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Open AccessArticle

Prediction of MoRFs in Protein Sequences with MLPs Based on Sequence Properties and Evolution Information

College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
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Entropy 2019, 21(7), 635; https://doi.org/10.3390/e21070635
Received: 19 April 2019 / Revised: 26 June 2019 / Accepted: 26 June 2019 / Published: 27 June 2019
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Abstract

Molecular recognition features (MoRFs) are one important type of intrinsically disordered proteins functional regions that can undergo a disorder-to-order transition through binding to their interaction partners. Prediction of MoRFs is crucial, as the functions of MoRFs are associated with many diseases and can therefore become the potential drug targets. In this paper, a method of predicting MoRFs is developed based on the sequence properties and evolutionary information. To this end, we design two distinct multi-layer perceptron (MLP) neural networks and present a procedure to train them. We develop a preprocessing process which exploits different sizes of sliding windows to capture various properties related to MoRFs. We then use the Bayes rule together with the outputs of two trained MLP neural networks to predict MoRFs. In comparison to several state-of-the-art methods, the simulation results show that our method is competitive. View Full-Text
Keywords: molecular recognition features; intrinsically disordered proteins; multi-layer perceptron molecular recognition features; intrinsically disordered proteins; multi-layer perceptron
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He, H.; Zhao, J.; Sun, G. Prediction of MoRFs in Protein Sequences with MLPs Based on Sequence Properties and Evolution Information. Entropy 2019, 21, 635.

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