ArticleBiomolecules2022
An Interpretable Machine-Learning Algorithm to Predict Disordered Protein Phase Separation Based on Biophysical Interactions.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed, 57 citations in OpenAlex.
- Engineering short-sequence elements for condensate-like assemblies by de novo design.Synthetic and systems biotechnology · 2027Article
- Designed IDPs phase separate and mix or demix according to sequence designed parameters.bioRxiv : the preprint server for biology · 2026Article
- RNA-binding protein family diversification correlates with neural complexity across metazoan evolution.iScience · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- FuzDrop: sequence-based prediction of the propensity of proteins for liquid-liquid phase separation and aggregation.Nature protocols · 2026Review
- The dynamic and heterogeneous composition of biomolecular condensates and its functional relevance.Nature reviews. Molecular cell biology · 2026Review
- A Systematic Evaluation of Protein Phase Separation Predictors across Diverse Protein Landscapes.Computational and structural biotechnology journal · 2026Article
- Amyloid-mediated RNA uptake by sperm for embryonic delivery.bioRxiv : the preprint server for biology · 2025Article
- Proteome-wide computational analyses reveal links between protein condensate formation and RNA biology.Science advances · 2025Article
- Missense mutations in intrinsically disordered protein regions link pathogenicity and phase separation.The Journal of biological chemistry · 2025Article
- Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation.Biomolecules · 2025Article
- Liquid‒liquid phase separation: a potentially fundamental mechanism of sepsis.Cell death discovery · 2025Review
- Characterizing protein sequence determinants of nuclear condensates by high-throughput pooled imaging with CondenSeq.Nature methods · 2025Article
- Functional specificity in biomolecular condensates revealed by genetic complementation.Nature reviews. Genetics · 2025Review
- Unraveling biomolecular and community grammars of RNA granules via machine learning.PNAS nexus · 2025Article
- Article
- Role of liquid-liquid phase separation in cancer: Mechanisms and therapeutic implications.Cancer innovation · 2024Review
- VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation.Orphanet journal of rare diseases · 2024Review
- AlphaFold2-based prediction of the co-condensation propensity of proteins.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Protein Condensate Atlas from predictive models of heteromolecular condensate composition.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Protein phase separation is increasingly understood to be an important mechanism of biological organization and biomaterial formation. Intrinsically disordered protein regions (IDRs) are often significant drivers of protein phase separation. A number of protein phase-separation-prediction algorithms are available, with many being specific for particular classes of proteins and others providing results that are not amenable to the interpretation of the contributing biophysical interactions. Here, we describe LLPhyScore, a new predictor of IDR-driven phase separation, based on a broad set of physical interactions or features. LLPhyScore uses sequence-based statistics from the RCSB PDB database of folded structures for these interactions, and is trained on a manually curated set of phase-separation-driving proteins with different negative training sets including the PDB and human proteome. Competitive training for a variety of physical chemical interactions shows the greatest contribution of solvent contacts, disorder, hydrogen bonds, pi-pi contacts, and kinked beta-structures to the score, with electrostatics, cation-pi contacts, and the absence of a helical secondary structure also contributing. LLPhyScore has strong phase-separation-prediction recall statistics and enables a breakdown of the contribution from each physical feature to a sequence's phase-separation propensity, while recognizing the interdependence of many of these features. The tool should be a valuable resource for guiding experiments and providing hypotheses for protein function in normal and pathological states, as well as for understanding how specificity emerges in defining individual biomolecular condensates.
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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.