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1.
Chin J Nat Med ; 18(12): 941-951, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33357725

RESUMO

As a representative drug for the treatment of severe community-acquired pneumonia and sepsis, Xuebijing (XBJ) injection is also one of the recommended drugs for the prevention and treatment of coronavirus disease 2019 (COVID-19), but its treatment mechanism for COVID-19 is still unclear. Therefore, this study aims to explore the potential mechanism of XBJ injection in the treatment of COVID-19 employing network pharmacology and molecular docking methods. The corresponding target genes of 45 main active ingredients in XBJ injection and COVID-19 were obtained by using multiple database retrieval and literature mining. 102 overlapping targets of them were screened as the core targets for analysis. Then built the PPI network, TCM-compound-target-disease, and disease-target-pathway networks with the help of Cytoscape 3.6.1 software. After that, utilized DAVID to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to predict the action mechanism of overlapping targets. Finally, by applying molecular docking technology, all compounds were docked with COVID-19 3 CL protease(3CLpro), spike protein (S protein), and angiotensin-converting enzyme II (ACE2). The results indicated that quercetin, luteolin, apigenin and other compounds in XBJ injection could affect TNF, MAPK1, IL6 and other overlapping targets. Meanwhile, anhydrosafflor yellow B (AHSYB), salvianolic acid B (SAB), and rutin could combine with COVID-19 crucial proteins, and then played the role of anti-inflammatory, antiviral and immune response to treat COVID-19. This study revealed the multiple active components, multiple targets, and multiple pathways of XBJ injection in the treatment of COVID-19, which provided a new perspective for the study of the mechanism of traditional Chinese medicine (TCM) in the treatment of COVID-19.


Assuntos
Medicamentos de Ervas Chinesas , Medicina Tradicional Chinesa/métodos , Simulação de Acoplamento Molecular/métodos , Transdução de Sinais/efeitos dos fármacos , /metabolismo , Disponibilidade Biológica , /metabolismo , /metabolismo , Medicamentos de Ervas Chinesas/farmacocinética , Medicamentos de Ervas Chinesas/uso terapêutico , Humanos , Mapeamento de Interação de Proteínas/métodos , /fisiologia , Glicoproteína da Espícula de Coronavírus/metabolismo
2.
BMC Bioinformatics ; 21(Suppl 16): 560, 2020 Dec 16.
Artigo em Inglês | MEDLINE | ID: mdl-33323115

RESUMO

BACKGROUND: Protein-protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consider missing and spurious interactions inherent in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a method that employs graph embeddings that learn vector representations from constructed Gene Ontology Annotation (GOA) graphs and then use embedded vectors to achieve the two tasks. Our method leverages on information from both term-term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. RESULTS: We compare our method with those methods that are based on information content (IC) and one method that is based on word embeddings, with experiments on three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods. CONCLUSION: Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GOA graphs for our defined missing and spurious PPI tasks.


Assuntos
Ontologia Genética , Anotação de Sequência Molecular , Mapeamento de Interação de Proteínas/métodos , Animais , Área Sob a Curva , Biologia Computacional/métodos , Humanos , Camundongos , Curva ROC , Saccharomyces cerevisiae/genética , Análise e Desempenho de Tarefas
3.
BMC Bioinformatics ; 21(Suppl 16): 537, 2020 Dec 16.
Artigo em Inglês | MEDLINE | ID: mdl-33323120

RESUMO

BACKGROUND: Protein-protein interactions (PPIs) are of great importance in cellular systems of organisms, since they are the basis of cellular structure and function and many essential cellular processes are related to that. Most proteins perform their functions by interacting with other proteins, so predicting PPIs accurately is crucial for understanding cell physiology. RESULTS: Recently, graph convolutional networks (GCNs) have been proposed to capture the graph structure information and generate representations for nodes in the graph. In our paper, we use GCNs to learn the position information of proteins in the PPIs networks graph, which can reflect the properties of proteins to some extent. Combining amino acid sequence information and position information makes a stronger representation for protein, which improves the accuracy of PPIs prediction. CONCLUSION: In previous research methods, most of them only used protein amino acid sequence as input information to make predictions, without considering the structural information of PPIs networks graph. We first time combine amino acid sequence information and position information to make representations for proteins. The experimental results indicate that our method has strong competitiveness compared with several sequence-based methods.


