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 Functional Characterization of Lipid Regulatory Effects of Three Genes Using Knockout Mouse Models

2021 ◽  
Author(s):  
Chen Yao ◽  
Holly Savage ◽  
Tong Hao ◽  
Gha Young Lee ◽  
Yuka Takemon ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Target Genes ◽  
Causal Effect ◽  
Functional Characterization ◽  
Analytical Techniques ◽  
Hdl Cholesterol ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Regulatory Effects ◽  
Association Data

Integrative analysis that combines genome-wide association data with expression quantitative trait analysis and network representation may illuminate causal relationships between genes and diseases. To identify causal lipid genes, we utilized genotype, gene expression, protein-protein interaction networks, and phenotype data from 5,257 Framingham Heart Study participants and performed Mendelian randomization to investigate possible mechanistic explanations for observed associations. We selected three putatively causal candidate genes (ABCA6, ALDH2, and SIDT2) for lipid traits (LDL cholesterol, HDL cholesterol and triglycerides) in humans and conducted mouse knockout studies for each gene to confirm its causal effect on the corresponding lipid trait. We conducted the RNA-seq from mouse livers to explore transcriptome-wide alterations after knocking out the target genes. Our work builds upon a lipid-related gene network and expands upon it by including protein-protein interactions. These resources, along with the innovative combination of emerging analytical techniques, provide a groundwork upon which future studies can be designed to more fully understand genetic contributions to cardiovascular diseases.

scholarly journals Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

2020 ◽  
Vol 21 (6) ◽  
pp. 454-463 ◽  
Author(s):  
Mst. Shamima Khatun ◽  
Watshara Shoombuatong ◽  
Md. Mehedi Hasan ◽  
Hiroyuki Kurata
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Disease Incidence ◽  
Computational Prediction ◽  
Future Perspective ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
High Throughput Technology ◽  
Protein Interaction Prediction ◽  
Therapy Design

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

scholarly journals A Comprehensive Arabidopsis Yeast Two-Hybrid Library for Protein-Protein Interaction Studies: A Resource to the Plant Research Community

2018 ◽  
Vol 31 (9) ◽  
pp. 899-902 ◽  
Author(s):  
Cleverson Carlos Matiolli ◽  
Maeli Melotto
Keyword(s):  
Protein Interactions ◽  
Stress Responses ◽  
Protein Function ◽  
Cdna Libraries ◽  
Optimum Conditions ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Biotic And Abiotic Stresses ◽  
Protein Protein Interaction ◽  
Two Hybrid

Yeast-two-hybrid (Y2H) cDNA library screening is a valuable tool to uncover protein-protein interactions and represents a widely used method to investigate protein function. However, low transcript representation in cDNA libraries limits the depth of the screening. We have developed a Y2H library with cDNA made from Arabidopsis leaves exposed to several stressors as well as untreated leaves. The library was built using pooled mRNA extracted from plants challenged with plant and human bacterial pathogens, the flg22 elicitor, the phytotoxin coronatine, and several hormones associated with environmental stress responses. The purpose of such a library is to maximize the discovery of protein-protein interactions that occur under optimum conditions as well as during biotic and abiotic stresses.

scholarly journals A split protease-E. coli ClpXP system quantifies protein–protein interactions in Escherichia coli cells

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Shengchen Wang ◽  
Faying Zhang ◽  
Meng Mei ◽  
Ting Wang ◽  
Yueli Yun ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
High Efficiency ◽  
Cellular Protein ◽  
Type I ◽  
Protein Protein Interactions ◽  
E Coli ◽  
Protein Protein Interaction ◽  
Quantitative Fluorescence ◽  
Temperature Adaptability

AbstractCharacterizing protein–protein interactions (PPIs) is an effective method to help explore protein function. Here, through integrating a newly identified split human Rhinovirus 3 C (HRV 3 C) protease, super-folder GFP (sfGFP), and ClpXP-SsrA protein degradation machinery, we developed a fluorescence-assisted single-cell methodology (split protease-E. coli ClpXP (SPEC)) to explore protein–protein interactions for both eukaryotic and prokaryotic species in E. coli cells. We firstly identified a highly efficient split HRV 3 C protease with high re-assembly ability and then incorporated it into the SPEC method. The SPEC method could convert the cellular protein-protein interaction to quantitative fluorescence signals through a split HRV 3 C protease-mediated proteolytic reaction with high efficiency and broad temperature adaptability. Using SPEC method, we explored the interactions among effectors of representative type I-E and I-F CRISPR/Cas complexes, which combining with subsequent studies of Cas3 mutations conferred further understanding of the functions and structures of CRISPR/Cas complexes.

