but it was attained following past structural achievements that were achieved, brick by brick, through the efforts of countless scientists and technologists.”

“Not surprisingly the problem was cracked using a neural network,” said Constellation Research Inc. analyst Holger Mueller. “It’s another achievement that underlines Google’s leading position in AI.”

Next, DeepMind will attempt to make the AlphaFold system accessible in a scalable way so that third-party researchers can better understand how different proteins impact specific diseases and try to create new treatments for them.

https://siliconangle.com/2020/11/30...ding-problem-one-biologys-biggest-challenges/

This breakthrough should win a Nobel Prize!

https://twitter.com/x/status/1333550953811861505

 

[Dual Wielder]

So.. fewer protein shakes to maintain my physique?

 

[F-22Raptor]

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.”

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 imaging, 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.”


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.”

https://www.nature.com/articles/d41586-020-03348-4

 

[Hamartia Antidote]

Alphabet Inc.’s artificial intelligence research lab DeepMind Technologies said today it has solved a 50-year-old “grand challenge” in biology by creating software that can predict the atomic structure that proteins will fold into in a matter of days.

The breakthrough could pave the way for a better understanding of diseases and new drug discoveries.

DeepMind’s software, known as “AlphaFold,” has solved what’s known as the “protein folding problem,” which refers to attempts to understand how a protein’s amino acid sequence shapes its 3D atomic structure. The form it folds into is dictated by interatomic forces and thermodynamics, and it’s extremely difficult to predict.

It’s an important discovery because every living cell contains thousands of different proteins that keep it alive and functioning. By predicting the shape a protein folds into, scientists can determine its function, and nearly all diseases, including cancer and dementia, are related to how proteins function.

The American biologist Christian Boehmer Anfinsen Jr. won the 1972 Nobel Prize in chemistry for theorizing that a protein’s amino acid sequence should fully determine its structure, and that hypothesis sparked a five-decade search for a computational simulation that could predict a protein’s structure based on its 1D amino acid sequence.

“A major challenge, however, is that the number of ways a protein could theoretically fold before settling into its final 3D structure is astronomical,” DeepMind’s AlphaFold team wrote in a blog post. “In 1969 Cyrus Levinthal noted that it would take longer than the age of the known universe to enumerate all possible configurations of a typical protein by brute force calculation – Levinthal estimated 10^300 possible conformations for a typical protein.”


The Critical Assessment for Structure Prediction, or CASP, was established in 1994 as a biannual challenge that aimed to catalyze research into the problem. CASP’s measure of success is the Global Distance Test, which is based on the percentage of amino acid residues that are predicted within a threshold distance of their correct position. The possible scores range from 0-100, with the unofficial benchmark being anything above 90 GDT.

DeepMind said finally cracked the problem in its 14th attempt, CASP14, scoring 92.4 GDT, which means its predictions have an average error of just 1.6 Angstroms, comparable to the width of an atom, or just 0.1 nanometer. That’s a significant improvement over its last effort, in 2018, which scored less than 60 GDT.

“For the latest version of AlphaFold, used at CASP14, we created an attention-based neural network system, trained end-to-end, that attempts to interpret the structure of this graph, while reasoning over the implicit graph that it’s building,” DeepMind explained. “It uses evolutionarily related sequences, multiple sequence alignment (MSA), and a representation of amino acid residue pairs to refine this graph.”


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. “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.”

DeepMind said the AlphaFold model was created using a neural network that runs on 128 of Google LLC’s latest TPU neural processing cores, crunching data from 170,000 protein from public and other databases. It took “a few weeks” to train the model, which is able to successfully predict the final structure of new proteins in just a few days time.

Analyst Charles King of Pund-IT Inc. told SiliconANGLE that DeepMind has scored an impressive achievement that could fundamentally alter the way scientists and clinicians understand and address protein structures.

“The DeepMind team and Alphabet should be over the moon with surpassing this milestone,” King said. “While solving the protein folding challenge certainly highlights the potential and potential value of AI, it also underscores the continuing vital role that computational technologies have in modern scientific inquiry. This is the latest peak but it was attained following past structural achievements that were achieved, brick by brick, through the efforts of countless scientists and technologists.”

“Not surprisingly the problem was cracked using a neural network,” said Constellation Research Inc. analyst Holger Mueller. “It’s another achievement that underlines Google’s leading position in AI.”

