scholarly journals Telomerase Biogenesis and Activities from the Perspective of Its Direct Interacting Partners

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1679 ◽  
Author(s):  
Kathryn T. T. T. Nguyen ◽  
Judy M. Y. Wong
Keyword(s):  
Protein Complexes ◽  
Detection Methods ◽  
Protein Protein Interaction ◽  
Interaction Detection ◽  
The Past ◽  
Binding Partners ◽  
A Cell ◽  
Direct Binding ◽  
Vitro Protein

Telomerase reverse transcriptase (TERT)—the catalytic subunit of telomerase—is reactivated in up to 90% of all human cancers. TERT is observed in heterogenous populations of protein complexes, which are dynamically regulated in a cell type- and cell cycle-specific manner. Over the past two decades, in vitro protein–protein interaction detection methods have discovered a number of endogenous TERT binding partners in human cells that are responsible for the biogenesis and functionalization of the telomerase holoenzyme, including the processes of TERT trafficking between subcellular compartments, assembly into telomerase, and catalytic action at telomeres. Additionally, TERT have been found to interact with protein species with no known telomeric functions, suggesting that these complexes may contribute to non-canonical activities of TERT. Here, we survey TERT direct binding partners and discuss their contributions to TERT biogenesis and functions. The goal is to review the comprehensive spectrum of TERT pro-malignant activities, both telomeric and non-telomeric, which may explain the prevalence of its upregulation in cancer.

scholarly journals Protein-Protein Interaction Detection: Methods and Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
V. Srinivasa Rao ◽  
K. Srinivas ◽  
G. N. Sujini ◽  
G. N. Sunand Kumar
Keyword(s):  
Protein Interaction ◽  
Protein Function ◽  
In Silico ◽  
Affinity Purification ◽  
Phylogenetic Profile ◽  
Detection Methods ◽  
Protein Protein Interaction ◽  
Interaction Detection

Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.

idenPC-MIIP: identify protein complexes from weighted PPI networks using mutual important interacting partner relation

2020 ◽  
Author(s):  
Zhourun Wu ◽  
Qing Liao ◽  
Bin Liu
Keyword(s):  
High Throughput ◽  
State Of The Art ◽  
Protein Complexes ◽  
Cell System ◽  
Protein Protein Interaction ◽  
The Past ◽  
Ppi Networks ◽  
A Cell ◽  
Relationship Of ◽  
Genome Scale

Abstract Protein complexes are key units for studying a cell system. During the past decades, the genome-scale protein–protein interaction (PPI) data have been determined by high-throughput approaches, which enables the identification of protein complexes from PPI networks. However, the high-throughput approaches often produce considerable fraction of false positive and negative samples. In this study, we propose the mutual important interacting partner relation to reflect the co-complex relationship of two proteins based on their interaction neighborhoods. In addition, a new algorithm called idenPC-MIIP is developed to identify protein complexes from weighted PPI networks. The experimental results on two widely used datasets show that idenPC-MIIP outperforms 17 state-of-the-art methods, especially for identification of small protein complexes with only two or three proteins.

Use of an in Vitro Protein Synthesizing System to Test the Mode of Action of Chloracetamides

Weed Science ◽  
1980 ◽  
Vol 28 (3) ◽  
pp. 334-340 ◽  
Author(s):  
Luanne M. Deal ◽  
J. T. Reeves ◽  
B. A. Larkins ◽  
F. D. Hess
Keyword(s):  
Protein Synthesis ◽  
Sand Culture ◽  
Leucine Incorporation ◽  
Insoluble Protein ◽  
In Vitro System ◽  
A Cell ◽  
Protein Incorporation ◽  
Vitro Protein

The effects of chloracetamides on protein synthesis were studied both in vivo and in vitro. Four chloracetamide herbicides, alachlor [2-chloro-2′,6′-diethyl-N-(methoxymethyl)acetanilide], metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide], CDAA (N–N-diallyl-2-chloroacetamide), and propachlor (2-chloro-N-isopropylacetanilide) were tested for inhibition of [3H]-leucine incorporation into protein. Incorporation of3H-leucine into trichloroacetic acid (TCA)-insoluble protein was inhibited in oat (Avena sativaL. ‘Victory’) seedlings grown in sand culture and treated 12 h at 1 × 10−4M with these chloracetamides. The herbicides were also tested in a cell-free protein synthesizing system containing polyribosomes purified from oat root cytoplasm. These herbicides had no effect on the rates of polypeptide elongation nor on the synthesis of specific polypeptides when herbicides (1 × 10−4M) were added directly to the system. Polypeptide formation was inhibited 89% when 1 × 10−4M cycloheximide was added during translation. Cytoplasmic polyribosomes were isolated from oat roots treated 12 h with 1 × 10−4M herbicide. Translation rates and products were not altered when these polyribosomes were added to the in vitro system. Protein synthesis is inhibited when tested in an in vivo system; however, the inhibition does not occur during the translation of mRNA into protein.

