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1.
Chaos ; 30(8): 081104, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32872802

RESUMO

The coronavirus 2019 (COVID-19) respiratory disease is caused by the novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), which uses the enzyme ACE2 to enter human cells. This disease is characterized by important damage at a multi-organ level, partially due to the abundant expression of ACE2 in practically all human tissues. However, not every organ in which ACE2 is abundant is affected by SARS-CoV-2, which suggests the existence of other multi-organ routes for transmitting the perturbations produced by the virus. We consider here diffusive processes through the protein-protein interaction (PPI) network of proteins targeted by SARS-CoV-2 as an alternative route. We found a subdiffusive regime that allows the propagation of virus perturbations through the PPI network at a significant rate. By following the main subdiffusive routes across the PPI network, we identify proteins mainly expressed in the heart, cerebral cortex, thymus, testis, lymph node, kidney, among others of the organs reported to be affected by COVID-19.


Assuntos
Betacoronavirus , Infecções por Coronavirus/fisiopatologia , Modelos Biológicos , Pneumonia Viral/fisiopatologia , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas , Proteoma , Biomarcadores/metabolismo , Infecções por Coronavirus/metabolismo , Difusão , Humanos , Pandemias , Pneumonia Viral/metabolismo , Fatores de Tempo
2.
Biomolecules ; 10(9)2020 09 21.
Artigo em Inglês | MEDLINE | ID: mdl-32967116

RESUMO

We report the results of our in silico study of approved drugs as potential treatments for COVID-19. The study is based on the analysis of normal modes of proteins. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). We applied the tools we developed and standard tools used in the structural biology community. Our results indicate that small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic sites over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general.


Assuntos
Antivirais/farmacologia , Infecções por Coronavirus/tratamento farmacológico , Pandemias , Pneumonia Viral/tratamento farmacológico , Monofosfato de Adenosina/análogos & derivados , Monofosfato de Adenosina/química , Monofosfato de Adenosina/farmacologia , Alanina/análogos & derivados , Alanina/química , Alanina/farmacologia , Anticorpos Antivirais/imunologia , Reações Antígeno-Anticorpo , Antivirais/química , Antivirais/uso terapêutico , Betacoronavirus , Sítios de Ligação , Cloroquina/química , Cloroquina/farmacologia , Infecções por Coronavirus/prevenção & controle , Reposicionamento de Medicamentos , Eflornitina/química , Eflornitina/farmacologia , Humanos , Interações Hidrofóbicas e Hidrofílicas , Ivermectina/química , Ivermectina/farmacologia , L-Lactato Desidrogenase/química , L-Lactato Desidrogenase/efeitos dos fármacos , Modelos Moleculares , Simulação de Acoplamento Molecular , Pandemias/prevenção & controle , Peptidil Dipeptidase A/química , Peptidil Dipeptidase A/efeitos dos fármacos , Pneumonia Viral/prevenção & controle , Prolina/análogos & derivados , Prolina/química , Prolina/farmacologia , Ligação Proteica , Conformação Proteica , Mapeamento de Interação de Proteínas , Receptores da Glicina/química , Receptores da Glicina/efeitos dos fármacos , Saposinas/química , Saposinas/efeitos dos fármacos , Sofosbuvir/química , Sofosbuvir/farmacologia , Glicoproteína da Espícula de Coronavírus/química , Glicoproteína da Espícula de Coronavírus/efeitos dos fármacos , Relação Estrutura-Atividade
3.
Medicine (Baltimore) ; 99(36): e22099, 2020 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-32899090

