Comparison of Biomolecular Condensate Localization and Protein Phase Separation Predictors

Kuechler, Erich R.; Huang, Alex L.; Bui, Jennifer M.; Mayor, Thibault; Gsponer, Jörg

doi:10.3390/biom13030527

Cited by 4 publications

(11 citation statements)

References 70 publications

(95 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…S6 H ) ( 41 , 42 ). We recently established several computational tools to predict localization of proteins in MLOs, such as the Membraneless organelle and Granule Z-Score (MaGS) ( 31 , 32 , 43 ). Accordingly, proteins in the young dataset have strikingly higher MaGS values relative to both the proteome and the old dataset indicating that they are more likely to be found in MLOs ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Shift of the insoluble content of the proteome in the aging mouse brain

Molzahn,

Kuechler,

Zemlyankina

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

During aging, the cellular response to unfolded proteins is believed to decline, resulting in diminished proteostasis. In model organisms, such as Caenorhabditis elegans, proteostatic decline with age has been linked to proteome solubility shifts and the onset of protein aggregation. However, this correlation has not been extensively characterized in aging mammals. To uncover age-dependent changes in the insoluble portion of a mammalian proteome, we analyzed the detergent-insoluble fraction of mouse brain tissue by mass spectrometry. We identified a group of 171 proteins, including the small heat shock protein α-crystallin, that become enriched in the detergent-insoluble fraction obtained from old mice. To enhance our ability to detect features associated with proteins in that fraction, we complemented our data with a meta-analysis of studies reporting the detergent-insoluble proteins in various mouse models of aging and neurodegeneration. Strikingly, insoluble proteins from young and old mice are distinct in several features in our study and across the collected literature data. In younger mice, proteins are more likely to be disordered, part of membraneless organelles, and involved in RNA binding. These traits become less prominent with age, as an increased number of structured proteins enter the pellet fraction. This analysis suggests that age-related changes to proteome organization lead a group of proteins with specific features to become detergent-insoluble. Importantly, these features are not consistent with those associated with proteins driving membraneless organelle formation. We see no evidence in our system of a general increase of condensate proteins in the detergent-insoluble fraction with age.

show abstract

Section: Resultsmentioning

confidence: 99%

Shift of the insoluble content of the proteome in the aging mouse brain

Molzahn,

Kuechler,

Zemlyankina

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…After we encode sequence-and structure-based features into a vector, we train multiple binary classifiers and we select the best performing model, which we call ROBOT, to define a LLPS propensity score for a protein. We tested catGRANULE 2.0 ROBOT on proteins belonging to different condensates in human and in different organisms and we show that it outperforms previous methods, both based on sequence features [7,30,31,59] but also on structural features [23]. Moreover, we provide an orthogonal validation of our predictions using thousands of antibody-based immunofluorescence (IF) confocal microscopy images obtained from the Human Protein Atlas [63].…”

Section: Introductionmentioning

confidence: 89%

“…One of the first LLPS predictors, catGRANULE 1.0, computes the protein propensity for granule formation based on structural disorder and nucleic acid-binding propensities [7]. Following this, the MaGS method was developed using a variety of features including protein abundance, intrinsic disorder percentage, phosphorylation site annotations, PScore, Camsol score, RNA interaction, and the composition of leucine and glycine [30,31]. A more recent method is PICNIC, which uses both sequence-based and structure-based features derived from AlphaFold2 models, focusing on sequence complexity, disorder scores, and amino acid co-occurrences [23].…”

Section: Introductionmentioning

confidence: 99%

“…The extensive training dataset used in catGRANULE 2.0 ROBOT comprises human proteins documented to undergo LLPS, sourced from various databases and resources [31,39,43,53,65,68,75,76]. It also includes a selection of negative proteins-those highly unlikely to undergo phase separation, specifically excluding known interactors of LLPS proteins [45].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accurate Predictions of Liquid-Liquid Phase Separating Proteins at Single Amino Acid Resolution

