Scientists today released a 'sneak preview' of a new open-source artificial intelligence (AI) model that predicts 3D structures of proteins, and say it is close to matching the performance of Google DeepMind's revolutionary protein-folding AI AlphaFold3. The system, called OpenFold3, was developed by the OpenFold Consortium1, a non-profit collaboration of academic and private research groups, headquartered in Davis, California. OpenFold3 uses proteins' amino acid sequences to map their 3D structures and model how they interact with other molecules, such as drugs or DNA. The tool still doesn't have the same functionality as AlphaFold3, but 'we wanted to get something out to the community as soon as possible', says Woody Sherman, the consortium's executive committee chair and chief innovation officer at the firm Psivant Therapeutics in Boston, Massachusetts. The team hopes to use researcher feedback after the preview release to improve the model. Unlike AlphaFold3, which is available for restricted academic use, any researcher or pharmaceutical company can use OpenFold3. 'It's a big step forward in terms of the democratization of AI structural-biology tools,' says Sherman....

As costs for diagnostic and sequencing technologies have plummeted in recent years, researchers have collected an unprecedented amount of data around disease and biology. Unfortunately, scientists hoping to go from data to new cures often require help from someone with experience in software engineering. Now, Watershed Bio is helping scientists and bioinformaticians run experiments and get insights with a platform that lets users analyze complex datasets regardless of their computational skills. The cloud-based platform provides workflow templates and a customizable interface to help users explore and share data of all types, including whole-genome sequencing, transcriptomics, proteomics, metabolomics, high-content imaging, protein folding, and more. 'Scientists want to learn about the software and data science parts of the field, but they don't want to become software engineers writing code just to understand their data,' co-founder and CEO Jonathan Wang '13, SM '15 says. 'With Watershed, they don't have to.'...

Proteins are the workhorses that keep our cells running, and there are many thousands of types of proteins in our cells, each performing a specialized function. Researchers have long known that the structure of a protein determines what it can do. More recently, researchers are coming to appreciate that a protein's localization is also critical for its function. Cells are full of compartments that help to organize their many denizens. Along with the well-known organelles that adorn the pages of biology textbooks, these spaces also include a variety of dynamic, membrane-less compartments that concentrate certain molecules together to perform shared functions. Knowing where a given protein localizes, and who it co-localizes with, can therefore be useful for better understanding that protein and its role in the healthy or diseased cell, but researchers have lacked a systematic way to predict this information. Meanwhile, protein structure has been studied for over half-a-century, culminating in the artificial intelligence tool AlphaFold, which can predict protein structure from a protein's amino acid code, the linear string of building blocks within it that folds to create its structure. AlphaFold and models like it have become widely used tools in research....

First, Hopfield and Hinton, two of the precursors of neural nets, the data-compressing algorithm that underpins developments like ChatGPT, were awarded the Nobel Prize in Physics and, yesterday, three other computer scientists/neuroscientists/chemists, two of which work at Google Deepmind (the CEO, Demis, and John, director), were awarded the Nobel Chemistry Prize, for their contributions to protein folding prediction. As for the Physics prize, the consensus is that both Hopfield and Hinton have been instrumental to the success of Deep Learning, the AI field that comprises neural networks and explains much of the progress of the field over the last two decades. However, their election has been met with mixed feelings, especially from the very own AI field. Especially harsh was Jurgen Schmidhuber, one of the most prominent AI researchers, who directly accused the Academy of 'rewarding plagiarism' as, according to him, both rewardees failed to cite crucial developments by other researchers like Shun-Ichi Amari in their respective research....