Transfer learning to leverage larger datasets for improved prediction of protein stability changes.
Transfer learning to leverage larger datasets for improved prediction of protein stability changes.
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迁移学习利用更大的数据集来改进对蛋白质稳定性变化的预测。
DOI：
10.1101/2023.07.27.550881
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Kuhlman,Brian
中科院分区：
文献类型：
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作者：
Dieckhaus,Henry;Brocidiacono,Michael;Randolph,Nicholas;Kuhlman,Brian
降低蛋白质热力学稳定性的氨基酸突变与许多疾病有关，具有增强稳定性的工程蛋白质在研究和医学中可能很重要。因此，预测突变如何扰乱蛋白质稳定性的计算方法引起了极大的兴趣。尽管最近使用深度学习在蛋白质设计方面取得了进展，但对稳定性变化的计算机预测仍然具有挑战性，部分原因是缺乏用于模型开发的大型高质量训练数据集。在这里，我们描述了ThermoMPNN，这是一种经过训练的深度神经网络，用于预测给定初始结构的蛋白质点突变的稳定性变化。在这样做的过程中，我们证明了最近发布的大规模稳定性数据集用于训练鲁棒稳定性模型的实用性。我们还使用迁移学习来利用第二个更大的数据集，方法是使用从ProteinMPNN中提取的学习特征，ProteinMPNN是一种经过训练的深度神经网络，可以根据蛋白质的三维结构预测蛋白质的氨基酸序列。我们表明，我们的方法在使用轻量级模型架构的已建立基准数据集上实现了最先进的性能，该架构允许快速，可扩展的预测。最后，我们使ThermoMPNN易于作为稳定性预测和设计的工具。
Amino acid mutations that lower a protein’s thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a deep neural network trained to predict a protein’s amino acid sequence given its three-dimensional structure. We show that our method achieves state-of-the-art performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design.