DeepCoil: a fast and accurate prediction of coiled-coil domains in protein 
sequences

Ludwiczak, J; Winski, A; Szczepaniak  , A; Alva, V; Dunin-Horkawicz, S

doi:10.1093/bioinformatics/bty1062

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DeepCoil: a fast and accurate prediction of coiled-coil domains in protein sequences

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Alva, V
Department Protein Evolution, Max Planck Institute for Developmental Biology, Max Planck Society;
Protein Bioinformatics Group, Department Protein Evolution, Max Planck Institute for Developmental Biology, Max Planck Society;

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Citation

Ludwiczak, J., Winski, A., Szczepaniak, A., Alva, V., & Dunin-Horkawicz, S. (2019). DeepCoil: a fast and accurate prediction of coiled-coil domains in protein sequences. Bioinformatics, 35(16), 2790-2795. doi:10.1093/bioinformatics/bty1062.

Cite as: https://hdl.handle.net/21.11116/0000-000A-6A55-0

Abstract

Motivation: Coiled coils are protein structural domains that mediate a plethora of biological interactions, and thus their reliable annotation is crucial for studies of protein structure and function. Results: Here, we report DeepCoil, a new neural network-based tool for the detection of coiled-coil domains in protein sequences. In our benchmarks, DeepCoil significantly outperformed current state-of-the-art tools, such as PCOILS and Marcoil, both in the prediction of canonical and non-canonical coiled coils. Furthermore, in a scan of the human genome with DeepCoil, we detected many coiled-coil domains that remained undetected by other methods. This higher sensitivity of DeepCoil should make it a method of choice for accurate genome-wide detection of coiled-coil domains. Availability and implementation: DeepCoil is written in Python and utilizes the Keras machine learning library. A web server is freely available at https://toolkit.tuebingen.mpg.de/#/tools/deepcoil and a standalone version can be downloaded at https://github.com/labstructbioinf/DeepCoil.