Assuntos
Mapeamento de Interação de Proteínas/métodos , Proteínas/química , Proteínas/metabolismo , Sequência de Aminoácidos , Bases de Dados de Proteínas , Humanos , Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo
4.
Sci Rep ; 10(1): 16809, 2020 10 08.
Artigo em Inglês | MEDLINE | ID: mdl-33033354

RESUMO

Both onco-suppressor PREP1 and the oncogene MEIS1 bind to PBX1. This interaction stabilizes the two proteins and allows their translocation into the nucleus and thus their transcriptional activity. Here, we have combined cross-linking mass-spectrometry and systematic mutagenesis to detail the binding geometry of the PBX1-PREP1 (and PBX1-MEIS1) complexes, under native in vivo conditions. The data confirm the existence of two distinct interaction sites within the PBC domain of PBX1 and unravel differences among the highly similar binding sites of MEIS1 and PREP1. The HR2 domain has a fundamental role in binding the PBC-B domain of PBX1 in both PREP1 and MEIS1. The HR1 domain of MEIS1, however, seem to play a less stringent role in PBX1 interaction with respect to that of PREP1. This difference is also reflected by the different binding affinity of the two proteins to PBX1. Although partial, this analysis provides for the first time some ideas on the tertiary structure of the complexes not available before. Moreover, the extensive mutagenic analysis of PREP1 identifies the role of individual hydrophobic HR1 and HR2 residues, both in vitro and in vivo.


Assuntos
Proteínas de Homeodomínio/metabolismo , Fator de Transcrição 1 de Leucemia de Células Pré-B/metabolismo , Mapeamento de Interação de Proteínas , Células A549 , Sítios de Ligação , Clonagem Molecular , Ensaio de Imunoadsorção Enzimática , Humanos , Espectrometria de Massas , Mutagênese , Proteína Meis1/metabolismo , Mapeamento de Interação de Proteínas/métodos
5.
Nucleic Acids Res ; 48(21): e122, 2020 12 02.
Artigo em Inglês | MEDLINE | ID: mdl-33053171

RESUMO

Protein-protein interactions are essential to ensure timely and precise recruitment of chromatin remodellers and repair factors to DNA damage sites. Conventional analyses of protein-protein interactions at a population level may mask the complexity of interaction dynamics, highlighting the need for a method that enables quantification of DNA damage-dependent interactions at a single-cell level. To this end, we integrated a pulsed UV laser on a confocal fluorescence lifetime imaging (FLIM) microscope to induce localized DNA damage. To quantify protein-protein interactions in live cells, we measured Förster resonance energy transfer (FRET) between mEGFP- and mCherry-tagged proteins, based on the fluorescence lifetime reduction of the mEGFP donor protein. The UV-FLIM-FRET system offers a unique combination of real-time and single-cell quantification of DNA damage-dependent interactions, and can distinguish between direct protein-protein interactions, as opposed to those mediated by chromatin proximity. Using the UV-FLIM-FRET system, we show the dynamic changes in the interaction between poly(ADP-ribose) polymerase 1, amplified in liver cancer 1, X-ray repair cross-complementing protein 1 and tripartite motif containing 33 after DNA damage. This new set-up complements the toolset for studying DNA damage response by providing single-cell quantitative and dynamic information about protein-protein interactions at DNA damage sites.


