Tyler O'Neal, Staff Editor INDUSTRY

Breakthrough in photosynthesis research: Deep learning applied to protein design

De novo Designed Bilin-Binding Proteins

In a groundbreaking study, researchers at the University of Washington in Seattle have achieved a breakthrough in protein design using a novel deep learning method called RoseTTAFold All-Atom (RFAA). This cutting-edge technique has opened up new possibilities for predicting and designing complexes of proteins, small molecules, and nucleic acids. By utilizing both deep learning and supercomputing power, scientists are now able to create proteins from scratch that can bind a wide range of cofactors and substrates, revolutionizing the field of protein design.

Led by renowned scientist Professor David Baker, the team at the University of Washington developed an additional tool, RFdiffusionAA, which enables the construction of protein structures around small molecules. This transformative advancement has paved the way for designing proteins that can effectively bind and interact with various types of small molecules, a crucial area of interest for many researchers in the scientific community.

The quest to find a suitable candidate small molecule to evaluate the effectiveness of RFdiffusionAA led Professor Neil Hunter at the University of Sheffield in the UK to propose bilins. Bilins are colorless and featureless compounds until they are securely bound within a defined binding site, at which point they become vibrantly colored and visibly emissive. Professor Hunter had previously worked with former PhD student Sam Barnett to create E. coli strains capable of synthesizing bilins, and they had successfully developed a native bilin-binding protein called CpcA.

To validate the efficiency of RFdiffusionAA, current Ph.D. student Felix Morey-Burrows from the Hunter/Hitchcock research group at Sheffield devised a multiwell assay that could rapidly screen a multitude of RFdiffusionAA-generated genes in parallel. By using E. coli cells capable of producing phycoerythrobilin (PEB), Morey-Burrows evaluated 94 designs simultaneously, leading to the identification of nine proteins that displayed pigmentation or fluorescence and were dissimilar to any known native bilin binders.

This crucial experiment not only confirmed the effectiveness of RFdiffusionAA but also demonstrated the immense potential of this method in modeling complex protein-small molecule interactions. These findings have far-reaching implications, particularly in the field of multicomponent biomolecular assemblies, where alternative methods are scarce. Additionally, this breakthrough could enable the design of small molecule binding proteins and sensors, expanding the horizons of biochemical research.

The remarkable aspect of this study lies in its implications for photosynthesis research. The ability to tailor the spectral profiles of designed biliproteins by manipulating the conformational flexibility of the bilin and the protein microenvironment opens up a world of possibilities. With just one round of design using a single chromophore, the researchers successfully covered the 34/30 nm range in absorption/emission. This advancement raises exciting prospects for developing de novo-designed antenna complexes that can harvest light across a wider range of the UV-visible spectrum, thereby enhancing photosynthetic energy capture and conversion. Furthermore, these findings offer the potential for creating fluorescent reporter probes with customizable excitation/emission maxima, valuable tools in biochemical research.

The use of deep learning and supercomputing has undoubtedly played a pivotal role in driving these breakthroughs in protein design. The vast computational power of modern supercomputers, such as those employed in the University of Washington's research, is fundamental to processing the large datasets required for training deep learning algorithms. Through their collaborative efforts, scientists have harnessed the potential of deep learning and supercomputing to unlock the secrets of protein design, propelling us into a new era of scientific discovery.

As researchers continue to explore the applications of deep learning and supercomputing in various fields, we can anticipate more paradigm-shifting developments and remarkable discoveries that will reshape our understanding of the natural world.

UF's HiPerGator supercomputer opens the secrets of ultralow frequency gravitational waves

Tyler O'Neal, Staff Editor INDUSTRY

Pushing the Boundary on Ultralow Frequency Gravitational Waves

A team of physicists at the University of Florida has recently made a groundbreaking discovery that could potentially unravel the mysteries surrounding the early phases of mergers between supermassive black holes - the heaviest objects in the universe. Their cutting-edge method of detecting ultralow frequency gravitational waves has set a new benchmark in the field and could offer profound insights into our cosmic history.

