DeepMind and several research partners have released a database containing the 3D structures of nearly every protein in the human body, as computationally determined by the breakthrough protein folding system demonstrated last year, AlphaFold. The freely available database represents an enormous advance and convenience for scientists across hundreds of disciplines and domains, and may very well form the foundation of a new phase in biology and medicine.
The AlphaFold Protein Structure Database is a collaboration between DeepMind, the European Bioinformatics Institute and others, and consists of hundreds of thousands of protein sequences with their structures predicted by AlphaFold — and the plan is to add millions more to create a “protein almanac of the world.”
“We believe that this work represents the most significant contribution AI has made to advancing the state of scientific knowledge to date, and is a great example of the kind of benefits AI can bring to society,” said DeepMind founder and CEO Demis Hassabis.
From genome to proteome
If you’re not familiar with proteomics in general — and it’s quite natural if that’s the case — the best way to think about this is perhaps in terms of another major effort: that of sequencing the human genome. As you may recall from the late ’90s and early ’00s, this was a huge endeavor undertaken by a large group of scientists and organizations across the globe and over many years. The genome, finished at last, has been instrumental to the diagnosis and understanding of countless conditions, and in the development of drugs and treatments for them.
It was, however, just the beginning of the work in that field — like finishing all the edge pieces of a giant puzzle. And one of the next big projects everyone turned their eyes toward in those years was understanding the human proteome — which is to say all the proteins used by the human body and encoded into the genome.
The problem with the proteome is that it’s much, much more complex. Proteins, like DNA, are sequences of known molecules; in DNA these are the handful of familiar bases (adenine, guanine, etc.), but in proteins they are the 20 amino acids (each of which is coded by multiple bases in genes). This in itself creates a great deal more complexity, but it’s only the start. The sequences aren’t simply “code” but actually twist and fold into tiny molecular origami machines that accomplish all kinds of tasks within our body. It’s like going from binary code to a complex language that manifests objects in the real world.
Practically speaking this means that the proteome is made up of not just 20,000 sequences of hundreds of acids each, but that each one of those sequences has a physical structure and function. And one of the hardest parts of understanding them is figuring out what shape is made from a given sequence. This is generally done experimentally using something like x-ray crystallography, a long, complex process that may take months or longer to figure out a single protein — if you happen to have the best labs and techniques at your disposal. The structure can also be predicted computationally, though the process has never been good enough to actually rely on — until AlphaFold came along.
Taking a discipline by surprise
Without going into the whole history of computational proteomics (as much as I’d like to), we essentially went from distributed brute-force tactics 15 years ago — remember Folding@home? — to more honed processes in the last decade. Then AI-based approaches came on the scene, making a splash in 2019 when DeepMind’s AlphaFold leapfrogged every other system in the world — then made another jump in 2020, achieving accuracy levels high enough and reliable enough that it prompted some experts to declare the problem of turning an arbitrary sequence into a 3D structure solved.
I’m only compressing this long history into one paragraph because it was extensively covered at the time, but it’s hard to overstate how sudden and complete this advance was. This was a problem that stumped the best minds in the world for decades, and it went from “we maybe have an approach that kind of works, but extremely slowly and at great cost” to “accurate, reliable, and can be done with off the shelf computers” in the space of a year.
Image Credits: DeepMind
The specifics of DeepMind’s advances and how it achieved them I will leave to specialists in the fields of computational biology and proteomics, who will no doubt be picking apart and iterating on this work over the coming months and years. It’s the practical results that concern us today, as the company employed its time since the publication of AlphaFold 2 (the version shown in 2020) not just tweaking the model, but running it… on every single protein sequence they could get their hands on.
The result is that 98.5% of the human proteome is now “folded,” as they say, meaning there is a predicted structure that the AI model is confident enough (and importantly, we are confident enough in its confidence) represents the real thing. Oh, and they also folded the proteome for 20 other organisms, like yeast and E. coli, amounting to about 350,000 protein structures total. It’s by far — by orders of magnitude — the largest and best collection of this absolutely crucial information.
All that will be made available as a freely browsable database that any researcher can simply plug a sequence or protein name into and immediately be provided the 3D structure. The details of the process and database can be found in a paper published today in the journal Nature.