Assuntos
Osteoblastos/efeitos da radiação , Poli(ADP-Ribose) Polimerase-1/genética , Mapeamento de Interação de Proteínas/métodos , Fatores de Transcrição/genética , Proteína 1 Complementadora Cruzada de Reparo de Raio-X/genética , Linhagem Celular Tumoral , Cromatina/química , Cromatina/metabolismo , Cromatina/efeitos da radiação , Dano ao DNA , Transferência Ressonante de Energia de Fluorescência , Regulação da Expressão Gênica , Genes Reporter , Proteínas de Fluorescência Verde/genética , Proteínas de Fluorescência Verde/metabolismo , Humanos , Lasers , Proteínas Luminescentes/genética , Proteínas Luminescentes/metabolismo , Imagem Óptica , Osteoblastos/citologia , Osteoblastos/metabolismo , Poli(ADP-Ribose) Polimerase-1/metabolismo , Ligação Proteica , Transdução de Sinais , Análise de Célula Única , Fatores de Transcrição/metabolismo , Raios Ultravioleta , Proteína 1 Complementadora Cruzada de Reparo de Raio-X/metabolismo
6.
Proc Natl Acad Sci U S A ; 117(43): 26710-26718, 2020 10 27.
Artigo em Inglês | MEDLINE | ID: mdl-33037152

RESUMO

Large-scale proteomic methods are essential for the functional characterization of proteins in their native cellular context. However, proteomics has lagged far behind genomic approaches in scalability, standardization, and cost. Here, we introduce in vivo mRNA display, a technology that converts a variety of proteomics applications into a DNA sequencing problem. In vivo-expressed proteins are coupled with their encoding messenger RNAs (mRNAs) via a high-affinity stem-loop RNA binding domain interaction, enabling high-throughput identification of proteins with high sensitivity and specificity by next generation DNA sequencing. We have generated a high-coverage in vivo mRNA display library of the Saccharomyces cerevisiae proteome and demonstrated its potential for characterizing subcellular localization and interactions of proteins expressed in their native cellular context. In vivo mRNA display libraries promise to circumvent the limitations of mass spectrometry-based proteomics and leverage the exponentially improving cost and throughput of DNA sequencing to systematically characterize native functional proteomes.


Assuntos
Sequenciamento de Nucleotídeos em Larga Escala/métodos , Mapeamento de Interação de Proteínas/métodos , Proteômica/métodos , RNA Mensageiro , DNA Fúngico/análise , DNA Fúngico/genética , Biblioteca Gênica , Proteoma/análise , Proteoma/genética , RNA Mensageiro/análise , RNA Mensageiro/genética , Proteínas de Saccharomyces cerevisiae/análise , Proteínas de Saccharomyces cerevisiae/genética , Análise de Sequência de DNA
7.
PLoS One ; 15(10): e0240628, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33048996

RESUMO

Protein Function Module (PFM) identification in Protein-Protein Interaction Networks (PPINs) is one of the most important and challenging tasks in computational biology. The quick and accurate detection of PFMs in PPINs can contribute greatly to the understanding of the functions, properties, and biological mechanisms in research on various diseases and the development of new medicines. Despite the performance of existing detection approaches being improved to some extent, there are still opportunities for further enhancements in the efficiency, accuracy, and robustness of such detection methods. Based on the uniqueness of the network-clustering problem in the context of PPINs, this study proposed a very effective and efficient model based on the Lin-Kernighan-Helsgaun algorithm for detecting PFMs in PPINs. To demonstrate the effectiveness and efficiency of the proposed model, computational experiments are performed using three different categories of species datasets. The computational results reveal that the proposed model outperforms existing detection techniques in terms of two key performance indices, i.e., the degree of polymerization inside PFMs (cohesion) and the deviation degree between PFMs (separation), while being very fast and robust. The proposed model can be used to help researchers decide whether to conduct further expensive and time-consuming biological experiments and to select target proteins from large-scale PPI data for further detailed research.


Assuntos
Modelos Biológicos , Mapeamento de Interação de Proteínas/métodos , Mapas de Interação de Proteínas , Algoritmos , Análise por Conglomerados , Biologia Computacional , Humanos
8.
BMC Bioinformatics ; 21(1): 442, 2020 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-33028186

RESUMO

BACKGROUND: Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions. RESULTS: According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse. CONCLUSION: Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.


