@article{GISLASON20216,
title = {Prediction of GPI-anchored proteins with pointer neural networks},
journal = {Current Research in Biotechnology},
volume = {3},
pages = {6-13},
year = {2021},
issn = {2590-2628},
doi = {https://doi.org/10.1016/j.crbiot.2021.01.001},
url = {https://www.sciencedirect.com/science/article/pii/S2590262821000010},
author = {Magnús Halldór Gíslason and Henrik Nielsen and José Juan {Almagro Armenteros} and Alexander Rosenberg Johansen},
keywords = {Glycosylphosphatidylinositol, Lipid anchored proteins, Post-translational modification, Protein sorting, Prediction, Neural networks},
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, 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.}
}