2024/12/05 by Daiheng Zhang, Zhang, Daiheng, Yan Zeng +5
Biochemistry, Genetics and Molecular Biology · Chemistry · Materials Science · #Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #Machine Learning in Materials Science #Mass Spectrometry Techniques and Applications #Protein Structure and Dynamics #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2412.04526
openalex publication_date 2024/12/05 · openalex created_date 2024/12/10 · openalex updated_date 2026/07/28
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.