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1.
Adv Exp Med Biol ; 1158: 83-100, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31452137

RESUMO

Mitochondria (mt) are double-membraned, dynamic organelles that play an essential role in a large number of cellular processes, and impairments in mt function have emerged as a causative factor for a growing number of human disorders. Given that most biological functions are driven by physical associations between proteins, the first step towards understanding mt dysfunction is to map its protein-protein interaction (PPI) network in a comprehensive and systematic fashion. While mass-spectrometry (MS) based approaches possess the high sensitivity ideal for such an endeavor, it also requires stringent biochemical purification of bait proteins to avoid detecting spurious, non-specific PPIs. Here, we outline a tagging-based affinity purification coupled with mass spectrometry (AP-MS) workflow for discovering new mt protein associations and providing novel insights into their role in mt biology and human physiology/pathology. Because AP-MS relies on the creation of proteins fused with affinity tags, we employ a versatile-affinity (VA) tag, consisting of 3× FLAG, 6 × His, and Strep III epitopes. For efficient delivery of affinity-tagged open reading frames (ORF) into mammalian cells, the VA-tag is cloned onto a specific ORF using Gateway recombinant cloning, and the resulting expression vector is stably introduced in target cells using lentiviral transduction. In this chapter, we show a functional workflow for mapping the mt interactome that includes tagging, stable transduction, selection and expansion of mammalian cell lines, mt extraction, identification of interacting protein partners by AP-MS, and lastly, computational assessment of protein complexes/PPI networks.


Assuntos
Cromatografia de Afinidade , Espectrometria de Massas , Proteínas Mitocondriais , Mapeamento de Interação de Proteínas/métodos , Fluxo de Trabalho , Animais , Humanos , Mitocôndrias/metabolismo , Proteínas Mitocondriais/isolamento & purificação
2.
Medicine (Baltimore) ; 98(27): e16277, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31277155

RESUMO

Kaposi sarcoma (KS) is an endothelial tumor etiologically related to Kaposi sarcoma herpesvirus (KSHV) infection. The aim of our study was to screen out candidate genes of KSHV infected endothelial cells and to elucidate the underlying molecular mechanisms by bioinformatics methods. Microarray datasets GSE16354 and GSE22522 were downloaded from Gene Expression Omnibus (GEO) database. the differentially expressed genes (DEGs) between endothelial cells and KSHV infected endothelial cells were identified. And then, functional enrichment analyses of gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. After that, Search Tool for the Retrieval of Interacting Genes (STRING) was used to investigate the potential protein-protein interaction (PPI) network between DEGs, Cytoscape software was used to visualize the interaction network of DEGs and to screen out the hub genes. A total of 113 DEGs and 11 hub genes were identified from the 2 datasets. GO enrichment analysis revealed that most of the DEGs were enrichen in regulation of cell proliferation, extracellular region part and sequence-specific DNA binding; KEGG pathway enrichments analysis displayed that DEGs were mostly enrichen in cell cycle, Jak-STAT signaling pathway, pathways in cancer, and Insulin signaling pathway. In conclusion, the present study identified a host of DEGs and hub genes in KSHV infected endothelial cells which may serve as potential key biomarkers and therapeutic targets, helping us to have a better understanding of the molecular mechanism of KS.


Assuntos
Biomarcadores Tumorais/genética , Biologia Computacional/métodos , Células Endoteliais/metabolismo , Regulação Neoplásica da Expressão Gênica , Herpesvirus Humano 8 , Mapas de Interação de Proteínas/genética , Sarcoma de Kaposi/genética , Biomarcadores Tumorais/biossíntese , DNA de Neoplasias/genética , Células Endoteliais/patologia , Células Endoteliais/virologia , Perfilação da Expressão Gênica/métodos , Ontologia Genética , Humanos , Mapeamento de Interação de Proteínas/métodos , Sarcoma de Kaposi/metabolismo , Sarcoma de Kaposi/virologia
3.
Adv Exp Med Biol ; 1140: 169-198, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31347048

RESUMO

Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.


