Small protein complex prediction algorithm based on protein–protein interaction network segmentation
Abstract Background Identifying protein complexes from protein-protein interaction network is one of significant tasks in the postgenome era. Protein complexes, none of which exceeds 10 in size play an irreplaceable role in life activities and are also a hotspot of scientific research, such as PSD-9...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-09-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04960-z |
| _version_ | 1828203646334009344 |
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| author | Jiaqing Lyu Zhen Yao Bing Liang Yiwei Liu Yijia Zhang |
| author_facet | Jiaqing Lyu Zhen Yao Bing Liang Yiwei Liu Yijia Zhang |
| author_sort | Jiaqing Lyu |
| collection | DOAJ |
| description | Abstract Background Identifying protein complexes from protein-protein interaction network is one of significant tasks in the postgenome era. Protein complexes, none of which exceeds 10 in size play an irreplaceable role in life activities and are also a hotspot of scientific research, such as PSD-95, CD44, PKM2 and BRD4. And in MIPS, CYC2008, SGD, Aloy and TAP06 datasets, the proportion of small protein complexes is over 75%. But up to now, protein complex identification methods do not perform well in the field of small protein complexes. Results In this paper, we propose a novel method, called BOPS. It is a three-step procedure. Firstly, it calculates the balanced weights to replace the original weights. Secondly, it divides the graphs larger than MAXP until the original PPIN is divided into small PPINs. Thirdly, it enumerates the connected subset of each small PPINs, identifies potential protein complexes based on cohesion and removes those that are similar. Conclusions In four yeast PPINs, experimental results have shown that BOPS has an improvement of about 5% compared with the SOTA model. In addition, we constructed a weighted Homo sapiens PPIN based on STRINGdb and BioGRID, and BOPS gets the best result in it. These results give new insights into the identification of small protein complexes, and the weighted Homo sapiens PPIN provides more data for related research. |
| first_indexed | 2024-04-12T12:09:26Z |
| format | Article |
| id | doaj.art-c5ebad98e75c472b933cc3bb778b8931 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T12:09:26Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c5ebad98e75c472b933cc3bb778b89312022-12-22T03:33:38ZengBMCBMC Bioinformatics1471-21052022-09-0123112010.1186/s12859-022-04960-zSmall protein complex prediction algorithm based on protein–protein interaction network segmentationJiaqing Lyu0Zhen Yao1Bing Liang2Yiwei Liu3Yijia Zhang4School of Computer Science and Technology, Dalian University of TechnologySchool of Chemical Engineering, Dalian University of TechnologySchool of Innovation and Entrepreneurship, Dalian University of TechnologySchool of Innovation and Entrepreneurship, Dalian University of TechnologySchool of Information Science and Technology, Dalian Maritime UniversityAbstract Background Identifying protein complexes from protein-protein interaction network is one of significant tasks in the postgenome era. Protein complexes, none of which exceeds 10 in size play an irreplaceable role in life activities and are also a hotspot of scientific research, such as PSD-95, CD44, PKM2 and BRD4. And in MIPS, CYC2008, SGD, Aloy and TAP06 datasets, the proportion of small protein complexes is over 75%. But up to now, protein complex identification methods do not perform well in the field of small protein complexes. Results In this paper, we propose a novel method, called BOPS. It is a three-step procedure. Firstly, it calculates the balanced weights to replace the original weights. Secondly, it divides the graphs larger than MAXP until the original PPIN is divided into small PPINs. Thirdly, it enumerates the connected subset of each small PPINs, identifies potential protein complexes based on cohesion and removes those that are similar. Conclusions In four yeast PPINs, experimental results have shown that BOPS has an improvement of about 5% compared with the SOTA model. In addition, we constructed a weighted Homo sapiens PPIN based on STRINGdb and BioGRID, and BOPS gets the best result in it. These results give new insights into the identification of small protein complexes, and the weighted Homo sapiens PPIN provides more data for related research.https://doi.org/10.1186/s12859-022-04960-zProtein complex identificationSmall protein complexProtein–protein interactionGraph segmentation |
| spellingShingle | Jiaqing Lyu Zhen Yao Bing Liang Yiwei Liu Yijia Zhang Small protein complex prediction algorithm based on protein–protein interaction network segmentation BMC Bioinformatics Protein complex identification Small protein complex Protein–protein interaction Graph segmentation |
| title | Small protein complex prediction algorithm based on protein–protein interaction network segmentation |
| title_full | Small protein complex prediction algorithm based on protein–protein interaction network segmentation |
| title_fullStr | Small protein complex prediction algorithm based on protein–protein interaction network segmentation |
| title_full_unstemmed | Small protein complex prediction algorithm based on protein–protein interaction network segmentation |
| title_short | Small protein complex prediction algorithm based on protein–protein interaction network segmentation |
| title_sort | small protein complex prediction algorithm based on protein protein interaction network segmentation |
| topic | Protein complex identification Small protein complex Protein–protein interaction Graph segmentation |
| url | https://doi.org/10.1186/s12859-022-04960-z |
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