ArticleBMC bioinformatics2024
Predicting condensate formation of protein and RNA under various environmental conditions.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Assessing the performance of multimodal large language models in experimental information extraction from liquid-liquid phase separation literature.Scientific reports · 2026Article
- Automated navigation of condensate phase behavior with active machine learning.Nature communications · 2025Article
- RNA-Protein Assemblies: A Review of Biophysical Principles and Coarse-Grained Modeling Approaches.Wiley interdisciplinary reviews. RNAReview
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundLiquid-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.
resultsTo 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.
conclusionsRNAPSEC and these prediction models are expected to accelerate our understanding of the roles of proteins, RNAs, and environmental factors in LLPS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.