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Prediction of GPI-anchored proteins with pointer neural networks

2021; Elsevier BV; Volume: 3; Linguagem: Inglês

10.1016/j.crbiot.2021.01.001

2590-2628

Magnús Halldór Gíslason, Henrik Nielsen, José Juan Almagro Armenteros, Alexander Rosenberg Johansen,

Biochemical and Structural Characterization

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.