“The database as you’ll see it tomorrow, it’s a search bar, it’s almost like Google search for protein structures,” said Hassabis in an interview with TechCrunch. “You can view it in the 3D visualizer, zoom around it, interrogate the genetic sequence… and the nice thing about doing it with EMBL-EBI is it’s linked to all their other databases. So you can immediately go and see related genes, And it’s linked to all these other databases, you can see related genes, related in other organisms, other proteins that have related functions, and so on.”
“As a scientist myself, who works on an almost unfathomable protein,” said EMBL-EBI’s Edith Heard (she didn’t specify which protein), “it’s really exciting to know that you can find out what the business end of a protein is now, in such a short time — it would have taken years. So being able to access the structure and say ‘aha, this is the business end,’ you can then focus on trying to work out what that business end does. And I think this is accelerating science by steps of years, a bit like being able to sequence genomes did decades ago.”
So new is the very idea of being able to do this that Hassabis said he fully expects the entire field to change — and change the database along with it.
“Structural biologists are not yet used to the idea that they can just look up anything in a matter of seconds, rather than take years to experimentally determine these things,” he said. “And I think that should lead to whole new types of approaches to questions that can be asked and experiments that can be done. Once we start getting wind of that, we may start building other tools that cater to this sort of serendipity: What if I want to look at 10,000 proteins related in a particular way? There isn’t really a normal way of doing that, because that isn’t really a normal question anyone would ask currently. So I imagine we’ll have to start producing new tools, and there’ll be demand for that once we start seeing how people interact with this.”
That includes derivative and incrementally improved versions of the software itself, which has been released in open source along with a great deal of development history. Already we have seen an independently developed system, RoseTTAFold, from researchers at the University of Washington’s Baker Lab, which extrapolated from AlphaFold’s performance last year to create something similar yet more efficient — though DeepMind seems to have taken the lead again with its latest version. But the point was made that the secret sauce is out there for all to use.
Although the prospect of structural bioinformaticians attaining their fondest dreams is heartwarming, it is important to note that there are in fact immediate and real benefits to the work DeepMind and EMBL-EBI have done. It is perhaps easiest to see in their partnership with the Drugs for Neglected Diseases Institute.
The DNDI focuses, as you might guess, on diseases that are rare enough that they don’t warrant the kind of attention and investment from major pharmaceutical companies and medical research outfits that would potentially result in discovering a treatment.
“This is a very practical problem in clinical genetics, where you have a suspected series of mutations, of changes in an affected child, and you want to try and work out which one is likely to be the reason why our child has got a particular genetic disease. And having widespread structural information, I am almost certain will improve the way we can do that,” said DNDI’s Ewan Birney in a press call ahead of the release.
Ordinarily examining the proteins suspected of being at the root of a given problem would be expensive and time-consuming, and for diseases that affect relatively few people, money and time are in short supply when they can be applied to more common problems like cancers or dementia-related diseases. But being able to simply call up the structures of 10 healthy proteins and 10 mutated versions of the same, insights may appear in seconds that might otherwise have taken years of painstaking experimental work. (The drug discovery and testing process still takes years, but maybe now it can start tomorrow for Chagas disease instead of in 2025.)
Illustration of RNA polymerase II ( a protein) in action in yeast. Image Credits: Getty Images / JUAN GAERTNER/SCIENCE PHOTO LIBRARY
Lest you think too much is resting on a computer’s prediction of experimentally unverified results, in another, totally different case, some of the painstaking work had already been done. John McGeehan of the University of Portsmouth, with whom DeepMind partnered for another potential use case, explained how this affected his team’s work on plastic decomposition.
“When we first sent our seven sequences to the DeepMind team, for two of those we already had experimental structures. So we were able to test those when they came back, and it was one of those moments, to be honest, when the hairs stood up on the back of my neck,” said McGeehan. “Because the structures that they produced were identical to our crystal structures. In fact, they contained even more information than the crystal structures were able to provide in certain cases. We were able to use that information directly to develop faster enzymes for breaking down plastics. And those experiments are already underway, immediately. So the acceleration to our project here is, I would say, multiple years.”
The plan is to, over the next year or two, make predictions for every single known and sequenced protein — somewhere in the neighborhood of a hundred million. And for the most part (the few structures not susceptible to this approach seem to make themselves known quickly) biologists should be able to have great confidence in the results.