RESUMO

Idiopathic pulmonary fibrosis is a chronic and irreversible respiratory disease with a high incidence worldwide and no specific treatment. Currently, the etiology and pathogenesis of this disease remain largely unknown. In main purpose of this study, bioinformatics analysis was used to uncover key genes and pathways related to idiopathic pulmonary fibrosis (IPF). Gene expression profiles of GSE2052 and GSE35145 were obtained. After combining the 2 chip groups; then, we normalized the data, eliminating batch difference. R software was used to process and to screen differentially expressed genes (DEGs) between the IPF and normal tissues. Then, functional enrichment analysis of these DEGs was carried out, and a protein-protein interaction network (PPI) was also constructed. A total of 276 DEGs (152 up and 134 down-regulated genes) were identified in the IPF lung samples. The PPI network was established with 227 nodes and 763 edges. The top 10 hub genes were CAM1, CDH1, CXCL12, JUN, CTGF, SERPINE1, CXCL1, EDN1, COL1A2, and SPARC. Analyzing the PPI network modules with close interaction, the 3 key modules in the whole PPI network were screened out. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for the module containing DEGs contained the viral protein interaction with cytokine and the cytokine receptor, the TNF signaling pathway, and the chemokine signaling pathway. The identified key genes and pathways may play an important role in the occurrence and development of IPF, and may be expected to be biomarkers or therapeutic targets for the diagnosis of IPF.


Assuntos
Fibrose Pulmonar Idiopática/genética , Fibrose Pulmonar Idiopática/patologia , Perfilação da Expressão Gênica , Humanos , Análise de Sequência com Séries de Oligonucleotídeos , Mapeamento de Interação de Proteínas
4.
Nat Commun ; 11(1): 4509, 2020 09 09.
Artigo em Inglês | MEDLINE | ID: mdl-32908151

RESUMO

Glycolysis is one of the primordial pathways of metabolism, playing a pivotal role in energy metabolism and biosynthesis. Glycolytic enzymes are known to form transient multi-enzyme assemblies. Here we examine the wider protein-protein interactions of plant glycolytic enzymes and reveal a moonlighting role for specific glycolytic enzymes in mediating the co-localization of mitochondria and chloroplasts. Knockout mutation of phosphoglycerate mutase or enolase resulted in a significantly reduced association of the two organelles. We provide evidence that phosphoglycerate mutase and enolase form a substrate-channelling metabolon which is part of a larger complex of proteins including pyruvate kinase. These results alongside a range of genetic complementation experiments are discussed in the context of our current understanding of chloroplast-mitochondrial interactions within photosynthetic eukaryotes.


Assuntos
Proteínas de Arabidopsis/metabolismo , Arabidopsis/fisiologia , Cloroplastos/enzimologia , Glicólise/fisiologia , Mitocôndrias/enzimologia , Arabidopsis/citologia , Proteínas de Arabidopsis/genética , Metabolismo Energético/fisiologia , Mutação , Fosfoglicerato Mutase/genética , Fosfoglicerato Mutase/metabolismo , Fosfopiruvato Hidratase/genética , Fosfopiruvato Hidratase/metabolismo , Fotossíntese/fisiologia , Plantas Geneticamente Modificadas , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas/fisiologia , Piruvato Quinase/genética , Piruvato Quinase/metabolismo
5.
Nat Commun ; 11(1): 3862, 2020 07 31.
Artigo em Inglês | MEDLINE | ID: mdl-32737291

RESUMO

Allostery in proteins influences various biological processes such as regulation of gene transcription and activities of enzymes and cell signaling. Computational approaches for analysis of allosteric coupling provide inexpensive opportunities to predict mutations and to design small-molecule agents to control protein function and cellular activity. We develop a computationally efficient network-based method, Ohm, to identify and characterize allosteric communication networks within proteins. Unlike previously developed simulation-based approaches, Ohm relies solely on the structure of the protein of interest. We use Ohm to map allosteric networks in a dataset composed of 20 proteins experimentally identified to be allosterically regulated. Further, the Ohm allostery prediction for the protein CheY correlates well with NMR CHESCA studies. Our webserver, Ohm.dokhlab.org, automatically determines allosteric network architecture and identifies critical coupled residues within this network.