Assuntos
Mapeamento de Interação de Proteínas/métodos , Mapas de Interação de Proteínas , Animais , Área Sob a Curva , Bases de Dados de Proteínas , Redes Reguladoras de Genes , Camundongos , Fenótipo , Curva ROC , Interface Usuário-Computador , Peixe-Zebra/metabolismo
9.
BMC Bioinformatics ; 21(Suppl 8): 262, 2020 Sep 16.
Artigo em Inglês | MEDLINE | ID: mdl-32938371

RESUMO

BACKGROUND: Properly scoring protein-protein docking models to single out the correct ones is an open challenge, also object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), a community-wide blind docking experiment. We introduced in the field CONSRANK (CONSensus RANKing), the first pure consensus method. Also available as a web server, CONSRANK ranks docking models in an ensemble based on their ability to match the most frequent inter-residue contacts in it. We have been blindly testing CONSRANK in all the latest CAPRI rounds, where we showed it to perform competitively with the state-of-the-art energy and knowledge-based scoring functions. More recently, we developed Clust-CONSRANK, an algorithm introducing a contact-based clustering of the models as a preliminary step of the CONSRANK scoring process. In the latest CASP13-CAPRI joint experiment, we participated as scorers with a novel pipeline, combining both our scoring tools, CONSRANK and Clust-CONSRANK, with our interface analysis tool COCOMAPS. Selection of the 10 models for submission was guided by the strength of the emerging consensus, and their final ranking was assisted by results of the interface analysis. RESULTS: As a result of the above approach, we were by far the first scorer in the CASP13-CAPRI top-1 ranking, having high/medium quality models ranked at the top-1 position for the majority of targets (11 out of the total 19). We were also the first scorer in the top-10 ranking, on a par with another group, and the second scorer in the top-5 ranking. Further, we topped the ranking relative to the prediction of binding interfaces, among all the scorers and predictors. Using the CASP13-CAPRI targets as case studies, we illustrate here in detail the approach we adopted. CONCLUSIONS: Introducing some flexibility in the final model selection and ranking, as well as differentiating the adopted scoring approach depending on the targets were the key assets for our highly successful performance, as compared to previous CAPRI rounds. The approach we propose is entirely based on methods made available to the community and could thus be reproduced by any user.


Assuntos
Biologia Computacional/métodos , Ligação Proteica/genética , Mapeamento de Interação de Proteínas/métodos , Proteínas/química , Algoritmos , Humanos , Conformação Proteica
10.
BMC Bioinformatics ; 21(Suppl 13): 381, 2020 Sep 17.
Artigo em Inglês | MEDLINE | ID: mdl-32938395

RESUMO

BACKGROUND: Identification of hot spots in protein-DNA interfaces provides crucial information for the research on protein-DNA interaction and drug design. As experimental methods for determining hot spots are time-consuming, labor-intensive and expensive, there is a need for developing reliable computational method to predict hot spots on a large scale. RESULTS: Here, we proposed a new method named sxPDH based on supervised isometric feature mapping (S-ISOMAP) and extreme gradient boosting (XGBoost) to predict hot spots in protein-DNA complexes. We obtained 114 features from a combination of the protein sequence, structure, network and solvent accessible information, and systematically assessed various feature selection methods and feature dimensionality reduction methods based on manifold learning. The results show that the S-ISOMAP method is superior to other feature selection or manifold learning methods. XGBoost was then used to develop hot spots prediction model sxPDH based on the three dimensionality-reduced features obtained from S-ISOMAP. CONCLUSION: Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.


