用深度学习提升单点突变对蛋白稳定性影响的预测精度
AlgoRxplorers | Precision in Mutation: Enhancing Drug Design with Advanced Protein Stability Prediction Tools
- 融合多模型特征与潜在传递技术构建蛋白稳定性表征
- ThermoMPNN+模型在ΔΔG预测上表现最佳
- 适合药物设计与疾病机制研究者参考
预测单点氨基酸突变对蛋白稳定性的影响，对于理解疾病机制和推动药物开发至关重要。蛋白稳定性通过吉布斯自由能变化（ΔΔG）量化，受突变影响。然而数据稀缺与模型解释复杂性制约了预测准确性。本研究提出利用深度神经网络，结合迁移学习并融合不同模型的互补信息，构建丰富的蛋白稳定性表征。我们开发了四个模型，其中第三模型ThermoMPNN+在ΔΔG预测中表现最优。该方法通过潜在传递技术整合多样特征集与嵌入表示，旨在优化ΔΔG预测，深化对蛋白动态的理解，有望推动疾病研究与药物发现进展。
原文摘要 · Abstract (English)
Predicting the impact of single-point amino acid mutations on protein stability is essential for understanding disease mechanisms and advancing drug development. Protein stability, quantified by changes in Gibbs free energy ($ΔΔG$), is influenced by these mutations. However, the scarcity of data and the complexity of model interpretation pose challenges in accurately predicting stability changes. This study proposes the application of deep neural networks, leveraging transfer learning and fusing complementary information from different models, to create a feature-rich representation of the protein stability landscape. We developed four models, with our third model, ThermoMPNN+, demonstrating the best performance in predicting $ΔΔG$ values. This approach, which integrates diverse feature sets and embeddings through latent transfusion techniques, aims to refine $ΔΔG$ predictions and contribute to a deeper understanding of protein dynamics, potentially leading to advancements in disease research and drug discovery.
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