Assuntos
Algoritmos , Proteínas Quimiotáticas Aceptoras de Metil/química , Mapeamento de Interação de Proteínas/estatística & dados numéricos , Software , Regulação Alostérica , Sítio Alostérico , Animais , Inibidores Enzimáticos/química , Inibidores Enzimáticos/farmacologia , Escherichia coli/enzimologia , Escherichia coli/genética , Humanos , Internet , Proteínas Quimiotáticas Aceptoras de Metil/antagonistas & inibidores , Proteínas Quimiotáticas Aceptoras de Metil/metabolismo , Simulação de Dinâmica Molecular , Estrutura Secundária de Proteína
6.
Gene ; 762: 145057, 2020 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-32805314

RESUMO

COVID-19 is a lurking calamitous disease caused by an unusual virus, SARS-CoV-2, causing massive deaths worldwide. Nonetheless, explicit therapeutic drugs or clinically approved vaccines are not available for COVID-19. Thus, a comprehensive research is crucially needed to decode the pathogenic tools, plausible drug targets, committed to the development of efficient therapy. Host-pathogen interactions via host cellular components is an emerging field of research in this respect. miRNAs have been established as vital players in host-virus interactions. Moreover, viruses have the capability to manoeuvre the host miRNA networks according to their own obligations. Besides protein coding mRNAs, noncoding RNAs might also be targeted in infected cells and viruses can exploit the host miRNA network via ceRNA effect. We have predicted a ceRNA network involving one miRNA (miR-124-3p), one mRNA (Ddx58), one lncRNA (Gm26917) and two circRNAs (Ppp1r10, C330019G07RiK) in SARS-CoV infected cells. We have identified 4 DEGs-Isg15, Ddx58, Oasl1, Usp18 by analyzing a mRNA GEO dataset. There is no notable induction of IFNs and IFN-induced ACE2, significant receptor responsible for S-protein binding mediated viral entry. Pathway enrichment and GO analysis conceded the enrichment of pathways associated with interferon signalling and antiviral-mechanism by IFN-stimulated genes. Further, we have identified 3 noncoding RNAs, playing as potential ceRNAs to the genes associated with immune mechanisms. This integrative analysis has identified noncoding RNAs and their plausible targets, which could effectively enhance the understanding of molecular mechanisms associated with viral infection. However, validation of these targets is further corroborated to determine their therapeutic efficacy.


Assuntos
Infecções por Coronavirus/genética , Redes Reguladoras de Genes , Interações Hospedeiro-Patógeno/genética , Pneumonia Viral/genética , RNA Circular/genética , RNA Longo não Codificante/genética , Animais , Betacoronavirus , Humanos , Camundongos , MicroRNAs/genética , Pandemias , Mapeamento de Interação de Proteínas , RNA Mensageiro/genética
7.
Nat Commun ; 11(1): 3942, 2020 08 07.
Artigo em Inglês | MEDLINE | ID: mdl-32770063

RESUMO

Though discovered over 100 years ago, the molecular foundation of sporadic Alzheimer's disease (AD) remains elusive. To better characterize the complex nature of AD, we constructed multiscale causal networks on a large human AD multi-omics dataset, integrating clinical features of AD, DNA variation, and gene- and protein-expression. These probabilistic causal models enabled detection, prioritization and replication of high-confidence master regulators of AD-associated networks, including the top predicted regulator, VGF. Overexpression of neuropeptide precursor VGF in 5xFAD mice partially rescued beta-amyloid-mediated memory impairment and neuropathology. Molecular validation of network predictions downstream of VGF was also achieved in this AD model, with significant enrichment for homologous genes identified as differentially expressed in 5xFAD brains overexpressing VGF. Our findings support a causal role for VGF in protecting against AD pathogenesis and progression.


Assuntos
Doença de Alzheimer/etiologia , Encéfalo/patologia , Fatores de Crescimento Neural/metabolismo , Mapas de Interação de Proteínas , Idoso , Idoso de 80 Anos ou mais , Doença de Alzheimer/patologia , Peptídeos beta-Amiloides/metabolismo , Animais , Conjuntos de Dados como Assunto , Modelos Animais de Doenças , Feminino , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Estudo de Associação Genômica Ampla , Humanos , Masculino , Camundongos , Camundongos Transgênicos , Fatores de Crescimento Neural/genética , Mapeamento de Interação de Proteínas , Proteômica
8.
J Neurovirol ; 26(5): 619-630, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32839951

