Designing Pooling Systems for Noisy High-Throughput Protein-Protein Interaction Experiments Using Boolean Compressed Sensing

2013 ◽  
Vol 10 (6) ◽  
pp. 1478-1490 ◽  
Author(s):  
Ramy Mourad ◽  
Zaher Dawy ◽  
Faruck Morcos
Keyword(s):  
Compressed Sensing ◽  
High Throughput ◽  
Protein Interaction ◽  
Protein Protein Interaction

scholarly journals Development of a Novel High-Throughput Screen and Identification of Small-Molecule Inhibitors of the Gα–RGS17 Protein–Protein Interaction Using AlphaScreen

2011 ◽  
Vol 16 (8) ◽  
pp. 869-877 ◽  
Author(s):  
Duncan I. Mackie ◽  
David L. Roman
Keyword(s):  
Small Molecule ◽  
High Throughput ◽  
Protein Interaction ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Screening Method ◽  
Fold Increase ◽  
Rgs Proteins ◽  
High Throughput Screen ◽  
Protein Protein Interaction

In this study, the authors used AlphaScreen technology to develop a high-throughput screening method for interrogating small-molecule libraries for inhibitors of the Gαo–RGS17 interaction. RGS17 is implicated in the growth, proliferation, metastasis, and the migration of prostate and lung cancers. RGS17 is upregulated in lung and prostate tumors up to a 13-fold increase over patient-matched normal tissues. Studies show RGS17 knockdown inhibits colony formation and decreases tumorigenesis in nude mice. The screen in this study uses a measurement of the Gαo–RGS17 protein–protein interaction, with an excellent Z score exceeding 0.73, a signal-to-noise ratio >70, and a screening time of 1100 compounds per hour. The authors screened the NCI Diversity Set II and determined 35 initial hits, of which 16 were confirmed after screening against controls. The 16 compounds exhibited IC50 <10 µM in dose–response experiments. Four exhibited IC50 values <6 µM while inhibiting the Gαo–RGS17 interaction >50% when compared to a biotinylated glutathione-S-transferase control. This report describes the first high-throughput screen for RGS17 inhibitors, as well as a novel paradigm adaptable to many other RGS proteins, which are emerging as attractive drug targets for modulating G-protein-coupled receptor signaling.

scholarly journals Versatile labeling and detection of endogenous proteins using tag-assisted split enzyme complementation

2020 ◽  
Author(s):  
Suraj Makhija ◽  
David Brown ◽  
Struan Bourke ◽  
Yina Wang ◽  
Shuqin Zhou ◽  
...  
Keyword(s):  
Protein Expression ◽  
High Throughput ◽  
Protein Interaction ◽  
Genome Engineering ◽  
Small Peptide ◽  
Protein Protein Interaction ◽  
Recent Advances ◽  
Cell Enrichment ◽  
Peptide Tags ◽  
Endogenous Proteins

AbstractRecent advances in genome engineering have expanded our capabilities to study proteins in their natural states. In particular, the ease and scalability of knocking-in small peptide tags has enabled high throughput tagging and analysis of endogenous proteins. To improve enrichment capacities and expand the functionality of knock-ins using short tags, we developed the tag-assisted split enzyme complementation (TASEC) approach, which uses two orthogonal small peptide tags and their cognate binders to conditionally drive complementation of a split enzyme upon labeled protein expression. Using this approach, we have engineered and optimized the tag-assisted split HaloTag complementation system (TA-splitHalo) and demonstrated its versatile applications in improving the efficiency of knock-in cell enrichment, detection of protein-protein interaction, and isolation of biallelic gene edited cells through multiplexing.

scholarly journals Megadock-Web: An Integrated Database of High-Throughput Structure-Based Protein-Protein Interaction Predictions

2019 ◽  
Vol 116 (3) ◽  
pp. 563a
Author(s):  
Masahito Ohue ◽  
Takanori Hayashi ◽  
Yuri Matsuzaki ◽  
Keisuke Yanagisawa ◽  
Yutaka Akiyama
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Protein Interaction

Classification Systems for Bacterial Protein-Protein Interaction Document Retrieval

2010 ◽  
Vol 1 (1) ◽  
pp. 34-44
Author(s):  
Hongfang Liu ◽  
Manabu Torii ◽  
Guixian Xu ◽  
Johannes Goll
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Document Retrieval ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Small Scale ◽  
Protein Protein Interaction ◽  
Manual Curation ◽  
Ppi Networks ◽  
Genomics And Proteomics

Protein-protein interaction (PPI) networks are essential to understand the fundamental processes governing cell biology. Recently, studying PPI networks becomes possible due to advances in experimental high-throughput genomics and proteomics technologies. Many interactions from such high-throughput studies and most interactions from small-scale studies are reported only in the scientific literature and thus are not accessible in a readily analyzable format. This has led to the birth of manual curation initiatives such as the International Molecular Exchange Consortium (IMEx). The manual curation of PPI knowledge can be accelerated by text mining systems to retrieve PPI-relevant articles (article retrieval) and extract PPI-relevant knowledge (information extraction). In this article, the authors focus on article retrieval and define the task as binary classification where PPI-relevant articles are positives and the others are negatives. In order to build such classifier, an annotated corpus is needed. It is very expensive to obtain an annotated corpus manually but a noisy and imbalanced annotated corpus can be obtained automatically, where a collection of positive documents can be retrieved from existing PPI knowledge bases and a large number of unlabeled documents (most of them are negatives) can be retrieved from PubMed. They compared the performance of several machine learning algorithms by varying the ratio of the number of positives to the number of unlabeled documents and the number of features used.

Integrative analysis of human omics data using biomolecular networks

2016 ◽  
Vol 12 (10) ◽  
pp. 2953-2964 ◽  
Author(s):  
Jonathan L. Robinson ◽  
Jens Nielsen
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Omics Data ◽  
Protein Protein Interaction ◽  
Biomolecular Networks ◽  
New Information ◽  
Protein Protein Interaction Networks ◽  
Genome Scale

Biomolecular networks, such as genome-scale metabolic models and protein–protein interaction networks, facilitate the extraction of new information from high-throughput omics data.

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