ArticleNature communications2024
PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms.
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 30 papers.
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30 citing papers in PubMed.
- Unlocking the bacterial condensome: from "dark matter" to design.Molecular biology of the cell · 2026Article
- Functionalized Coacervates for Cancer Therapy: Advances and Prospects in Phase-Separation-Based Intelligent Drug Delivery.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Proteome-scale quantification of the interactions driving condensate formation of intrinsically disordered proteins.Nature communications · 2026Article
- Taxonomy-aware, disorder-matched benchmarking of phase-separating protein predictors.Genome biology · 2026Article
- Self-assembling proteins compose the chemically resistant shell biomaterial of planktonic tintinnid ciliates.Nature communications · 2026Article
- amyloid-predict and LLPS-predict: Predicting phase separation propensities in the intrinsically disordered proteome.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Sketching microprotein portraits.Protein science : a publication of the Protein Society · 2026Review
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Engineering Design of Artificial Phase-Separating Proteins.Biotechnology journal · 2026Review
- From biomolecular condensates to functional nanomaterials: LLPS-inspired frameworks for nanoscale hydrogels and adaptive materials.Journal of nanobiotechnology · 2026Review
- Integrative profiling of condensation-prone RNAs during early development.Cell genomics · 2026Article
- Liquid liquid phase separation of the intrinsically disordered protein JPT2 compartmentalizes components of NAADP-evoked CabioRxiv : the preprint server for biology · 2026Article
- Molecular grammars of predicted intrinsically disordered regions that span the human proteome.Cell · 2026Article
- CD-CODE 2.0: an enhanced condensate knowledgebase integrating pathobiology, condensate modulating drugs, and host-pathogen interactions.Nucleic acids research · 2026Article
- Sticky interactions govern sequence-dependent dynamics in biomolecular condensates.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- PICNIC web server for predicting proteins involved in biomolecular condensates.Bioinformatics (Oxford, England) · 2026Article
- A Systematic Evaluation of Protein Phase Separation Predictors across Diverse Protein Landscapes.Computational and structural biotechnology journal · 2026Article
- Phase separation and biomolecular condensate formation drive plant endomembrane and autophagy crosstalk.Journal of experimental botany · 2025Review
- Proteome-wide computational analyses reveal links between protein condensate formation and RNA biology.Science advances · 2025Article
- How the Extent of Protein Folding and Oligomerization Modulate Condensate Formation and Properties.The journal of physical chemistry letters · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins in cells to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards proteins with high structural disorder content with focus on self-phase separating (driver) proteins. Here, we present a machine learning algorithm, PICNIC (Proteins Involved in CoNdensates In Cells) to classify proteins that localize to biomolecular condensates regardless of their role in condensate formation. PICNIC successfully predicts condensate members by learning amino acid patterns in the protein sequence and structure in addition to the intrinsic disorder. Extensive experimental validation of 24 positive predictions in cellulo shows an overall ~82% accuracy regardless of the structural disorder content of the tested proteins. While increasing disorder content is associated with organismal complexity, our analysis of 26 species reveals no correlation between predicted condensate proteome content and disorder content across organisms. Overall, we present a machine learning classifier to interrogate condensate components at whole-proteome levels across the tree of life.
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