RESUMO

The recent pandemic outbreak of coronavirus is pathogenic and a highly transmittable viral infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2). In this time of ongoing pandemic, many emerging reports suggested that the SARS-CoV-2 has inimical effects on neurological functions, and even causes serious neurological damage. The neurological symptoms associated with COVID-19 include headache, dizziness, depression, anosmia, encephalitis, stroke, epileptic seizures, and Guillain-Barre syndrome along with many others. The involvement of the CNS may be related with poor prognosis and disease worsening. Here, we review the evidence of nervous system involvement and currently known neurological manifestations in COVID-19 infections caused by SARS-CoV-2. We prioritize the 332 human targets of SARS-CoV-2 according to their association with brain-related disease and identified 73 candidate genes. We prioritize these 73 genes according to their spatio-temporal expression in the different regions of brain and also through evolutionary intolerance analysis. The prioritized genes could be considered potential indicators of COVID-19-associated neurological symptoms and thus act as a possible therapeutic target for the prevention and treatment of CNS manifestations associated with COVID-19 patients.


Assuntos
Betacoronavirus/patogenicidade , Encéfalo/metabolismo , Infecções por Coronavirus/genética , Interações Hospedeiro-Patógeno/genética , Proteínas do Tecido Nervoso/genética , Pneumonia Viral/genética , Proteínas Virais/genética , Encéfalo/patologia , Encéfalo/virologia , Infecções por Coronavirus/complicações , Infecções por Coronavirus/patologia , Infecções por Coronavirus/virologia , Depressão , Tontura/complicações , Tontura/genética , Tontura/patologia , Tontura/virologia , Encefalite/complicações , Encefalite/genética , Encefalite/patologia , Encefalite/virologia , Síndrome de Guillain-Barré/complicações , Síndrome de Guillain-Barré/genética , Síndrome de Guillain-Barré/patologia , Síndrome de Guillain-Barré/virologia , Cefaleia/complicações , Cefaleia/genética , Cefaleia/patologia , Cefaleia/virologia , Humanos , Proteínas do Tecido Nervoso/classificação , Proteínas do Tecido Nervoso/metabolismo , Transtornos do Olfato/complicações , Transtornos do Olfato/genética , Transtornos do Olfato/patologia , Transtornos do Olfato/virologia , Pandemias , Pneumonia Viral/complicações , Pneumonia Viral/patologia , Pneumonia Viral/virologia , Mapeamento de Interação de Proteínas , Convulsões/complicações , Convulsões/genética , Convulsões/patologia , Convulsões/virologia , Índice de Gravidade de Doença , Acidente Vascular Cerebral/complicações , Acidente Vascular Cerebral/genética , Acidente Vascular Cerebral/patologia , Acidente Vascular Cerebral/virologia , Proteínas Virais/metabolismo
9.
BMC Bioinformatics ; 21(1): 356, 2020 Aug 12.
Artigo em Inglês | MEDLINE | ID: mdl-32787845

RESUMO

BACKGROUND: Complex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model. Coalitional game theory (CGT) is an exciting method that can consider the combinatorial effect of groups of variants working in concert to produce a phenotype. CGT has been applied to associate likely-gene-disrupting variants encoded from whole genome sequence data to ASD; however, this previous approach cannot take into account for prior biological knowledge. Here we extend CGT to incorporate a priori knowledge from biological networks through a game theoretic centrality measure based on Shapley value to rank genes by their relevance-the individual gene's synergistic influence in a gene-to-gene interaction network. Game theoretic centrality extends the notion of Shapley value to the evaluation of a gene's contribution to the overall connectivity of its corresponding node in a biological network. RESULTS: We implemented and applied game theoretic centrality to rank genes on whole genomes from 756 multiplex autism families. Top ranking genes with the highest game theoretic centrality in both the weighted and unweighted approaches were enriched for pathways previously associated with autism, including pathways of the immune system. Four of the selected genes HLA-A, HLA-B, HLA-G, and HLA-DRB1-have also been implicated in ASD and further support the link between ASD and the human leukocyte antigen complex. CONCLUSIONS: Game theoretic centrality can prioritize influential, disease-associated genes within biological networks, and assist in the decoding of polygenic associations to complex disorders like autism.