Assuntos
Espectrometria de Massas , Mapeamento de Interação de Proteínas/métodos , Processamento de Proteína Pós-Traducional , Proteômica/métodos , Humanos , Simulação de Acoplamento Molecular , Proteoma
4.
Nat Commun ; 10(1): 3015, 2019 07 09.
Artigo em Inglês | MEDLINE | ID: mdl-31289271

RESUMO

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.


Assuntos
Biologia Computacional/métodos , Neoplasias/genética , Mapeamento de Interação de Proteínas/métodos , Mapas de Interação de Proteínas/efeitos dos fármacos , Análise de Dados , Bases de Dados Genéticas/estatística & dados numéricos , Bases de Dados de Produtos Farmacêuticos/estatística & dados numéricos , Conjuntos de Dados como Assunto , Humanos , Mapas de Interação de Proteínas/genética , Software
5.
BMC Bioinformatics ; 20(1): 355, 2019 Jun 24.
Artigo em Inglês | MEDLINE | ID: mdl-31234779

RESUMO

BACKGROUND: Essential proteins are distinctly important for an organism's survival and development and crucial to disease analysis and drug design as well. Large-scale protein-protein interaction (PPI) data sets exist in Saccharomyces cerevisiae, which provides us with a valuable opportunity to predict identify essential proteins from PPI networks. Many network topology-based computational methods have been designed to detect essential proteins. However, these methods are limited by the completeness of available PPI data. To break out of these restraints, some computational methods have been proposed by integrating PPI networks and multi-source biological data. Despite the progress in the research of multiple data fusion, it is still challenging to improve the prediction accuracy of the computational methods. RESULTS: In this paper, we design a novel iterative model for essential proteins prediction, named Randomly Walking in the Heterogeneous Network (RWHN). In RWHN, a weighted protein-protein interaction network and a domain-domain association network are constructed according to the original PPI network and the known protein-domain association network, firstly. And then, we establish a new heterogeneous matrix by combining the two constructed networks with the protein-domain association network. Based on the heterogeneous matrix, a transition probability matrix is established by normalized operation. Finally, an improved PageRank algorithm is adopted on the heterogeneous network for essential proteins prediction. In order to eliminate the influence of the false negative, information on orthologous proteins and the subcellular localization information of proteins are integrated to initialize the score vector of proteins. In RWHN, the topology, conservative and functional features of essential proteins are all taken into account in the prediction process. The experimental results show that RWHN obviously exceeds in predicting essential proteins ten other competing methods. CONCLUSIONS: We demonstrated that integrating multi-source data into a heterogeneous network can preserve the complex relationship among multiple biological data and improve the prediction accuracy of essential proteins. RWHN, our proposed method, is effective for the prediction of essential proteins.


Assuntos
Mapeamento de Interação de Proteínas/métodos , Proteínas de Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/metabolismo , Algoritmos , Domínios Proteicos , Mapas de Interação de Proteínas , Proteínas de Saccharomyces cerevisiae/química
6.
BMC Bioinformatics ; 20(1): 308, 2019 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-31182027

RESUMO

BACKGROUND: Although various machine learning-based predictors have been developed for estimating protein-protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein-protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features. RESULTS: We developed a stacked generalization scheme that integrates five learning algorithms. We also designed three types of protein-pair features based on the physicochemical properties of amino acids, gene ontology annotations, and interaction network topologies. When tested on 19 published datasets collected from eight species, the proposed approach achieved a significantly higher or comparable overall performance, compared with seven competitive predictors. CONCLUSION: We introduced an ensemble learning approach for PPI prediction that integrated multiple learning algorithms and different protein-pair representations. The extensive comparisons with other state-of-the-art prediction tools demonstrated the feasibility and superiority of the proposed method.


