Spectroscopic and electron microscopic analysis of bi-ligand functionalized glycopolymer/FITC–gold nanoparticles

RSC Advances ◽  
2016 ◽  
Vol 6 (50) ◽  
pp. 44392-44401 ◽  
Author(s):  
Kashyap Dave ◽  
N. Naga Malleswara Rao ◽  
Mummuluri Trinadh ◽  
B. Anu Monisha ◽  
Annadanam V. Sesha Sainath ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Binding Affinity ◽  
Self Assembly ◽  
Microscopic Analysis ◽  
Electron Microscopic Analysis ◽  
Assembly Process ◽  
Electron Microscopic ◽  
Relative Binding Affinity ◽  
Relative Binding

We report a strategy to quantify the relative binding affinity of glycopolymers on FITC-AuNP by release of the FITC via self-assembly process and which was improved by introducing a PEG segment to the glycopolymer of similar functionalities.

Synthesis and electron microscopic analysis of the self-assembly of polymer and ferritin core-shell structures

2010 ◽  
Vol 74 (7) ◽  
pp. 636-641 ◽  
Author(s):  
Laying Wu ◽  
Tao Li ◽  
Douglas Blom ◽  
Jibin Zhao ◽  
Soumitra Ghoshroy ◽  
...  
Keyword(s):  
Self Assembly ◽  
Microscopic Analysis ◽  
The Self ◽  
Electron Microscopic Analysis ◽  
Shell Structures ◽  
Core Shell ◽  
Electron Microscopic

GRAM: A True Null Model for Relative Binding Affinity Predictions

2019 ◽  
Author(s):  
Guanglei Cui ◽  
Alan P. Graves ◽  
Eric S. Manas
Keyword(s):  
Binding Affinity ◽  
Performance Metrics ◽  
Null Model ◽  
Performance Measure ◽  
Interval Estimate ◽  
Empirical Observation ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Relative Binding Affinity ◽  
Relative Binding

Relative binding affinity prediction is a critical component in computer aided drug design. Significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are 1) it provides the uncertainty range in the predicted activities, which is important in prospective applications; 2) a true null model with well-defined PI can be established. We provide one such example termed Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, s). Having an analytically defined PI that only depends on the variation in the activities, GRAM should in principle allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.<br>

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