用于从头蛋白质设计的生成式人工智能
Generative artificial intelligence for de novo protein design
AI总结：
本文综述了生成式人工智能（如语言模型和扩散模型）在从头蛋白质设计中的应用，指出其实现近20%实验成功率，并讨论了领域挑战及整合生化知识的重要性。
摘要：
工程化具有期望功能和性质的新分子，有望扩展我们设计蛋白质的能力，超越自然界迄今进化出的范围。所谓的“从头”设计问题的最新进展，近期由人工智能的发展所推动。生成式架构，如语言模型和扩散过程，似乎擅长生成新颖且逼真的蛋白质，这些蛋白质展现出期望的性质并执行特定的功能。最先进的设计方案现在实现了接近20%的实验成功率，从而拓宽了获取从头设计蛋白质的途径。尽管取得了广泛进展，但领域内仍存在明显的挑战，例如确定最佳的计算指标以优先选择用于实验测试的设计，以及设计能够经历大的构象变化或受翻译后修饰及其他细胞过程调控的蛋白质。随着开发的模型数量增加，本综述提供了一个框架，以理解这些工具如何融入从头蛋白质设计的整体流程。在整个过程中，我们强调了整合生物化学知识以提高性能和可解释性的力量。
英文摘要：
Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called "de novo" design problem have recently been brought forward by developments in artificial intelligence. Generative architectures, such as language models and diffusion processes, seem adept at generating novel, yet realistic proteins that display desirable properties and perform specified functions. State-of-the-art design protocols now achieve experimental success rates nearing 20%, thus widening the access to de novo designed proteins. Despite extensive progress, there are clear field-wide challenges, for example in determining the best in silico metrics to prioritise designs for experimental testing, and in designing proteins that can undergo large conformational changes or be regulated by post-translational modifications and other cellular processes. With an increase in the number of models being developed, this review provides a framework to understand how these tools fit into the overall process of de novo protein design. Throughout, we highlight the power of incorporating biochemical knowledge to improve performance and interpretability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
