Jacques Dubochet, Joachim Frank and Richard Henderson have won the 2017 Nobel Prize in chemistry for developing a way to image our cells and biomolecules at cold temperatures. Jacques Dubochet, Joachim Frank and Richard Henderson have won the 2017 Nobel Prize in chemistry for developing a way to image biomolecules at cold temperatures. Cryo-electron microscopy -- or cool microscopy -- transformed biology and medicine because it can take snapshots of our body's materials, like proteins, which are constantly moving at rapid speeds. The technique allowed scientists to figure out how drugs interact with components of our cells and exposed the most intimate corners of our cells on an atomic level. Dubochet, 75, is a Swiss biophysicist who currently conducts his research at University of Lausanne in Lausanne, Switzerland.
Scientists from EPFL (Ecole Polytechnique Federale de Lausanne) have developed nanosensors on AI, which allow researchers to observe different types of biological molecules without disturbing them. The world of biomolecules is rich in captivating interactions between many different agents such as intricate nanomachines (proteins), shape-shifting vessels (lipid complexes), chains of vital information (DNA), and energy fuel (carbohydrates). However, the ways in which biomolecules meet and interact to define an essential symphony are incredibly complex. Scientists at the Bionanophotonic Systems Laboratory in EPFL's School of Engineering have developed a new biosensor that can be used to monitor all major classes of nanoworld biomolecules without disturbing them. Their innovative method uses nanotechnology, metasurfaces, infrared light, and artificial intelligence.
A 3D rendering of a protein complex structures predicted from protein sequences by AF2Complex. From the muscle fibers that move us to the enzymes that replicate our DNA, proteins are the molecular machinery that makes life possible. Protein function heavily depends on their three-dimensional structure, and researchers around the world have long endeavored to answer a seemingly simple inquiry to bridge function and form: if you know the building blocks of these molecular machines, can you predict how they are assembled into their functional shape? This question is not so easy to answer. With complex structures dependent on intricate physical interactions, researchers have turned to artificial neural network models – mathematical frameworks that convert complex patterns into numerical representations – to predict and "see" the shape of proteins in 3D.
The tiny world of biomolecules is rich in fascinating interactions between a plethora of different agents such as intricate nanomachines (proteins), shape-shifting vessels (lipid complexes), chains of vital information (DNA) and energy fuel (carbohydrates). Yet the ways in which biomolecules meet and interact to define the symphony of life is exceedingly complex. Scientists at the Bionanophotonic Systems Laboratory in EPFL's School of Engineering have developed a biosensor that can be used to observe all major biomolecule classes of the nanoworld without disturbing them. Their innovative technique uses nanotechnology, metasurfaces, infrared light and artificial intelligence. The team's research has been published in Advanced Materials.
Scientist are now combining recent advances in evolutionary analysis and deep learning to build three-dimensional models of how most proteins in eukaryotes interact. The research effort has implications for understanding the biochemical processes that are common to all animals, plants, and fungi. The open-access work appears Nov. 11 in Science. As part of a multi-institutional collaboration, the lab of David Baker at the UW Medicine Institute for Protein Design helped guide this new development. "To really understand the cellular conditions that give rise to health and disease, it's essential to know how different proteins in a cell work together," Baker said.