Assuntos
Algoritmos , Teoria do Jogo , Redes Reguladoras de Genes , Estudos de Associação Genética , Transtorno do Espectro Autista/genética , Estudo de Associação Genômica Ampla , Humanos , Mapeamento de Interação de Proteínas , Reprodutibilidade dos Testes
10.
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
11.
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
12.
Proc Natl Acad Sci U S A ; 117(30): 18099-18109, 2020 07 28.
Artigo em Inglês | MEDLINE | ID: mdl-32669441

RESUMO

Quantitative disease resistance (QDR) represents the predominant form of resistance in natural populations and crops. Surprisingly, very limited information exists on the biomolecular network of the signaling machineries underlying this form of plant immunity. This lack of information may result from its complex and quantitative nature. Here, we used an integrative approach including genomics, network reconstruction, and mutational analysis to identify and validate molecular networks that control QDR in Arabidopsis thaliana in response to the bacterial pathogen Xanthomonas campestris To tackle this challenge, we first performed a transcriptomic analysis focused on the early stages of infection and using transgenic lines deregulated for the expression of RKS1, a gene underlying a QTL conferring quantitative and broad-spectrum resistance to X campestris RKS1-dependent gene expression was shown to involve multiple cellular activities (signaling, transport, and metabolism processes), mainly distinct from effector-triggered immunity (ETI) and pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) responses already characterized in A thaliana Protein-protein interaction network reconstitution then revealed a highly interconnected and distributed RKS1-dependent network, organized in five gene modules. Finally, knockout mutants for 41 genes belonging to the different functional modules of the network revealed that 76% of the genes and all gene modules participate partially in RKS1-mediated resistance. However, these functional modules exhibit differential robustness to genetic mutations, indicating that, within the decentralized structure of the QDR network, some modules are more resilient than others. In conclusion, our work sheds light on the complexity of QDR and provides comprehensive understanding of a QDR immune network.


Assuntos
Resistência à Doença/imunologia , Suscetibilidade a Doenças/imunologia , Interações Hospedeiro-Patógeno , Imunomodulação , Modelos Biológicos , Doenças das Plantas/etiologia , Imunidade Vegetal , Biologia Computacional/métodos , Perfilação da Expressão Gênica , Regulação da Expressão Gênica de Plantas , Genes de Plantas , Interações Hospedeiro-Patógeno/genética , Interações Hospedeiro-Patógeno/imunologia , Fenótipo , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas , Transcriptoma
13.
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
14.
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
15.
Am J Psychiatry ; 177(9): 855-866, 2020 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-32600152

RESUMO

OBJECTIVE: Attention deficit hyperactivity disorder (ADHD) is a highly heritable psychiatric disorder. The objective of this study was to define ADHD-associated candidate genes and their associated molecular modules and biological themes, based on the analysis of rare genetic variants. METHODS: The authors combined data from 11 published copy number variation studies in 6,176 individuals with ADHD and 25,026 control subjects and prioritized genes by applying an integrative strategy based on criteria including recurrence in individuals with ADHD, absence in control subjects, complete coverage in copy number gains, and presence in the minimal region common to overlapping copy number variants (CNVs), as well as on protein-protein interactions and information from cross-species genotype-phenotype annotation. RESULTS: The authors localized 2,241 eligible genes in the 1,532 reported CNVs, of which they classified 432 as high-priority ADHD candidate genes. The high-priority ADHD candidate genes were significantly coexpressed in the brain. A network of 66 genes was supported by ADHD-relevant phenotypes in the cross-species database. Four significantly interconnected protein modules were found among the high-priority ADHD genes. A total of 26 genes were observed across all applied bioinformatic methods. Lookup in the latest genome-wide association study for ADHD showed that among those 26 genes, POLR3C and RBFOX1 were also supported by common genetic variants. CONCLUSIONS: Integration of a stringent filtering procedure in CNV studies with suitable bioinformatics approaches can identify ADHD candidate genes at increased levels of credibility. The authors' analytic pipeline provides additional insight into the molecular mechanisms underlying ADHD and allows prioritization of genes for functional validation in validated model organisms.