Assuntos
Algoritmos , Mapeamento de Interação de Proteínas/métodos , Animais , Área Sob a Curva , Bases de Dados de Proteínas , Ontologia Genética , Humanos , Anotação de Sequência Molecular
7.
Nat Commun ; 10(1): 2822, 2019 06 27.
Artigo em Inglês | MEDLINE | ID: mdl-31249300

RESUMO

Interactions between proteins play an essential role in metabolic and signaling pathways, cellular processes and organismal systems. We report the development of splitFAST, a fluorescence complementation system for the visualization of transient protein-protein interactions in living cells. Engineered from the fluorogenic reporter FAST (Fluorescence-Activating and absorption-Shifting Tag), which specifically and reversibly binds fluorogenic hydroxybenzylidene rhodanine (HBR) analogs, splitFAST displays rapid and reversible complementation, allowing the real-time visualization of both the formation and the dissociation of a protein assembly.


Assuntos
Mapeamento de Interação de Proteínas/métodos , Proteínas/química , Linhagem Celular , Fluorescência , Genes Reporter , Humanos , Proteínas Luminescentes/química , Proteínas Luminescentes/metabolismo , Ligação Proteica , Proteínas/metabolismo , Rodanina/química , Proteínas de Ligação a Tacrolimo/química , Proteínas de Ligação a Tacrolimo/metabolismo
8.
Mol Med Rep ; 19(6): 5281-5290, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31059043

RESUMO

Heart failure (HF) secondary to acute myocardial infarction (AMI) is a public health concern. The current study aimed to investigate differentially expressed genes (DEGs) and their possible function in HF post­myocardial infarction. The GSE59867 dataset included microarray data from peripheral blood samples obtained from HF and non­HF patients following AMI at 4 time points (admission, discharge, and 1 and 6 months post­AMI). Time­series DEGs were analyzed using R Bioconductor. Functional enrichment analysis was performed, followed by analysis of protein­protein interactions (PPIs). A total of 108 DEGs on admission, 32 DEGs on discharge, 41 DEGs at 1 month post­AMI and 19 DEGs at 6 months post­AMI were identified. Among these DEGs, 4 genes were downregulated at all the 4 time points. These included fatty acid desaturase 2, leucine rich repeat neuronal protein 3, G­protein coupled receptor 15 and adenylate kinase 5. Functional enrichment analysis revealed that these DEGs were mainly enriched in 'inflammatory response', 'immune response', 'toll­like receptor signaling pathway' and 'NF­κß signaling pathway'. Furthermore, PPI network analysis revealed that C­X­C motif chemokine ligand 8 and interleukin 1ß were hub genes. The current study identified candidate DEGs and pathways that may serve important roles in the development of HF following AMI. The results obtained in the current study may guide the development of novel therapeutic agents for HF following AMI.


Assuntos
Biologia Computacional/métodos , Regulação da Expressão Gênica , Insuficiência Cardíaca/patologia , Infarto do Miocárdio/patologia , Adulto , Idoso , Bases de Dados Genéticas , Regulação para Baixo , Ácidos Graxos Dessaturases/genética , Feminino , Insuficiência Cardíaca/etiologia , Insuficiência Cardíaca/genética , Humanos , Interleucina-1beta/genética , Interleucina-8/genética , Masculino , Proteínas de Membrana/genética , Pessoa de Meia-Idade , Infarto do Miocárdio/complicações , Proteínas de Neoplasias/genética , Mapeamento de Interação de Proteínas/métodos
9.
BMC Genomics ; 20(1): 352, 2019 May 09.
Artigo em Inglês | MEDLINE | ID: mdl-31072324

RESUMO

BACKGROUND: Gene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets. RESULTS: Here, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks. CONCLUSIONS: Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis.


Assuntos
Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes , Mapeamento de Interação de Proteínas/métodos , Software , Algoritmos , Animais , Diabetes Mellitus Tipo 2/genética , Regulação da Expressão Gênica , Humanos , Neoplasias/genética
10.
Chem Commun (Camb) ; 55(41): 5777-5780, 2019 May 16.
Artigo em Inglês | MEDLINE | ID: mdl-31041432

RESUMO

Investigating the interplay in a minimal redox complex of cytochrome-P450 and its reductase is crucial for understanding cytochrome-P450's enzymatic activity. Probing the hotspots of dynamic structural interactions using NMR revealed the engagement of loop residues from P450-reductase to be responsible for the enhanced affinity of CYP450 towards its obligate redox partner.


