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Sciencedaily reporter, Liu Xia<\/p>\n
In 2020, Google’s Deep Thinking Company launched its artificial intelligence model, Alpha Fold 2. Today, it can predict the structures of almost all the more than 200 million known proteins, shortening the time it takes to determine protein structures manually from years to minutes, demonstrating the enormous potential of AI to advance science.<\/p>\n
In the past five years, \u201cAlpha Fold 2\u201d has not only changed the way structural biology is studied, but also promoted the progress of computational biology. However, it will take time to translate its biological insights into practical applications such as drug development.<\/p>\n
Assist in scientific research and exploration<\/p>\n
\u201cAlpha Fold 2\u201d has been used in many scientific scenarios.<\/p>\n
Andrea Pauli’s team at the Institute of Molecular Pathology in Vienna, Austria, has long studied how sperm and eggs combine. In 2018, they found that a Bouncer protein on the surface of zebrafish eggs is essential for fertilisation, but failed to resolve the mechanism by which it recognises sperm.<\/p>\n
The advent of\u201cAlpha Fold 2\u201d has brought about a turnaround. Models predict that a protein called TMEM81 forms a complex with two sperm proteins and binds to bouncers. Experiments soon confirmed this result.<\/p>\n
Wolfgang Pauli says the process has been significantly accelerated by the alpha fold 2, \u201cWhich we now use in every project\u201d. 2024, the team has published a paper on the subject. The paper is one of nearly 40,000 that cite the original\u201cAlpha Fold 2\u201d paper published in nature. In July 2021, deep thinking open-sourced the model and published an article in nature detailing its approach.<\/p>\n
Unlike other highly cited papers, the scientific community’s attention to\u201cAlpha-fold 2\u201d has not abated.<\/p>\n
The key to its rapid adoption, researchers say, is open sharing. Deep thinking not only made its algorithmic code public, it also partnered with European Molecular Biology Laboratory to create the Alpha Fold Database, which now contains more than 240 million structural predictions, it covers the vast majority of known proteins and supports 3.3 million researchers in more than 100 countries.<\/p>\n
Reshaping the face of the discipline<\/p>\n
Nowhere has the impact of alpha-fold 2 been felt more deeply than in structural biology. It has\u201cReshaped the landscape of structural biology\u201d, says Janet Thornton of the European Bioinformatics Institute.<\/p>\n
The data showed that researchers using the\u201cAlpha fold 2\u201d model submitted structures to the protein database about 50% more often than those who were not using it, submission rates are also better than for teams using other AI methods or traditional techniques.<\/p>\n
Even better, this model, trained on protein databases, is feeding back into experimental science. John Jumper, the developer of Alpha Fold 2, stresses that the predicted structure will help researchers interpret experimental data from X-ray crystallography and cryo-em, this is especially useful for the researchers who provide it with training data.<\/p>\n
The computational biology also welcomes the new development by this. Davide Santon observes that\u201cAlpha-fold 2\u201d is now mentioned in almost every presentation at a conference. Ai-assisted drug development, protein design and other directions are particularly prominent in the direct citation of its literature.<\/p>\n
Muhammed Alkureich, a computational biologist at Columbia University in the United States, said that when the\u201cAlpha Fold 2\u201d was first published, many of his colleagues had experienced an\u201cExistential crisis.\u201d The core problem of worrying about protein structure prediction has been thoroughly solved. But it has opened up new avenues of innovation, including AI protein design, which has inspired new research and more funding.<\/p>\n
Now, scientists have used the\u201cAlpha Fold 2\u201d to design strategies to combat antibiotic resistance, find new treatments for diseases such as malaria, and gain insight into disease mechanisms to accelerate the development of targeted drugs.<\/p>\n
The effects of the radiation are far-reaching<\/p>\n
Citing papers directly or using databases is just the tip of the iceberg for the impact of Alpha Fold 2.<\/p>\n
A comprehensive analysis by scientists George Richardson and Davy Ampudia Vincent of the London Laboratory for innovative growth found that, more than 200,000 studies and nearly 800,000 scientists have benefited directly or indirectly from alpha folding 2.<\/p>\n
But when they tried to quantify the impact, they found a mixed picture: output of laboratory papers rose by about 5 per cent, compared with just 2 per cent for individual researchers. Richardson points out that\u201cAlpha-fold 2\u201d did not produce a\u201cPaper factory\u201d, but its value goes deeper.<\/p>\n
The impact is equally diverse in terms of clinical and commercial translation. May Be because 5 years is still short, not enough to be fully reflected in the patent and other achievements. Jumper agrees that translating biological insights into practical applications will take time.<\/p>\n
The 2024, \u201cAlpha Fold 3,\u201d has even greater potential in drug discovery, predicting potential drug-protein interactions.<\/p>\n
Jumper stressed that the findings based on the\u201cAlpha Fold 2\u201d are already helping scientists to uncover the mechanisms of disease and that in the future, patients will be able to recover from the technique.<\/p>\n","protected":false},"excerpt":{"rendered":"
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