[Dataset] Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies
Proteins self-organize in dynamic cellular environments by assembling into reversible biomolecular condensates through liquid-liquid phase separation (LLPS). These condensates can comprise single or multiple proteins, with different roles in the ensemble's structural and functional integrity. D...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/395374 |
| Acceso en línea: | http://hdl.handle.net/10261/395374 https://doi.org/10.20350/digitalCSIC/17447 https://digital.csic.es/handle/10261/395373 |
| Access Level: | acceso abierto |
| Palabra clave: | Proteins Benchmark Client Datasets Disorder Driver Integration Liquid–liquid phase separation Machine learning Negative http://metadata.un.org/sdg/3 http://metadata.un.org/sdg/9 Ensure healthy lives and promote well-being for all at all ages Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation |
| Sumario: | Proteins self-organize in dynamic cellular environments by assembling into reversible biomolecular condensates through liquid-liquid phase separation (LLPS). These condensates can comprise single or multiple proteins, with different roles in the ensemble's structural and functional integrity. Driver proteins form condensates autonomously, while client proteins just localize within them. Although several databases exist to catalog proteins undergoing LLPS, they often contain divergent data that impedes interoperability between these resources. Additionally, there is a lack of consensus on selecting proteins without explicit experimental association with condensates under physiological conditions (non-LLPS proteins or negative proteins). These two aspects have prevented the generation of reliable predictive models and fair benchmarks. |
|---|