蛋白质结构预测模型AlphaFold2的应用进展

Advances in the application of AlphaFold2: a protein structure prediction model

摘要:

蛋白质结构预测是生命科学和医学的重要研究领域，也是人工智能在科学研究中的重要应用场景。AlphaFold2是由DeepMind开发的一种基于深度学习的蛋白质结构预测系统，可以从氨基酸序列中高效地生成原子级精度的蛋白质空间结构。由于AlphaFold2优越的性能，自问世以来对蛋白质结构预测方面的研究提供了前所未有的助力，因此备受关注和研究。本文介绍了AlphaFold2的模型架构、亮点、局限性和应用进展，列举了几种其他类型的蛋白质结构预测模型，并讨论了其能力、优势及局限性并思考该蛋白质结构预测模型的未来发展方向。

Abstract:

Protein structure prediction is an important research field in life sciences and medicine, and it is also a key application scenario of artificial intelligence in scientific research. AlphaFold2 is a protein structure prediction system developed by DeepMind based on deep learning, capable of efficiently generating the atomic-scale spatial structure of a protein from the amino acid sequence. It has demonstrated superior performance in the prediction of protein structures since its inception, thus attracting much attention and research. This paper introduces the model architecture, highlights, limitations, and application progress of AlphaFold2. Furthermore, it briefs the capabilities, highlights, and limitations of several other types of protein structure prediction models and prospects the future development direction in this field.