| Download | - View final version: ProtInteract: a deep learning framework for predicting protein–protein interactions (PDF, 7.9 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1016/j.csbj.2023.01.028 |
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| Author | Search for: Soleymani, Farzan; Search for: Paquet, Eric1ORCID identifier: https://orcid.org/0000-0001-6515-2556; Search for: Viktor, Herna Lydia; Search for: Michalowski, WojtekORCID identifier: https://orcid.org/0000-0002-9198-6439; Search for: Spinello, Davide |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | protein–protein interaction; autoencoder; long short-term memory; temporal convolutional; network; convolutional neural network; sequential pattern; feature selection |
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| Abstract | Proteins mainly perform their functions by interacting with other proteins. Protein–protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein–protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein’s primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein’s primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein’s amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions. |
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| Publication date | 2023-01-25 |
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| Publisher | Elsevier BV |
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| Copyright statement | - Crown Copyright © 2023 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology
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| Language | English |
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| Peer reviewed | Yes |
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| Identifier | S2001037023000296 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | fbe35cca-8c98-48ca-b7af-be3b9647ec36 |
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| Record created | 2023-03-04 |
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| Record modified | 2023-03-06 |
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