ProtInteract: A deep learning framework for predicting protein–protein interactions

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 interacti...
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Gespeichert in:

Autor*in:	Farzan Soleymani [verfasserIn] Eric Paquet [verfasserIn] Herna Lydia Viktor [verfasserIn] Wojtek Michalowski [verfasserIn] Davide Spinello [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Protein–Protein interaction Autoencoder Long short-term memory Temporal convolutional, Network Convolutional neural network Sequential pattern

Übergeordnetes Werk:	In: Computational and Structural Biotechnology Journal - Elsevier, 2013, 21(2023), Seite 1324-1348
Übergeordnetes Werk:	volume:21 ; year:2023 ; pages:1324-1348

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.csbj.2023.01.028

Katalog-ID:	DOAJ080728154

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520			\|a 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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allfields	10.1016/j.csbj.2023.01.028 doi (DE-627)DOAJ080728154 (DE-599)DOAJ686268db195147ce8e154664ebca585c DE-627 ger DE-627 rakwb eng TP248.13-248.65 Farzan Soleymani verfasserin aut ProtInteract: A deep learning framework for predicting protein–protein interactions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Protein–Protein interaction Autoencoder Long short-term memory Temporal convolutional, Network Convolutional neural network Sequential pattern Biotechnology Eric Paquet verfasserin aut Herna Lydia Viktor verfasserin aut Wojtek Michalowski verfasserin aut Davide Spinello verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 21(2023), Seite 1324-1348 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:21 year:2023 pages:1324-1348 https://doi.org/10.1016/j.csbj.2023.01.028 kostenfrei https://doaj.org/article/686268db195147ce8e154664ebca585c kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037023000296 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2023 1324-1348
spelling	10.1016/j.csbj.2023.01.028 doi (DE-627)DOAJ080728154 (DE-599)DOAJ686268db195147ce8e154664ebca585c DE-627 ger DE-627 rakwb eng TP248.13-248.65 Farzan Soleymani verfasserin aut ProtInteract: A deep learning framework for predicting protein–protein interactions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Protein–Protein interaction Autoencoder Long short-term memory Temporal convolutional, Network Convolutional neural network Sequential pattern Biotechnology Eric Paquet verfasserin aut Herna Lydia Viktor verfasserin aut Wojtek Michalowski verfasserin aut Davide Spinello verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 21(2023), Seite 1324-1348 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:21 year:2023 pages:1324-1348 https://doi.org/10.1016/j.csbj.2023.01.028 kostenfrei https://doaj.org/article/686268db195147ce8e154664ebca585c kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037023000296 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2023 1324-1348
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callnumber-first	T - Technology
author	Farzan Soleymani
spellingShingle	Farzan Soleymani misc TP248.13-248.65 misc Protein–Protein interaction misc Autoencoder misc Long short-term memory misc Temporal convolutional, Network misc Convolutional neural network misc Sequential pattern misc Biotechnology ProtInteract: A deep learning framework for predicting protein–protein interactions
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topic_title	TP248.13-248.65 ProtInteract: A deep learning framework for predicting protein–protein interactions Protein–Protein interaction Autoencoder Long short-term memory Temporal convolutional, Network Convolutional neural network Sequential pattern
topic	misc TP248.13-248.65 misc Protein–Protein interaction misc Autoencoder misc Long short-term memory misc Temporal convolutional, Network misc Convolutional neural network misc Sequential pattern misc Biotechnology
topic_unstemmed	misc TP248.13-248.65 misc Protein–Protein interaction misc Autoencoder misc Long short-term memory misc Temporal convolutional, Network misc Convolutional neural network misc Sequential pattern misc Biotechnology
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title	ProtInteract: A deep learning framework for predicting protein–protein interactions
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title_full	ProtInteract: A deep learning framework for predicting protein–protein interactions
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title_sort	protinteract: a deep learning framework for predicting protein–protein interactions
callnumber	TP248.13-248.65
title_auth	ProtInteract: A deep learning framework for predicting protein–protein interactions
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.
abstractGer	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.
abstract_unstemmed	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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title_short	ProtInteract: A deep learning framework for predicting protein–protein interactions
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