ArticleACS chemical biology2020
Quantification of ERK Kinase Activity in Biological Samples Using Differential Sensing.
Article in ACS chemical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Nanosensor-Enabled Detection and Identification of Intracellular Bacterial Infections in Macrophages.Biosensors · 2024Article
- Phosphorylation-Assisted Luciferase Complementation Assay Designed to Monitor Kinase Activity and Kinase-Domain-Mediated Protein-Protein Binding.International journal of molecular sciences · 2023Article
- Molecules that Generate Fingerprints: A New Class of Fluorescent Sensors for Chemical Biology, Medical Diagnosis, and Cryptography.Accounts of chemical research · 2023Review
- Electrochemical Detection of Different Foodborne Bacteria for Point-of-Care Applications.Biosensors · 2023Article
- Chemically programmable bacterial probes for the recognition of cell surface proteins.Materials today. Bio · 2023Article
- A Highly Sensitive Fluorescent Akt Biosensor Reveals Lysosome-Selective Regulation of Lipid Second Messengers and Kinase Activity.ACS central science · 2021Article
- Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage.Food chemistry · 2021Article
- A fluorescent probe for monitoring PTP-PEST enzymatic activity.The Analyst · 2020Article
- Application of Chemometrics in Biosensing: A Review.Biosensors · 2020Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The understanding of complex biological systems requires an ability to evaluate interacting networks of genes, proteins, and cellular reactions. Enabling technologies that support the rapid quantification of these networks will facilitate the development of biological models and help to identify treatment targets and to assess treatment plans. The biochemical process of protein phosphorylation, which underlies almost all aspects of cell signaling, is typically evaluated by immunoblotting procedures (Western blot) or more recently proteomics procedures, which provide qualitative estimates of the concentration of proteins and their modifications in cells. However, protein modifications are difficult to correlate with activity, and while immunoblotting and proteomics approaches have the potential to be quantitative, they require a complex series of steps that diminish reproducibility. Here, a complementary approach is presented that allows for the rapid quantification of a protein kinase activity in cell lysates and tumor samples. Using the activity of cellular ERK (extracellular signal-regulated kinase) as a test case, an array sensing approach that utilizes a library of differential peptide-based biosensors and chemometric tools was used to rapidly quantify nanograms of active ERK in micrograms of unfractionated cell lysates and tumor extracts. This approach has the potential both for high-throughput and for quantifying the activities of multiple protein kinases in a single biological sample. The critical advantages of this differential sensing approach over others are that it removes the need for the addition of exogenous inhibitors to suppress the activities of major off-target kinases and allows us to quantitate the amount of active kinase in tested samples rather than measuring the changes in its activity upon induction or inhibition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.