Hypoxia inducible factor (HIF) as a model for studying inhibition of protein–protein interactions

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.

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Furan warheads for covalent trapping of weak protein-protein interactions: cross-linking of thymosin β4 to actin

Chemical Communications ◽

10.1039/d1cc01731d ◽

2021 ◽

Author(s):

Laia Miret Casals ◽

Willem Vannecke ◽

Kurt Hoogewijs ◽

Gianluca Arauz ◽

Marina Gay ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

3D Structure ◽

Cross Linking ◽

Thymosin Β4 ◽

Protein Protein Interactions ◽

Site Specific ◽

Protein Protein Interaction ◽

Covalent Trapping

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...

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HMNPPID—human malignant neoplasm protein–protein interaction database

Human Genomics ◽

10.1186/s40246-019-0223-5 ◽

2019 ◽

Vol 13 (S1) ◽

Author(s):

Qingqing Li ◽

Zhihao Yang ◽

Zhehuan Zhao ◽

Ling Luo ◽

Zhiheng Li ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Malignant Neoplasm ◽

Biomedical Literature ◽

Malignant Neoplasms ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Interaction Database ◽

Protein Interaction Database

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.

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Protein-protein interaction inhibitors: advances in anticancer drug design

Expert Opinion on Drug Discovery ◽

10.1080/17460441.2016.1223038 ◽

2016 ◽

Vol 11 (10) ◽

pp. 957-968 ◽

Cited By ~ 13

Author(s):

Leonardo G. Ferreira ◽

Glaucius Oliva ◽

Adriano D. Andricopulo

Keyword(s):

Drug Design ◽

Anticancer Drug ◽

Protein Interaction ◽

Protein Protein Interaction

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

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1707335114 ◽

2017 ◽

Vol 114 (40) ◽

pp. E8333-E8342 ◽

Cited By ~ 13

Author(s):

Maximilian G. Plach ◽

Florian Semmelmann ◽

Florian Busch ◽

Markus Busch ◽

Leonhard Heizinger ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Complexes ◽

Evolutionary Strategy ◽

Structural Level ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Evolutionary Diversification

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

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How helpful are the protein-protein interaction databases and which ones?

10.1101/566372 ◽

2019 ◽

Author(s):

Akhilesh Kumar Bajpai ◽

Sravanthi Davuluri ◽

Kriti Tiwary ◽

Sithalechumi Narayanan ◽

Sailaja Oguru ◽

...

Keyword(s):

Breast Cancer ◽

Protein Interaction ◽

Protein Interactions ◽

Protein Protein Interactions ◽

Web Interfaces ◽

Protein Protein Interaction ◽

Usage Frequency ◽

First Time ◽

Different Tissues ◽

The Web

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.

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Feature Design for Protein Interface hotspots using KFC2 and Rosetta

10.1101/514372 ◽

2019 ◽

Author(s):

Franziska Seeger ◽

Anna Little ◽

Yang Chen ◽

Tina Woolf ◽

Haiyan Cheng ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Scoring Function ◽

Selection Strategy ◽

Support Vector ◽

Protein Protein Interactions ◽

Forward Selection ◽

Protein Protein Interaction ◽

Combined Features ◽

Improved Performance

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.

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The TCP transcription factor HvTB2 heterodimerizes with VRS5(HvTB1) and controls spike architecture in barley

10.1101/2021.04.14.439785 ◽

2021 ◽

Author(s):

Tatiana de Souza Moraes ◽

Sam W. van Es ◽

Inmaculada Hernández-Pinzón ◽

Gwendolyn K. Kirschner ◽

Froukje van der Wal ◽

...

Keyword(s):

Transcription Factor ◽

Protein Interaction ◽

Protein Interactions ◽

Plant Architecture ◽

Transcriptional Regulators ◽

Class Ii ◽

Barley Plant ◽

Cereal Crop ◽

Protein Protein Interactions ◽

Protein Protein Interaction

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.

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MIPPIE: the mouse integrated protein–protein interaction reference

Database ◽

10.1093/database/baaa035 ◽

2020 ◽

Vol 2020 ◽

Cited By ~ 3

Author(s):

Gregorio Alanis-Lobato ◽

Jannik S Möllmann ◽

Martin H Schaefer ◽

Miguel A Andrade-Navarro

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Model Organism ◽

Web Interface ◽

Protein Protein Interactions ◽

Environmental Signals ◽

Protein Protein Interaction ◽

Ppi Networks ◽

User Friendly

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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Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions

International Journal of Knowledge Discovery in Bioinformatics ◽

10.4018/ijkdb.2014010103 ◽

2014 ◽

Vol 4 (1) ◽

pp. 24-41 ◽

Cited By ~ 2

Author(s):

Morihiro Hayashida ◽

Tatsuya Akutsu

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Domains ◽

Cellular Systems ◽

Support Vector ◽

Protein Protein Interactions ◽

Scale Free ◽

Free Distribution ◽

Protein Protein Interaction ◽

Domain Interactions

Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

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