Screening Assay for the Detection of the Protein-Protein Interaction Between HIV-1 Nef Protein and the SH3 Domain of Hck

1998 ◽  
Vol 3 (1) ◽  
pp. 37-41 ◽  
Author(s):  
Anne E. Jones ◽  
Kalle Saksela ◽  
Stephen M. Game ◽  
Gerard O'Beirne ◽  
Neil D. Cook
Keyword(s):  
Protein Interactions ◽  
Sh3 Domain ◽  
Protein Protein Interactions ◽  
Screening Assay ◽  
Native Form ◽  
Protein Protein Interaction ◽  
Src Homology ◽  
Equilibrium Dissociation ◽  
High Degree ◽  
Hiv 1

The interaction between the Human Immunodeficiency Virus Nef protein (HIV-1 Nef) and the Src Homology Region 3 (SH3) domain of Hck was studied using scintillation proximity assay (SPA). SPA is a quick and sensitive method that does not require a separation step, thus allowing assays to be performed in a homogeneous environment. In contrast to most conventional techniques, SPA may also be used to detect low affinity protein-protein interactions. In this study, the assay was configured using biotinylated Hck SH3 domain expressed both as a GST fusion protein and synthesized chemically in its' native form. Biotinylated Hck protein was immobilized to streptavidin-coated fluoromicrosphere SPA beads and the binding of [3H]Nef was detected by scintillation counting. Analysis of binding yielded an average equilibrium dissociation constant (KD) of 183 ± 30 nM for the interaction in line with reported values by other methods. The data presented demonstrates that using SPA, protein-protein interactions of relatively low affinity can be detected with a high degree of sensitivity and screening studies of inhibitors of these associations could be facilitated by the high sample throughput achievable with SPA.

scholarly journals Membrane Binding of HIV-1 Matrix Protein: Dependence on Bilayer Composition and Protein Lipidation

2016 ◽  
Vol 90 (9) ◽  
pp. 4544-4555 ◽  
Author(s):  
Marilia Barros ◽  
Frank Heinrich ◽  
Siddhartha A. K. Datta ◽  
Alan Rein ◽  
Ioannis Karageorgos ◽  
...  
Keyword(s):  
Plasma Membrane ◽  
Protein Interactions ◽  
Matrix Protein ◽  
Full Length ◽  
Protein Protein Interactions ◽  
Binding Studies ◽  
Protein Protein Interaction ◽  
Gag Polyprotein ◽  
Protein Lipidation ◽  
Hiv 1

ABSTRACTBy assembling in a protein lattice on the host's plasma membrane, the retroviral Gag polyprotein triggers formation of the viral protein/membrane shell. The MA domain of Gag employs multiple signals—electrostatic, hydrophobic, and lipid-specific—to bring the protein to the plasma membrane, thereby complementing protein-protein interactions, located in full-length Gag, in lattice formation. We report the interaction of myristoylated and unmyristoylated HIV-1 Gag MA domains with bilayers composed of purified lipid components to dissect these complex membrane signals and quantify their contributions to the overall interaction. Surface plasmon resonance on well-defined planar membrane models is used to quantify binding affinities and amounts of protein and yields free binding energy contributions, ΔG, of the various signals. Charge-charge interactions in the absence of the phosphatidylinositide PI(4,5)P2attract the protein to acidic membrane surfaces, and myristoylation increases the affinity by a factor of 10; thus, our data do not provide evidence for a PI(4,5)P2trigger of myristate exposure. Lipid-specific interactions with PI(4,5)P2, the major signal lipid in the inner plasma membrane, increase membrane attraction at a level similar to that of protein lipidation. While cholesterol does not directly engage in interactions, it augments protein affinity strongly by facilitating efficient myristate insertion and PI(4,5)P2binding. We thus observe that the isolated MA protein, in the absence of protein-protein interaction conferred by the full-length Gag, binds the membrane with submicromolar affinities.IMPORTANCELike other retroviral species, the Gag polyprotein of HIV-1 contains three major domains: the N-terminal, myristoylated MA domain that targets the protein to the plasma membrane of the host; a central capsid-forming domain; and the C-terminal, genome-binding nucleocapsid domain. These domains act in concert to condense Gag into a membrane-bounded protein lattice that recruits genomic RNA into the virus and forms the shell of a budding immature viral capsid. In binding studies of HIV-1 Gag MA to model membranes with well-controlled lipid composition, we dissect the multiple interactions of the MA domain with its target membrane. This results in a detailed understanding of the thermodynamic aspects that determine membrane association, preferential lipid recruitment to the viral shell, and those aspects of Gag assembly into the membrane-bound protein lattice that are determined by MA.

scholarly journals Sulfotyrosine, an interaction specificity determinant for extracellular protein-protein interactions

2021 ◽  
Author(s):  
Valley Stewart ◽  
Pamela C. Ronald
Keyword(s):  
Protein Interactions ◽  
Chemokine Receptors ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Protein Protein Interaction ◽  
Coreceptor Binding ◽  
Receptor Interactions ◽  
Technical Advances ◽  
Affinity Peptide ◽  
Hiv 1

