Screening membraneless organelle participants with machine-learning models that integrate multimodal features.

Protein self-assembly is one of the formation mechanisms of biomolecular condensates. However, most phase-separating systems (PS) demand multiple partners in biological conditions. In this study, we divided PS proteins into two groups according to the mechanism by which they undergo PS: PS-Self proteins can self-assemble spontaneously to form droplets, while PS-Part proteins interact with partners to undergo PS. Analysis of the amino acid composition revealed differences in the sequence pattern between the two protein groups. Existing PS predictors, when evaluated on two test protein sets, preferentially predicted self-assembling proteins. Thus, a comprehensive predictor is required. Herein, we propose that properties other than sequence composition can provide crucial information in screening PS proteins. By incorporating phosphorylation frequencies and immunofluorescence image-based droplet-forming propensity with other PS-related features, we built two independent machine-learning models to separately predict the two protein categories. Results of independent testing suggested the superiority of integrating multimodal features. We performed experimental verification on the top-scored proteins DHX9, Ki-67, and NIFK. Their PS behavior in vitro revealed the effectiveness of our models in PS prediction. Further validation on the proteome of membraneless organelles confirmed the ability of our models to identify PS-Part proteins. We implemented a web server named PhaSePred (http://predict.phasep.pro/) that incorporates our two models together with representative PS predictors. PhaSePred displays proteome-level quantiles of different features, thus profiling PS propensity and providing crucial information for identification of candidate proteins.

Proteome-scale analysis of phase-separated proteins in immunofluorescence images.

Phase separation is an important mechanism that mediates the spatial distribution of proteins in different cellular compartments. While phase-separated proteins share certain sequence characteristics, including intrinsically disordered regions (IDRs) and prion-like domains, such characteristics are insufficient for making accurate predictions; thus, a proteome-wide understanding of phase separation is currently lacking. Here, we define phase-separated proteomes based on the systematic analysis of immunofluorescence images of 12 073 proteins in the Human Protein Atlas. The analysis of these proteins reveals that phase-separated candidate proteins exhibit higher IDR contents, higher mean net charge and lower hydropathy and prefer to bind to RNA. Kinases and transcription factors are also enriched among these candidate proteins. Strikingly, both phase-separated kinases and phase-separated transcription factors display significantly reduced substrate specificity. Our work provides the first global view of the phase-separated proteome and suggests that the spatial proximity resulting from phase separation reduces the requirement for motif specificity and expands the repertoire of substrates. The source code and data are available at https://github.com/cheneyyu/deepphase.

Computational Screening of Phase-separating Proteins.

Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tools are commonly used in the screening of these proteins. However, current phase separation predictors are mostly designed for IDR-containing proteins, thus inevitably overlook the phase-separating proteins with relatively low IDR content. Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins: protein-protein interaction (PPI) networks show multivalent interactions that underlie phase separation process; post-translational modifications (PTMs) are crucial in the regulation of phase separation behavior; spherical structures revealed in immunofluorescence (IF)images indicate condensed droplets formed by phase-separating proteins, distinguishing these proteins from non-phase-separating proteins. Here, we summarize the sequence-based tools for predicting phase-separating proteins and highlight the importance of incorporating PPIs, PTMs, and IF images into phase separation prediction in future studies.

PhaSepDB: a database of liquid-liquid phase separation related proteins.

It's widely appreciated that liquid-liquid phase separation (LLPS) underlies the formation of membraneless organelles, which function to concentrate proteins and nucleic acids. In the past few decades, major efforts have been devoted to identify the phase separation associated proteins and elucidate their functions. To better utilize the knowledge dispersed in published literature, we developed PhaSepDB (http://db.phasep.pro/), a manually curated database of phase separation associated proteins. Currently, PhaSepDB includes 2914 non-redundant proteins localized in different organelles curated from published literature and database. PhaSepDB provides protein summary, publication reference and sequence features of phase separation associated proteins. The sequence features which reflect the LLPS behavior are also available for other human protein candidates. The online database provides a convenient interface for the research community to easily browse, search and download phase separation associated proteins. As a centralized resource, we believe PhaSepDB will facilitate the future study of phase separation.

Molecular mechanisms of the effects of the ethanolic extract of Muntingia calabura Linn. fruit on lipopolysaccharide-induced pro-inflammatory mediators in macrophages.

Four flavonoids (epicatechin, rutin, diosmin and luteolin) and 11 phenolic acids (gallic acid, gentisic acid, p-hydroxybezoic acid, vanillic acid, caffeic acid, p-coumaric acid, ferulic acid, sinapic acid, syringic acid, p-anisic acid and rosmarinic acid) were determined in the ethanolic extract of M. calabura Linn. fruit gathered in Taiwan. The extract suppressed the lipopolysaccharide-stimulated expressions of inducible nitric oxide synthase and cyclooxygenase-2 as well as the productions of nitric oxide, prostaglandin E2 and pro-inflammatory cytokines [tumour necrosis factor-α, interleukin (IL)-1ß and IL-6] in RAW264.7 macrophages. The extract modulated the inflammatory processes through inactivation of nuclear factor-κB (NF-κB), mitogen-activated protein kinases (MAPKs) p38 and c-Jun NH2-terminal kinase 1/2 (JNK1/2), and Janus kinase 2 (JAK2)/signal transducers and activators of transcription 1/3 (STAT1/3). Moreover, the activation of nuclear factor erythroid-2-related factor 2 (Nrf2) followed by inducing the production of heme oxygenase-1 (HO-1) is also related to the anti-inflammatory effect of the extract.

Phenolic compositions and antioxidant attributes of leaves and stems from three inbred varieties of Lycium chinense Miller harvested at various times.

Antioxidant components and properties (assayed by scavenging DPPH radicals, TEAC, reducing power, and inhibiting Cu(2+)-induced human LDL oxidation) of leaves and stems from three inbred varieties of Lycium chinense Miller, namely ML01, ML02 and ML02-TY, harvested from January to April were studied. Their flavonoid and phenolic acid compositions were also analyzed by HPLC. For each variety, the leaves and stems collected in higher temperature month had higher contents of total phenol, total flavonoid and condensed tannin. Contents of these components in the samples collected in different months were in the order: April (22.3°C)>March (18.0°C)>January (15.6°C)>February (15.4°C). Antioxidant activities of the leaves and stems for all assays also showed similar trends. The samples from different varieties collected in the same month also possessed different phenolic compositions and contents and antioxidant activities. Their antioxidant activities were significantly correlated with flavonoid and phenolic contents.