School of Physics and Astronomy, & Shanghai National Center for Applied Mathematics (SJTU Center), & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.
Shanghai Artificial Intelligence Laboratory, Shanghai 200030, China.
Sci Adv. 2024 Nov 29;10(48):eadr2641. doi: 10.1126/sciadv.adr2641. Epub 2024 Nov 27.
Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants with improved stability and activity without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on the public mutagenesis dataset across 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the impact of the top 30 to 45 single-site mutations on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize nonnatural nucleic acid or resilience to extreme alkaline conditions. More than 30% of PRIME-recommended mutants exhibited superior performance compared to their premutation counterparts across all proteins and desired properties. We developed an efficient and effective method based on PRIME to rapidly obtain multisite mutants with enhanced activity and stability. Hence, PRIME demonstrates broad applicability in protein engineering.
设计兼具高稳定性和活性的蛋白质突变体是蛋白质工程中的一项关键而具有挑战性的任务。在这里，我们介绍了 PRIME，这是一种深度学习模型，它可以在没有指定蛋白质任何先前实验突变数据的情况下，提出具有改善稳定性和活性的蛋白质突变体。利用温度感知语言建模，PRIME 在 283 个蛋白质测定中，在公共突变数据集上的表现优于当前最先进的模型。此外，我们在五个蛋白质上验证了 PRIME 的预测，研究了前 30 到 45 个单点突变对各种蛋白质特性的影响，包括热稳定性、抗原抗体结合亲和力以及聚合非天然核酸的能力和对极端碱性条件的恢复能力。超过 30%的 PRIME 推荐的突变体在所有蛋白质和所需特性上都表现出优于其前突变体的性能。我们开发了一种基于 PRIME 的高效有效的方法来快速获得具有增强活性和稳定性的多位点突变体。因此，PRIME 在蛋白质工程中具有广泛的适用性。
