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.
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.)
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.
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.”
NASA, Boeing launch Starliner to the ISS: How to watch test flight live – CNET
Boeing is set to relaunch its Starliner crew capsule for a second attempt at docking with the International Space Station this Tuesday, Aug. 3 (there won’t be any humans aboard). Boeing’s to reach the ISS but landed safely back on Earth.
The mission was originally scheduled to take off Friday, but it’s now aiming for Tuesday after anfiring its thrusters shortly after docking with the station.
“The International Space Station team will use the time to continue working checkouts of the newly arrived Roscosmos Nauka multipurpose laboratory module (MLM) and to ensure the station will be ready for Starliner’s arrival,” said NASA in a statement.
Software defects and a communications link problem led to a premature end to the original Boeing test flight in 2019, though the CST-100 Starliner capsule landed safely back on Earth. The upcoming Orbital Flight Test-2 (OFT-2) mission is a chance for Boeing to thoroughly vet its hardware and software before a crew of three American astronauts flies on Starliner.
Both Boeing and SpaceX are part of NASA’s Commercial Crew Program, which is all about sending astronauts to the ISS from American soil. SpaceX has now delivered 10 astronauts to the ISS, and Boeing would like to catch up. First, it’ll need to show that its Starliner can safely reach the ISS and return to Earth.
NASA will livestream the launch, which is scheduled to occur at 10:20 a.m. PT (1:20 p.m. ET) on Tuesday Aug. 3. Coverage is expected to begin at 9:30 a.m. PT.
Starliner will lift off on a United Launch Alliance (ULA) Atlas V rocket. The capsule will be packed with around 400 pounds of crew supplies and cargo. If all goes well, it’ll dock with the space station about 24 hours later, on Wednesday Aug. 4. Docking will also be covered live by NASA’s NASA TV.
ULA shared some scenic photos from the launch site on Monday as it prepares for liftoff.
Starliner will spend between five and 10 days at the ISS before bringing research samples back to Earth. Boeing will aim to bring the spacecraft back for a gentle parachute landing in the desert of New Mexico.
“OFT-2 will provide valuable data that will help NASA certify Boeing’s crew transportation system to carry astronauts to and from the space station,” NASA said in a statement July 22 after successfully concluding a flight readiness review.
The mission is a key step for NASA’s plans to run regular crewed launches from the US, ending its reliance on Russian Soyuz spacecraft. If all goes well, the first crewed mission, Boe-CFT, could launch in the next six months.
Follow CNET’s 2021 Space Calendar to stay up to date with all the latest space news this year. You can even add it to your own Google Calendar.
Meteor Shower 2021: Why There Are Only A Few Precious Hours In 2021 When You Can Reliably See ‘Shooting Stars’ – Forbes
Have you ever seen a “shooting star?” If you haven’t, you’ll no doubt have read articles imploring you to go outside and experience a “shower” of meteors.
There’s no such thing as a “meteor shower.”
Meteoroids don’t behave like that. “Shooting stars” are caused by Earth’s atmosphere colliding with clumps of dust left along its orbital path by a passing comet. They look like streaks and they last around a second, depending on the “shower” in question.
“Shooting stars” are sudden events that can happen anywhere in the night sky, but they’re sporadic. They rarely happen together. For instance, you might see one out of the corner of your eye and, five minutes later, see another one in a completely different part of the sky. Many of them you will miss. There are never two or three—or more—“raining down” at the same time, as composite photographs would suggest.
Besides, when you read that a “meteor shower” like the Lyrids, Orionids or Geminids could have “up to 150 shooting stars per hour,” what it really means that it might be possible to see that many (the so-called zenithal hourly rate or ZHR) in perfect conditions. That scenario is, in practice, impossible to achieve—you would need to be observing the entire night sky constantly, for many hours either side of the absolute “peak” of activity, and in super-dark skies.
However, the biggest factor that determines what you’re likely to see—and one many meteor shower-promoters fail to point out—is the effect of Moon and moonlight.
If there’s a first quarter Moon or anything brighter, particularly a full Moon, in the sky during the peak night(s) of a meteor shower, you can forget seeing anything other than the very brightest of “shooting stars.” And they’re very rare.
If the Moon is big and bright then, in effect, you’ll be observing from under a heavily light-polluted night sky even if you’ve gone to a dark sky destination.
So which meteor showers are the ones to go for in 2021? There are going to be three meteor showers in 2021 that will occur under near-ideal conditions.
