DeepCoil—a fast and accurate prediction of coiled-coil domains in protein sequences
| Autor: | Aleksander Winski, Jan Ludwiczak, Vikram Alva, Krzysztof Szczepaniak, Stanislaw Dunin-Horkawicz |
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| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Protein structure and function Coiled coil 0303 health sciences Computer science 030302 biochemistry & molecular biology Proteins computer.software_genre Biochemistry Computer Science Applications Machine Learning 03 medical and health sciences Computational Mathematics Protein Domains Computational Theory and Mathematics Humans Human genome Amino Acid Sequence Data mining Molecular Biology computer Software 030304 developmental biology |
| Zdroj: | Bioinformatics. 35:2790-2795 |
| ISSN: | 1460-2059 1367-4803 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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