Single-molecule force spectroscopy (SMFS) techniques, such as atomic force microscopy (AFM), have revolutionized the study of biomolecular structure and dynamics. However, the computational analysis of experimental SMFS data remains a challenge due to the high dimensionality and complex nature of the underlying molecular processes. Here we introduce a deep learning-based approach, termed Force-Field Neural Network (FFNN), to analyze SMFS data and extract real-time molecular conformational information. FFNN is trained on data generated from molecular simulations, which capture the underlying molecular dynamics governed by a force field. By utilizing a deep neural network architecture, FFNN accurately predicts the instantaneous molecular conformations from experimental SMFS data, overcoming the limitations of traditional analysis methods. We demonstrate the effectiveness of FFNN for monitoring the conformational dynamics of a single-molecule protein, the titin immunoglobulin (Ig) domain I27, in real time. We show that FFNN can accurately capture the transient unfolding and refolding events of I27, as well as the associated force-extension behavior. Furthermore, FFNN enables the identification of distinct unfolding pathways and intermediate states, providing insights into the underlying molecular mechanisms. This study showcases the potential of deep learning for the analysis of SMFS data, paving the way for a deeper understanding of biomolecular dynamics at the single-molecule level.

Li, X., Hwang, H., Zhao, Y., Yu, H., Hyeon, C., Grubmüller, H., Voth, G. A.