The bad news?
The first (and by far the best) one isn’t until August 2021.
The good news?
It’s the Perseids, arguably the most famous and easiest meteor shower to observe in the northern hemisphere … largely because it occurs in the middle of summer when it’s easiest to be outdoors at night.
The best three meteor showers in 2021, these will be best observed after midnight, with the exception of the Draconids, which can be observed right after dark.
1. Perseid meteor shower 2021
When: Thursday/Friday, August 12/13, 2021
Moon phase: 23%-lit crescent Moon
2. Draconid meteor shower 2021
When: Friday/Saturday, October 8/9, 2021
Moon phase: 10%-lit crescent Moon
3. South Taurid meteor shower 2021
When: Thursday/Friday, November 4/5, 2021
Moon phase: 0.1%-lit crescent Moon
Wishing you clear skies and wide eyes.
Lake Huron sinkhole surprise: The rise of oxygen on early Earth linked to changing planetary rotation rate – Phys.org
The rise of oxygen levels early in Earth’s history paved the way for the spectacular diversity of animal life. But for decades, scientists have struggled to explain the factors that controlled this gradual and stepwise process, which unfolded over nearly 2 billion years.
Now an international research team is proposing that increasing day length on the early Earth—the spinning of the young planet gradually slowed over time, making the days longer—may have boosted the amount of oxygen released by photosynthetic cyanobacteria, thereby shaping the timing of Earth’s oxygenation.
Their conclusion was inspired by a study of present-day microbial communities growing under extreme conditions at the bottom of a submerged Lake Huron sinkhole, 80 feet below the water’s surface. The water in the Middle Island Sinkhole is rich in sulfur and low in oxygen, and the brightly colored bacteria that thrive there are considered good analogs for the single-celled organisms that formed mat-like colonies billions of years ago, carpeting both land and seafloor surfaces.
The researchers show that longer day length increases the amount of oxygen released by photosynthetic microbial mats. That finding, in turn, points to a previously unconsidered link between Earth’s oxygenation history and its rotation rate. While the Earth now spins on its axis once every 24 hours, day length was possibly as brief as 6 hours during the planet’s infancy.
The team’s findings are scheduled for publication Aug. 2 in the journal Nature Geoscience.
Lead authors are Judith Klatt of the Max Planck Institute for Marine Microbiology and Arjun Chennu of the Leibniz Centre for Tropical Marine Research. Klatt is a former postdoctoral researcher in the lab of University of Michigan geomicrobiologist Gregory Dick, who is one of the study’s two corresponding authors. The other co-authors are from U-M and Grand Valley State University.
“An enduring question in the Earth sciences has been how did Earth’s atmosphere get its oxygen, and what factors controlled when this oxygenation took place,” Dick said from the deck of the R/V Storm, a 50-foot NOAA research vessel that carried a team of scientists and scuba divers on a sample-collection trip from the town of Alpena, Michigan, to the Middle Island Sinkhole, several miles offshore.
“Our research suggests that the rate at which the Earth is spinning—in other words, its day length—may have had an important effect on the pattern and timing of Earth’s oxygenation,” said Dick, a professor in the U-M Department of Earth and Environmental Sciences.
The researchers simulated the gradual slowing of Earth’s rotation rate and showed that longer days would have boosted the amount of oxygen released by early cyanobacterial mats in a manner that helps explain the planet’s two great oxygenation events.
The project began when co-author Brian Arbic, a physical oceanographer in the U-M Department of Earth and Environmental Sciences, heard a public lecture about Klatt’s work and noted that day length changes could play a role, over geological time, in the photosynthesis story that Dick’s lab was developing.
Cyanobacteria get a bad rap these days because they are the main culprits behind the unsightly and toxic algal blooms that plague Lake Erie and other water bodies around the world.
But these microbes, formerly known as blue-green algae, have been around for billions of years and were the first organisms to figure out how to capture energy from sunlight and use it to produce organic compounds through photosynthesis—releasing oxygen as a byproduct.
Masses of these simple organisms living in primeval seas are credited with releasing oxygen that later allowed for the emergence of multicellular animals. The planet was slowly transformed from one with vanishingly small amounts of oxygen to present-day atmospheric levels of around 21%.
At the Middle Island Sinkhole in Lake Huron, purple oxygen-producing cyanobacteria compete with white sulfur-oxidizing bacteria that use sulfur, not sunlight, as their main energy source.
