Liquid-liquid phase separation, driven by collective interactions among multivalent and intrinsically disordered proteins, is thought to mediate the formation of membrane-less organelles in cells. ...Using parallel cellular and in vitro assays, we show that the Nephrin intracellular domain (NICD), a disordered protein, drives intracellular phase separation via complex coacervation, whereby the negatively charged NICD co-assembles with positively charged partners to form protein-rich dense liquid droplets. Mutagenesis reveals that the driving force for phase separation depends on the overall amino acid composition and not the precise sequence of NICD. Instead, phase separation is promoted by one or more regions of high negative charge density and aromatic/hydrophobic residues that are distributed across the protein. Many disordered proteins share similar sequence characteristics with NICD, suggesting that complex coacervation may be a widely used mechanism to promote intracellular phase separation.
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•Disordered Nephrin intracellular domain (NICD) forms phase-separated nuclear bodies•NICD phase separates via complex coacervation•Aromatic/hydrophobic residues and high (−) charge density promote phase separation•Disordered regions with NICD-like sequence features are common in human proteome
Pak et al. describe cellular liquid-liquid phase separation of a negatively charged intrinsically disordered protein, the Nephrin intracellular domain. Phase separation is driven by co-assembly with positively charged partners, a process termed complex coacervation. Disordered regions with NICD-like sequence features are common in the human proteome, suggesting complex coacervation may be widespread.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Siz1 is a founding member of the Siz/PIAS RING family of SUMO E3 ligases. The X-ray structure of an active Siz1 ligase revealed an elongated tripartite architecture comprised of an N-terminal PINIT ...domain, a central zinc-containing RING-like SP-RING domain, and a C-terminal domain we term the SP-CTD. Structure-based mutational analysis and biochemical studies show that the SP-RING and SP-CTD are required for activation of the E2∼SUMO thioester, while the PINIT domain is essential for redirecting SUMO conjugation to the proliferating cell nuclear antigen (PCNA) at lysine 164, a nonconsensus lysine residue that is not modified by the SUMO E2 in the absence of Siz1. Mutational analysis of Siz1 and PCNA revealed surfaces on both proteins that are required for efficient SUMO modification of PCNA in vitro and in vivo.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
E2 conjugating proteins that transfer ubiquitin and ubiquitin-like modifiers to substrate lysine residues must first activate the lysine nucleophile for conjugation. Genetic complementation revealed ...three side chains of the E2 Ubc9 that were crucial for normal growth. Kinetic analysis revealed modest binding defects but substantially lowered catalytic rates for these mutant alleles with respect to wild-type Ubc9. X-ray structures for wild-type and mutant human Ubc9-RanGAP1 complexes showed partial loss of contacts to the substrate lysine in mutant complexes. Computational analysis predicted pK perturbations for the substrate lysine, and Ubc9 mutations weakened pK suppression through improper side chain coordination. Biochemical studies with p53, RanGAP1 and the Nup358/RanBP2 E3 were used to determine rate constants and pK values, confirming both structural and computational predictions. It seems that Ubc9 uses an indirect mechanism to activate lysine for conjugation that may be conserved among E2 family members.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Damage-associated molecular patterns (DAMPs) are molecules released by dead cells that trigger sterile inflammation and, in vertebrates, adaptive immunity. Actin is a DAMP detected in mammals by the ...receptor, DNGR-1, expressed by dendritic cells (DCs). DNGR-1 is phosphorylated by Src-family kinases and recruits the tyrosine kinase Syk to promote DC cross-presentation of dead cell-associated antigens. Here we report that actin is also a DAMP in invertebrates that lack DCs and adaptive immunity. Administration of actin to
triggers a response characterised by selective induction of STAT target genes in the fat body through the cytokine Upd3 and its JAK/STAT-coupled receptor, Domeless. Notably, this response requires signalling via Shark, the
orthologue of Syk, and Src42A, a
Src-family kinase, and is dependent on Nox activity. Thus, extracellular actin detection via a Src-family kinase-dependent cascade is an ancient means of detecting cell injury that precedes the evolution of adaptive immunity.
VopL is an effector protein from Vibrio parahaemolyticus that nucleates actin filaments. VopL consists of a VopL C-terminal domain (VCD) and an array of three WASP homology 2 (WH2) motifs. Here, we ...report the crystal structure of the VCD dimer bound to actin. The VCD organizes three actin monomers in a spatial arrangement close to that found in the canonical actin filament. In this arrangement, WH2 motifs can be modeled into the binding site of each actin without steric clashes. The data suggest a mechanism of nucleation wherein VopL creates filament-like structures, organized by the VCD with monomers delivered by the WH2 array, that can template addition of new subunits. Similarities with Arp2/3 complex and formin proteins suggest that organization of monomers into filament-like structures is a general and central feature of actin nucleation.
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•Crystal structure of an actin trimer bound to the VopL C-terminal domain (VCD)•The dimeric VCD organizes the actin monomers into a filament-like configuration•Tandem WH2 motifs deliver each actin to the VCD through high-affinity interactions•Actin nucleation factors combine distinct delivery and organization functions
A pathogen effector protein assembles actin filaments through the collaborative action of an organizing domain that templates filament structure through low-affinity interactions and a delivery domain featuring WH2 motifs that recruits actin monomers through high-affinity binding.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
SUMO conjugation to protein substrates requires the concerted action of a dedicated E2 ubiquitin conjugation enzyme (Ubc9) and associated E3 ligases. Although Ubc9 can directly recognize and modify ...substrate lysine residues that occur within a consensus site for SUMO modification, E3 ligases can redirect specificity and enhance conjugation rates during SUMO conjugation in vitro and in vivo. In this chapter, we will describe methods utilized to purify SUMO conjugating enzymes and model substrates which can be used for analysis of SUMO conjugation in vitro. We will also describe methods to extract kinetic parameters during E3-dependent or E3-independent substrate conjugation.
