Reference - PB_REF:0000007 - Prediction of GPI-anchored proteins with pointer neural networks
Reference summary
- PubMed ID
- PB_REF:0000007
- Title
- Prediction of GPI-anchored proteins with pointer neural networks
- Authors
- Magnús Halldór Gíslason, Henrik Nielsen, José Juan Almagro Armenteros, Alexander Rosenberg Johansen.
- Abstract
- 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, computatinal 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.