Assuntos
Proteínas de Ligação a DNA/metabolismo , Mapeamento de Interação de Proteínas/métodos , Humanos , Modelos Moleculares
11.
PLoS One ; 15(9): e0238915, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32970681

RESUMO

Protein-protein interactions (PPIs) are essential for most biological processes. However, current PPI networks present high levels of noise, sparseness and incompleteness, which limits our ability to understand the cell at the system level from the PPI network. Predicting novel (missing) links in noisy PPI networks is an essential computational method for automatically expanding the human interactome and for identifying biologically legitimate but undetected interactions for experimental determination of PPIs, which is both expensive and time-consuming. Recently, graph convolutional networks (GCN) have shown their effectiveness in modeling graph-structured data, which employ a 1-hop neighborhood aggregation procedure and have emerged as a powerful architecture for node or graph representations. In this paper, we propose a novel node (protein) embedding method by combining GCN and PageRank as the latter can significantly improve the GCN's aggregation scheme, which has difficulty in extending and exploring topological information of networks across higher-order neighborhoods of each node. Building on this novel node embedding model, we develop a higher-order GCN variational auto-encoder (HO-VGAE) architecture, which can learn a joint node representation of higher-order local and global PPI network topology for novel protein interaction prediction. It is worth noting that our method is based exclusively on network topology, with no protein attributes or extra biological features used. Extensive computational validations on PPI prediction task demonstrate our method without leveraging any additional biological information shows competitive performance-outperforms all existing graph embedding-based link prediction methods in both accuracy and robustness.


Assuntos
Biologia Computacional/métodos , Mapeamento de Interação de Proteínas/métodos , Algoritmos , Humanos , Redes Neurais de Computação
12.
J Struct Biol ; 212(2): 107617, 2020 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-32919067

RESUMO

Corona virus spike protein S is a large homo-trimeric protein anchored in the membrane of the virion particle. Protein S binds to angiotensin-converting-enzyme 2, ACE2, of the host cell, followed by proteolysis of the spike protein, drastic protein conformational change with exposure of the fusion peptide of the virus, and entry of the virion into the host cell. The structural elements that govern conformational plasticity of the spike protein are largely unknown. Here, we present a methodology that relies upon graph and centrality analyses, augmented by bioinformatics, to identify and characterize large H-bond clusters in protein structures. We apply this methodology to protein S ectodomain and find that, in the closed conformation, the three protomers of protein S bring the same contribution to an extensive central network of H-bonds, and contribute symmetrically to a relatively large H-bond cluster at the receptor binding domain, and to a cluster near a protease cleavage site. Markedly different H-bonding at these three clusters in open and pre-fusion conformations suggest dynamic H-bond clusters could facilitate structural plasticity and selection of a protein S protomer for binding to the host receptor, and proteolytic cleavage. From analyses of spike protein sequences we identify patches of histidine and carboxylate groups that could be involved in transient proton binding.


Assuntos
Betacoronavirus/química , Gráficos por Computador , Infecções por Coronavirus/virologia , Peptidil Dipeptidase A/metabolismo , Pneumonia Viral/virologia , Mapeamento de Interação de Proteínas/métodos , Glicoproteína da Espícula de Coronavírus , Algoritmos , Betacoronavirus/fisiologia , Biologia Computacional/métodos , Humanos , Ligação de Hidrogênio , Modelos Moleculares , Pandemias , Peptidil Dipeptidase A/química , Ligação Proteica , Domínios e Motivos de Interação entre Proteínas , Mapas de Interação de Proteínas , Estrutura Quaternária de Proteína , Estrutura Secundária de Proteína , Glicoproteína da Espícula de Coronavírus/química , Glicoproteína da Espícula de Coronavírus/metabolismo , Internalização do Vírus
13.
Nat Methods ; 17(10): 1010-1017, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32958922

RESUMO

Measuring the binding kinetics of single proteins represents one of the most important and challenging tasks in protein analysis. Here we show that this is possible using a surface plasmon resonance (SPR) scattering technique. SPR is a popular label-free detection technology because of its extraordinary sensitivity, but it has never been used for imaging single proteins. We overcome this limitation by imaging scattering of surface plasmonic waves by proteins. This allows us to image single proteins, measure their sizes and identify them based on their specific binding to antibodies. We further show that it is possible to quantify protein binding kinetics by counting the binding of individual molecules, providing a digital method to measure binding kinetics and analyze heterogeneity of protein behavior. We anticipate that this imaging method will become an important tool for single protein analysis, especially for low volume samples, such as single cells.