Tyrosine sulfation, a post-translational modification, can enhance and often determine protein-protein interaction specificity. Sulfotyrosyl residues (sTyr) are formed by tyrosyl-protein sulfotransferases (TPSTs) during maturation of certain secreted proteins. Here we consider three contexts for sTyr function. First, a single sTyr residue is critical for high-affinity peptide-receptor interactions in plant peptide hormones and animal receptors for glycopeptide hormones. Second, structurally flexible anionic segments often contain a cluster of two or three sTyr residues within a six-residue span. These sTyr residues are essential for coreceptor binding of the HIV-1 envelope spike protein during virus entry and for chemokine interactions with many chemokine receptors. Third, several proteins that interact with thrombin, central to normal blood-clotting, require the presence of sTyr residues in the context of acidic sequences termed hirudin-like motifs. Consequently, many proven and potential therapeutic proteins derived from blood-consuming invertebrates depend on sTyr residues for their activity. Technical advances in generating and documenting site-specific sTyr substitutions facilitate discovery and analysis, and promise to enable engineering of defined interaction determinants.

scholarly journals Determinants of the Src Homology Domain 3-Like Fold

2003 ◽  
Vol 185 (14) ◽  
pp. 4081-4086 ◽  
Author(s):  
J. Alejandro D'Aquino ◽  
Dagmar Ringe
Keyword(s):  
Protein Interactions ◽  
Sequence Similarity ◽  
Structural Alignment ◽  
Sh3 Domain ◽  
Protein Protein Interactions ◽  
Sh3 Domains ◽  
Terminal Domain ◽  
Protein Architecture ◽  
Src Homology ◽  
Domain 3

ABSTRACT In eukaryotes, the Src homology domain 3 (SH3) is a very important motif in signal transduction. SH3 domains recognize poly-proline-rich peptides and are involved in protein-protein interactions. Until now, the existence of SH3 domains has not been demonstrated in prokaryotes. However, the structure of the C-terminal domain of DtxR clearly shows that the fold of this domain is very similar to that of the SH3 domain. In addition, there is evidence that the C-terminal domain of DtxR binds to poly-proline-rich regions. Other bacterial proteins have domains that are structurally similar to the SH3 domain but whose functions are unknown or differ from that of the SH3 domain. The observed similarities between the structures of the C-terminal domain of DtxR and the SH3 domain constitute a perfect system to gain insight into their function and information about their evolution. Our results show that the C-terminal domain of DtxR shares a number of conserved key hydrophobic positions not recognizable from sequence comparison that might be responsible for the integrity of the SH3-like fold. Structural alignment of an ensemble of such domains from unrelated proteins shows a common structural core that seems to be conserved despite the lack of sequence similarity. This core constitutes the minimal requirements of protein architecture for the SH3-like fold.

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.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals Universal Screening Methods and Applications of ThermoFluor®

2006 ◽  
Vol 11 (7) ◽  
pp. 854-863 ◽  
Author(s):  
Maxwell D. Cummings ◽  
Michael A. Farnum ◽  
Marina I. Nelen
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Unfolding ◽  
Direct Detection ◽  
Functional Characterization ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Bacterial Enzyme ◽  
Research Problems

The genomics revolution has unveiled a wealth of poorly characterized proteins. Scientists are often able to produce milligram quantities of proteins for which function is unknown or hypothetical, based only on very distant sequence homology. Broadly applicable tools for functional characterization are essential to the illumination of these orphan proteins. An additional challenge is the direct detection of inhibitors of protein-protein interactions (and allosteric effectors). Both of these research problems are relevant to, among other things, the challenge of finding and validating new protein targets for drug action. Screening collections of small molecules has long been used in the pharmaceutical industry as 1 method of discovering drug leads. Screening in this context typically involves a function-based assay. Given a sufficient quantity of a protein of interest, significant effort may still be required for functional characterization, assay development, and assay configuration for screening. Increasingly, techniques are being reported that facilitate screening for specific ligands for a protein of unknown function. Such techniques also allow for function-independent screening with better characterized proteins. ThermoFluor®, a screening instrument based on monitoring ligand effects on temperature-dependent protein unfolding, can be applied when protein function is unknown. This technology has proven useful in the decryption of an essential bacterial enzyme and in the discovery of a series of inhibitors of a cancer-related, protein-protein interaction. The authors review some of the tools relevant to these research problems in drug discovery, and describe our experiences with 2 different proteins.

scholarly journals Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dan Tan ◽  
Qiang Li ◽  
Mei-Jun Zhang ◽  
Chao Liu ◽  
Chengying Ma ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Structural Information ◽  
Affinity Purification ◽  
Protein Protein Interactions ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Lysine Residues ◽  
Spacer Arm ◽  
Cross Linker ◽  
Protein Nucleic Acid

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
Author(s):  
Sharanya Sarkar ◽  
Khushboo Gulati ◽  
Manikyaprabhu Kairamkonda ◽  
Amit Mishra ◽  
Krishna Mohan Poluri
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Small Molecule Inhibitors ◽  
Computational Techniques ◽  
Protein Protein Interactions ◽  
Computational Tools ◽  
Computational Approaches ◽  
Protein Protein Interaction ◽  
Wide Range ◽  
Therapeutic Measures

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

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