Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome
Abstract In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bia...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-024-10082-y |
| _version_ | 1797259531049238528 |
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| author | Steven Tavis Robert L. Hettich |
| author_facet | Steven Tavis Robert L. Hettich |
| author_sort | Steven Tavis |
| collection | DOAJ |
| description | Abstract In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bias analyses since these proteins can carry out functions which impact the engineering of organisms. As a consequence of predicting protein function across all organisms, function prediction tools generally fail to use all of the types of data available for any specific organism, including protein and transcript expression information. Additionally, the release of Alphafold predictions for all Uniprot proteins provides a novel opportunity for leveraging structural information. We constructed a bespoke machine learning model to predict the function of recalcitrant proteins of unknown function in Pseudomonas putida based on these sources of data, which annotated 1079 terms to 213 proteins. Among the predicted functions supplied by the model, we found evidence for a significant overrepresentation of nitrogen metabolism and macromolecule processing proteins. These findings were corroborated by manual analyses of selected proteins which identified, among others, a functionally unannotated operon that likely encodes a branch of the shikimate pathway. |
| first_indexed | 2024-04-24T23:10:54Z |
| format | Article |
| id | doaj.art-d606eb770ec5438dad8e3fb21607c319 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-24T23:10:54Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-d606eb770ec5438dad8e3fb21607c3192024-03-17T12:16:32ZengBMCBMC Genomics1471-21642024-03-0125111510.1186/s12864-024-10082-yMulti-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteomeSteven Tavis0Robert L. Hettich1Genome Science and Technology Graduate Program, University of Tennessee KnoxvilleBiosciences Division, Oak Ridge National LaboratoryAbstract In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bias analyses since these proteins can carry out functions which impact the engineering of organisms. As a consequence of predicting protein function across all organisms, function prediction tools generally fail to use all of the types of data available for any specific organism, including protein and transcript expression information. Additionally, the release of Alphafold predictions for all Uniprot proteins provides a novel opportunity for leveraging structural information. We constructed a bespoke machine learning model to predict the function of recalcitrant proteins of unknown function in Pseudomonas putida based on these sources of data, which annotated 1079 terms to 213 proteins. Among the predicted functions supplied by the model, we found evidence for a significant overrepresentation of nitrogen metabolism and macromolecule processing proteins. These findings were corroborated by manual analyses of selected proteins which identified, among others, a functionally unannotated operon that likely encodes a branch of the shikimate pathway.https://doi.org/10.1186/s12864-024-10082-yMulti-omics integrationProteins of unknown functionMachine learningGene ontologyPseudomonas putidaFunction prediction |
| spellingShingle | Steven Tavis Robert L. Hettich Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome BMC Genomics Multi-omics integration Proteins of unknown function Machine learning Gene ontology Pseudomonas putida Function prediction |
| title | Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome |
| title_full | Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome |
| title_fullStr | Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome |
| title_full_unstemmed | Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome |
| title_short | Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome |
| title_sort | multi omics integration can be used to rescue metabolic information for some of the dark region of the pseudomonas putida proteome |
| topic | Multi-omics integration Proteins of unknown function Machine learning Gene ontology Pseudomonas putida Function prediction |
| url | https://doi.org/10.1186/s12864-024-10082-y |
| work_keys_str_mv | AT steventavis multiomicsintegrationcanbeusedtorescuemetabolicinformationforsomeofthedarkregionofthepseudomonasputidaproteome AT robertlhettich multiomicsintegrationcanbeusedtorescuemetabolicinformationforsomeofthedarkregionofthepseudomonasputidaproteome |