scholarly journals Augmenting protein network embeddings with sequence information

2019 ◽  
Author(s):  
Hassan Kané ◽  
Mohamed Coulibali ◽  
Ali Abdalla ◽  
Pelkins Ajanoh
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Quaternary Structure ◽  
Protein Function Prediction ◽  
Representation Learning ◽  
Specific Protein ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Tissue Specific ◽  
Protein Protein Interaction

ABSTRACTComputational methods that infer the function of proteins are key to understanding life at the molecular level. In recent years, representation learning has emerged as a powerful paradigm to discover new patterns among entities as varied as images, words, speech, molecules. In typical representation learning, there is only one source of data or one level of abstraction at which the learned representation occurs. However, proteins can be described by their primary, secondary, tertiary, and quaternary structure or even as nodes in protein-protein interaction networks. Given that protein function is an emergent property of all these levels of interactions in this work, we learn joint representations from both amino acid sequence and multilayer networks representing tissue-specific protein-protein interactions. Using these hybrid representations, we show that simple machine learning models trained using these hybrid representations outperform existing network-based methods on the task of tissue-specific protein function prediction on 13 out of 13 tissues. Furthermore, these representations outperform existing ones by 14% on average.

scholarly journals Experimental and bioinformatic approaches for interrogating protein–protein interactions to determine protein function

2005 ◽  
Vol 34 (2) ◽  
pp. 263-280 ◽  
Author(s):  
Arnaud Droit ◽  
Guy G Poirier ◽  
Joanna M Hunter
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Protein Protein Interactions ◽  
Biologically Relevant ◽  
Protein Protein Interaction ◽  
Bioinformatic Approaches ◽  
Disparate Data ◽  
Protein Protein Interaction Networks ◽  
Novel Protein

An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein–protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein–protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein–protein interactions.

scholarly journals InterPred: A pipeline to identify and model protein-protein interactions

2016 ◽  
Author(s):  
Claudio Mirabello ◽  
Björn Wallner
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Function ◽  
Structural Features ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Interaction Detection ◽  
Molecular Detail ◽  
Better Than

AbstractProtein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modelling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein-protein interaction models. We show that InterPred represents a major improvement in protein-protein interaction detection with a performance comparable or better than experimental high-throughput techniques. We also show that our full-atom protein-protein complex modelling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment.InterPred source code can be downloaded from http://wallnerlab.org/InterPred

scholarly journals 2HybridTools, a handy software to facilitate clone identification and mutation mapping from yeast two-hybrid screening

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7245
Author(s):  
Pierre Cauchy ◽  
Brigitte Kahn-Perlès ◽  
Pierre Ferrier ◽  
Jean Imbert ◽  
Patrick Lécine
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Sanger Sequencing ◽  
Full Range ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Protein Protein Interaction ◽  
Java Program ◽  
Two Hybrid

Yeast Two-Hybrid (Y2H) and reverse Two-Hybrid (RY2H) are powerful protein–protein interaction screening methods that rely on the interaction of bait and prey proteins fused to DNA binding (DB) and activation domains (AD), respectively. Y2H allows identification of protein interaction partners using screening libraries, while RY2H is used to determine residues critical to a given protein–protein interaction by exploiting site-directed mutagenesis. Currently, both these techniques still rely on sequencing of positive clones using conventional Sanger sequencing. For Y2H, a screen can yield several positives; the identification of such clones is further complicated by the fact that sequencing products usually contain vector sequence. For RY2H, obtaining a complete sequence is required to identify the full range of residues involved in protein–protein interactions. However, with Sanger sequencing limited to 500–800 nucleotides, sequencing is usually carried from both ends for clones greater than this length. Analysis of such RY2H data thus requires assembly of sequencing products combined with trimming of vector sequences and of low-quality bases at the beginning and ends of sequencing products. Further, RY2H analysis requires collation of mutations that abrogate a DB/AD interaction. Here, we present 2HybridTools, a Java program with a user-friendly interface that allows addressing all these issues inherent to both Y2H and RY2H. Specifically, for Y2H, 2HybridTools enables automated identification of positive clones, while for RY2H, 2HybridTools provides detailed mutation reports as a basis for further investigation of given protein–protein interactions.

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.

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