Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins
Abstract Background Predicting and studying essential proteins not only helps to understand the fundamental requirements for cell survival and growth regulation mechanisms but also deepens our understanding of disease mechanisms and drives drug development. Existing methods for identifying essential...
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BMC
2025-03-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06093-5 |
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| author | He Zhao Huan Xu Tao Wang Guixia Liu |
| author_facet | He Zhao Huan Xu Tao Wang Guixia Liu |
| author_sort | He Zhao |
| collection | DOAJ |
| description | Abstract Background Predicting and studying essential proteins not only helps to understand the fundamental requirements for cell survival and growth regulation mechanisms but also deepens our understanding of disease mechanisms and drives drug development. Existing methods for identifying essential proteins primarily focus on PPI networks within a single species, without fully exploiting interspecies homologous relationships. These homologous relationships connect proteins from different species, forming multilayer PPI networks. Some methods only construct interlayer edges based on homologous relationships between two species, without incorporating appropriate biological attributes to assess the biological significance of these edges. Furthermore, homologous proteins are often highly conserved across multiple species, and expanding homologous relationships to more species allows for a more accurate assessment of interlayer edge importance. Results To address these issues, we propose a novel model, MLPR, which constructs a multilayer PPI network based on homologous proteins and integrates multiple PageRank algorithms to identify essential proteins. This study combines homologous protein data from three species to construct interlayer transition matrices and assigns weights to interlayer edges by integrating the biological attributes of homologous proteins and cross-species GO annotations. The MLPR model uses multiple PageRank methods to comprehensively consider homologous relationships across species and designs three key parameters to find the optimal combination that balances random walks within layers, global jumps, interlayer biases, and interspecies homologous relationships. Conclusions Experimental results show that MLPR outperforms other state-of-the-art methods in terms of performance. Ablation experiments further validate that integrating homologous relationships across three species effectively enhances the overall performance of MLPR and demonstrates the advantages of the multiple PageRank model in identifying essential proteins. |
| format | Article |
| id | doaj-art-a4efe44a4be4430f8023933a08eaa781 |
| institution | DOAJ |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-a4efe44a4be4430f8023933a08eaa7812025-08-20T03:01:35ZengBMCBMC Bioinformatics1471-21052025-03-0126112710.1186/s12859-025-06093-5Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteinsHe Zhao0Huan Xu1Tao Wang2Guixia Liu3College of Computer Science and Technology, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityCollege of Computer Science and Technology, Jilin UniversityAbstract Background Predicting and studying essential proteins not only helps to understand the fundamental requirements for cell survival and growth regulation mechanisms but also deepens our understanding of disease mechanisms and drives drug development. Existing methods for identifying essential proteins primarily focus on PPI networks within a single species, without fully exploiting interspecies homologous relationships. These homologous relationships connect proteins from different species, forming multilayer PPI networks. Some methods only construct interlayer edges based on homologous relationships between two species, without incorporating appropriate biological attributes to assess the biological significance of these edges. Furthermore, homologous proteins are often highly conserved across multiple species, and expanding homologous relationships to more species allows for a more accurate assessment of interlayer edge importance. Results To address these issues, we propose a novel model, MLPR, which constructs a multilayer PPI network based on homologous proteins and integrates multiple PageRank algorithms to identify essential proteins. This study combines homologous protein data from three species to construct interlayer transition matrices and assigns weights to interlayer edges by integrating the biological attributes of homologous proteins and cross-species GO annotations. The MLPR model uses multiple PageRank methods to comprehensively consider homologous relationships across species and designs three key parameters to find the optimal combination that balances random walks within layers, global jumps, interlayer biases, and interspecies homologous relationships. Conclusions Experimental results show that MLPR outperforms other state-of-the-art methods in terms of performance. Ablation experiments further validate that integrating homologous relationships across three species effectively enhances the overall performance of MLPR and demonstrates the advantages of the multiple PageRank model in identifying essential proteins.https://doi.org/10.1186/s12859-025-06093-5Essential proteinsHomologous proteinsMultilayer PPI networksMultiple PageRank |
| spellingShingle | He Zhao Huan Xu Tao Wang Guixia Liu Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins BMC Bioinformatics Essential proteins Homologous proteins Multilayer PPI networks Multiple PageRank |
| title | Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins |
| title_full | Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins |
| title_fullStr | Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins |
| title_full_unstemmed | Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins |
| title_short | Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins |
| title_sort | constructing multilayer ppi networks based on homologous proteins and integrating multiple pagerank to identify essential proteins |
| topic | Essential proteins Homologous proteins Multilayer PPI networks Multiple PageRank |
| url | https://doi.org/10.1186/s12859-025-06093-5 |
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