Related Experiment Video
Updated: Jan 25, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
Published on: February 23, 2024
Computational prediction and in vitro validation of VEGFR1 as a novel protein target for
Kumaraswamy Naidu Chitrala1, Xiaoming Yang1, Brandon Busbee1
1Department of Pathology, Microbiology and Immunology, University of South Carolina School of Medicine, Columbia, SC, 29208, USA.
Abstract:
The toxic manifestations of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), an environmental contaminant, primarily depend on its ability to activate aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor belonging to the superfamily of basic-helix-loop-helix DNA-binding proteins. In the present study, we aimed to identify novel protein receptor targets for TCDD using computational and in vitro validation experiments. Interestingly, results from computational methods predicted that Vascular Endothelial Growth Factor Receptor 1 (VEGFR1) could be one of the potential targets for TCDD in both mouse and humans. Results from molecular docking studies showed that human VEGFR1 (hVEGFR1) has less affinity towards TCDD compared to the mouse VEGFR1 (mVEGFR1). In vitro validation results showed that TCDD can bind and phosphorylate hVEGFR1. Further, results from molecular dynamic simulation studies showed that hVEGFR1 interaction with TCDD is stable throughout the simulation time. Overall, the present study has identified VEGFR1 as a novel target for TCDD, which provides the basis for further elucidating the role of TCDD in angiogenesis.
Related Concept Videos
Reliability and Validity
In Vitro Drug Release Testing: Overview, Development and Validation
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Predicting Molecular Geometry
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...

