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RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins
Open accessAbstract
Motivation: Recent studies have found many proteins containing regions that do not form well-defined three-dimensional structures in their native states. The study and detection of such disordered regions is important both for understanding protein function and for facilitating structural analysis since disordered regions may affect solubility and/or crystallizability. Results: We have developed the regional order neural network (RONN) software as an application of our recently developed ‘bio-basis function neural network’ pattern recognition algorithm to the detection of natively disordered regions in proteins. The results of blind-testing a panel of nine disorder prediction tools (including RONN) against 80 protein sequences derived from the Protein Data Bank shows that, based on the probability excess measure, RONN performed the best. Availability: RONN is available at http://www.strubi.ox.ac.uk/RONN. Requests for the RONN software and the database of disorder (XML format) can be directed to the corresponding author. Contact: robert@strubi.ox.ac.uk Supplementary information: Details of all predictions made during blind testing, also available at http://www.strubi.ox.ac.uk/RONN3_Supplementary.pdf
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“Three further measures are defined: the accuracy is (TN+ TP)/(TN+ FN+ FP+ TP); the Matthews’ correlation coefficient is (TN· TP− FN · FP)/√[(FN + TP)(TN+ FP)(FP+ TP)(FN+ TN)] (Matthews, 1975) and the probability excess is (TN · TP− FN · FP)/[(FN+ TP)(TN+ FP)].”
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Cited by
Protein production and purification
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Hanson, J., Yang, Y., Paliwal, K., & Zhou, Y. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw678 · Cited by 292
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning
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Metadata
- Authors
- Yang, Z. R., Thomson, R., McNeil, P., & Esnouf, R. M.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-06-09
- Volume
- 21
- Issue
- 16
- Pages
- 3369-3376
- ISSN
- 0266-7061 (ISSN-L)
- 1367-4803
- 1460-2059
- OpenAlex
- W2156678798
- Cited by
- 644
- References
- 4
- FWCI
- 9.10
- Type
- journal article
- Language
- en
- Open access
- bronze
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