DeepSP: A Deep Learning Framework for Spatial Proteomics

The study of protein subcellular localization (PSL) is
a fundamental
step toward understanding the mechanism of protein function. The recent
development of mass spectrometry (MS)-based spatial proteomics to
quantify the distribution of proteins across subcellular fractions
provides us a high-throughput approach to predict unknown PSLs based
on known PSLs. However, the accuracy of PSL annotations in spatial
proteomics is limited by the performance of existing PSL predictors
based on traditional machine learning algorithms. In this study, we
present a novel deep learning framework named DeepSP for PSL prediction
of an MS-based spatial proteomics data set. DeepSP constructs the
new feature map of a difference matrix by capturing detailed changes
between different subcellular fractions of protein occupancy profiles
and uses the convolutional block attention module to improve the prediction
performance of PSL. DeepSP achieved significant improvement in accuracy
and robustness for PSL prediction in independent test sets and unknown
PSL prediction compared to current state-of-the-art machine learning
predictors. As an efficient and robust framework for PSL prediction,
DeepSP is expected to facilitate spatial proteomics studies and contributes
to the elucidation of protein functions and the regulation of biological
processes.