Li Daixi, Zhu Yuqi, Zhang Wujie, Liu Jing, Yang Xiaochen, Liu Zhihong, Wei Dongqing
Institute of Biothermal Engineering, University of Shanghai for Science and Technology, Shanghai, 20093, China.
Pengcheng Laboratory, Shenzhen, 518055, China.
Interdiscip Sci. 2025 Mar;17(1):101-113. doi: 10.1007/s12539-024-00662-7. Epub 2024 Oct 5.
The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, while pursuing high thermodynamic stability and rigidity of proteins, inevitably sacrifices biological functions closely related to protein flexibility. The thermodynamic stability of proteins is not always optimal when they are highest to perfectly perform their biological functions. Extensive theoretical and experimental screening is often required to obtain stable protein structures. Thus, it becomes critically important to develop a stability prediction model based on the balance between protein stability and bioactivity. To design protein drugs with better functionality in a broader structural space, a novel protein structural stability predictor called PSSP has been developed in this study. PSSP is a mean pooled dual graph convolutional network (GCN) model based on sequence characteristics and secondary structure, distance matrix, graph, and residue properties of a nanoprotein to provide rapid prediction and judgment. This model exhibits excellent robustness in predicting the structural stability of nanoproteins. Comparing with previous artificial intelligence algorithms, the results indicate this model can provide a rapid and accurate assessment of the structural stability of artificially designed proteins, which shows the great promises for promoting the robust development of protein design.
蛋白质的结构稳定性是生物技术、制药和酶学等各个领域的一个重要课题。具体而言，了解蛋白质的结构稳定性对于蛋白质设计至关重要。人工设计在追求蛋白质的高热力学稳定性和刚性时，不可避免地会牺牲与蛋白质柔韧性密切相关的生物学功能。当蛋白质具有最高的热力学稳定性以完美地执行其生物学功能时，其热力学稳定性并不总是最佳的。通常需要进行广泛的理论和实验筛选来获得稳定的蛋白质结构。因此，基于蛋白质稳定性和生物活性之间的平衡开发一种稳定性预测模型变得至关重要。为了在更广阔的结构空间中设计具有更好功能的蛋白质药物，本研究开发了一种名为PSSP的新型蛋白质结构稳定性预测器。PSSP是一种基于纳米蛋白质的序列特征、二级结构、距离矩阵、图谱和残基性质的平均池化双图卷积网络（GCN）模型，以提供快速的预测和判断。该模型在预测纳米蛋白质的结构稳定性方面表现出优异的稳健性。与先前的人工智能算法相比，结果表明该模型可以对人工设计的蛋白质的结构稳定性提供快速准确的评估，这为推动蛋白质设计的稳健发展显示出巨大的前景。