Assuntos
Sistema Enzimático do Citocromo P-450/metabolismo , NADPH-Ferri-Hemoproteína Redutase/metabolismo , Mapas de Interação de Proteínas , Animais , Sistema Enzimático do Citocromo P-450/química , Humanos , Modelos Moleculares , NADPH-Ferri-Hemoproteína Redutase/química , Ressonância Magnética Nuclear Biomolecular/métodos , Oxirredução , Mapeamento de Interação de Proteínas/métodos , Coelhos
11.
BMC Bioinformatics ; 20(1): 225, 2019 May 02.
Artigo em Inglês | MEDLINE | ID: mdl-31046665

RESUMO

BACKGROUND: Characterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics. This is made possible by the rising of high-throughput technology. Unfortunately, computational methods to identify functional modules were limited by the data quality issues of high-throughput techniques. This study aims to integrate knowledge extracted from literature to further improve the accuracy of functional module identification. RESULTS: Our new model and algorithm were applied to both yeast and human interactomes. Predicted functional modules have covered over 90% of the proteins in both organisms, while maintaining a comparable overall accuracy. We found that the combination of both mRNA expression information and biomedical knowledge greatly improved the performance of functional module identification, which is better than those only using protein interaction network weighted with transcriptomic data, literature knowledge, or simply unweighted protein interaction network. Our new algorithm also achieved better performance when comparing with some other well-known methods, especially in terms of the positive predictive value (PPV), which indicated the confidence of novel discovery. CONCLUSION: Higher PPV with the multiplex approach suggested that information from both sources has been effectively integrated to reduce false positive. With protein coverage higher than 90%, our algorithm is able to generate more novel biological hypothesis with higher confidence.


Assuntos
Algoritmos , Mapeamento de Interação de Proteínas/métodos , Análise por Conglomerados , Perfilação da Expressão Gênica , Genes Fúngicos , Humanos , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/metabolismo
12.
Med Sci Monit ; 25: 2488-2504, 2019 Apr 05.
Artigo em Inglês | MEDLINE | ID: mdl-30948703

RESUMO

BACKGROUND Globally, gastric cancer (GC) is the third most common source of cancer-associated mortality. The aim of this study was to identify key genes and circular RNAs (circRNAs) in GC diagnosis, prognosis, and therapy and to further explore the potential molecular mechanisms of GC. MATERIAL AND METHODS Differentially expressed genes (DEGs) and circRNAs (DE circRNAs) between GC tissues and adjacent non-tumor tissues were identified from 3 mRNA and 3 circRNA expression profiles. Functional analyses were performed, and protein-protein interaction (PPI) networks were constructed. The significant modules and key genes in the PPI networks were identified. Kaplan-Meier analysis was performed to evaluate the prognostic value of these key genes. Potential miRNA-binding sites of the DE circRNAs and target genes of these miRNAs were predicted and used to construct DE circRNA-miRNA-mRNA networks. RESULTS A total of 196 upregulated and 311 downregulated genes were identified in GC. The results of functional analysis showed that these DEGs were significantly enriched in a variety of functions and pathways, including extracellular matrix-related pathways. Ten hub genes (COL1A1, COL3A1, COL1A2, COL5A2, FN1, THBS1, COL5A1, SPARC, COL18A1, and COL11A1) were identified via PPI network analysis. Kaplan-Meier analysis revealed that 7 of these were associated with a poor overall survival in GC patients. Furthermore, we identified 2 DE circRNAs, hsa_circ_0000332 and hsa_circ_0021087. To reveal the potential molecular mechanisms of circRNAs in GC, DE circRNA-microRNA-mRNA networks were constructed. CONCLUSIONS Key candidate genes and circRNAs were identified, and novel PPI and circRNA-microRNA-mRNA networks in GC were constructed. These may provide useful information for the exploration of potential biomarkers and targets for the diagnosis, prognosis, and therapy of GC.


