The lane, which goes from Nuevo León, Mexico, into Texas, is only to be used by Tesla and its suppliers.
Current approaches to de novo design of proteins harboring a desired binding or catalytic motif require pre-specification of an overall fold or secondary structure composition, and hence considerable trial and error can be required to identify protein structures capable of scaffolding an arbitrary functional site. Here we describe two complementary approaches to the general functional site design problem that employ the RosettaFold and AlphaFold neural networks which map input sequences to predicted structures. In the first “constrained hallucination” approach, we carry out gradient descent in sequence space to optimize a loss function which simultaneously rewards recapitulation of the desired functional site and the ideality of the surrounding scaffold, supplemented with problem-specific interaction terms, to design candidate immunogens presenting epitopes recognized by neutralizing antibodies, receptor traps for escape-resistant viral inhibition, metalloproteins and enzymes, and target binding proteins with designed interfaces expanding around known binding motifs. In the second “missing information recovery” approach, we start from the desired functional site and jointly fill in the missing sequence and structure information needed to complete the protein in a single forward pass through an updated RoseTTAFold trained to recover sequence from structure in addition to structure from sequence. We show that the two approaches have considerable synergy, and AlphaFold2 structure prediction calculations suggest that the approaches can accurately generate proteins containing a very wide array of functional sites.
Current approaches to de novo design of proteins harboring a desired binding or catalytic motif require pre-specification of an overall fold or secondary structure composition, and hence considerable trial and error can be required to identify protein structures capable of scaffolding an arbitrary functional site. Here we describe two complementary approaches to the general functional site design problem that employ the RosettaFold and AlphaFold neural networks which map input sequences to predicted structures. In the first “constrained hallucination” approach, we carry out gradient descent in sequence space to optimize a loss function which simultaneously rewards recapitulation of the desired functional site and the ideality of the surrounding scaffold, supplemented with problem-specific interaction terms, to design candidate immunogens presenting epitopes recognized by neutralizing antibodies, receptor traps for escape-resistant viral inhibition, metalloproteins and enzymes, and target binding proteins with designed interfaces expanding around known binding motifs. In the second “missing information recovery” approach, we start from the desired functional site and jointly fill in the missing sequence and structure information needed to complete the protein in a single forward pass through an updated RoseTTAFold trained to recover sequence from structure in addition to structure from sequence. We show that the two approaches have considerable synergy, and AlphaFold2 structure prediction calculations suggest that the approaches can accurately generate proteins containing a very wide array of functional sites.The authors have declared no competing interest.
A team of University of Kentucky researchers led by College of Engineering Professor Dibakar Bhattacharyya, Ph.D., and his Ph.D. student, Rollie Mills, have developed a medical face mask membrane that can capture and deactivate the SARS-CoV-2 spike protein on contact.
At the beginning of the COVID-19 pandemic in 2020, Bhattacharyya, known to friends and colleagues as “DB,” along with collaborators across disciplines at UK set out to create the material. Their work was published in Communications Materials on May 24.
SARS-CoV-2 is covered in spike proteins, which allow the virus to enter host cells once in the body. The team developed a membrane that includes proteolytic enzymes that attach to the protein spikes and deactivates them.
Silicon is one of the most abundant elements on Earth, and in its pure form the material has become the foundation of much of modern technology, from solar cells to computer chips. But silicon’s properties as a semiconductor are far from ideal.
For one thing, although silicon lets electrons whizz through its structure easily, it is much less accommodating to “holes”—electrons’ positively charged counterparts—and harnessing both is important for some kinds of chips. What’s more, silicon is not very good at conducting heat, which is why overheating issues and expensive cooling systems are common in computers.
Now, a team of researchers at MIT, the University of Houston, and other institutions has carried out experiments showing that a material known as cubic boron arsenide overcomes both of these limitations. It provides high mobility to both electrons and holes, and has excellent thermal conductivity. It is, the researchers say, the best semiconductor material ever found, and maybe the best possible one.
Theoretical physicists at the Max Planck Institute for the Structure and Dynamics of Matter (MPSD) have demonstrated how the coupling between intense lasers, the motion of electrons, and their spin influences the emission of light on the ultrafast timescale.
Electrons, which are present in all kinds of matter, are charged particles and therefore react to the application of light. When an intense light field hits a solid, electrons experience a force, called the Lorentz force, that drives them and induces some exquisite dynamics reflecting the properties of the material. This, in turn, results in the emission of light by the electrons at various wavelengths, a well-known phenomenon called high-harmonic generation.
Exactly how the electrons move under the influence of the light field depends on a complex mixture of properties of the solid, including its symmetries, topology, and band structure, as well as the nature of the light pulse. Additionally, electrons are like spinning tops. They have a propensity to rotate either clockwise or counter-clockwise, a property called the “spin” of the electrons in quantum mechanics.
The widely celebrated James Webb Space Telescope has received damage to one of its mirrors from a micrometeoroid, but NASA says not to worry. — Videos from The Weather Channel | weather.com
Scientists have developed artificial intelligence software that can create proteins that may be useful as vaccines, cancer treatments, or even tools for pulling carbon pollution out of the air.
This research, reported today in the journal Science, was led by the University of Washington School of Medicine and Harvard University. The article is titled “Scaffolding protein functional sites using deep learning.”
“The proteins we find in nature are amazing molecules, but designed proteins can do so much more,” said senior author David Baker, an HHMI Investigator and professor of biochemistry at UW Medicine. “In this work, we show that machine learning can be used to design proteins with a wide variety of functions.”
Proteins serve a variety of purposes in plants in addition to being the fundamental building blocks of life. More than 20 billion protein molecules make up a typical plant cell, helping to stabilize its structure and sustain cellular metabolism.
Researchers at Heidelberg University’s Centre for Organismal Studies have shed light on a biological process that increases the life of plant proteins. They have now discovered a crucial protein, called N-terminal acetylation, that controls this mechanism. The study’s findings were published in the journals Molecular Plant and Science Advances.
N-terminal acetylation is a chemical marker that develops during the production of proteins. Plants do this by affixing an acetic acid.
Circa 2021
Mycelium is very light in weight, it naturally floats on water, it can withstand the cold of space where we don’t have to worry about cold welding, and we can add in fine strains of metal material which is used to transmit almost any type of signal. As you can see, there are numerous reasons why mycelium is quite suitable for our satellites in space, on land, and in the air on its way to space.
Of course, there’s also the all-important issue of space debris, which is projected to become a severe hazard to satellites and spacecraft in Low Earth Orbit (LEO) in the coming years.
Simpler, faster, smaller, and cheaper chips are a key to low-power computing — even in AI.
RISC-V is taking off like a rocket.
In this video I discuss how RISC-V will reshape chip design industry.
#RISCV
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