scholarly journals HMNPPID—human malignant neoplasm protein–protein interaction database

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Qingqing Li ◽  
Zhihao Yang ◽  
Zhehuan Zhao ◽  
Ling Luo ◽  
Zhiheng Li ◽  
...  

Abstract Background Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. Results In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. Conclusions HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

2018 ◽  
Author(s):  
Helen Victoria Cook ◽  
Nadezhda Tsankova ◽  
Damian Szklarczyk ◽  
Christian von Mering ◽  
Lars Juhl Jensen

AbstractAs viruses continue to pose risks to global health, having a better un-derstanding of virus–host protein–protein interactions aids in the development of treatments and vaccines. Here, we introduce Viruses.STRING, a protein–protein interaction database specifically catering to virus-virus and virus-host interactions. This database combines evidence from experimental and text-mining channels to provide combined probabilities for interactions between viral and host proteins. The database contains 177,425 interactions between 239 viruses and 319 hosts. The database is publicly available at viruses.string-db.org, and the interaction data can also be accessed through the latest version of the Cytoscape STRING app.


Viruses ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 519 ◽  
Author(s):  
Helen Cook ◽  
Nadezhda Doncheva ◽  
Damian Szklarczyk ◽  
Christian von Mering ◽  
Lars Jensen

As viruses continue to pose risks to global health, having a better understanding of virus–host protein–protein interactions aids in the development of treatments and vaccines. Here, we introduce Viruses.STRING, a protein–protein interaction database specifically catering to virus–virus and virus–host interactions. This database combines evidence from experimental and text-mining channels to provide combined probabilities for interactions between viral and host proteins. The database contains 177,425 interactions between 239 viruses and 319 hosts. The database is publicly available at viruses.string-db.org, and the interaction data can also be accessed through the latest version of the Cytoscape STRING app.


Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.


2021 ◽  
Author(s):  
Laia Miret Casals ◽  
Willem Vannecke ◽  
Kurt Hoogewijs ◽  
Gianluca Arauz ◽  
Marina Gay ◽  
...  

We describe furan as a triggerable ‘warhead’ for site-specific cross-linking using the actin and thymosin β4 (Tβ4)-complex as model of a weak and dynamic protein-protein interaction with known 3D structure...


2017 ◽  
Vol 114 (40) ◽  
pp. E8333-E8342 ◽  
Author(s):  
Maximilian G. Plach ◽  
Florian Semmelmann ◽  
Florian Busch ◽  
Markus Busch ◽  
Leonhard Heizinger ◽  
...  

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


2019 ◽  
Author(s):  
Akhilesh Kumar Bajpai ◽  
Sravanthi Davuluri ◽  
Kriti Tiwary ◽  
Sithalechumi Narayanan ◽  
Sailaja Oguru ◽  
...  

AbstractProtein-protein interactions (PPIs) are critical, and so are the databases and tools (resources) concerning PPIs. But in absence of systematic comparisons, biologists/bioinformaticians may be forced to make a subjective selection among such protein interaction databases and tools. In fact, a comprehensive list of such bioinformatics resources has not been reported so far. For the first time, we compiled 375 PPI resources, short-listed and performed preliminary comparison of 125 important ones (both lists available publicly at startbioinfo.com), and then systematically compared human PPIs from 16 carefully-selected databases. General features have been first compared in detail. The coverage of ‘experimentally verified’ vs. all PPIs, as well as those significant in case of disease-associated and other types of genes among the chosen databases has been compared quantitatively. This has been done in two ways: outputs manually obtained using web-interfaces, and all interactions downloaded from the databases. For the first approach, PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes associated with different tissues (specific to kidney, testis, and uterus, and ubiquitous) or diseases (breast cancer, lung cancer, Alzheimer’s, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. PPI-coverage for well-studied genes was also compared with that of less-studied ones. For the second approach, the back-end-data from the databases was downloaded and compared. Based on the results, we recommend the use of STRING and UniHI for retrieving the majority of ‘experimentally verified’ protein interactions, and hPRINT and STRING for obtaining maximum number of ‘total’ (experimentally verified as well as predicted) PPIs. The analysis of experimentally verified PPIs found exclusively in each database revealed that STRING contributed about 71% of exclusive hits. Overall, hPRINT, STRING and IID together retrieved ~94% of ‘total’ protein interactions available in the databases. The coverage of certain databases was skewed for some gene-types. The results also indicate that the database usage frequency may not correlate with their advantages, thereby justifying the need for more frequent studies of this nature.


Author(s):  
João Botelho ◽  
Paulo Mascarenhas ◽  
José João Mendes ◽  
Vanessa Machado

Recent studies supported a clinical association between Parkinson’s Disease (PD) and periodontitis. Hence, investigating possible protein interactions between these two conditions is of interest. In this study, we conducted a protein-protein network interaction analysis with recognized genes encoding proteins for PD and periodontitis. Genes of interest were collected via GWAS database. Then, we conducted a protein interaction analysis using STRING database, with a highest confidence cut-off of 0.9. Our protein network casted a comprehensive analysis of potential protein-protein interactions between PD and periodontitis. This analysis may underpin valuable information for new candidate molecular mechanisms between PD and periodontitis and may serve new potential targets for research purposes. These results should be carefully interpreted giving the limitations of this approach.


2019 ◽  
Author(s):  
Franziska Seeger ◽  
Anna Little ◽  
Yang Chen ◽  
Tina Woolf ◽  
Haiyan Cheng ◽  
...  

