From statistics to deep learning in single-molecule fluorescence resonance energy transfer analysis

Single-molecule fluorescence resonance energy transfer (smFRET) is a versatile technique for studying biomolecular dynamics and function by detecting nanoscale movements as fluorescence signals. Analysing such signals is a complex exercise, which has recently been the focus of approaches relying on deep learning. Here, we survey such artificial-intelligence-based approaches and compare them with classical methods for smFRET analysis. The use of deep learning has shown potential to enhance precision, accuracy, and speed in analysing massive smFRET datasets.