Few-shot deep learning for AFM force curve characterization of single-molecule interactions

Abstract

Deep learning (DL)-based analytics has the scope to transform the field of atomic force microscopy (AFM) with regard to fast and bias-free measurement characterization. For example, AFM force-distance curves can help estimate important parameters of binding kinetics, such as the most probable rupture force, binding probability, association, and dissociation constants, as well as receptor density on live cells. Other than the ideal single-rupture event in the force-distance curves, there can be no-rupture, double-rupture, or multiple-rupture events. The current practice is to go through such datasets manually, which can be extremely tedious work for the experimentalists. We address this issue by adopting a few-shot learning approach to build sample-efficient DL models that demonstrate better performance than shallow ML models while matching the performance of moderately trained humans. We also release our AFM force curve dataset and annotations publicly as a benchmark for the research community.