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

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.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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