人工智能alpha fold 2开放代码与蛋白质开放数据库

在2021年7月22日，DeepMind和欧洲分子生物学实验室（European Bioinformatics Institute）联合宣布，两家合作，用自己开发的人工智能分析工具AlphaFold2预测了36.5万种蛋白质的结构，然后把这些结果做成数据库，免费给全球所有科研人员使用。

 

 

Deepmind

https://deepmind.com/

 

 

EMBL-EBI

https://www.ebi.ac.uk/

 

 

 

这不仅会推动生物学相关的研究，也在改变科学研究的面貌——人工智能、数据科学可能带来科学研究领域划时代的变革。

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’8, has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.