ArticleNature communications2024
Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- A machine learning framework for predicting and modulating condition-dependent protein phase separation.Nature communications · 2026Article
- Kinase condensates enrich ATP and trigger autophosphorylation.Cell reports · 2026Article
- Taxonomy-aware, disorder-matched benchmarking of phase-separating protein predictors.Genome biology · 2026Article
- Dysregulation of liquid-liquid phase separation: from molecular mechanisms to targeted therapies in cardiovascular diseases.Journal of nanobiotechnology · 2026Review
- MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning.Briefings in bioinformatics · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Small molecules modulate the biomolecular condensates and liquid‒liquid phase separation.Acta pharmaceutica Sinica. B · 2026Review
- Engineering Design of Artificial Phase-Separating Proteins.Biotechnology journal · 2026Review
- Prediction of plant phase-separating proteins using positive-unlabeled learning.Genome biology · 2026Article
- Phase separation of PGL-3 driven by structured domains that oligomerize and interact with RGG motifs.EMBO reports · 2026Article
- Interpretable and generative deep learning models explicate phase separating intrinsically disordered motifs.Nature communications · 2026Article
- PhaSepDB 3.0: a comprehensive knowledgebase of phase separation-related proteins from AI-assisted curation.Nucleic acids research · 2026Article
- Decoding intrinsically disordered regions in biomolecular condensates.Fundamental research · 2026Review
- A Systematic Evaluation of Protein Phase Separation Predictors across Diverse Protein Landscapes.Computational and structural biotechnology journal · 2026Article
- Prediction of liquid-liquid phase separation proteins based on protein language model.Briefings in bioinformatics · 2025Article
- Phase Separation-Regulated Fungal Growth, Sexual Development, Adaptation and Synthetic Biology Applications.Journal of fungi (Basel, Switzerland) · 2025Review
- Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation.Biomolecules · 2025Article
- Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies.Genome biology · 2025Article
- Prediction of liquid-phase separation proteins using Siamese network with feature fusion.Briefings in bioinformatics · 2025Article
- Unveiling the dynamic drivers: phase separation's pivotal role in stem cell biology and therapeutic potential.Stem cell research & therapy · 2025Review
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6 authors.
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Abstract
The burgeoning comprehension of protein phase separation (PS) has ushered in a wealth of bioinformatics tools for the prediction of phase-separating proteins (PSPs). These tools often skew towards PSPs with a high content of intrinsically disordered regions (IDRs), thus frequently undervaluing potential PSPs without IDRs. Nonetheless, PS is not only steered by IDRs but also by the structured modular domains and interactions that aren't necessarily reflected in amino acid sequences. In this work, we introduce PSPire, a machine learning predictor that incorporates both residue-level and structure-level features for the precise prediction of PSPs. Compared to current PSP predictors, PSPire shows a notable improvement in identifying PSPs without IDRs, which underscores the crucial role of non-IDR, structure-based characteristics in multivalent interactions throughout the PS process. Additionally, our biological validation experiments substantiate the predictive capacity of PSPire, with 9 out of 11 chosen candidate PSPs confirmed to form condensates within cells.
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