Deep-Channel: A Deep Convolution and Recurrent Neural Network for Detection of Single Molecule Events

Single molecule research delivers a unique biological insight by capturing the movement of individual proteins in real time, unobscured by whole-cell ensemble averaging. The critical first step in single molecule analyses is event detection, so called "idealisation", where noisy raw data are turned into discrete records of protein movement. The most common type of single molecule research is electrophysiological patch-clamp recording of ion channel gating. To date, there have been practical limitations in the analyses pipelines for ion channel or other single molecule data; they are typically manually performed; laborious and require human supervision. In addition, this task can become infeasible with complex biological data containing many distinct native single ion channel proteins gating simultaneously. In this report we describe an "artificial intelligence" deep learning model for analyses of single molecule data, based on convolutional neural networks (CNN) and long short-term memory (LSTM) architecture. This network automatically idealises complex single-molecule activity more accurately and faster than manual "threshold crossing" analyses. We believe this is the first use of deep learning to analyse single-molecule datasets and such methods may revolutionise the unsupervised automatic detection of ion channel and other single-molecule transition events in the future.