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尹志
(2023-11-30 16:36):
#paper Hamed Khakzad, Ilia Igashov, Arne Schneuing, et al. A new age in protein design empowered by deep learning. Cell Systems 14, 925–939 (2023). https://doi.org/10.1016/j.cels.2023.10.006.
蛋白质作为细胞的主要组成，参与了包括酶促反应、信号转导等在内的各种生命反应，其意义毋庸置疑。但是如何通过人工的方式设计特定的蛋白质，从而解决疾病治疗、药物研发等一系列生命科学问题，一直是科学家的追求。人工智能的发展，特别是深度学习的发展，给这个主题带来了特别巨大的进展。这篇最新的综述就是对使用深度学习进行蛋白质设计的几类范式和sota方法进行了介绍。从方法角度看，介绍的非常全面。有意思的是，我们会发现目前生成式模型在AI的冲击已经迁移到蛋白质设计领域，并孵化出独有的味道。图神经网络、物理启发的模型、语言模型的模仿、深度生成模型的利用在蛋白质设计领域都展现出不错的性能，特别是当把几何先验通过数学的手段，比如群轮与深度学习进行结合，往往可以较好的捕获蛋白质精巧晦涩的结构信息。当然，考虑到蛋白质设计所涉及的序列、结构、功能三者的精密联系，如何协调序列建模、结构建模等方法，也成为未来发展的关键问题。文章中对数据、benchmark等方面的讨论也很有价值。当然，问题也是一大堆，最令人不爽的是，拥有生命科学基本属性的蛋白质设计，最终的效果需要实验甚至实际效果进行验证，因此计算方法论上再优秀的设计，也需要湿实验、临床实验的验证。希望随着技术的进步，这个领域的自动化agent技术会带来全新的范式。
Abstract:
The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading …
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The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.
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