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

• Published in: Bioinformatics (Oxford, England) Vol. 35; no. 16; pp. 2790 - 2795
• Main Authors: Ludwiczak, Jan, Winski, Aleksander, Szczepaniak, Krzysztof, Alva, Vikram, Dunin-Horkawicz, Stanislaw
• Format: Journal Article
• Language: English
• Published: England 15.08.2019
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/bty1062

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. 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. 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 data are available at Bioinformatics online.