Prediction of GPI-anchored proteins with pointer neural networks

GPI‐anchors constitute a very important post‐translational modification, linking many proteins to the outer face of the plasma membrane in eukaryotic cells. Since experimental validation of GPI‐anchoring signals is slow and costly, computational approaches for predicting them from amino acid sequences are needed. However, the most recent GPI predictor is more than a decade old and considerable progress has been made in machine learning since then. We present a new dataset and a novel method, NetGPI, for GPI signal prediction. NetGPI is based on recurrent neural networks, incorporating an attention mechanism that simultaneously detects GPI‐anchoring signals and points out the location of their ω‐sites. The performance of NetGPI is superior to existing methods with regards to discrimination between GPI‐anchored proteins and other secretory proteins and approximate (±1 position) placement of the ω‐site.
NetGPI is available at: https://services.healthtech.dtu.dk/service.php?NetGPI.
The code repository is available at: https://github.com/mhgislason/netgpi-1.1