Predicting condensate formation of protein and RNA under various environmental conditions
Abstract Background Liquid–liquid phase separation (LLPS) by biomolecules plays a central role in various biological phenomena and has garnered significant attention. The behavior of LLPS is strongly influenced by the characteristics of RNAs and environmental factors such as pH and temperature, as w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-024-05764-z |
| _version_ | 1827291303418789888 |
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| author | Ka Yin Chin Shoichi Ishida Yukio Sasaki Kei Terayama |
| author_facet | Ka Yin Chin Shoichi Ishida Yukio Sasaki Kei Terayama |
| author_sort | Ka Yin Chin |
| collection | DOAJ |
| description | Abstract Background Liquid–liquid phase separation (LLPS) by biomolecules plays a central role in various biological phenomena and has garnered significant attention. The behavior of LLPS is strongly influenced by the characteristics of RNAs and environmental factors such as pH and temperature, as well as the properties of proteins. Recently, several databases recording LLPS-related biomolecules have been established, and prediction models of LLPS-related phenomena have been explored using these databases. However, a prediction model that concurrently considers proteins, RNAs, and experimental conditions has not been developed due to the limited information available from individual experiments in public databases. Results To address this challenge, we have constructed a new dataset, RNAPSEC, which serves each experiment as a data point. This dataset was accomplished by manually collecting data from public literature. Utilizing RNAPSEC, we developed two prediction models that consider a protein, RNA, and experimental conditions. The first model can predict the LLPS behavior of a protein and RNA under given experimental conditions. The second model can predict the required conditions for a given protein and RNA to undergo LLPS. Conclusions RNAPSEC and these prediction models are expected to accelerate our understanding of the roles of proteins, RNAs, and environmental factors in LLPS. |
| first_indexed | 2024-04-24T12:35:14Z |
| format | Article |
| id | doaj.art-8dd62faed4844fe288b118ef60b843ca |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-24T12:35:14Z |
| publishDate | 2024-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-8dd62faed4844fe288b118ef60b843ca2024-04-07T11:32:42ZengBMCBMC Bioinformatics1471-21052024-04-0125111410.1186/s12859-024-05764-zPredicting condensate formation of protein and RNA under various environmental conditionsKa Yin Chin0Shoichi Ishida1Yukio Sasaki2Kei Terayama3Graduate School of Medical Life Science, Yokohama City UniversityGraduate School of Medical Life Science, Yokohama City UniversityGraduate School of Medical Life Science, Yokohama City UniversityGraduate School of Medical Life Science, Yokohama City UniversityAbstract Background Liquid–liquid phase separation (LLPS) by biomolecules plays a central role in various biological phenomena and has garnered significant attention. The behavior of LLPS is strongly influenced by the characteristics of RNAs and environmental factors such as pH and temperature, as well as the properties of proteins. Recently, several databases recording LLPS-related biomolecules have been established, and prediction models of LLPS-related phenomena have been explored using these databases. However, a prediction model that concurrently considers proteins, RNAs, and experimental conditions has not been developed due to the limited information available from individual experiments in public databases. Results To address this challenge, we have constructed a new dataset, RNAPSEC, which serves each experiment as a data point. This dataset was accomplished by manually collecting data from public literature. Utilizing RNAPSEC, we developed two prediction models that consider a protein, RNA, and experimental conditions. The first model can predict the LLPS behavior of a protein and RNA under given experimental conditions. The second model can predict the required conditions for a given protein and RNA to undergo LLPS. Conclusions RNAPSEC and these prediction models are expected to accelerate our understanding of the roles of proteins, RNAs, and environmental factors in LLPS.https://doi.org/10.1186/s12859-024-05764-zLiquid–liquid phase separationMachine learningProteinRNAExperimental conditions |
| spellingShingle | Ka Yin Chin Shoichi Ishida Yukio Sasaki Kei Terayama Predicting condensate formation of protein and RNA under various environmental conditions BMC Bioinformatics Liquid–liquid phase separation Machine learning Protein RNA Experimental conditions |
| title | Predicting condensate formation of protein and RNA under various environmental conditions |
| title_full | Predicting condensate formation of protein and RNA under various environmental conditions |
| title_fullStr | Predicting condensate formation of protein and RNA under various environmental conditions |
| title_full_unstemmed | Predicting condensate formation of protein and RNA under various environmental conditions |
| title_short | Predicting condensate formation of protein and RNA under various environmental conditions |
| title_sort | predicting condensate formation of protein and rna under various environmental conditions |
| topic | Liquid–liquid phase separation Machine learning Protein RNA Experimental conditions |
| url | https://doi.org/10.1186/s12859-024-05764-z |
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