Assuntos
Transtorno do Deficit de Atenção com Hiperatividade , Encéfalo/metabolismo , Perfilação da Expressão Gênica/métodos , RNA Polimerase III , Fatores de Processamento de RNA , Transtorno do Deficit de Atenção com Hiperatividade/genética , Transtorno do Deficit de Atenção com Hiperatividade/metabolismo , Variações do Número de Cópias de DNA/fisiologia , Bases de Dados Genéticas , Estudos de Associação Genética/métodos , Predisposição Genética para Doença , Humanos , Mapeamento de Interação de Proteínas/métodos , RNA Polimerase III/genética , RNA Polimerase III/metabolismo , Fatores de Processamento de RNA/genética , Fatores de Processamento de RNA/metabolismo
16.
Nature ; 583(7815): 271-276, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32612234

RESUMO

Plant hormones coordinate responses to environmental cues with developmental programs1, and are fundamental for stress resilience and agronomic yield2. The core signalling pathways underlying the effects of phytohormones have been elucidated by genetic screens and hypothesis-driven approaches, and extended by interactome studies of select pathways3. However, fundamental questions remain about how information from different pathways is integrated. Genetically, most phenotypes seem to be regulated by several hormones, but transcriptional profiling suggests that hormones trigger largely exclusive transcriptional programs4. We hypothesized that protein-protein interactions have an important role in phytohormone signal integration. Here, we experimentally generated a systems-level map of the Arabidopsis phytohormone signalling network, consisting of more than 2,000 binary protein-protein interactions. In the highly interconnected network, we identify pathway communities and hundreds of previously unknown pathway contacts that represent potential points of crosstalk. Functional validation of candidates in seven hormone pathways reveals new functions for 74% of tested proteins in 84% of candidate interactions, and indicates that a large majority of signalling proteins function pleiotropically in several pathways. Moreover, we identify several hundred largely small-molecule-dependent interactions of hormone receptors. Comparison with previous reports suggests that noncanonical and nontranscription-mediated receptor signalling is more common than hitherto appreciated.


Assuntos
Proteínas de Arabidopsis/metabolismo , Arabidopsis/metabolismo , Reguladores de Crescimento de Planta/metabolismo , Mapas de Interação de Proteínas , Transdução de Sinais , Arabidopsis/genética , Proteínas de Arabidopsis/genética , Ligação Proteica , Mapeamento de Interação de Proteínas , Reprodutibilidade dos Testes , Transcrição Genética
17.
J Transl Med ; 18(1): 233, 2020 06 10.
Artigo em Inglês | MEDLINE | ID: covidwho-592324

RESUMO

BACKGROUND: Epidemiological, virological and pathogenetic characteristics of SARS-CoV-2 infection are under evaluation. A better understanding of the pathophysiology associated with COVID-19 is crucial to improve treatment modalities and to develop effective prevention strategies. Transcriptomic and proteomic data on the host response against SARS-CoV-2 still have anecdotic character; currently available data from other coronavirus infections are therefore a key source of information. METHODS: We investigated selected molecular aspects of three human coronavirus (HCoV) infections, namely SARS-CoV, MERS-CoV and HCoV-229E, through a network based-approach. A functional analysis of HCoV-host interactome was carried out in order to provide a theoretic host-pathogen interaction model for HCoV infections and in order to translate the results in prediction for SARS-CoV-2 pathogenesis. The 3D model of S-glycoprotein of SARS-CoV-2 was compared to the structure of the corresponding SARS-CoV, HCoV-229E and MERS-CoV S-glycoprotein. SARS-CoV, MERS-CoV, HCoV-229E and the host interactome were inferred through published protein-protein interactions (PPI) as well as gene co-expression, triggered by HCoV S-glycoprotein in host cells. RESULTS: Although the amino acid sequences of the S-glycoprotein were found to be different between the various HCoV, the structures showed high similarity, but the best 3D structural overlap shared by SARS-CoV and SARS-CoV-2, consistent with the shared ACE2 predicted receptor. The host interactome, linked to the S-glycoprotein of SARS-CoV and MERS-CoV, mainly highlighted innate immunity pathway components, such as Toll Like receptors, cytokines and chemokines. CONCLUSIONS: In this paper, we developed a network-based model with the aim to define molecular aspects of pathogenic phenotypes in HCoV infections. The resulting pattern may facilitate the process of structure-guided pharmaceutical and diagnostic research with the prospect to identify potential new biological targets.


