scholarly journals Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

10.3791/51809 ◽  
2014 ◽  
Author(s):  
Mirella Vivoli ◽  
Halina R. Novak ◽  
Jennifer A. Littlechild ◽  
Nicholas J. Harmer
Keyword(s):  
Protein Ligand Interactions ◽  
Differential Scanning Fluorimetry ◽  
Ligand Interactions

scholarly journals SimpleDSFviewer: a tool to analyse and view differential scanning fluorimetry data for characterising protein thermal stability and interactions

2015 ◽  
Author(s):  
Changye Sun ◽  
Yong Li ◽  
Edwin A Yates ◽  
David G Fernig
Keyword(s):  
Data Analysis ◽  
Fibroblast Growth Factors ◽  
Fibroblast Growth ◽  
Protein Ligand Interactions ◽  
Melting Curves ◽  
Differential Scanning Fluorimetry ◽  
Ligand Interactions ◽  
Protein Thermal Stability ◽  
User Friendly ◽  
Thermal Shift

Differential scanning fluorimetry (DSF) is used widely as a thermal shift assay to study protein stability and protein-ligand interactions. The benefit of DSF is that it is simple, cheap and can generate melting curves in 96-well plates providing good throughput. However, data analysis remains a challenge, and requires different methods to optimise and analyse the collected raw data. Here, the program SimpleDSFviewer is introduced to help view and analyse DSF data in an efficient way and with a user-friendly interface. The data analysis, optimisation and view methods provided by the program are described, using sample melting curves of fibroblast growth factors.

Studies of protein–ligand interactions by NMR

2003 ◽  
Vol 31 (5) ◽  
pp. 1006-1009 ◽  
Author(s):  
J. Clarkson ◽  
I.D. Campbell
Keyword(s):  
Chemical Shift ◽  
Structure Determination ◽  
Dissociation Constants ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Small Proteins ◽  
Ligand Interactions

Solution-state NMR has become an accepted method for studying the structure of small proteins in solution. This has resulted in over 3000 NMR-based co-ordinate sets being deposited in the Protein Databank. It is becoming increasingly apparent, however, that NMR is also a very powerful tool for accessing interactions between macromolecules and various ligands. These interactions can be assessed at a wide variety of levels, e.g. qualitative screening of libraries of pharmaceuticals and ‘chemical shift mapping’. Dissociation constants can sometimes be obtained in such cases. Another example would be the complete three-dimensional structure determination of a protein–ligand complex. Here we briefly describe a few of the principles involved and illustrate the method with recent examples.

scholarly journals Graph Convolutional Neural Networks for Predicting Drug-Target Interactions

2018 ◽  
Author(s):  
Wen Torng ◽  
Russ B. Altman
Keyword(s):  
Drug Target ◽  
Accurate Determination ◽  
Supplementary Information ◽  
Fixed Size ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Comparable Performance ◽  
Benchmark Datasets ◽  
Protein Pockets

AbstractAccurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a two-staged graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. We first describe an unsupervised graph-autoencoder to learn fixed-size representations of protein pockets. Two Graph-CNNs are then trained to automatically extract features from pocket graphs and 2D molecular graphs, respectively. We demonstrate that graph-autoencoders can learn meaningful fixed-size representation for protein pockets of varying sizes and the Graph-CNN framework can effectively capture protein-ligand binding interactions without relying on target-ligand co-complexes. Across several metrics, Graph-CNNs achieved better or comparable performance to 3DCNN ligand-scoring, AutoDock Vina, RF-Score, and NNScore on common virtual screening benchmark datasets. Visualization of key pocket residues and ligand atoms contributing to the classification decisions confirms that our networks recognize meaningful interactions between pockets and ligands.Availability and ImplementationContact: [email protected] information:

scholarly journals SimpleDSFviewer: a tool to analyse and view differential scanning fluorimetry data for characterising protein thermal stability and interactions

2015 ◽  
Author(s):  
Changye Sun ◽  
Yong Li ◽  
Edwin A Yates ◽  
David G Fernig
Keyword(s):  
Data Analysis ◽  
Fibroblast Growth Factors ◽  
Fibroblast Growth ◽  
Protein Ligand Interactions ◽  
Melting Curves ◽  
Differential Scanning Fluorimetry ◽  
Ligand Interactions ◽  
Protein Thermal Stability ◽  
User Friendly ◽  
Thermal Shift

Differential scanning fluorimetry (DSF) is used widely as a thermal shift assay to study protein stability and protein-ligand interactions. The benefit of DSF is that it is simple, cheap and can generate melting curves in 96-well plates providing good throughput. However, data analysis remains a challenge, and requires different methods to optimise and analyse the collected raw data. Here, the program SimpleDSFviewer is introduced to help view and analyse DSF data in an efficient way and with a user-friendly interface. The data analysis, optimisation and view methods provided by the program are described, using sample melting curves of fibroblast growth factors.

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