Dr. Jeff Dror, an assistant professor of physics at UF and co-author of the study, describes the detected gravitational waves as "reaching us from the farthest corners of the universe, capable of affecting how light travels." These waves, oscillating just once every thousand years, are a hundred times slower than any gravitational waves previously measured. Dror's research could potentially provide a complete picture of our cosmic history, similar to the monumental discovery of the cosmic microwave background.

Gravitational waves, like ripples in space, are characterized by their frequency and amplitude. They offer valuable information about their origin and age. While previous efforts focused on detecting higher-frequency gravitational waves, the UF team's innovative approach involves studying ultralow frequency waves, undetectable by the human ear. To capture these waves, the researchers turn their attention to pulsars – highly regular radio wave-emitting neutron stars.

The team hypothesizes that the gradual slowdown in the arrivals of these pulsar pulses could reveal new gravitational waves. By analyzing existing pulsar data, Dror successfully extended the range of detectable frequencies to as low as 10 picohertz, a hundred times lower than previous nanohertz-level efforts.

The origin of these ultralow-frequency gravitational waves remains a mystery, and there are two competing theories. One suggests that these waves result from the merger of two supermassive black holes, allowing researchers to explore the behavior of these colossal objects that reside at the core of every galaxy. The other theory proposes that these waves were triggered by cataclysmic events in the early universe. By studying these waves at lower frequencies, scientists hope to differentiate between these possibilities.

To further unravel cosmic history, Dror plans to run simulations using the University of Florida's HiPerGator supercomputer. This cutting-edge technology will enable the team to efficiently analyze large and complex datasets, significantly reducing the time required for their research.

UF's HiPerGator supercomputer has long been recognized for its computational power and its ability to facilitate revolutionary scientific discoveries. With its vast capabilities, the supercomputer is poised to play a crucial role in pushing the boundaries of our understanding of ultralow-frequency gravitational waves.

"The datasets we used were primarily from 2014 and 2015," Dror shared, "and a huge number of pulsar observations have been undertaken since that time." This indicates that there is still much more to be discovered and understood in this increasingly exciting field of gravitational wave research.

The study was supported in part by the National Science Foundation and the Department of Energy. As scientists around the world eagerly look forward to analyzing newer datasets and running simulations on UF's HiPerGator, there is no doubt that we are on the brink of unlocking profound secrets about the origins and evolution of our universe.

German researchers reveal Betelgeuse's boiling surface

Tyler O'Neal, Staff Editor INDUSTRY

Betelgeuse, a red supergiant star located in the constellation of Orion, has always fascinated astronomers and stargazers. Recently, a team of scientists from Garching, Germany, has been studying Betelgeuse's behavior, using the power of supercomputer simulations to uncover its mysteries.

The scientists are challenging the prevailing theory about Betelgeuse's rapid rotation and imminent explosion, offering an alternative explanation based on its convective surface activity. They collaborated across disciplines, using new telescope technology, specifically the Atacama Large Millimeter/submillimeter Array (ALMA), to investigate the star's outer layer's dipolar radial velocity map. The ALMA telescope's limited resolution led to the misinterpretation of the star's convective motions as evidence of rapid rotation.

The scientists' pioneering work highlights the importance of comprehensive understanding and diverse perspectives in the field of astronomy. They recognize that further observations are needed to refine their understanding of Betelgeuse's true nature and validate their predictions. They also acknowledge that collaboration and the data collected from telescopes like ALMA are essential to answering deep astronomical questions.

The scientists at the Max Planck Institute for Astrophysics inspire us to embrace cross-disciplinary approaches, pushing the boundaries of human knowledge and revealing the wonders of the universe. They remind us that with insatiable curiosity, a collaborative spirit, and the might of supercomputer simulations, there are no boundaries to what we can uncover.