Protein condensates that form by undergoing liquid-liquid phase separation will show changes in their rheological properties with time, a process known as aging. Jawerth et al. used laser tweezer–based active and microbead-based passive rheology to characterize the time-dependent material properties of protein condensates (see the Perspective by Zhang). They found that condensate aging is not gelation of the condensates, but rather a changing viscoelastic Maxwell liquid with a viscosity that strongly increases with age, whereas the elastic modulus stays the same. Science , this issue p. ; see also p.  Protein condensates are complex fluids that can change their material properties with time. However, an appropriate rheological description of these fluids remains missing. We characterize the time-dependent material properties of in vitro protein condensates using laser tweezer–based active and microbead-based passive rheology. For different proteins, the condensates behave at all ages as viscoelastic Maxwell fluids. Their viscosity strongly increases with age while their elastic modulus varies weakly. No significant differences in structure were seen by electron microscopy at early and late ages. We conclude that protein condensates can be soft glassy materials that we call Maxwell glasses with age-dependent material properties. We discuss possible advantages of glassy behavior for modulation of cellular biochemistry. : /lookup/doi/10.1126/science.aaw4951 : /lookup/doi/10.1126/science.abe9745
Here, we show that the prion domain of Sup35 drives the reversible phase separation of the translation termination factor into biomolecular condensates. These condensates are distinct and different from fibrillar amyloid-like prion particles. Combining genetic analysis in cells with in vitro reconstitution protein biochemistry and quantitative biophysical methods, we demonstrate that Sup35 condensates form by pH-induced liquid-like phase separation as a response to sudden stress. The condensates are liquid-like initially but subsequently solidify to form protective protein gels. Cryo–electron tomography demonstrates that these gel-like condensates consist of cross-linked Sup35 molecules forming a porous meshwork.
Imagine packing all the people in the world into the Great Salt Lake in Utah--all of us jammed shoulder to shoulder, yet also charging past one another at insanely high speeds. That gives you some idea of how densely crowded the 5 billion proteins in a typical cell are, said Anthony Hyman, a British cell biologist and a director of the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research develop ments and trends in mathe matics and the physical and life sciences. Somehow in that bustling cytoplasm, enzymes need to find their substrates, and signaling molecules need to find their receptors, so the cell can carry out the work of growing, dividing and surviving. If cells were sloshing bags of evenly mixed cytoplasm, that would be difficult to achieve.
Powerful algorithms used by Netflix, Amazon and Facebook can'predict' the biological language of cancer and neurodegenerative diseases like Alzheimer's, scientists have found. Big data produced during decades of research was fed into a computer language model to see if artificial intelligence can make more advanced discoveries than humans. Academics based at St John's College, University of Cambridge, found the machine-learning technology could decipher the'biological language' of cancer, Alzheimer's, and other neurodegenerative diseases. Their ground-breaking study has been published in the scientific journal PNAS today (April 8 2021) and could be used in the future to'correct the grammatical mistakes inside cells that cause disease'. Professor Tuomas Knowles, lead author of the paper and a Fellow at St John's College, said: "Bringing machine-learning technology into research into neurodegenerative diseases and cancer is an absolute game-changer. Ultimately, the aim will be to use artificial intelligence to develop targeted drugs to dramatically ease symptoms or to prevent dementia happening at all."
Algorithms similar to those used by Netflix, Amazon and Facebook have shown the ability to decipher the'biological language' of cancer, Alzheimer's and other neurodegenerative diseases. Researchers trained a large-scale language model with a recommendation AI to look at what happens when something goes wrong with proteins that leads to the development of a disease. The work, conducted by St. John's College and the University of Cambridge, programed the algorithm to learn the language of shapeshifting droplets of proteins found in cells in order to understand their function and malfunction. By learning these protein droplets' language, the team can then'correct the grammatical mistakes inside cells that cause disease.'' Professor Tuomas Knowles, a Fellow at St John's College, said: 'Any defects connected with these protein droplets can lead to diseases such as cancer. 'This is why bringing natural language processing technology into research into the molecular origins of protein malfunction is vital if we want to be able to correct the grammatical mistakes inside cells that cause disease.' Machine learning technology has made waves in the tech industry – Netflix uses it to recommend series, Facebook's suggest someone to friend and Amazon's Alexa has an algorithm to recognize people based on their voice.