Prediction of GPI-anchored proteins with pointer neural networks

• Published in: Current research in biotechnology Vol. 3; pp. 6 - 13
• Main Authors: Gíslason, Magnús Halldór, Nielsen, Henrik, Almagro Armenteros, José Juan, Johansen, Alexander Rosenberg
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2021; Elsevier
• Subjects: Glycosylphosphatidylinositol; Lipid anchored proteins; Neural networks; Post-translational modification; Prediction; Protein sorting; Post-translational modification; Lipid anchored proteins; Protein sorting; Glycosylphosphatidylinositol; Neural networks; Prediction
• ISSN: 2590-2628; 2590-2628
• DOI: 10.1016/j.crbiot.2021.01.001

[Display omitted] •Deep learning approach for glycosylphosphatidylinositol (GPI) anchoring signal prediction.•A novel, carefully homology partitioned, dataset.•Recurrent neural networks with an attention mechanism.•Exploring biological features uncovered by deep learning. 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.