Assuntos
RNA/genética , Neoplasias Gástricas/genética , Biomarcadores , Biologia Computacional/métodos , Regulação Neoplásica da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Humanos , Estimativa de Kaplan-Meier , MicroRNAs/genética , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Prognóstico , Mapeamento de Interação de Proteínas/métodos , Mapas de Interação de Proteínas/genética , RNA/metabolismo , RNA Mensageiro/genética
13.
Methods Mol Biol ; 1977: 115-143, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30980326

RESUMO

Protein complexes perform key roles in nearly all aspects of biology. Identification of the composition of these complexes offers insights into how different cellular processes are carried out. The use of affinity purification coupled to mass spectrometry has become a method of choice for identifying protein-protein interactions, but has been most frequently applied to cell model systems using tagged and overexpressed bait proteins. Although valuable, this approach can create several potential artifacts due to the presence of a tag on a protein and the higher abundance of the protein of interest (bait). The isolation of endogenous proteins using antibodies raised against the proteins of interest instead of an epitope tag offers a means to examine protein interactions in any cellular or animal model system and without the caveats of overexpressed, tagged proteins. Although conceptually simple, the limited use of this approach has been primarily driven by challenges associated with finding adequate antibodies and experimental conditions for effective isolations. In this chapter, we present a protocol for the optimization of lysis conditions, antibody evaluation, affinity purification, and ultimately identification of protein complexes from endogenous immunoaffinity purifications using quantitative mass spectrometry. We also highlight the increased use of targeted mass spectrometry analyses, such as parallel reaction monitoring (PRM) for orthogonal validation of protein isolation and interactions initially identified via data-dependent mass spectrometry analyses.


Assuntos
Cromatografia de Afinidade , Espectrometria de Massas , Proteínas/química , Proteínas/isolamento & purificação , Proteômica , Cromatografia de Afinidade/métodos , Análise de Dados , Espectrometria de Massas/métodos , Mapeamento de Interação de Proteínas/métodos , Proteômica/métodos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Fluxo de Trabalho
14.
Methods Mol Biol ; 1977: 249-261, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30980333

RESUMO

Affinity proteomics (AP-MS) is growing in importance for characterizing protein-protein interactions (PPIs) in the form of protein complexes and signaling networks. The AP-MS approach necessitates several different software tools, integrated into reproducible and accessible workflows. However, if the scientist (e.g., a bench biologist) lacks a computational background, then managing large AP-MS datasets can be challenging, manually formatting AP-MS data for input into analysis software can be error-prone, and data visualization involving dozens of variables can be laborious. One solution to address these issues is Galaxy, an open source and web-based platform for developing and deploying user-friendly computational pipelines or workflows. Here, we describe a Galaxy-based platform enabling AP-MS analysis. This platform enables researchers with no prior computational experience to begin with data from a mass spectrometer (e.g., peaklists in mzML format) and perform peak processing, database searching, assignment of interaction confidence scores, and data visualization with a few clicks of a mouse. We provide sample data and a sample workflow with step-by-step instructions to quickly acquaint users with the process.


Assuntos
Cromatografia de Afinidade , Biologia Computacional/métodos , Espectrometria de Massas , Proteômica , Software , Cromatografia de Afinidade/métodos , Análise de Dados , Bases de Dados de Proteínas , Espectrometria de Massas/métodos , Mapeamento de Interação de Proteínas/métodos , Proteômica/métodos , Navegador
15.
Int J Mol Sci ; 20(9)2019 Apr 26.
Artigo em Inglês | MEDLINE | ID: mdl-31027327