Assuntos
Proteínas/química , Imagem Individual de Molécula , Humanos , Imunoglobulina A/química , Imunoglobulina A/metabolismo , Imunoglobulina M/química , Imunoglobulina M/metabolismo , Cinética , Ligação Proteica , Mapeamento de Interação de Proteínas/métodos , Proteínas/metabolismo , Ressonância de Plasmônio de Superfície
14.
Sci Rep ; 10(1): 15522, 2020 09 23.
Artigo em Inglês | MEDLINE | ID: mdl-32968143

RESUMO

Enzymes are fundamental to biological processes and involved in most pathologies. Here we demonstrate the concept of simultaneously mapping multiple enzyme activities (EA) by applying enzyme substrate libraries to tissue sections and analyzing their conversion by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). To that end, we spray-applied a solution of 20 naturally derived peptides that are known substrates for proteases, kinases, and phosphatases to zinc-fixed paraffin tissue sections of mouse kidneys. After enzyme conversion for 5 to 120 min at 37 °C and matrix application, the tissue sections were imaged by MALDI-IMS. We could image incubation time-dependently 16 of the applied substrates with differing signal intensities and 12 masses of expected products. Utilizing inherent enzyme amplification, EA-IMS can become a powerful tool to locally study multiple, potentially even lowly expressed, enzyme activities, networks, and their pharmaceutical modulation. Differences in the substrate detectability highlight the need for future optimizations.


Assuntos
Enzimas/metabolismo , Imagem Molecular/métodos , Peptídeos/metabolismo , Mapeamento de Interação de Proteínas/métodos , Bibliotecas de Moléculas Pequenas , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz/métodos , Animais , Enzimas/ultraestrutura , Humanos , Bibliotecas de Moléculas Pequenas/metabolismo
15.
Nat Protoc ; 15(10): 3182-3211, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32778839

RESUMO

Affinity purification coupled with mass spectrometry (AP-MS) and proximity-dependent biotinylation identification (BioID) methods have made substantial contributions to interaction proteomics studies. Whereas AP-MS results in the identification of proteins that are in a stable complex, BioID labels and identifies proteins that are in close proximity to the bait, resulting in overlapping yet distinct protein identifications. Integration of AP-MS and BioID data has been shown to comprehensively characterize a protein's molecular context, but interactome analysis using both methods in parallel is still labor and resource intense with respect to cell line generation and protein purification. Therefore, we developed the Multiple Approaches Combined (MAC)-tag workflow, which allows for both AP-MS and BioID analysis with a single construct and with almost identical protein purification and mass spectrometry (MS) identification procedures. We have applied the MAC-tag workflow to a selection of subcellular markers to provide a global view of the cellular protein interactome landscape. This localization database is accessible via our online platform ( http://proteomics.fi ) to predict the cellular localization of a protein of interest (POI) depending on its identified interactors. In this protocol, we present the detailed three-stage procedure for the MAC-tag workflow: (1) cell line generation for the MAC-tagged POI; (2) parallel AP-MS and BioID protein purification followed by MS analysis; and (3) protein interaction data analysis, data filtration and visualization with our localization visualization platform. The entire procedure can be completed within 25 d.