AbstractProtein-protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally charac-terizing protein residues that contribute the most to protein-protein interaction affin-ity and specificity is laborious. Thus, developing models that accurately characterize hotspots at protein-protein interfaces provides important information about how to inhibit therapeutically relevant protein-protein interactions. During the course of the ICERM WiSDM workshop 2017, we combined the KFC2a protein-protein interaction hotspot prediction features with Rosetta scoring function terms and interface filter metrics. A 2-way and 3-way forward selection strategy was employed to train support vector machine classifiers, as was a reverse feature elimination strategy. From these results, we identified subsets of KFC2a and Rosetta combined features that show improved performance over KFC2a features alone.


2021 ◽  
Author(s):  
Tatiana de Souza Moraes ◽  
Sam W. van Es ◽  
Inmaculada Hernández-Pinzón ◽  
Gwendolyn K. Kirschner ◽  
Froukje van der Wal ◽  
...  

AbstractBarley is the fourth largest cereal crop grown worldwide, and essential for food and feed production. Phenotypically, the barley spike, which is unbranched, occurs in two main architectural shapes: two-rowed or six-rowed. In the 6-rowed cultivars, all three florets of the triple floret meristem develop into seeds while in 2-rowed lines only the central floret forms a seed. VRS5(HvTB1), act as inhibitor of lateral seed outgrowth and vrs5(hvtb1) mutants display a six-rowed spike architecture. VRS5(HvTB1) is a member of the TCP transcription factor (TF) family, which often form protein-protein interactions with other transcriptional regulators to modulate the expression of their target genes.Despite the key role of VRS5(HvTB1) in regulating barley plant architecture, there is hardly any knowledge on its molecular mode-of-action. We performed an extensive phylogenetic analysis of the TCP transcription factor family, followed by an in-vitro protein-protein interaction study using yeast-two-hybrid. Our analysis shows that VRS5(HvTB1) has a diverse interaction capacity, interacting with class II TCP’s, NF-Y TF, but also chromatin modellers. Further analysis of the interaction capacity of VRS5(HvTB1) with other TCP TFs shows that VRS5(HvTB1) preferably interacts with other class II TCP TFs within the TB1 clade. One of these interactors, encoded by HvTB2, shows a similar expression pattern when compared to VRS5(HvTB1). Haplotype analysis of HvTB2 suggest that this gene is highly conserved and shows hardly any variation in cultivars or wild barley. Induced mutations in HvTB2 trough CRISPR-CAS9 mutagenesis in cv. Golden Promise resulted in barley plants that lost their characteristic unbranched spike architecture. hvtb2 mutants exhibited branches arising at the main spike, suggesting that, similar to VRS5(HvTB1), HvTB2 act as inhibitor of branching. Taken together, our protein-protein interaction studies of VRS5(HvTB1) resulted in the identification of HvTB2, another key regulator of spike architecture in barley. Understanding the molecular network, including protein-protein interactions, of key regulators of plant architecture such as VRS5(HvTB1) provide new routes towards the identification of other key regulators of plant architecture in barley.Author summaryTranscriptional regulation is one of the basic molecular processes that drives plant growth and development. The key TCP transcriptional regulator TEOSINTE BRANCHED 1 (TB1) is one of these key regulators that has been targeted during domestication of several crops for its role as modulator of branching. Also in barley, a key cereal crop, HvTB1 (also referred to as VRS5), inhibits the outgrowth or side shoots, or tillers, and seeds. Despite its key role in barley development, there is hardly any knowledge on the molecular network that is utilized by VRS5(HvTB1). Transcriptional regulators form homo- and heterodimers to regulate the expression of their downstream targets. Here, we performed an extensive phylogenetic analysis of TCP transcription factors (TFs) in barley, followed by protein-protein interaction studies of VRS5(HvTB1). Our analysis indicates, that VRS5(HvTB1) has a diverse capacity of interacting with class II TCPs, NF-Y TF, but also chromatin modellers. Induced mutagenesis trough CRISPR-CAS mutagenesis of one of the putative VRS5(HvTB1) interactors, HvTB2, resulted in barley plants with branched spikes. This shows that insight into the VRS5(HvTB1) interactome, followed by detailed functional analysis of potential interactors is essential to truly understand how TCPs modulate plant architecture. The study presented here provides a first step to underpin the protein-protein interactome of VRS5(HvTB1) and identify other, yet unknown, key regulators of barley plant architecture.


Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Jannik S Möllmann ◽  
Martin H Schaefer ◽  
Miguel A Andrade-Navarro

Abstract Cells operate and react to environmental signals thanks to a complex network of protein–protein interactions (PPIs), the malfunction of which can severely disrupt cellular homeostasis. As a result, mapping and analyzing protein networks are key to advancing our understanding of biological processes and diseases. An invaluable part of these endeavors has been the house mouse (Mus musculus), the mammalian model organism par excellence, which has provided insights into human biology and disorders. The importance of investigating PPI networks in the context of mouse prompted us to develop the Mouse Integrated Protein–Protein Interaction rEference (MIPPIE). MIPPIE inherits a robust infrastructure from HIPPIE, its sister database of human PPIs, allowing for the assembly of reliable networks supported by different evidence sources and high-quality experimental techniques. MIPPIE networks can be further refined with tissue, directionality and effect information through a user-friendly web interface. Moreover, all MIPPIE data and meta-data can be accessed via a REST web service or downloaded as text files, thus facilitating the integration of mouse PPIs into follow-up bioinformatics pipelines.


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