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Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. Th...

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Autores principales: Li, Hang, Gong, Xiu-Jun, Yu, Hua, Zhou, Chang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6222503/
https://www.ncbi.nlm.nih.gov/pubmed/30071670
http://dx.doi.org/10.3390/molecules23081923
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author Li, Hang
Gong, Xiu-Jun
Yu, Hua
Zhou, Chang
author_facet Li, Hang
Gong, Xiu-Jun
Yu, Hua
Zhou, Chang
author_sort Li, Hang
collection PubMed
description Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success in various fields. However, the over-fitting and generalization of its models are not yet well investigated in most scenarios. Here, we present a deep neural network framework (DNN-PPI) for predicting PPIs using features learned automatically only from protein primary sequences. Within the framework, the sequences of two interacting proteins are sequentially fed into the encoding, embedding, convolution neural network (CNN), and long short-term memory (LSTM) neural network layers. Then, a concatenated vector of the two outputs from the previous layer is wired as the input of the fully connected neural network. Finally, the Adam optimizer is applied to learn the network weights in a back-propagation fashion. The different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, CNN and LSTM layers, respectively. When the model was trained on Pan’s human PPI dataset, it achieved a prediction accuracy of 98.78% at the Matthew’s correlation coefficient (MCC) of 97.57%. The prediction accuracies for six external datasets ranged from 92.80% to 97.89%, making them superior to those achieved with previous methods. When performed on Escherichia coli, Drosophila, and Caenorhabditis elegans datasets, DNN-PPI obtained prediction accuracies of 95.949%, 98.389%, and 98.669%, respectively. The performances in cross-species testing among the four species above coincided in their evolutionary distances. However, when testing Mus Musculus using the models from those species, they all obtained prediction accuracies of over 92.43%, which is difficult to achieve and worthy of note for further study. These results suggest that DNN-PPI has remarkable generalization and is a promising tool for identifying protein interactions.
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spelling pubmed-62225032018-11-13 Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences Li, Hang Gong, Xiu-Jun Yu, Hua Zhou, Chang Molecules Article Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success in various fields. However, the over-fitting and generalization of its models are not yet well investigated in most scenarios. Here, we present a deep neural network framework (DNN-PPI) for predicting PPIs using features learned automatically only from protein primary sequences. Within the framework, the sequences of two interacting proteins are sequentially fed into the encoding, embedding, convolution neural network (CNN), and long short-term memory (LSTM) neural network layers. Then, a concatenated vector of the two outputs from the previous layer is wired as the input of the fully connected neural network. Finally, the Adam optimizer is applied to learn the network weights in a back-propagation fashion. The different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, CNN and LSTM layers, respectively. When the model was trained on Pan’s human PPI dataset, it achieved a prediction accuracy of 98.78% at the Matthew’s correlation coefficient (MCC) of 97.57%. The prediction accuracies for six external datasets ranged from 92.80% to 97.89%, making them superior to those achieved with previous methods. When performed on Escherichia coli, Drosophila, and Caenorhabditis elegans datasets, DNN-PPI obtained prediction accuracies of 95.949%, 98.389%, and 98.669%, respectively. The performances in cross-species testing among the four species above coincided in their evolutionary distances. However, when testing Mus Musculus using the models from those species, they all obtained prediction accuracies of over 92.43%, which is difficult to achieve and worthy of note for further study. These results suggest that DNN-PPI has remarkable generalization and is a promising tool for identifying protein interactions. MDPI 2018-08-01 /pmc/articles/PMC6222503/ /pubmed/30071670 http://dx.doi.org/10.3390/molecules23081923 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Hang
Gong, Xiu-Jun
Yu, Hua
Zhou, Chang
Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences
title Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences
title_full Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences
title_fullStr Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences
title_full_unstemmed Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences
title_short Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences
title_sort deep neural network based predictions of protein interactions using primary sequences
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6222503/
https://www.ncbi.nlm.nih.gov/pubmed/30071670
http://dx.doi.org/10.3390/molecules23081923
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