Prediction of protein-protein interaction sites in heterocomplexes with neural networks
European Journal of Biochemistry, ISSN: 0014-2956, Vol: 269, Issue: 5, Page: 1356-1361
2002
- 231Citations
- 118Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations231
- Citation Indexes231
- 231
- CrossRef188
- Captures118
- Readers118
- 118
Article Description
In this paper we address the problem of extracting features relevant for predicting protein-protein interaction sites from the three-dimensional structures of protein complexes. Our approach is based on information about evolutionary conservation and surface disposition. We implement a neural network based system, which uses a cross validation procedure and allows the correct detection of 73% of the residues involved in protein interactions in a selected database comprising 226 heterodimers. Our analysis confirms that the chemico-physical properties of interacting surfaces are difficult to distinguish from those of the whole protein surface. However neural networks trained with a reduced representation of the interacting patch and sequence profile are sufficient to generalize over the different features of the contact patches and to predict whether a residue in the protein surface is or is not in contact. By usinga blind test, we report the prediction of the surface interacting sites of three structural components of the Dnak molecular chaperone system, and find close agreement with previously published experimental results. We propose that the predictor can significantly complement results from structural and functional proteomics.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=0036122073&origin=inward; http://dx.doi.org/10.1046/j.1432-1033.2002.02767.x; http://www.ncbi.nlm.nih.gov/pubmed/11874449; https://febs.onlinelibrary.wiley.com/doi/10.1046/j.1432-1033.2002.02767.x; http://doi.wiley.com/10.1046/j.1432-1033.2002.02767.x; https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1432-1033.2002.02767.x
Wiley
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