NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences
Intrinsically disordered proteins are those proteins with intrinsically disordered regions. One of the unique characteristics of intrinsically disordered proteins is the existence of functional segments in intrinsically disordered regions. These segments are involved in binding to partner molecules...
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| Format: | Article |
| Language: | English |
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The Biophysical Society of Japan
2020-11-01
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| Series: | Biophysics and Physicobiology |
| Subjects: | |
| Online Access: | https://doi.org/10.2142/biophysico.BSJ-2020026 |
| _version_ | 1828888357814403072 |
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| author | Hiroto Anbo Hiroki Amagai Satoshi Fukuchi |
| author_facet | Hiroto Anbo Hiroki Amagai Satoshi Fukuchi |
| author_sort | Hiroto Anbo |
| collection | DOAJ |
| description | Intrinsically disordered proteins are those proteins with intrinsically disordered regions. One of the unique characteristics of intrinsically disordered proteins is the existence of functional segments in intrinsically disordered regions. These segments are involved in binding to partner molecules, such as protein and DNA, and play important roles in signaling pathways and/or transcriptional regulation. Although there are databases that gather information on such disordered binding regions, data remain limited. Therefore, it is desirable to develop programs to predict the disordered binding regions without using data for the binding regions. We developed a program, NeProc, to predict the disordered binding regions, which can be regarded as intrinsically disordered regions with a structural propensity. We only used data for the structural domains and intrinsically disordered regions to detect such regions. NeProc accepts a query amino acid sequence converted into a position specific score matrix, and uses two neural networks that employ different window sizes, a neural network of short windows, and a neural network of long windows. The performance of NeProc was comparable to that of existing programs of the disordered binding region prediction. This result presents the possibility to overcome the shortage of the disordered binding region data in the development of the prediction programs for these binding regions. NeProc is available at http://flab.neproc.org/neproc/index.html |
| first_indexed | 2024-12-13T12:21:14Z |
| format | Article |
| id | doaj.art-cb5cc176d7e646abbb1a82c90895017a |
| institution | Directory Open Access Journal |
| issn | 2189-4779 |
| language | English |
| last_indexed | 2024-12-13T12:21:14Z |
| publishDate | 2020-11-01 |
| publisher | The Biophysical Society of Japan |
| record_format | Article |
| series | Biophysics and Physicobiology |
| spelling | doaj.art-cb5cc176d7e646abbb1a82c90895017a2022-12-21T23:46:35ZengThe Biophysical Society of JapanBiophysics and Physicobiology2189-47792020-11-011710.2142/biophysico.BSJ-2020026NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequencesHiroto Anbo0Hiroki Amagai1Satoshi Fukuchi2Department of Life Science and Informatics, Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma 371-0816, JapanDepartment of Life Science and Informatics, Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma 371-0816, JapanDepartment of Life Science and Informatics, Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma 371-0816, JapanIntrinsically disordered proteins are those proteins with intrinsically disordered regions. One of the unique characteristics of intrinsically disordered proteins is the existence of functional segments in intrinsically disordered regions. These segments are involved in binding to partner molecules, such as protein and DNA, and play important roles in signaling pathways and/or transcriptional regulation. Although there are databases that gather information on such disordered binding regions, data remain limited. Therefore, it is desirable to develop programs to predict the disordered binding regions without using data for the binding regions. We developed a program, NeProc, to predict the disordered binding regions, which can be regarded as intrinsically disordered regions with a structural propensity. We only used data for the structural domains and intrinsically disordered regions to detect such regions. NeProc accepts a query amino acid sequence converted into a position specific score matrix, and uses two neural networks that employ different window sizes, a neural network of short windows, and a neural network of long windows. The performance of NeProc was comparable to that of existing programs of the disordered binding region prediction. This result presents the possibility to overcome the shortage of the disordered binding region data in the development of the prediction programs for these binding regions. NeProc is available at http://flab.neproc.org/neproc/index.htmlhttps://doi.org/10.2142/biophysico.BSJ-2020026intrinsically disordered proteinbinding regionsstructure predictionneural network |
| spellingShingle | Hiroto Anbo Hiroki Amagai Satoshi Fukuchi NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences Biophysics and Physicobiology intrinsically disordered protein binding regions structure prediction neural network |
| title | NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences |
| title_full | NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences |
| title_fullStr | NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences |
| title_full_unstemmed | NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences |
| title_short | NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences |
| title_sort | neproc predicts binding segments in intrinsically disordered regions without learning binding region sequences |
| topic | intrinsically disordered protein binding regions structure prediction neural network |
| url | https://doi.org/10.2142/biophysico.BSJ-2020026 |
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