scholarly journals On the importance of composite protein multiple ligand interactions in protein pockets

2016 ◽  
Vol 38 (15) ◽  
pp. 1252-1259 ◽  
Author(s):  
Sam Tonddast-Navaei ◽  
Bharath Srinivasan ◽  
Jeffrey Skolnick
Keyword(s):  
Ligand Interactions ◽  
Protein Pockets ◽  
Multiple Ligand

scholarly journals Direct Determination of Multiple Ligand Interactions with the Extracellular Domain of the Calcium-sensing Receptor

2014 ◽  
Vol 289 (48) ◽  
pp. 33529-33542 ◽  
Author(s):  
Chen Zhang ◽  
You Zhuo ◽  
Heather A. Moniz ◽  
Shuo Wang ◽  
Kelley W. Moremen ◽  
...  
Keyword(s):  
Direct Determination ◽  
Extracellular Domain ◽  
Calcium Sensing Receptor ◽  
Calcium Sensing ◽  
Ligand Interactions ◽  
Multiple Ligand

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:

New insights into heparan sulphate biosynthesis from the study of mutant mice

2002 ◽  
Vol 69 ◽  
pp. 47-57 ◽  
Author(s):  
Catherine L. R. Merry ◽  
John T. Gallagher
Keyword(s):  
Growth Factors ◽  
Biosynthetic Pathway ◽  
Heparan Sulphate ◽  
Mutant Mice ◽  
Gene Products ◽  
Multiple Gene ◽  
Adhesion Proteins ◽  
Ligand Interactions

Heparan sulphate (HS) is an essential co-receptor for a number of growth factors, morphogens and adhesion proteins. The biosynthetic modifications involved in the generation of a mature HS chain may determine the strength and outcome of HS–ligand interactions. These modifications are catalysed by a complex family of enzymes, some of which occur as multiple gene products. Various mutant mice have now been generated, which lack the function of isolated components of the HS biosynthetic pathway. In this discussion, we outline the key findings of these studies, and use them to put into context our own work concerning the structure of the HS generated by the Hs2st-/- mice.

Activated CD4+ T cells enriched from spleen or colon induce apoptosis in intestinal epithellal cells via fas/fas ligand interactions

2001 ◽  
Vol 120 (5) ◽  
pp. A192-A192
Author(s):  
H TAKAISHI ◽  
T DENNING ◽  
K ITO ◽  
R MIFFLIN ◽  
P ERNST
Keyword(s):  
T Cells ◽  
Cd4 T Cells ◽  
Fas Ligand ◽  
Ligand Interactions

Modulation of Platelet Activation by Native DNA – Initial Description of Platelet Receptor Type, Number and Discrimination for Native DNA

1981 ◽  
Vol 45 (03) ◽  
pp. 263-266 ◽  
Author(s):  
B A Fiedel ◽  
M E Frenzke
Keyword(s):  
Platelet Aggregation ◽  
Human Platelets ◽  
Scatchard Analysis ◽  
Type Number ◽  
Single Class ◽  
Receptor Ligand ◽  
Platelet Receptor ◽  
Platelet Binding ◽  
Ligand Interactions ◽  
Initial Description

SummaryNative DNA (dsDNA) induces the aggregation of isolated human platelets. Using isotopically labeled dsDNA (125I-dsDNA) and Scatchard analysis, a single class of platelet receptor was detected with a KD = 190 pM and numbering ~275/platelet. This receptor was discriminatory in that heat denatured dsDNA, poly A, poly C, poly C · I and poly C · poly I failed to substantially inhibit either the platelet binding of, or platelet aggregation induced by, dsDNA; by themselves, these polynucleotides were ineffective as platelet agonists. However, poly G, poly I and poly G · I effectively and competitively inhibited platelet binding of the radioligand, independently activated the platelet and when used at a sub-activating concentration decreased the extent of dsDNA stimulated platelet aggregation. These data depict a receptor on human platelets for dsDNA and perhaps certain additional polynucleotides and relate receptor-ligand interactions to a physiologic platelet function.

Solvent-Ligand Interactions in Colloidal Nanocrystals

2018 ◽  
Author(s):  
Jonathan De Roo ◽  
Nuri Yazdani ◽  
Emile Drijvers ◽  
Alessandro Lauria ◽  
Jorick Maes ◽  
...  
Keyword(s):  
Direct Method ◽  
Line Broadening ◽  
H Nmr ◽  
Size Dependent ◽  
Line Shape Analysis ◽  
Wide Range ◽  
Nanocrystal Synthesis ◽  
Ligand Interactions ◽  
Indispensable Tool ◽  
Theoretical Evidence

<p>Although solvent-ligand interactions play a major role in nanocrystal synthesis, dispersion formulation and assembly, there is currently no direct method to study this. Here we examine the broadening of <sup>1</sup>H NMR resonances associated with bound ligands, and turn this poorly understood descriptor into a tool to assess solvent-ligand interactions. We show that the line broadening has both a homogeneous and a heterogeneous component. The former is nanocrystal-size dependent and the latter results from solvent-ligand interactions. Our model is supported by experimental and theoretical evidence that correlates broad NMR lines with poor ligand solvation. This correlation is found across a wide range of solvents, extending from water to hexane, for both hydrophobic and hydrophilic ligand types, and for a multitude of oxide, sulfide and selenide nanocrystals. Our findings thus put forward NMR line shape analysis as an indispensable tool to form, investigate and manipulate nanocolloids.</p>

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