From mono-PEGylation towards anti-nonspecific protein interaction: comparison of dihydrolipoic acid versus glutathione-capped fluorescent gold nanoclusters using gel electrophoresis

Nanoscale ◽  
2020 ◽  
Vol 12 (34) ◽  
pp. 17786-17794
Author(s):  
Tzu-Yin Hou ◽  
Fang-Yu Shao ◽  
Yi-Tang Sun ◽  
Kai-Shiang Yang ◽  
Walter H. Chang ◽  
...  
Keyword(s):  
Polyethylene Glycol ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Gel Electrophoresis ◽  
Gold Nanoclusters ◽  
Dihydrolipoic Acid ◽  
Acid Versus ◽  
Bioconjugate Chemistry ◽  
First Time

Comparison of two promising candidates for future nanomedicine, i.e. DHLA- versus GSH-capped AuNCs, was conducted for the first time to clarify their polyethylene glycol-related bioconjugate chemistry (PEGylation) and protein interactions.

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

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.

scholarly journals EGAT: Edge Aggregated Graph Attention Networks and Transfer Learning Improve Protein-Protein Interaction Site Prediction

2020 ◽  
Author(s):  
Sazan Mahbub ◽  
Md Shamsuzzoha Bayzid
Keyword(s):  
Transfer Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Protein Interactions ◽  
Attention Networks ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Interaction Site Prediction ◽  
First Time

AbstractMotivationProtein-protein interactions are central to most biological processes. However, reliable identification of protein-protein interaction (PPI) sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computational methods to identify PPI sites.ResultsWe present EGAT, a highly accurate deep learning based method for PPI site prediction, where we have introduced a novel edge aggregated graph attention network to effectively leverage the structural information. We, for the first time, have used transfer learning in PPI site prediction. Our proposed edge aggregated network, together with transfer learning, has achieved remarkable improvement over the best alternate methods. Furthermore, EGAT offers a more interpretable framework than the typical black-box deep neural networks.AvailabilityEGAT is freely available as an open source project at https://github.com/Sazan-Mahbub/EGAT.

An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Epidermal Cells ◽  
Cyclin Dependent Kinase ◽  
Protein Subcellular Localization ◽  
Protein Protein Interactions ◽  
Efficient System ◽  
Protein Protein Interaction ◽  
Onion Epidermal Cells

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.

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

scholarly journals Interactome Analysis of KIN (Kin17) Shows New Functions of This Protein

2021 ◽  
Vol 43 (2) ◽  
pp. 767-781
Author(s):  
Vanessa Pinatto Gaspar ◽  
Anelise Cardoso Ramos ◽  
Philippe Cloutier ◽  
José Renato Pattaro Junior ◽  
Francisco Ferreira Duarte Junior ◽  
...  
Keyword(s):  
Dna Replication ◽  
Protein Interactions ◽  
Ribosome Biogenesis ◽  
Cell Metabolism ◽  
Cell Types ◽  
Protein Protein Interactions ◽  
Cellular Mechanisms ◽  
Cancerous Cell ◽  
First Time

KIN (Kin17) protein is overexpressed in a number of cancerous cell lines, and is therefore considered a possible cancer biomarker. It is a well-conserved protein across eukaryotes and is ubiquitously expressed in all cell types studied, suggesting an important role in the maintenance of basic cellular function which is yet to be well determined. Early studies on KIN suggested that this nuclear protein plays a role in cellular mechanisms such as DNA replication and/or repair; however, its association with chromatin depends on its methylation state. In order to provide a better understanding of the cellular role of this protein, we investigated its interactome by proximity-dependent biotin identification coupled to mass spectrometry (BioID-MS), used for identification of protein–protein interactions. Our analyses detected interaction with a novel set of proteins and reinforced previous observations linking KIN to factors involved in RNA processing, notably pre-mRNA splicing and ribosome biogenesis. However, little evidence supports that this protein is directly coupled to DNA replication and/or repair processes, as previously suggested. Furthermore, a novel interaction was observed with PRMT7 (protein arginine methyltransferase 7) and we demonstrated that KIN is modified by this enzyme. This interactome analysis indicates that KIN is associated with several cell metabolism functions, and shows for the first time an association with ribosome biogenesis, suggesting that KIN is likely a moonlight protein.

scholarly journals Comparison of α-Tocopherol, α-Tocopherol Acetate, and α-Tocopheryl Polyethylene Glycol Succinate 1000 Absorption by Caco-2 TC7 Intestinal Cells

