Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding
The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied “dark” members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the e...
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| Vydáno v: | PeerJ (San Francisco, CA) Ročník 11; s. e15815 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
PeerJ. Ltd
18.10.2023
PeerJ Inc |
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| ISSN: | 2167-8359, 2167-8359 |
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| Abstract | The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied “dark” members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing. |
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| AbstractList | The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing. The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing.The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing. |
| ArticleNumber | e15815 |
| Audience | Academic |
| Author | Huang, Liang-Chin Kannan, Natarajan Keshavarzi, Abbas Salcedo, Mariah V. Kochut, Krzysztof J. Gravel, Nathan |
| Author_xml | – sequence: 1 givenname: Mariah V. surname: Salcedo fullname: Salcedo, Mariah V. organization: Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America – sequence: 2 givenname: Nathan surname: Gravel fullname: Gravel, Nathan organization: Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America – sequence: 3 givenname: Abbas surname: Keshavarzi fullname: Keshavarzi, Abbas organization: School of Computing, University of Georgia, Athens, GA, United States of America – sequence: 4 givenname: Liang-Chin surname: Huang fullname: Huang, Liang-Chin organization: Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America – sequence: 5 givenname: Krzysztof J. surname: Kochut fullname: Kochut, Krzysztof J. organization: School of Computing, University of Georgia, Athens, GA, United States of America – sequence: 6 givenname: Natarajan surname: Kannan fullname: Kannan, Natarajan organization: Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America, Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37868056$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1039_D5MD00306G crossref_primary_10_1016_j_csbj_2024_06_022 crossref_primary_10_1016_j_drudis_2024_103894 |
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| Keywords | Link prediction Illuminating Druggable Genome (IDG) Data integration Random walk Classification Pathway prediction Ontologies Evolution Drug discovery Signaling networks |
| Language | English |
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| Snippet | The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied “dark”... The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark"... |
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| SubjectTerms | Biochemistry Bioinformatics Case studies Computational Biology Computational linguistics Computational Science Data integration Data Mining and Machine Learning Data Science Genomics Humans Illuminating Druggable Genome (IDG) Knowledge Language processing Learning Link prediction Natural language interfaces Ontologies Pathway prediction Pattern Recognition, Automated Protein kinases Protein-protein interactions Proteins Proteins - genetics Random walk |
| Title | Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding |
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