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bioRxiv · 10.1101/838680

Prediction of GPI-Anchored proteins with pointer neural networks

Gislason, M. H.·Nielsen, H.·Almagro Armenteros, J. J.·Johansen, A. R.
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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{omega} -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{omega} -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

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Gislason, M. H., Nielsen, H., Almagro Armenteros, J. J., Johansen, A. R.. 2019-11-18. Prediction of GPI-Anchored proteins with pointer neural networks. https://doi.org/10.1101/838680

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