Nutrients ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 129
Author(s):  
Charlotte Cuerq ◽  
Claire Bordat ◽  
Charlotte Halimi ◽  
Emilie Blond ◽  
Marion Nowicki ◽  
...  
Keyword(s):  
Polyethylene Glycol ◽  
High Performance ◽  
Lipid Extraction ◽  
Free Form ◽  
Glycol Succinate ◽  
Tocopherol Acetate ◽  
Apical Side ◽  
Basolateral Side ◽  
Human Enterocyte ◽  
First Time

(1) Background: vitamin E is often supplemented in the form of tocopherol acetate, but it has poor bioavailability and can fail to correct blood tocopherol concentrations in some patients with severe cholestasis. In this context, α-tocopheryl polyethylene glycol succinate 1000 (TPGS) has been of value, but very little is known about the mechanisms of its absorption. The aim of our work was to evaluate the mechanisms of absorption/secretion of TPGS compared to tocopherol acetate (TAC) and α-tocopherol by human enterocyte-like Caco-2 TC7 cells. (2) Methods: two weeks post-confluence Caco-2 cells were incubated with tocopherol- or TAC- or TPGS-rich mixed micelles up to 24 h and, following lipid extraction, TAC and tocopherol amounts were measured by high performance liquid chromatography (HPLC) in apical, cellular, and basolateral compartments. (3) Results: at equivalent concentrations of tocopherol in the apical side, the amounts of tocopherol secreted at the basolateral pole of Caco-2 cells are (i) significantly greater when the tocopherol is in the free form in the micelles; (ii) intermediate when it is in the TAC form in the micelles (p < 0.001); and (iii) significantly lower with the TPGS form (p < 0.0001). Interestingly, our results show, for the first time, that Caco-2 cells secrete one or more esterified forms of the vitamin contained in TPGS at the basolateral side.

scholarly journals Mutations and Protein Interaction Landscape Reveal Key Cellular Events Perturbed in Upper Motor Neurons with HSP and PLS

2021 ◽  
Vol 11 (5) ◽  
pp. 578
Author(s):  
Oge Gozutok ◽  
Benjamin Ryan Helmold ◽  
P. Hande Ozdinler
Keyword(s):  
Motor Neurons ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Cortical Neurons ◽  
Therapeutic Interventions ◽  
Functional Identification ◽  
Protein Protein Interaction ◽  
Cellular Events ◽  
Interaction Domains ◽  
Upper Motor Neurons

Hereditary spastic paraplegia (HSP) and primary lateral sclerosis (PLS) are rare motor neuron diseases, which affect mostly the upper motor neurons (UMNs) in patients. The UMNs display early vulnerability and progressive degeneration, while other cortical neurons mostly remain functional. Identification of numerous mutations either directly linked or associated with HSP and PLS begins to reveal the genetic component of UMN diseases. Since each of these mutations are identified on genes that code for a protein, and because cellular functions mostly depend on protein-protein interactions, we hypothesized that the mutations detected in patients and the alterations in protein interaction domains would hold the key to unravel the underlying causes of their vulnerability. In an effort to bring a mechanistic insight, we utilized computational analyses to identify interaction partners of proteins and developed the protein-protein interaction landscape with respect to HSP and PLS. Protein-protein interaction domains, upstream regulators and canonical pathways begin to highlight key cellular events. Here we report that proteins involved in maintaining lipid homeostasis and cytoarchitectural dynamics and their interactions are of great importance for UMN health and stability. Their perturbation may result in neuronal vulnerability, and thus maintaining their balance could offer therapeutic interventions.

Rotational Diffusion Analysis of Polyethylene Glycol Induced Protein Interactions

2011 ◽  
Vol 115 (41) ◽  
pp. 11786-11792 ◽  
Author(s):  
Daisuke Takahashi ◽  
Shoji Yamashita ◽  
Om Prakash ◽  
Etsuko Nishimoto
Keyword(s):  
Polyethylene Glycol ◽  
Protein Interactions ◽  
Rotational Diffusion ◽  
Diffusion Analysis

scholarly journals An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3174 ◽  
Author(s):  
Xin Xue ◽  
Gang Bao ◽  
Hai-Qing Zhang ◽  
Ning-Yi Zhao ◽  
Yuan Sun ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Drug Target ◽  
P53 Protein ◽  
Pharmacophore Model ◽  
Cellular Level ◽  
Complex Structures ◽  
Ligand Complex ◽  
Null Cell ◽  
Protein Protein Interaction ◽  
First Time

: The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of Ki from 102 to 103 nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.

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