Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network
In recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain hete...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-06-01
|
Series: | Frontiers in Genetics |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.708162/full |
id |
doaj-ae28baea4d7d43ee8d4df03913bfc97e |
---|---|
record_format |
Article |
spelling |
doaj-ae28baea4d7d43ee8d4df03913bfc97e2021-06-29T05:49:13ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-06-011210.3389/fgene.2021.708162708162Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain NetworkXin He0Linai Kuang1Zhiping Chen2Yihong Tan3Lei Wang4Lei Wang5College of Computer, Xiangtan University, Xiangtan, ChinaCollege of Computer, Xiangtan University, Xiangtan, ChinaCollege of Computer Engineering & Applied Mathematics, Changsha University, Changsha, ChinaCollege of Computer Engineering & Applied Mathematics, Changsha University, Changsha, ChinaCollege of Computer, Xiangtan University, Xiangtan, ChinaCollege of Computer Engineering & Applied Mathematics, Changsha University, Changsha, ChinaIn recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain heterogeneous network is established first by combining known protein-protein interactions with known associations between proteins and domains. Next, based on key topological characteristics extracted from the newly constructed protein-domain network and functional characteristics extracted from multiple biological information of proteins, a new computational method is designed to effectively integrate multiple biological features to infer potential essential proteins based on an improved PageRank algorithm. Finally, in order to evaluate the performance of KFPM, we compared it with 13 state-of-the-art prediction methods, experimental results show that, among the top 1, 5, and 10% of candidate proteins predicted by KFPM, the prediction accuracy can achieve 96.08, 83.14, and 70.59%, respectively, which significantly outperform all these 13 competitive methods. It means that KFPM may be a meaningful tool for prediction of potential essential proteins in the future.https://www.frontiersin.org/articles/10.3389/fgene.2021.708162/fullessential proteinsprotein-protein networkcomputational modeldomain-domain networkprotein-domain network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xin He Linai Kuang Zhiping Chen Yihong Tan Lei Wang Lei Wang |
spellingShingle |
Xin He Linai Kuang Zhiping Chen Yihong Tan Lei Wang Lei Wang Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network Frontiers in Genetics essential proteins protein-protein network computational model domain-domain network protein-domain network |
author_facet |
Xin He Linai Kuang Zhiping Chen Yihong Tan Lei Wang Lei Wang |
author_sort |
Xin He |
title |
Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network |
title_short |
Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network |
title_full |
Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network |
title_fullStr |
Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network |
title_full_unstemmed |
Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network |
title_sort |
method for identifying essential proteins by key features of proteins in a novel protein-domain network |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2021-06-01 |
description |
In recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain heterogeneous network is established first by combining known protein-protein interactions with known associations between proteins and domains. Next, based on key topological characteristics extracted from the newly constructed protein-domain network and functional characteristics extracted from multiple biological information of proteins, a new computational method is designed to effectively integrate multiple biological features to infer potential essential proteins based on an improved PageRank algorithm. Finally, in order to evaluate the performance of KFPM, we compared it with 13 state-of-the-art prediction methods, experimental results show that, among the top 1, 5, and 10% of candidate proteins predicted by KFPM, the prediction accuracy can achieve 96.08, 83.14, and 70.59%, respectively, which significantly outperform all these 13 competitive methods. It means that KFPM may be a meaningful tool for prediction of potential essential proteins in the future. |
topic |
essential proteins protein-protein network computational model domain-domain network protein-domain network |
url |
https://www.frontiersin.org/articles/10.3389/fgene.2021.708162/full |
work_keys_str_mv |
AT xinhe methodforidentifyingessentialproteinsbykeyfeaturesofproteinsinanovelproteindomainnetwork AT linaikuang methodforidentifyingessentialproteinsbykeyfeaturesofproteinsinanovelproteindomainnetwork AT zhipingchen methodforidentifyingessentialproteinsbykeyfeaturesofproteinsinanovelproteindomainnetwork AT yihongtan methodforidentifyingessentialproteinsbykeyfeaturesofproteinsinanovelproteindomainnetwork AT leiwang methodforidentifyingessentialproteinsbykeyfeaturesofproteinsinanovelproteindomainnetwork AT leiwang methodforidentifyingessentialproteinsbykeyfeaturesofproteinsinanovelproteindomainnetwork |
_version_ |
1721355455192629248 |