The team at Link\u00f6ping University has created AF_unmasked, an enhanced version of AlphaFold that addresses previous limitations of the original software. The new tool demonstrates particular prowess in handling very large protein compounds and can effectively process experimental and incomplete data sets.<\/p>\n

\u201cWe\u2019re giving a new type of input to AlphaFold. The idea is to get the whole picture, both from experiments and neural networks, making it possible to build larger structures. But you can also have a draft of a structure that you feed into AlphaFold and get a relatively accurate result,\u201d explains Claudio Mirabello, docent at the Department of Physics, Chemistry and Biology at Link\u00f6ping University.<\/p>\n

Historical context and progression<\/h4>\n

The development of AlphaFold, released as open-source software by Deepmind in 2020, represented a major breakthrough in the field of protein structure prediction, ultimately contributing to the 2024 Nobel Prize in Chemistry. The success of AlphaFold was built upon decades of protein structure data collection, with approximately 200,000 different proteins documented since the 1970s providing essential training data for the AI system.<\/p>\n

Notably, the Link\u00f6ping team\u2019s influence on protein structure prediction predates the current advancement. Professor Bj\u00f6rn Wallner, who has collaborated with one of the Nobel laureates, developed a precursor to AlphaFold alongside Claudio Mirabello. Their work, particularly their innovation in encoding protein evolutionary history within neural networks, published in 2019, significantly influenced DeepMind\u2019s development of AlphaFold.<\/p>\n

\u201cThe possibilities for protein design are endless, only the imagination sets limits. It\u2019s possible to develop proteins for use both inside and outside the body. You always have to find new, more difficult problems when you have solved the old ones. And within our field, finding problems is no problem,\u201d notes Wallner.<\/p>\n