The small ubiquitin-like modifier (SUMO) can be conjugated to lysine residues directly by the ubiquitin-conjugating protein Ubc9. SUMO conjugation can be catalyzed in vitro using only E1, Ubc9 (E2), ...mature SUMO, and ATP because Ubc9 directly recognizes consensus SUMO modification sites found in many identified targets of SUMO conjugation. This article describes methods to prepare Ubc9 and provides details for assay conditions used to evaluate E2 thioester formation and E2-mediated SUMO conjugation under single turnover and multiple turnover conditions. It also briefly describes parameters used to evaluate E3-mediated SUMO conjugation. Conservation of the SUMO conjugation apparatus from yeast to human has enabled in vivo assessment of human Ubc9 function through yeast complementation assays.
Stat1 and SUMO modification Song, Li; Bhattacharya, Samita; Yunus, Ali A. ...
Blood,
11/2006, Volume:
108, Issue:
10
Journal Article
Peer reviewed
Open access
Many proteins are known to undergo small ubiquitin-related modifier (SUMO) modification by an E1-, E2-, and E3-dependent ligation process. Recognition that protein inhibitor of activated signal ...transducers and activators of transcription (STATs) (PIAS) proteins are SUMO E3 ligases raised the possibility that STATs may also be regulated by SUMO modification. Consistent with this possibility, a SUMO-ylation consensus site (ΨKxE; Ψ indicates hydrophobic residue, and x indicates any residue) was identified in Stat1 (ie, 702IKTE705), but not in other STATs. Biochemical analysis confirmed that Stat1 K703 could be SUMO modified in vitro. Mutation of this critical lysine (ie, Stat1K703R) yielded a protein that, when expressed in Stat1–/– mouse embryonic fibroblasts (MEFs), exhibited enhanced DNA binding and nuclear retention. This was associated with modest changes in transcriptional and antiviral activity. However, mutation of the second critical residue in the SUMO consensus site, E705 (ie, Stat1E705A), yielded a protein with wild-type DNA binding, nuclear retention, and transcriptional and antiviral activity. Similar observations were made when these mutants were expressed in primary Stat1–/– macrophages. These observations suggest that although Stat1 can uniquely be SUMO-ylated in vitro, this modification is unlikely to play an important role in regulating Stat1 activity in vivo.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Breast cancer is one of the main causes of mortality for women around the world. Such mortality rate could be reduced if it is possible to diagnose breast cancer at the primary stage. It is hard to ...determine the causes of this disease that may lead to the development of breast cancer. But it is still important in predicting the probability of cancer. We can assess the likelihood of occurrence of breast cancer using machine learning algorithms and routine diagnosis data. Although a variety of patient information attributes are stored in cancer datasets not all of the attributes are important in predicting cancer. In such situations, feature selection approaches can be applied to keep the pertinent feature set. In this research, a comprehensive analysis of Machine Learning (ML) classification algorithms with and without feature selection on Wisconsin Breast Cancer Original (WBCO), Wisconsin Diagnosis Breast Cancer (WDBC), and Wisconsin Prognosis Breast Cancer (WPBC) datasets is performed for breast cancer prediction. We employed wrapper-based feature selection and three different classifiers Logistic Regression (LR), Linear Support Vector Machine (LSVM), and Quadratic Support Vector Machine (QSVM) for breast cancer prediction. Based on experimental results, it is shown that the LR classifier with feature selection performs significantly better with an accuracy of 97.1% and 83.5% on WBCO and WPBC datasets respectively. On WDBC datasets, the result reveals that the QSVM classifier without feature selection achieved an accuracy of 97.9% and these results outperform the existing methods.
Diabetes is a globally prevalent disease that can cause microvascular compilation such as Diabetic Retinopathy (DR) in the human eye organs and it might prompt a significant reason for visual ...deficiency. The present study aimed to develop an automatic detection and classification system to diagnosing diabetic retinopathy from digital fundus images. An automated diabetic retinopathy detection and classification system from retinal images is proposed in our work to reduce the workload of ophthalmologists. This work comprises three main stages. Our proposed method first extracts the blood vessels from color fundus image. Secondly, the method detects whatever the input image as normal or diabetic retinopathy and then illustrates an automatic diabetic retinopathy classification technique through statistical texture features. It embeds Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for second-order and higher-order statistical texture feature as a feature extraction technique into three renowned classifiers namely K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM). The evaluation results containing a dataset of 644 retinal images indicate that the proposed method based on random forest classifier is found to be effective with a weighted sensitivity, precision, F1-score and accuracy of 95.53% 96.45%, 95.38% and 95.19% respectively for the detection and classification of diabetic retinopathy. These outcomes propose, that the method could decrease the cost of screening and diagnosis while achieving higher than suggested performance and that the system could be implemented in clinical assessments requiring better evaluating.
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