In a microbial dance repeated daily at the bottom of the Middle Island Sinkhole, filmy sheets of purple and white microbes jockey for position as the day progresses and as environmental conditions slowly shift. The white sulfur-eating bacteria physically cover the purple cyanobacteria in the morning and evening, blocking their access to sunlight and preventing them from carrying out oxygen-producing photosynthesis.
But when sunlight levels increase to a critical threshold, the sulfur-oxidizing bacteria migrate back down below the photosynthetic cyanobacteria, enabling them to start producing oxygen.
The vertical migration of sulfur-oxidizing bacteria has been observed before. What’s new is that the authors of the Nature Geoscience study are the first to link these microbial movements, and the resultant rates of oxygen production, to changing day length throughout Earth’s history.
“Two groups of microbes in the Middle Island Sinkhole mats compete for the uppermost position, with sulfur-oxidizing bacteria sometimes shading the photosynthetically active cyanobacteria,” Klatt said while processing a core sample from Middle Island Sinkhole microbial mats in an Alpena laboratory. “It’s possible that a similar type of competition between microbes contributed to the delay in oxygen production on the early Earth.”
A key to understanding the proposed link between changing day length and Earth’s oxygenation is that longer days extend the afternoon high-light period, allowing photosynthetic cyanobacteria to crank out more oxygen.
“The idea is that with a shorter day length and shorter window for high-light conditions in the afternoon, those white sulfur-eating bacteria would be on top of the photosynthetic bacteria for larger portions of the day, limiting oxygen production,” Dick said as the boat rocked on choppy waters, moored a couple hundred yards from Middle Island.
The present-day Lake Huron microbes are believed to be good analogs for ancient organisms in part because the extreme environment at the bottom of the Middle Island Sinkhole likely resembles the harsh conditions that prevailed in the shallow seas of early Earth.
Lake Huron is underlain by 400-million-year-old limestone, dolomite and gypsum bedrock that formed from the saltwater seas that once covered the continent. Over time, the movement of groundwater dissolved some of that bedrock, forming caves and cracks that later collapsed to create both on-land and submerged sinkholes near Alpena.
Cold, oxygen-poor, sulfur-rich groundwater seeps into the bottom of the 300-foot-diameter Middle Island Sinkhole today, driving away most plants and animals but creating an ideal home for certain specialized microbes.
Dick’s team, in collaboration with co-author Bopaiah Biddanda of the Annis Water Resources Institute at Grand Valley State University, has been studying the microbial mats on the floor of Middle Island Sinkhole for several years, using a variety of techniques. With the help of scuba divers from NOAA’s Thunder Bay National Marine Sanctuary—which is best known for its shipwrecks but is also home to the Middle Island Sinkhole and several others like it—the researchers deployed instruments to the lake floor to study the chemistry and biology there.
They also brought mat samples to the lab to conduct experiments under controlled conditions.
Klatt hypothesized that the link between day length and oxygen release can be generalized to any given mat ecosystem, based on the physics of oxygen transport. She teamed up with Chennu to conduct detailed modeling studies to relate microbial mat processes to Earth-scale patterns over geological timescales.
The modeling studies revealed that day length does, in fact, shape oxygen release from the mats.
“Simply speaking, there is just less time for the oxygen to leave the mat in shorter days,” Klatt said.
This led the researchers to posit a possible link between longer day lengths and increasing atmospheric oxygen levels. The models show that this proposed mechanism might help explain the distinctive stepwise pattern of Earth’s oxygenation, as well as the persistence of low-oxygen periods through most of the planet’s history.
Throughout most of Earth’s history, atmospheric oxygen was only sparsely available and is believed to have increased in two broad steps. The Great Oxidation Event occurred about 2.4 billion years ago and has generally been credited to the earliest photosynthesizing cyanobacteria. Nearly 2 billion years later a second surge in oxygen, known as the Neoproterozoic Oxygenation Event, occurred.
Earth’s rotation rate has been slowly decreasing since the planet formed about 4.6 billion years ago due to the relentless tug of the moon’s gravity, which creates tidal friction.
Possible link between Earth’s rotation rate and oxygenation, Nature Geoscience (2021). DOI: 10.1038/s41561-021-00784-3 , www.nature.com/articles/s41561-021-00784-3
University of Michigan
Lake Huron sinkhole surprise: The rise of oxygen on early Earth linked to changing planetary rotation rate (2021, August 2)
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