Assuntos
Espectrometria de Massas/métodos , Mapeamento de Interação de Proteínas/métodos , Purificação por Afinidade em Tandem/métodos , Biotinilação , Linhagem Celular , Cromatografia de Afinidade/métodos , Humanos , Mapas de Interação de Proteínas/fisiologia , Proteínas/metabolismo , Proteômica/métodos , Fluxo de Trabalho
16.
Proc Natl Acad Sci U S A ; 117(36): 22068-22079, 2020 09 08.
Artigo em Inglês | MEDLINE | ID: mdl-32839320

RESUMO

RNA-protein interactions underlie a wide range of cellular processes. Improved methods are needed to systematically map RNA-protein interactions in living cells in an unbiased manner. We used two approaches to target the engineered peroxidase APEX2 to specific cellular RNAs for RNA-centered proximity biotinylation of protein interaction partners. Both an MS2-MCP system and an engineered CRISPR-Cas13 system were used to deliver APEX2 to the human telomerase RNA hTR with high specificity. One-minute proximity biotinylation captured candidate binding partners for hTR, including more than a dozen proteins not previously linked to hTR. We validated the interaction between hTR and the N 6-methyladenosine (m6A) demethylase ALKBH5 and showed that ALKBH5 is able to erase the m6A modification on endogenous hTR. ALKBH5 also modulates telomerase complex assembly and activity. MS2- and Cas13-targeted APEX2 may facilitate the discovery of novel RNA-protein interactions in living cells.


Assuntos
DNA Liase (Sítios Apurínicos ou Apirimidínicos)/metabolismo , Endonucleases/metabolismo , Enzimas Multifuncionais/metabolismo , Mapeamento de Interação de Proteínas/métodos , RNA/metabolismo , Telomerase/metabolismo , Homólogo AlkB 5 da RNA Desmetilase/genética , Homólogo AlkB 5 da RNA Desmetilase/metabolismo , Biotinilação , Sistemas CRISPR-Cas , Metilação de DNA , DNA Liase (Sítios Apurínicos ou Apirimidínicos)/genética , Endonucleases/genética , Células HEK293 , Humanos , Espectrometria de Massas , Enzimas Multifuncionais/genética , Ligação Proteica , RNA/genética , Telomerase/genética
17.
BMC Bioinformatics ; 21(1): 323, 2020 Jul 21.
Artigo em Inglês | MEDLINE | ID: mdl-32693790

RESUMO

BACKGROUND: Protein-protein interactions (PPIs) are central to many biological processes. Considering that the experimental methods for identifying PPIs are time-consuming and expensive, it is important to develop automated computational methods to better predict PPIs. Various machine learning methods have been proposed, including a deep learning technique which is sequence-based that has achieved promising results. However, it only focuses on sequence information while ignoring the structural information of PPI networks. Structural information of PPI networks such as their degree, position, and neighboring nodes in a graph has been proved to be informative in PPI prediction. RESULTS: Facing the challenge of representing graph information, we introduce an improved graph representation learning method. Our model can study PPI prediction based on both sequence information and graph structure. Moreover, our study takes advantage of a representation learning model and employs a graph-based deep learning method for PPI prediction, which shows superiority over existing sequence-based methods. Statistically, Our method achieves state-of-the-art accuracy of 99.15% on Human protein reference database (HPRD) dataset and also obtains best results on Database of Interacting Protein (DIP) Human, Drosophila, Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegan) datasets. CONCLUSION: Here, we introduce signed variational graph auto-encoder (S-VGAE), an improved graph representation learning method, to automatically learn to encode graph structure into low-dimensional embeddings. Experimental results demonstrate that our method outperforms other existing sequence-based methods on several datasets. We also prove the robustness of our model for very sparse networks and the generalization for a new dataset that consists of four datasets: HPRD, E.coli, C.elegan, and Drosophila.


