A High Throughput Screen to Identify Inhibitors of the KIF15-TPX2 Protein-Protein Interaction for Ovarian Cancer

2018 ◽  
Author(s):  
Rebecca Wates ◽  
Anuradha Roy ◽  
Frank J Schoenen ◽  
Jeffrey Hirst ◽  
Anne Cooper ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Throughput ◽  
Protein Interaction ◽  
High Throughput Screen ◽  
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 Selective killing of homologous recombination-deficient cancer cell lines by inhibitors of the RPA:RAD52 protein-protein interaction

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248941
Author(s):  
Mona Al-Mugotir ◽  
Jeffrey J. Lovelace ◽  
Joseph George ◽  
Mika Bessho ◽  
Dhananjaya Pal ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Homologous Recombination ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Protein Interaction ◽  
Ovarian Cancer Cells ◽  
Protein Protein Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs

Synthetic lethality is a successful strategy employed to develop selective chemotherapeutics against cancer cells. Inactivation of RAD52 is synthetically lethal to homologous recombination (HR) deficient cancer cell lines. Replication protein A (RPA) recruits RAD52 to repair sites, and the formation of this protein-protein complex is critical for RAD52 activity. To discover small molecules that inhibit the RPA:RAD52 protein-protein interaction (PPI), we screened chemical libraries with our newly developed Fluorescence-based protein-protein Interaction Assay (FluorIA). Eleven compounds were identified, including FDA-approved drugs (quinacrine, mitoxantrone, and doxorubicin). The FluorIA was used to rank the compounds by their ability to inhibit the RPA:RAD52 PPI and showed mitoxantrone and doxorubicin to be the most effective. Initial studies using the three FDA-approved drugs showed selective killing of BRCA1-mutated breast cancer cells (HCC1937), BRCA2-mutated ovarian cancer cells (PE01), and BRCA1-mutated ovarian cancer cells (UWB1.289). It was noteworthy that selective killing was seen in cells known to be resistant to PARP inhibitors (HCC1937 and UWB1 SYr13). A cell-based double-strand break (DSB) repair assay indicated that mitoxantrone significantly suppressed RAD52-dependent single-strand annealing (SSA) and mitoxantrone treatment disrupted the RPA:RAD52 PPI in cells. Furthermore, mitoxantrone reduced radiation-induced foci-formation of RAD52 with no significant activity against RAD51 foci formation. The results indicate that the RPA:RAD52 PPI could be a therapeutic target for HR-deficient cancers. These data also suggest that RAD52 is one of the targets of mitoxantrone and related compounds.

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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