Monti,

Fiorentino,

Miltiadis-Vrachnos

et al. 2024

Preprint

View full text Add to dashboard Cite

Liquid-liquid phase separation (LLPS) is a molecular mechanism that leads to the formation of membraneless organelles inside the cell. Despite recent advances in the experimental probing and computational prediction of proteins involved in this process, the identification of the protein regions driving LLPS and the prediction of the effect of mutations on LLPS are lagging behind.Here, we introduce catGRANULE 2.0 ROBOT (R - Ribonucleoprotein, O - Organization, in B - Biocondensates, O - Organelle, T - Types), an advanced algorithm for predicting protein LLPS at single amino acid resolution. Integrating physico-chemical properties of the proteins and structural features derived from AlphaFold models, catGRANULE 2.0 ROBOT significantly surpasses traditional sequence-based and state-of-the-art structure-based methods in performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.76 or higher. We present a comprehensive evaluation of the algorithm across multiple organisms and cellular components, demonstrating its effectiveness in predicting LLPS propensities at the single amino acid level and the impacts of mutations on LLPS. Our results are robustly supported by experimental validations, including immunofluorescence microscopy images from the Human Protein Atlas.catGRANULE 2.0 ROBOT’s potential in protein design and mutation control can improve our understanding of proteins’ propensity to form subcellular compartments and help develop strategies to influence biological processes through LLPS. catGRANULE 2.0 ROBOT is freely available athttps://tools.tartaglialab. com/catgranule2.

show abstract

“…The role of proteins in the formation of RNP condensates may be determined, with high efficiency, by analyzing their amino acid sequence and identifying specific regions such as low complexity regions or RNA binding domains. 76 However, this kind of approximation is not feasible with RNA, since all molecules are made up of only four different building block nucleotides, whereas proteins are chains of up to 20 different amino acids able to generate many different combinations. Nevertheless, the elements modulating the RNA interactivity go further than just the four-nucleotide code (Figure 2).…”

Section: Physico-chemical Determinants Of Rna-mediated Interactionsmentioning

confidence: 99%

RNA: The Unsuspected Conductor in the Orchestra of Macromolecular Crowding

Zacco,

Broglia,

Kurihara

et al. 2024

Chem. Rev.

View full text Add to dashboard Cite

This comprehensive Review delves into the chemical principles governing RNA-mediated crowding events, commonly referred to as granules or biological condensates. We explore the pivotal role played by RNA sequence, structure, and chemical modifications in these processes, uncovering their correlation with crowding phenomena under physiological conditions. Additionally, we investigate instances where crowding deviates from its intended function, leading to pathological consequences. By deepening our understanding of the delicate balance that governs molecular crowding driven by RNA and its implications for cellular homeostasis, we aim to shed light on this intriguing area of research. Our exploration extends to the methodologies employed to decipher the composition and structural intricacies of RNA granules, offering a comprehensive overview of the techniques used to characterize them, including relevant computational approaches. Through two detailed examples highlighting the significance of noncoding RNAs, NEAT1 and XIST, in the formation of phase-separated assemblies and their influence on the cellular landscape, we emphasize their crucial role in cellular organization and function. By elucidating the chemical underpinnings of RNAmediated molecular crowding, investigating the role of modifications, structures, and composition of RNA granules, and exploring both physiological and aberrant phase separation phenomena, this Review provides a multifaceted understanding of the intriguing world of RNA-mediated biological condensates.

show abstract

Comparison of Biomolecular Condensate Localization and Protein Phase Separation Predictors

Cited by 4 publications

References 70 publications

Shift of the insoluble content of the proteome in the aging mouse brain

Shift of the insoluble content of the proteome in the aging mouse brain

Accurate Predictions of Liquid-Liquid Phase Separating Proteins at Single Amino Acid Resolution

RNA: The Unsuspected Conductor in the Orchestra of Macromolecular Crowding

Contact Info

Product

Resources

About