scholarly journals Chemical aminoacylation of tRNAs with fluorinated amino acids for in vitro protein mutagenesis

2010 ◽  
Vol 6 ◽  
Author(s):  
Shijie Ye ◽  
Allison Ann Berger ◽  
Dominique Petzold ◽  
Oliver Reimann ◽  
Benjamin Matt ◽  
...  
Keyword(s):  
Amino Acids ◽  
Translation System ◽  
Biologically Relevant ◽  
Fluorinated Amino Acids ◽  
Free Translation ◽  
Trna Species ◽  
A Cell ◽  
Protein Environment ◽  
Vitro Protein

This article describes the chemical aminoacylation of the yeast phenylalanine suppressor tRNA with a series of amino acids bearing fluorinated side chains via the hybrid dinucleotide pdCpA and ligation to the corresponding truncated tRNA species. Aminoacyl-tRNAs can be used to synthesize biologically relevant proteins which contain fluorinated amino acids at specific sites by means of a cell-free translation system. Such engineered proteins are expected to contribute to our understanding of discrete fluorines’ interaction with canonical amino acids in a native protein environment and to enable the design of fluorinated proteins with arbitrary desired properties.

scholarly journals Regulation of SH3PX1 by dNedd4-long at the Drosophila neuromuscular junction

2018 ◽  
Vol 294 (5) ◽  
pp. 1739-1752 ◽  
Author(s):  
Samantha S. Wasserman ◽  
Alina Shteiman-Kotler ◽  
Kathryn Harris ◽  
Konstantin G. Iliadi ◽  
Avinash Persaud ◽  
...  
Keyword(s):  
Neurotransmitter Release ◽  
Neuromuscular Junctions ◽  
Cell Periphery ◽  
Middle Region ◽  
Binding Partners ◽  
Direct Binding ◽  
Specific Regulation ◽  
Neuromuscular Synaptogenesis

Drosophila Nedd4 (dNedd4) is a HECT E3 ubiquitin ligase present in two major isoforms: short (dNedd4S) and long (dNedd4Lo), with the latter containing two unique regions (N terminus and Middle). Although dNedd4S promotes neuromuscular synaptogenesis (NMS), dNedd4Lo inhibits it and impairs larval locomotion. To explain how dNedd4Lo inhibits NMS, MS analysis was performed to find its binding partners and identified SH3PX1, which binds dNedd4Lo unique Middle region. SH3PX1 contains SH3, PX, and BAR domains and is present at neuromuscular junctions, where it regulates active zone ultrastructure and presynaptic neurotransmitter release. Here, we demonstrate direct binding of SH3PX1 to the dNedd4Lo Middle region (which contains a Pro-rich sequence) in vitro and in cells, via the SH3PX1-SH3 domain. In Drosophila S2 cells, dNedd4Lo overexpression reduces SH3PX1 levels at the cell periphery. In vivo overexpression of dNedd4Lo post-synaptically, but not pre-synaptically, reduces SH3PX1 levels at the subsynaptic reticulum and impairs neurotransmitter release. Unexpectedly, larvae that overexpress dNedd4Lo post-synaptically and are heterozygous for a null mutation in SH3PX1 display increased neurotransmission compared with dNedd4Lo or SH3PX1 mutant larvae alone, suggesting a compensatory effect from the remaining SH3PX1 allele. These results suggest a post-synaptic–specific regulation of SH3PX1 by dNedd4Lo.

scholarly journals Evolutionary diversification of protein–protein interactions by interface add-ons

2017 ◽  
Vol 114 (40) ◽  
pp. E8333-E8342 ◽  
Author(s):  
Maximilian G. Plach ◽  
Florian Semmelmann ◽  
Florian Busch ◽  
Markus Busch ◽  
Leonhard Heizinger ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Evolutionary Strategy ◽  
Structural Level ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Evolutionary Diversification

Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein–protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein–protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein–protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein–protein interactions.

