Abstract
Background
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 (non-LLPS proteins or negative data). These two aspects have prevented the generation of reliable predictive models and fair benchmarks.
Results
In this work, we used an integrated biocuration protocol to analyze information from all relevant LLPS databases and generate confident datasets of client and driver proteins. Besides, we introduce standardized negative datasets, encompassing both globular and disordered proteins. To validate our datasets, we investigated specific physicochemical traits related to LLPS across different subsets of protein sequences. We observed significant differences not only between positive and negative instances but also among LLPS proteins themselves. The datasets from this study are publicly available as a website at https://llpsdatasets.ppmclab.com and as a data repository at https://github.com/PPMC-lab/llps-datasets.
Conclusions
Our datasets offer a reliable means for confidently assessing the specific roles of proteins in LLPS and identifying key differences in physicochemical properties underlying this process. These high-confidence datasets are poised to train a new generation of multilabel models, build more standardized benchmarks, and mitigate sequential biases associated with the presence of intrinsically disordered regions.