{"id":54026,"date":"2022-11-07T18:04:00","date_gmt":"2022-11-07T17:04:00","guid":{"rendered":"https:\/\/www.embl.org\/news\/?p=54026"},"modified":"2022-11-08T10:36:07","modified_gmt":"2022-11-08T09:36:07","slug":"alphafold-community-applications","status":"publish","type":"post","link":"https:\/\/www.embl.org\/news\/science\/alphafold-community-applications\/","title":{"rendered":"AlphaFold applications \u2013 a community assessment"},"content":{"rendered":"\n

In the era of social media, researchers studying similar topics can work together despite being based in different parts of the world. Social platforms have become a tool for scientific collaboration, including testing the validity of new methods and technologies, such as the AlphaFold AI system for predicting protein structures. <\/p>\n\n\n\n

A paper published in Nature Structural and Molecular Biology<\/em><\/a> has brought together results from a number of groups who, when the AlphaFold code and protein structure predictions were first made public<\/a>, coordinated their efforts through social media to evaluate the use of the predictions in a range of applications. <\/p>\n\n\n\n

Building on the buzz<\/strong><\/h2>\n\n\n\n

\u201cAfter the AlphaFold launch, there was so much buzz in the structural biology world about what this was going to mean for our day to day work,\u201d explained Pedro Beltrao, Associate Professor at ETH and former Group Leader at EMBL-EBI. \u201cLots of people were sharing initial results and excitement on social media, exploring the method and the predictions. I contacted several of them to see if they were interested in a joint study rounding up all the results that were being shared left and right on social media.\u201d<\/p>\n\n\n\n

Beltrao found that there was a lot of interest in his idea. \u201cIt picked up speed very quickly. At one point I was even receiving results on social media and, bit by bit, this paper got built up from social media interactions. It was a real bottom-up approach, building on the excitement of the initial results.<\/p>\n\n\n\n

Beltrao mentioned seeing a similar thing with COVID \u2013 scientists who had never met before, based in different corners of the world, working together to uncover insights about the virus. On this occasion, the trigger wasn\u2019t a health crisis, it was an exciting new discovery. <\/p>\n\n\n\n

\u201cI think this really illustrates a new way of doing science. It\u2019s like maximising the capacity of everyone and bringing everything together while trying to avoid duplication of efforts. And personally I see a lot of value in this, it\u2019s an exciting way to work.\u201d<\/p>\n\n\n\n

The versatility of structure predictions<\/strong><\/h2>\n\n\n\n

There is a whole world of applications for protein structure predictions. But when EMBL-EBI and DeepMind made the first batch of 350,000 AlphaFold predictions openly available to the scientific community through the AlphaFold Database<\/a> in the summer of 2021, researchers had many unanswered questions about how accurate and useful the predictions would be.<\/p>\n\n\n\n

In the end, this paper brought together researchers from 18 institutes in 11 countries. They pooled their results evaluating the use of AlphaFold predictions in the following areas:<\/p>\n\n\n\n