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 ($\Delta\Delta G$), 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 $\Delta\Delta G$ values. This approach, which integrates diverse feature sets and embeddings through latent transfusion techniques, aims to refine $\Delta\Delta G$ predictions and contribute to a deeper understanding of protein dynamics, potentially leading to advancements in disease research and drug discovery.

翻译：预测单点氨基酸突变对蛋白质稳定性的影响对于理解疾病机制和推进药物研发至关重要。蛋白质稳定性通过吉布斯自由能变化（$\Delta\Delta G$）进行量化，并受这些突变的影响。然而，数据的稀缺性和模型解释的复杂性给准确预测稳定性变化带来了挑战。本研究提出应用深度神经网络，利用迁移学习并融合来自不同模型的互补信息，以构建特征丰富的蛋白质稳定性图谱表征。我们开发了四种模型，其中第三个模型ThermoMPNN+在预测$\Delta\Delta G$值方面表现出最佳性能。该方法通过潜在信息融合技术整合多样化的特征集和嵌入表示，旨在优化$\Delta\Delta G$预测，并促进对蛋白质动力学的深入理解，有望推动疾病研究和药物发现领域的进展。