Assuntos
Betacoronavirus/fisiologia , Infecções por Coronavirus/genética , Infecções por Coronavirus/virologia , Redes Reguladoras de Genes , Interações Hospedeiro-Patógeno , Modelos Biológicos , Pneumonia Viral/genética , Pneumonia Viral/virologia , Mapeamento de Interação de Proteínas , Humanos , Glicoproteínas de Membrana/metabolismo , Pandemias , Transdução de Sinais/genética , Proteínas do Envelope Viral
19.
PLoS One ; 15(6): e0233445, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32497048

RESUMO

OBJECTIVE: The present study aims to explore the role of smoking factors in the risk of lung cancer and screen the feature risk pathways of smoking-induced lung cancer. METHODS: The expression profiles of the patient data from GEO database were standardized, and differentially expressed genes (DEGs) were analyzed by limma algorithm. Samples and genes were analyzed by Unsupervised hierarchical clustering method, while GO and KEGG enrichment analyses were performed on DEGs. The data of the protein-protein interaction (PPI) network were downloaded from the BioGrid and HPRD databases, and the DEGs were mapped into the PPI network to identify the interaction relationship. The enriched significant pathways were used to calculate the anomaly score and RFE method was used to optimize the feature sets. The model was trained using the support vector machine (SVM) and the predicted results were plotted into ROC curves. The AUC value was calculated to evaluate the predictive performance of the SVM model. RESULTS: A total of 1923 DEGs were obtained, of which 826 were down-regulated and 1097 were up-regulated. Unsupervised hierarchical clustering analysis showed that the diagnosis accuracy of lung cancer smokers was 74%, and that of non-lung cancer smokers was 75%. Five optimal feature pathway sets were obtained by screening, the clinical diagnostic ability of which was detected by SVM model with the accuracy improved to 84%. The diagnostic accuracy was 90% after combining clinical information. CONCLUSION: We verified that five signaling pathways combined with clinical information could be used as a feature risk pathway for identifying lung cancer smokers and non-lung cancer smokers and increased the diagnostic accuracy.


Assuntos
Perfilação da Expressão Gênica , Neoplasias Pulmonares/etiologia , Fumar/efeitos adversos , Máquina de Vetores de Suporte , Área Sob a Curva , Análise por Conglomerados , DNA de Neoplasias/análise , DNA de Neoplasias/genética , Bases de Dados Genéticas , Ontologia Genética , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/epidemiologia , Neoplasias Pulmonares/genética , Mapeamento de Interação de Proteínas , Curva ROC , Fatores de Risco , Transdução de Sinais
20.
Nat Commun ; 11(1): 3128, 2020 06 19.
Artigo em Inglês | MEDLINE | ID: mdl-32561732

RESUMO

Whole-cell cross-linking coupled to mass spectrometry is one of the few tools that can probe protein-protein interactions in intact cells. A very attractive reagent for this purpose is formaldehyde, a small molecule which is known to rapidly penetrate into all cellular compartments and to preserve the protein structure. In light of these benefits, it is surprising that identification of formaldehyde cross-links by mass spectrometry has so far been unsuccessful. Here we report mass spectrometry data that reveal formaldehyde cross-links to be the dimerization product of two formaldehyde-induced amino acid modifications. By integrating the revised mechanism into a customized search algorithm, we identify hundreds of cross-links from in situ formaldehyde fixation of human cells. Interestingly, many of the cross-links could not be mapped onto known atomic structures, and thus provide new structural insights. These findings enhance the use of formaldehyde cross-linking and mass spectrometry for structural studies.


Assuntos
Reagentes para Ligações Cruzadas/química , Formaldeído/química , Mapeamento de Interação de Proteínas/métodos , Proteínas/química , Aminoácidos/química , Linhagem Celular Tumoral , Humanos , Espectrometria de Massas , Simulação de Acoplamento Molecular , Proteínas/metabolismo
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