Denoising Protein–Protein interaction network via variational graph auto-encoder for protein complex detection

2020 ◽  
Vol 18 (03) ◽  
pp. 2040010 ◽  
Author(s):  
Heng Yao ◽  
Jihong Guan ◽  
Tianying Liu
Keyword(s):  
Protein Interaction ◽  
Protein Complex ◽  
Protein Complexes ◽  
Empirical Evaluation ◽  
High Rate ◽  
Detection Methods ◽  
Convolutional Network ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Complex Detection

Identifying protein complexes is an important issue in computational biology, as it benefits the understanding of cellular functions and the design of drugs. In the past decades, many computational methods have been proposed by mining dense subgraphs in Protein–Protein Interaction Networks (PINs). However, the high rate of false positive/negative interactions in PINs prevents accurately detecting complexes directly from the raw PINs. In this paper, we propose a denoising approach for protein complex detection by using variational graph auto-encoder. First, we embed a PIN to vector space by a stacked graph convolutional network (GCN), then decide which interactions in the PIN are credible. If the probability of an interaction being credible is less than a threshold, we delete the interaction. In such a way, we reconstruct a reliable PIN. Following that, we detect protein complexes in the reconstructed PIN by using several typical detection methods, including CPM, Coach, DPClus, GraphEntropy, IPCA and MCODE, and compare the results with those obtained directly from the original PIN. We conduct the empirical evaluation on four yeast PPI datasets (Gavin, Krogan, DIP and Wiphi) and two human PPI datasets (Reactome and Reactomekb), against two yeast complex benchmarks (CYC2008 and MIPS) and three human complex benchmarks (REACT, REACT_uniprotkb and CORE_COMPLEX_human), respectively. Experimental results show that with the reconstructed PINs obtained by our denoising approach, complex detection performance can get obviously boosted, in most cases by over 5%, sometimes even by 200%. Furthermore, we compare our approach with two existing denoising methods (RWS and RedNemo) while varying different matching rates on separate complex distributions. Our results show that in most cases (over 2/3), the proposed approach outperforms the existing methods.

Molecular Characterization of a PU.1 Transcription Complex Formed on the IL-1β Proximal Promoter.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 3547-3547
Author(s):  
Trang Hoang ◽  
Benoit Grondin ◽  
Martin Lefrancois ◽  
Marianne St Denis ◽  
Daniel G. Tenen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stress Response ◽  
Dna Binding ◽  
Leucine Zipper ◽  
Protein Complexes ◽  
Proximal Promoter ◽  
Stress Exposure ◽  
Protein Protein Interaction ◽  
Nuclear Extracts

Abstract The gene coding for the pro-inflammatory cytokine IL-1β is induced at the transcription level in differentiating macrophages and in stress response. Interestingly, PU.1 and C/EBPβ, two transcription factors implicated in IL-1β gene expression are not induced by stress exposure, while c-Jun is strongly induced. Strikingly, this upregulation of c-Jun is required for IL-1β induction, as cells expressing a c-Jun antisense construct fail to respond to stress exposure. We have mapped the induction of IL-1β gene expression to its proximal promoter and show that it is mediated by the transcriptional synergy between C/EBPβ, c-Jun and PU.1 via specific DNA binding sites for C/EBPβ and PU.1 only. To elucidate how PU.1 and C/EBPβ cooperate with c-Jun at the molecular level, we have optimized a DNA binding assay based on IL-1β promoter fragments immobilized on beads to isolate protein complexes from nuclear extracts, which were subsequently eluted and identified by Western blotting. We show that PU.1 or C/EBPβ alone directly bind this promoter fragment via specific sequences while purified recombinant c-Jun fails to do so. However, the presence of either PU.1 or C/EBPβ on the promoter allows for a recruitment of c-Jun to the DNA template, mediated by direct protein-protein interaction. Interestingly, the leucine zipper domain of c-Jun is essential for its interaction with C/EBPβ while dispensable for PU.1 interaction in vitro whereas its basic domain is required for both interactions. Furthermore, we show that PU.1 and C/EBPβ cooperatively bind the IL-1β promoter, resulting in a synergistic recruitment of c-Jun. Finally, we show that the strength of interaction of c-Jun mutants with PU.1 or C/EBPβ determine the strength of transcription output and c-Jun mutants that fail to associate with either PU.1 or C/EBPβ are transcriptionally inactive. In contrast, c-Jun mutants exhibiting increased homodimerization are more active that the wild type protein. Taken together, our data suggest that c-Jun homodimers can be targeted to the IL-1β promoter in the absence of a specific DNA binding element, and conclude that PU.1 and C/EBPβ are specifically tethered to the IL-1β promoter while c-Jun cooperatively binds these proteins and acts as a transcriptional co-activator. We propose a mechanism based on an initial binding of PU.1 and C/EBPβ to the IL-1β promoter followed by a cooperative recruitment of c-Jun, resulting in transcriptional synergy and IL-1β gene expression in stress response.