Assuntos
Mapeamento de Interação de Proteínas/métodos , Animais , Caenorhabditis elegans/metabolismo , Simulação por Computador , Bases de Dados de Proteínas , Drosophila/metabolismo , Escherichia coli/metabolismo , Humanos , Aprendizado de Máquina , Redes Neurais de Computação
18.
PLoS One ; 15(7): e0234978, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32614833

RESUMO

In this study, we deal with the problem of biological network alignment (NA), which aims to find a node mapping between species' molecular networks that uncovers similar network regions, thus allowing for the transfer of functional knowledge between the aligned nodes. We provide evidence that current NA methods, which assume that topologically similar nodes (i.e., nodes whose network neighborhoods are isomorphic-like) have high functional relatedness, do not actually end up aligning functionally related nodes. That is, we show that the current topological similarity assumption does not hold well. Consequently, we argue that a paradigm shift is needed with how the NA problem is approached. So, we redefine NA as a data-driven framework, called TARA (data-driven NA), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity. TARA makes no assumptions about what nodes should be aligned, distinguishing it from existing NA methods. Specifically, TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns (features). We find that TARA is able to make accurate predictions. TARA then takes each pair of nodes that are predicted as related to be part of an alignment. Like traditional NA methods, TARA uses this alignment for the across-species transfer of functional knowledge. TARA as currently implemented uses topological but not protein sequence information for functional knowledge transfer. In this context, we find that TARA outperforms existing state-of-the-art NA methods that also use topological information, WAVE and SANA, and even outperforms or complements a state-of-the-art NA method that uses both topological and sequence information, PrimAlign. Hence, adding sequence information to TARA, which is our future work, is likely to further improve its performance. The software and data are available at http://www.nd.edu/~cone/TARA/.


Assuntos
Mapeamento de Interação de Proteínas/métodos , Mapas de Interação de Proteínas , Algoritmos , Biologia Computacional , Ontologia Genética , Humanos , Proteômica/métodos , Software
19.
BMC Bioinformatics ; 21(1): 289, 2020 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-32631222

RESUMO

BACKGROUND: The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. RESULTS: In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ∆SASsa1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods. CONCLUSION: In this study, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/ .


Assuntos
Mapeamento de Interação de Proteínas/métodos , Proteínas/química , Humanos
20.
Mol Cell ; 79(3): 504-520.e9, 2020 08 06.
Artigo em Inglês | MEDLINE | ID: mdl-32707033

RESUMO

Protein kinases are essential for signal transduction and control of most cellular processes, including metabolism, membrane transport, motility, and cell cycle. Despite the critical role of kinases in cells and their strong association with diseases, good coverage of their interactions is available for only a fraction of the 535 human kinases. Here, we present a comprehensive mass-spectrometry-based analysis of a human kinase interaction network covering more than 300 kinases. The interaction dataset is a high-quality resource with more than 5,000 previously unreported interactions. We extensively characterized the obtained network and were able to identify previously described, as well as predict new, kinase functional associations, including those of the less well-studied kinases PIM3 and protein O-mannose kinase (POMK). Importantly, the presented interaction map is a valuable resource for assisting biomedical studies. We uncover dozens of kinase-disease associations spanning from genetic disorders to complex diseases, including cancer.


Assuntos
Redes Reguladoras de Genes , Doenças Genéticas Inatas/genética , Neoplasias/genética , Proteínas Quinases/genética , Proteínas Serina-Treonina Quinases/genética , Proteínas Proto-Oncogênicas/genética , Biologia Computacional/métodos , Conjuntos de Dados como Assunto , Regulação da Expressão Gênica , Ontologia Genética , Doenças Genéticas Inatas/enzimologia , Doenças Genéticas Inatas/patologia , Humanos , Redes e Vias Metabólicas/genética , Anotação de Sequência Molecular , Distrofias Musculares/enzimologia , Distrofias Musculares/genética , Distrofias Musculares/patologia , Neoplasias/enzimologia , Neoplasias/patologia , Doenças Neurodegenerativas/enzimologia , Doenças Neurodegenerativas/genética , Doenças Neurodegenerativas/patologia , Mapeamento de Interação de Proteínas/métodos , Proteínas Quinases/química , Proteínas Quinases/classificação , Proteínas Quinases/metabolismo , Proteínas Serina-Treonina Quinases/química , Proteínas Serina-Treonina Quinases/metabolismo , Proteínas Proto-Oncogênicas/química , Proteínas Proto-Oncogênicas/metabolismo , Transdução de Sinais
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