Capsule network for protein ubiquitination site prediction

Luo, Y.; Huang, Q.; Jiang, J.; Li, W.; Wang, Y.

doi:10.1101/2021.01.07.425697

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bioRxiv · 10.1101/2021.01.07.425697

Capsule network for protein ubiquitination site prediction

Luo, Y.·Huang, Q.·Jiang, J.·Li, W.·Wang, Y.

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Abstract

Ubiquitination modification is one of the most important protein posttranslational modifications used in many biological processes. Traditional ubiquitination site determination methods are expensive and time-consuming, whereas calculation-based prediction methods can accurately and efficiently predict ubiquitination sites. This study used a convolutional neural network and a capsule network in deep learning to design a deep learning model, "Caps-Ubi," for multispecies ubiquitination site prediction. Two encoding methods, one-of-K and the amino acid continuous type were used to characterize the sequence pattern of ubiquitination sites. The proposed Caps-Ubi predictor achieved an accuracy of 0.91, a sensitivity of 0.93, a specificity of 0.89, a measure-correlate-prediction of 0.83, and an area under receiver operating characteristic curve value of 0.96, which outperformed the other tested predictors.

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Luo, Y., Huang, Q., Jiang, J., Li, W., Wang, Y.. 2021-01-07. Capsule network for protein ubiquitination site prediction. https://doi.org/10.1101/2021.01.07.425697

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