Next, DeepMind will attempt to make the AlphaFold system accessible in a scalable way so that third-party researchers can better understand how different proteins impact specific diseases and try to create new treatments for them.

https://siliconangle.com/2020/11/30...ding-problem-one-biologys-biggest-challenges/

This breakthrough should win a Nobel Prize!

https://twitter.com/x/status/1333550953811861505

Nobel Prize for DeepMind

 

[TheTruth]

Good job to the Asians that make up 40-60% of Google's research team, as well as the vultures with blood money that pay them.

 

[Hamartia Antidote]

Good job to the Asians that make up 40-60% of Google's research team, as well as the vultures with blood money that pay them.

Um...not many seen on that DeepMind AlphaFold team chat video.

They probably quit to join one of the Chinese competing CASP teams that lost.

Good job to them all.

 

[dbc]

Um...not many seen on that DeepMind AlphaFold team chat video.

They probably quit to join one of the Chinese competing CASP teams that lost.

Good job to them all.

It like the Indian claim of NASA being 80% Indian - LAMAO
The Chinese are becoming more Indian.

 

[Hamartia Antidote]

It like the Indian claim of NASA being 80% Indian - LAMAO
The Chinese are becoming more Indian.

You know...I was going to say the same thing
They are birds of a feather...

 

[TheTruth]

Um...not many seen on that DeepMind AlphaFold team chat video.

They probably quit to join one of the Chinese competing CASP teams that lost.

Good job to them all.

DeepMind's protein-folding breakthrough triggers fierce debate among skeptical scientists: 'Until they share their code, nobody in the field cares'

One professor at the University of California, Merced, branded DeepMind's announcement "laughable."

www.businessinsider.com

Oh

Looks like they need to hire more Asians. Average white guy isn't very smart, outnumber Asians 12 to 1 but there are more Asians in tech positions in FAGMAN companies.

 

[Hamartia Antidote]

DeepMind's protein-folding breakthrough triggers fierce debate among skeptical scientists: 'Until they share their code, nobody in the field cares'

One professor at the University of California, Merced, branded DeepMind's announcement "laughable."

www.businessinsider.com

Oh

View attachment 692951

Looks like they need to hire more Asians. Average white guy isn't very smart, outnumber Asians 12 to 1 but there are more Asians in tech positions in FAGMAN companies.

LOL! Look at the title of the article the pic is from. They are trying to make it more diverse.

These 7 graphs lay bare Google's diversity problem

Google has published its 2018 diversity report. The figures show that while Google's diversity is improving, it's moving at a very slow pace.

www.businessinsider.com

These 7 graphs lay bare Google's diversity problem

Written by the company's chief diversity and inclusion officer, Danielle Brown, the report shows that while Google's diversity is getting better, improvement
is moving at a slow pace. ( Seems hard to replace the white men)

Seems Asians don't have good leadership skills. Blacks fared better.
BTW I think of those Asians a good percentage are probably Indians vs Chinese.

 

[TheTruth]

DeepMind's protein-folding breakthrough triggers fierce debate among skeptical scientists: 'Until they share their code, nobody in the field cares'

One professor at the University of California, Merced, branded DeepMind's announcement "laughable."

www.businessinsider.com

Oh

LOL! Look at the title of the article the pic is from. They are trying to make it more diverse.

These 7 graphs lay bare Google's diversity problem

Google has published its 2018 diversity report. The figures show that while Google's diversity is improving, it's moving at a very slow pace.

www.businessinsider.com

These 7 graphs lay bare Google's diversity problem
View attachment 692953
Written by the company's chief diversity and inclusion officer, Danielle Brown, the report shows that while Google's diversity is getting better, improvement
is moving at a slow pace.

View attachment 692957
Seems Asians don't have good leadership skills. Blacks fared better.
BTW I think of those Asians a good percentage are probably Indians vs Chinese.

"Leadership skills" aka old boys club + *** licking.

Yes, I know a lot of India's top .00000001 really strive to get into these companies, while most Chinese found their own. The split is pretty even. From what I've heard from friends the Chinese are more technically skilled, but the Indians are quite good at brown-nosing.

 

[Hamartia Antidote]

"Leadership skills" aka old boys club + *** licking.

Yes, I know a lot of India's top .00000001 really strive to get into these companies, while most Chinese found their own. The split is pretty even. From what I've heard from friends the Chinese are more technically skilled, but the Indians are quite good at brown-nosing.

I have a feeling you both are brown-nosing...