Inspecting molecular structure in 3D has been possible for decades, but finding that structure in the first place is difficult. Image Credits: DeepMind
The process AlphaFold uses to predict structures is, in some cases, better than experimental options. And although there is an amount of uncertainty in how any AI model achieves its results, Hassabis was clear that this is not just a black box.
“For this particular case, I think explainability was not just a nice-to-have, which often is the case in machine learning, but it was a must-have, given the seriousness of what we wanted it to be used for,” he said. “So I think we’ve done the most we’ve ever done on a particular system to make the case with explainability. So there’s both explainability on a granular level on the algorithm, and then explainability in terms of the outputs, as well the predictions and the structures, and how much you should or shouldn’t trust them, and which of the regions are the reliable areas of prediction.”
Nevertheless, his description of the system as “miraculous” attracted my special sense for potential headline words. Hassabis said that there’s nothing miraculous about the process itself, but rather that he’s a bit amazed that all their work has produced something so powerful.
“This was by far the hardest project we’ve ever done,” he said. “And, you know, even when we know every detail of how the code works, and the system works, and we can see all the outputs, it’s still just still a bit miraculous when you see what it’s doing… that it’s taking this, this 1D amino acid chain and creating these beautiful 3D structures, a lot of them aesthetically incredibly beautiful, as well as scientifically and functionally valuable. So it was more a statement of a sort of wonder.”
Fold after fold
The impact of AlphaFold and the proteome database won’t be felt for some time at large, but it will almost certainly — as early partners have testified — lead to some serious short-term and long-term breakthroughs. But that doesn’t mean that the mystery of the proteome is solved completely. Not by a long shot.
As noted above, the complexity of the genome is nothing compared to that of the proteome at a fundamental level, but even with this major advance we have only scratched the surface of the latter. AlphaFold solves a very specific, though very important problem: given a sequence of amino acids, predict the 3D shape that sequence takes in reality. But proteins don’t exist in a vacuum; they’re part of a complex, dynamic system in which they are changing their conformation, being broken up and reformed, responding to conditions, the presence of elements or other proteins, and indeed then reshaping themselves around those.
In fact a great deal of the human proteins for which AlphaFold gave only a middling level of confidence to its predictions may be fundamentally “disordered” proteins that are too variable to pin down the way a more static one can be (in which case the prediction would be validated as a highly accurate predictor for that type of protein). So the team has its work cut out for it.
“It’s time to start looking at new problems,” said Hassabis. “Of course, there are many, many new challenges. But the ones you mentioned, protein interaction, protein complexes, ligand binding, we’re working actually on all these things, and we have early, early stage projects on all those topics. But I do think it’s worth taking, you know, a moment to just talk about delivering this big step… it’s something that the computational biology community’s been working on for 20, 30 years, and I do think we have now broken the back of that problem.”
Astronomers Discover an Intermediate-Mass Black Hole as it Destroys a Star – Universe Today
Supermassive black holes (SMBH) reside in the center of galaxies like the Milky Way. They are mind-bogglingly massive, ranging from 1 million to 10 billion solar masses. Their smaller brethren, intermediate-mass black holes (IMBH), ranging between 100 and 100,000 solar masses, are harder to find.
Astronomers have spotted an intermediate-mass black hole destroying a star that got too close. They’ve learned a lot from their observations and hope to find even more of these black holes. Observing more of them may lead to understanding how SMBHs got so massive.
When a star gets too close to a powerful black hole, a tidal disruption event (TDE) occurs. The star is torn apart and its constituent matter is drawn to the black hole, where it gets caught in the hole’s accretion disk. The event releases an enormous amount of energy, outshining all the stars in the galaxy for months, even years.
That’s what happened with TDE 3XMM J215022.4-055108, which is more readily known as TDE J2150. Astronomers were only able to spot the elusive IMBH because of the burst of x-rays emitted by the hot gas from the star as it was torn apart. J2150 is about 740 million light-years from Earth in the direction of the Aquarius constellation. Now a team of researchers has used observations of the distant J2150 and existing scientific models to learn more about the IMBH.
They’ve published their results in a paper titled “Mass, Spin, and Ultralight Boson Constraints from the Intermediate Mass Black Hole in the Tidal Disruption Event 3XMM J215022.4?055108.” The lead author is Sixiang Wen from the University of Arizona. The paper is published in The Astrophysical Journal.
