A reinforcement learning based method for protein's differential scanning calorimetry signal separation

Differential scanning calorimetry (DSC) is a powerful technique to study protein stability, since the DSC test data provides valuable insights to characterize protein folding thermodynamics. Researchers in the drug discovery field need to manually analyze the DSC curves in multiple steps, such as baseline subtraction, data fitting, integration, and domain deconvolution. To improve the efficiency and consistency of data processing, machine learning methods for automatic DSC peak identification and baseline estimation were seen in prior research. However, the DSC's automatic peak separation remained unexplored, despite its significant role in explaining the multi-domain protein unfolding. In this research, we propose a method based on reinforcement learning to separate the overlapping peaks of the DSC signal. We use two types of protein data to verify the effectiveness of this method. It automatically deconvolutes the peak signals into multiple sub-peaks. Our automated analysis method could lead to improved efficiency in DSC signal analysis when high volume data is involved. The code and data for this work can be found at: https://github.com/shuyu-wang/DSC_analysis_peak_separation.

Automated summary

Differential scanning calorimetry (DSC) is a powerful technique for studying protein stability, and researchers have proposed a method based on reinforcement learning to automatically separate overlapping peaks in the DSC signal, improving the efficiency of DSC data analysis.