RESUMO

The MAPPIT (mammalian protein protein interaction trap) method allows high-throughput detection of protein interactions by very simple co-transfection of three plasmids in HEK293T cells, followed by a luciferase readout. MAPPIT detects a large percentage of all protein interactions, including those requiring posttranslational modifications and endogenous or exogenous ligands. Here, we present a straightforward method that allows detailed mapping of interaction interfaces via MAPPIT. The method provides insight into the interaction mechanism and reveals how this is affected by disease-associated mutations. By combining error-prone polymerase chain reaction (PCR) for random mutagenesis, 96-well DNA prepping, Sanger sequencing, and MAPPIT via 384-well transfections, we test the effects of a large number of mutations of a selected protein on its protein interactions. The entire screen takes less than three months and interactions with multiple partners can be studied in parallel. The effect of mutations on the MAPPIT readout is mapped on the protein structure, allowing unbiased identification of all putative interaction sites. We have thus far analysed 6 proteins and mapped their interfaces for 16 different interaction partners. Our method is broadly applicable as the required tools are simple and widely available.


Assuntos
Mutagênese/genética , Mapeamento de Interação de Proteínas/métodos , Animais , Humanos , Ligação Proteica
16.
Methods Mol Biol ; 1947: 269-285, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30969422

RESUMO

Mass spectrometry is a sensitive technique used in the field of proteomics that allows for simultaneous detection and characterization of several proteins in a sample. It is also a powerful methodology to elucidate protein-protein interactions in a sequence-dependent and unbiased manner. G protein-coupled receptors (GPCRs) seldom function in isolation and characterization of proteins present in the receptor complex (or its interactome) is critical for understanding the vast spectrum of functions these receptors perform in a context-dependent manner. Here, we describe a mass spectrometry-based method to sequence and characterize proteins present in heteromeric complexes formed by corticotropin-releasing factor (CRF) receptors that belong to class B GPCRs. CRF receptor heteromeric complexes were identified in HEK293 cells co-transfected with tagged CRF receptors 1 and 2. CRF receptors were immunoprecipitated using antibodies against the tags from transfected HEK293 cells and proteins in their interactome were identified using liquid chromatography mass spectrometry method (LC-MS/MS). Both CRF receptors were identified in this interactome. A few of the proteins identified in the CRF receptor interactome using MS were confirmed to be true interactions using traditional co-immunoprecipitation and Western blotting methods.


Assuntos
Mapeamento de Interação de Proteínas/métodos , Multimerização Proteica , Receptores de Hormônio Liberador da Corticotropina/química , Receptores de Hormônio Liberador da Corticotropina/metabolismo , Espectrometria de Massas em Tandem/métodos , Humanos , Proteômica
17.
Nat Chem Biol ; 15(4): 410-418, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30886434

RESUMO

The use of competitive inhibitors to disrupt protein-protein interactions (PPIs) holds great promise for the treatment of disease. However, the discovery of high-affinity inhibitors can be a challenge. Here we report a platform for improving the affinity of peptide-based PPI inhibitors using non-canonical amino acids. The platform utilizes size exclusion-based enrichment from pools of synthetic peptides (1.5-4 kDa) and liquid chromatography-tandem mass spectrometry-based peptide sequencing to identify high-affinity binders to protein targets, without the need for 'reporter' or 'encoding' tags. Using this approach-which is inherently selective for high-affinity binders-we realized gains in affinity of up to ~100- or ~30-fold for binders to the oncogenic ubiquitin ligase MDM2 or HIV capsid protein C-terminal domain, which inhibit MDM2-p53 interaction or HIV capsid protein C-terminal domain dimerization, respectively. Subsequent macrocyclization of select MDM2 inhibitors rendered them cell permeable and cytotoxic toward cancer cells, demonstrating the utility of the identified compounds as functional PPI inhibitors.


