融合多模态蛋白表示，提升熔解温度预测精度
Leveraging Multi-modal Representations to Predict Protein Melting Temperatures
- 结合语言模型与结构信息，构建多模态特征表示
- 在s571数据集上达0.50的皮尔逊相关系数，刷新性能纪录
- 适合蛋白质工程与稳定性评估研究者参考
准确预测蛋白质熔解温度变化（Delta Tm）对于评估蛋白稳定性及指导蛋白工程至关重要。利用多模态蛋白表示能有效捕捉序列、结构与功能间的复杂关系。本研究基于ESM-2、ESM-3和AlphaFold等强大蛋白语言模型，采用多种特征提取方法，提升预测准确性。通过使用ESM-3模型，在s571测试集上取得0.50的皮尔逊相关系数（PCC），达到当前最优水平。此外，我们对不同蛋白语言模型在Delta Tm预测任务中的表现进行了公平评估。结果表明，融合多模态蛋白表示可显著提升熔解温度预测能力。
原文摘要 · Abstract (English)
Accurately predicting protein melting temperature changes (Delta Tm) is fundamental for assessing protein stability and guiding protein engineering. Leveraging multi-modal protein representations has shown great promise in capturing the complex relationships among protein sequences, structures, and functions. In this study, we develop models based on powerful protein language models, including ESM-2, ESM-3 and AlphaFold, using various feature extraction methods to enhance prediction accuracy. By utilizing the ESM-3 model, we achieve a new state-of-the-art performance on the s571 test dataset, obtaining a Pearson correlation coefficient (PCC) of 0.50. Furthermore, we conduct a fair evaluation to compare the performance of different protein language models in the Delta Tm prediction task. Our results demonstrate that integrating multi-modal protein representations could advance the prediction of protein melting temperatures.
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