IMBHs are elusive and difficult to study. Astronomers have found several of them in the Milky Way and in nearby galaxies. Mostly they’ve been spotted because of their low-luminosity active galactic nuclei. In 2019 the LIGO and Virgo gravitational wave observatories spotted a gravitational wave from the merger of two IMBHs. As it stands now, there’s a catalogue of only 305 IMBH candidates, even though scientists think they could be common in galactic centers.
One of the problems in seeing them is their low mass itself. While SMBHs can be found by observing how their mass affects the stellar dynamics of nearby stars, IMBHs are typically too small to do the same. Their gravity isn’t powerful enough to change the orbits of nearby stars.
“The fact that we were able to catch this black hole while it was devouring a star offers a remarkable opportunity to observe what otherwise would be invisible,” said Ann Zabludoff, UArizona professor of astronomy and co-author on the paper. “Not only that, by analyzing the flare we were able to better understand this elusive category of black holes, which may well account for the majority of black holes in the centers of galaxies.”
It was the eruption of x-rays that made the event visible. The team compared the observed x-rays with models and was able to confirm the presence of an IMBH. “The X-ray emissions from the inner disk formed by the debris of the dead star made it possible for us to infer the mass and spin of this black hole and classify it as an intermediate black hole,” lead author Wen said.
This is the first time that observations have been detailed enough to be able to use a TDE flare to confirm the presence of an IMBH. It’s a big deal, because though we know that SMBHs lie in the center of galaxies like the Milky Way and larger, our understanding of smaller galaxies and their IMBHs is much more limited. They’re just really hard to see.
“We still know very little about the existence of black holes in the centers of galaxies smaller than the Milky Way,” said co-author Peter Jonker of Radboud University and SRON Netherlands Institute for Space Research, both in the Netherlands. “Due to observational limitations, it is challenging to discover central black holes much smaller than 1 million solar masses.”
The mystery surrounding IMBHs feeds into the mystery surrounding SMBHs. We can see SMBHs at the heart of large galaxies, but we don’t know exactly how they got that massive. Did they go through mergers? Maybe. Through the accretion of matter? Maybe. Astrophysicists mostly agree that both mechanisms may play a role.
Another question surrounds SMBH “seeds.” The seeds could be IMBHs of tens or hundreds of solar masses. The IMBHs themselves could’ve grown from stellar-mass black holes that grew into IMBHs through the accretion of matter. Another possibility is that long before there were actual stars, there were large gas clouds that collapsed into quasi-stars, that then collapsed into black holes. These strange entities would collapse directly from quasi-star to black hole without ever becoming a star, and are known as direct collapse black holes. But these are all hypotheses and models. Astrophysicists need more actual observations, like in the case of TDE J2150, to confirm or rule anything out.
“Therefore, if we get a better handle of how many bona fide intermediate black holes are out there, it can help determine which theories of supermassive black hole formation are correct,” Jonker said.
The team of researchers was also able to measure the black hole’s spin, which has implications for black hole growth, and maybe for particle physics, too. The black hole is spinning quickly, but it’s not spinning as fast as possible. It begs the question, how did the IMBH attain a speed in this range? The spin opens up some possibilities and eliminates others.
“It’s possible that the black hole formed that way and hasn’t changed much since, or that two intermediate-mass black holes merged recently to form this one,” Zabludoff said. “We do know that the spin we measured excludes scenarios where the black hole grows over a long time from steadily eating gas or from many quick gas snacks that arrive from random directions.”
The spin rate may shed some light on potential particle candidates for dark matter, too. One of the hypotheses says that dark matter is made up of particles never seen in a laboratory, called ultralight bosons. These exotic particles, if they exist, would have less than one-billionth the mass of an electron. The IMBHs spin rate may preclude the existence of these candidate particles.
“If those particles exist and have masses in a certain range, they will prevent an intermediate-mass black hole from having a fast spin,” co-author Nicholas Stone said. “Yet J2150’s black hole is spinning fast. So, our spin measurement rules out a broad class of ultralight boson theories, showcasing the value of black holes as extraterrestrial laboratories for particle physics.”
This discovery will build toward a better understanding of dwarf galaxies and their black holes, too. But for that to happen, astrophysicists need to observe more of these IMBH tidal disruption events.
