Order from disordered proteins
Peer-Reviewed Publication
Updates every hour. Last Updated: 29-Oct-2025 10:11 ET (29-Oct-2025 14:11 GMT/UTC)
Researchers at Harvard and Northwestern have developed a machine learning method that can design intrinsically disordered proteins with custom properties, addressing nearly 30% of all human proteins that are currently out of reach of AI tools like AlphaFold. The new approach uses automatic differentiation, traditionally a deep learning tool, to optimize protein sequences for desired properties.
MIT researchers developed a framework that can help engineers design systems that involve many interconnected parts in a way that explicitly accounts for the uncertainty in each component’s performance. This could lead to electronic devices like drones and robots that are more robust and reliable in unpredictable, real-world situations.
In the era of instant data exchange and growing risks of cyberattacks, scientists are seeking secure methods of transmitting information. One promising solution is quantum cryptography – a quantum technology that uses single photons to establish encryption keys. A team from the Faculty of Physics at the University of Warsaw has developed and tested in urban infrastructure a novel system for quantum key distribution (QKD). The system employs so-called high-dimensional encoding. The proposed setup is simpler to build and scale than existing solutions, while being based on a phenomenon known to physicists for nearly two centuries – the Talbot effect. The research results have been published in prestigious journals: “Optica Quantum”, “Optica”, and “Physical Review Applied”.