MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses
Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been develo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2022.1025887/full |
| _version_ | 1811313390226767872 |
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| author | Muhammad Nabeel Asim Muhammad Nabeel Asim Ahtisham Fazeel Ahtisham Fazeel Muhammad Ali Ibrahim Muhammad Ali Ibrahim Andreas Dengel Andreas Dengel Sheraz Ahmed |
| author_facet | Muhammad Nabeel Asim Muhammad Nabeel Asim Ahtisham Fazeel Ahtisham Fazeel Muhammad Ali Ibrahim Muhammad Ali Ibrahim Andreas Dengel Andreas Dengel Sheraz Ahmed |
| author_sort | Muhammad Nabeel Asim |
| collection | DOAJ |
| description | Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/. |
| first_indexed | 2024-04-13T10:52:57Z |
| format | Article |
| id | doaj.art-00499a84d5494676b76832505ef29112 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-13T10:52:57Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-00499a84d5494676b76832505ef291122022-12-22T02:49:36ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-11-01910.3389/fmed.2022.10258871025887MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and virusesMuhammad Nabeel Asim0Muhammad Nabeel Asim1Ahtisham Fazeel2Ahtisham Fazeel3Muhammad Ali Ibrahim4Muhammad Ali Ibrahim5Andreas Dengel6Andreas Dengel7Sheraz Ahmed8Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, GermanyGerman Research Center for Artificial Intelligence GmbH, Kaiserslautern, GermanyDepartment of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, GermanyGerman Research Center for Artificial Intelligence GmbH, Kaiserslautern, GermanyDepartment of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, GermanyGerman Research Center for Artificial Intelligence GmbH, Kaiserslautern, GermanyDepartment of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, GermanyGerman Research Center for Artificial Intelligence GmbH, Kaiserslautern, GermanyGerman Research Center for Artificial Intelligence GmbH, Kaiserslautern, GermanyViral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.https://www.frontiersin.org/articles/10.3389/fmed.2022.1025887/fullvirus-host protein-protein interactionmeta predictorfeature agglomerationSARSCoV-2Ebola virusH1N1 virus |
| spellingShingle | Muhammad Nabeel Asim Muhammad Nabeel Asim Ahtisham Fazeel Ahtisham Fazeel Muhammad Ali Ibrahim Muhammad Ali Ibrahim Andreas Dengel Andreas Dengel Sheraz Ahmed MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses Frontiers in Medicine virus-host protein-protein interaction meta predictor feature agglomeration SARSCoV-2 Ebola virus H1N1 virus |
| title | MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses |
| title_full | MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses |
| title_fullStr | MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses |
| title_full_unstemmed | MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses |
| title_short | MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses |
| title_sort | mp vhppi meta predictor for viral host protein protein interaction prediction in multiple hosts and viruses |
| topic | virus-host protein-protein interaction meta predictor feature agglomeration SARSCoV-2 Ebola virus H1N1 virus |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2022.1025887/full |
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