“If it turns out that most dwarf galaxies contain intermediate-mass black holes, then they will dominate the rate of stellar tidal disruption,” Stone said. “By fitting the X-ray emission from these flares to theoretical models, we can conduct a census of the intermediate-mass black hole population in the universe,” Wen added.
As is often the case in astronomy, astrophysics, and cosmology, future telescopes and observatories should advance our knowledge considerably. In this, the Vera C. Rubin Observatory could play a role. The Rubin could discover thousands of TDEs each year.
Then we may finally be able to piece together the story of not only IMBHs but also SMBHs.
NASA splits human spaceflight unit in two, reflecting new orbital economy – CTV News
NASA is splitting its human spaceflight department into two separate bodies – one centred on big, future-oriented missions to the moon and Mars, the other on the International Space Station and other operations closer to Earth.
The reorganization, announced by NASA chief Bill Nelson on Tuesday, reflects an evolving relationship between private companies, such as SpaceX, that have increasingly commercialized rocket travel and the federal agency that had exercised a U.S. monopoly over spaceflight for decades.
Nelson said the shake-up was also spurred by a recent proliferation of flights and commercial investment in low-Earth orbit even as NASA steps up its development of deep-space aspirations.
“Today is more than organizational change,” Nelson said at a press briefing. “It’s setting the stage for the next 20 years, it’s defining NASA’s future in a growing space economy.”
The move breaks up NASA’s Human Exploration and Operations Mission Directorate, currently headed by Kathy Leuders, into two separate branches.
Leuders will keep her associate administrator title as head of the new Exploration Systems Development Mission Directorate, focusing on NASA’s most ambitious, long-term programs, such as plans to return astronauts to the moon under project Artemis, and eventual human exploration of Mars.
A retired deputy associate administrator, James Free, who played key roles in NASA’s space station and commercial crew and cargo programs, will return to the agency as head of the new Space Operations Mission Directorate.
His branch will primarily oversee more routine launch and spaceflight activities, including missions involving the space station and privatization of low-Earth orbit, as well as sustaining lunar operations once those have been established.
“This approach with two areas focused on human spaceflight allows one mission directorate to operate in space while the other builds future space systems,” NASA said in a press release announcing the move.
The announcement came less than a week after SpaceX, which had already flown numerous astronaut missions and cargo payloads to the space station for NASA, launched the first all-civilian crew ever to reach orbit and returned them safely to Earth.
(Reporting by Steve Gorman in Los Angeles; Editing by Leslie Adler)
Elon Musk trolls Biden with Trump line over perceived Inspiration4 snub – CNET
Elon Musk, SpaceX founder and leading orbital travel agent, was feeling a bit slighted by the world’s most powerful man after President Joe Biden failed to acknowledge the company’sthat sent four civilians on a three-day trip in orbit of our planet.
The flight was bankrolled by billionaire Jared Isaacman, who commanded the mission aboard a Crew Dragon capsule, alongside geologist Sian Proctor, data engineer Chris Sembroski and St. Jude Children’s Research Hospital employee Hayley Arceneaux. The quartetoff the coast of Florida on Saturday.
The mission served as a fundraiser for St. Jude, with over $60 million raised from the public so far. Isaacman also pledged $100 million and Musk added $50 million.
When a Twitter user asked why the president hadn’t acknowledged Inspiration4, Musk hopped into the replies.
“He’s still sleeping,” the CEO wrote, in an apparent reference to Donald Trump’s favorite nickname for his former adversary, “sleepy” Joe Biden.
It seems fair to point out, as a number of other Twitter users have, that the president may have a few other things on his plate at the moment, like continuing to manage the response to a global pandemic, climate crisis and various national security threats.
For what it’s worth, NASA administrator Bill Nelson, a Biden appointee, did offer his congratulations to the crew multiple times.
The White House did not immediately respond to a request for comment.
Inspiration4 is the latest in a string ofthis year. Richard Branson flew to the edge of space on the first fully crewed flight of his Virgin Galactic spaceplane in July. Nine days later, Amazon and Blue Origin founder Jeff Bezos cruised a bit higher with three other passengers on his New Shepard spacecraft.
Unlike those flights, which lasted under 15 minutes each, the Inspiration4 mission was a much more complex venture that saw the four passengers performing scientific research during the multiple day flight as they orbited Earth over 40 times.
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