scholarly journals The quantification of protein-protein interaction interfaces using solvent-excluded surface-defined properties

2018 ◽  
Author(s):  
Lincong Wang
Keyword(s):  
Protein Interaction ◽  
Protein Complexes ◽  
Protein Docking ◽  
Water Soluble ◽  
Surface Model ◽  
Protein Protein Interaction ◽  
Ring Model ◽  
Water Soluble Protein ◽  
Site Identification

AbstractProtein-protein interaction (PPI) is the cornerstone of nearly every biological process. During last forty years PPI interfaces have been investigated extensively both in vitro and in silico in order to understand both the strength and specificity of PPI. At least three different models, the buried surface model, the O-ring model and the rim- and-core model, have been proposed for PPI interface. However none of them provide much detail about PPI and a single model that reconciles them remains elusive. To identify common physical and geometrical features shared by various PPI interfaces we have analyzed several solvent-excluded surface (SES)-defined properties for a set of well-studied protein-protein complexes with crystal structures. Our analysis shows that the SES-defined properties for the interface atoms of a PPI partner are in general different from those for the surface atoms of a water-soluble protein. Most significantly we find that the partially-buried atoms of a PPI partner have unique SES-defined properties that set them well apart from either the buried atoms or the accessible atoms. Based on distinct SES-defined properties for the accessible, buried and partially-buried atoms shared by various PPI interfaces we propose a new model specified by a list of SES-defined properties shared by various PPI interfaces. Our model is quantitative in nature and should be useful for PPI site identification, protein-protein docking and structure-based design of chemicals targeting PPI.

scholarly journals An implicit lipid model for efficient reaction-diffusion simulations of protein binding to surfaces of arbitrary topology

2019 ◽  
Author(s):  
Yiben Fu ◽  
Osman N. Yogurtcu ◽  
Ruchita Kothari ◽  
Gudrun Thorkelsdottir ◽  
Alexander J. Sodt ◽  
...  
Keyword(s):  
Excellent Agreement ◽  
Protein Complexes ◽  
Specific Binding ◽  
Surface Adsorption ◽  
Reaction Diffusion ◽  
Surface Binding ◽  
Adsorption Models ◽  
Protein Protein Interaction

AbstractLocalization of proteins to a membrane is an essential step in a broad range of biological processes such as signaling, virion formation, and clathrin-mediated endocytosis. The strength and specificity of proteins binding to a membrane depend on the lipid composition. Single-particle reaction-diffusion methods offer a powerful tool for capturing lipid-specific binding to membrane surfaces by treating lipids explicitly as individual diffusible binding sites. However, modeling lipid particle populations is expensive. Here we present an algorithm for reversible binding of proteins to continuum surfaces with implicit lipids, providing dramatic speed-ups to many body simulations. Our algorithm can be readily integrated into most reaction-diffusion software packages. We characterize changes to kinetics that emerge from explicit versus implicit lipids as well as surface adsorption models, showing excellent agreement between our method and the full explicit lipid model. Compared to models of surface adsorption, which couple together binding affinity and lipid concentration, our implicit lipid model decouples them to provide more flexibility for controlling surface binding properties and lipid inhomogeneity, and thus reproducing binding kinetics and equilibria. Crucially, we demonstrate our method’s application to membranes of arbitrary curvature and topology, modeled via a subdivision limit surface, again showing excellent agreement with explicit lipid simulations. Unlike adsorption models, our method retains the ability to bind lipids after proteins are localized to the surface (through e.g. a protein-protein interaction), which can greatly increase stability of multi-protein complexes on the surface. Our method will enable efficient cell-scale simulations involving proteins localizing to realistic membrane models, which is a critical step for predictive modeling and quantification of in vitro and in vivo dynamics.

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