Deep learning-based event classification of mass photometry data for optimal mass measurement at the single-molecule level

Abstract

Mass photometry (MP) is a powerful technique for studying biomolecular structure, interactions, and dynamics in solution. It detects and quantifies small reflectivity changes at a glass-water interface during protein (un)binding, with signals typically averaged over 100 milliseconds. However, particle motion at the point of single-molecule measurement can compromise key metrics such as mass resolution, sensitivity, and concentration. We present a three-dimensional convolutional residual network trained via supervised learning to classify landing events based on their spatiotemporal dynamics. By analysing 3D event thumbnails, our method isolates optimal single-molecule measurements, eliminating cumulative histogram artifacts and improving resolving power by up to a factor of 2. Validated across diverse experimental datasets--including resolved and partially resolved samples, and varying masses, concentrations, and integration times--our approach delivers robust performance under (sub)optimal conditions. Our approach provides measurement-level data-driven feedback, facilitating high quality MP measurements in challenging scenarios.