Assuntos
Peptídeos/síntese química , Ligação Proteica/fisiologia , Mapeamento de Interação de Proteínas/métodos , Sequência de Aminoácidos , Aminoácidos/metabolismo , Animais , Cromatografia Líquida , Humanos , Modelos Moleculares , Multimerização Proteica , Proteínas Proto-Oncogênicas c-mdm2 , Espectrometria de Massas em Tandem/métodos , Proteína Supressora de Tumor p53
18.
BMC Bioinformatics ; 20(Suppl 3): 131, 2019 Mar 29.
Artigo em Inglês | MEDLINE | ID: mdl-30925866

RESUMO

BACKGROUND: Detecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems. METHODS: In this study, we present a novel algorithm, named Improved Flower Pollination Algorithm (IFPA), to identify protein complexes in multi-relation reconstructed dynamic PPI networks. Specifically, we first introduce a concept called co-essentiality, which considers the protein essentiality to search essential interactions, Then, we devise the multi-relation reconstructed dynamic PPI networks (MRDPNs) and discover the potential cores of protein complexes in MRDPNs. Finally, an IFPA algorithm is put forward based on the flower pollination mechanism to generate protein complexes by simulating the process of pollen find the optimal pollination plants, namely, attach the peripheries to the corresponding cores. RESULTS: The experimental results on three different datasets (DIP, MIPS and Krogan) show that our IFPA algorithm is more superior to some representative methods in the prediction of protein complexes. CONCLUSIONS: Our proposed IFPA algorithm is powerful in protein complex detection by building multi-relation reconstructed dynamic protein networks and using improved flower pollination algorithm. The experimental results indicate that our IFPA algorithm can obtain better performance than other methods.


Assuntos
Biologia Computacional/métodos , Flores/fisiologia , Proteínas de Plantas/metabolismo , Polinização/fisiologia , Mapas de Interação de Proteínas , Algoritmos , Bases de Dados de Proteínas , Complexo de Golgi/metabolismo , Proteínas de Plantas/química , Mapeamento de Interação de Proteínas/métodos
19.
Methods Mol Biol ; 1957: 107-120, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30919350

RESUMO

Nonvisual arrestins (arrestin-2/arrestin-3) interact with hundreds of G protein-coupled receptor (GPCR) subtypes and dozens of non-receptor signaling proteins. Here we describe the methods used to identify the interaction sites of arrestin-binding partners on arrestin-3 and the use of monofunctional individual arrestin-3 elements in cells. Our in vitro pull-down assay with purified proteins demonstrates that relatively few elements in arrestin engage each partner, whereas cell-based functional assays indicate that certain arrestin elements devoid of other functionalities can perform individual functions in living cells.


Assuntos
Arrestina/metabolismo , Bioensaio/métodos , Mapeamento de Interação de Proteínas/métodos , Animais , Células COS , Cercopithecus aethiops , Células HEK293 , Humanos , Proteínas Imobilizadas/metabolismo , Camundongos , Ligação Proteica , Proteínas Recombinantes de Fusão/metabolismo
20.
Methods Mol Biol ; 1957: 139-158, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30919352

RESUMO

ß-Arrestins 1 and 2 (ß-arr1 and ß-arr2) are ubiquitous proteins with common and distinct functions. They were initially identified as proteins recruited to stimulated G protein-coupled receptors (GPCRs), regulating their desensitization and internalization. The discovery that ß-arrs could also interact with more than 400 non-GPCR protein partners brought to light their central roles as multifunctional scaffold proteins regulating multiple signalling pathways from the plasma membrane to the nucleus, downstream of GPCRs or independently from these receptors. Through the regulation of the activities and subcellular localization of their binding partners, ß-arrs control various cell processes such as proliferation, cytoskeletal rearrangement, cell motility, and apoptosis. Thus, the identification of ß-arrs binding partners and the characterization of their mode of interaction in cells are central to the understanding of their function. Here we provide methods to explore the molecular interaction of ß-arrs with other proteins in cellulo.


Assuntos
Mapeamento de Interação de Proteínas/métodos , beta-Arrestinas/metabolismo , Técnicas de Transferência de Energia por Ressonância de Bioluminescência , Células HEK293 , Humanos , Imunoprecipitação , Ligação Proteica , Saccharomyces cerevisiae/metabolismo , Técnicas do Sistema de Duplo-Híbrido
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