scholarly journals RELATIONSHIPS BETWEEN RELATIVE BINDING AFFINITY AND ELECTROPHORETIC BEHAVIOR OF RABBIT ANTIBODIES TO STREPTOCOCCAL CARBOHYDRATES

1971 ◽  
Vol 133 (3) ◽  
pp. 424-441 ◽  
Author(s):  
K. Eichmann ◽  
J. Greenblatt
Keyword(s):  
Binding Affinity ◽  
Variable Region ◽  
Reciprocal Relationship ◽  
Binding Affinities ◽  
Igg Antibody ◽  
Intravenous Injections ◽  
Relative Binding Affinity ◽  
Relative Binding ◽  
Electrophoretic Behavior ◽  
Microzone Electrophoresis

After repeated intravenous injections with Group C streptococcal vaccine, most rabbit antisera were shown to contain one or more IgG antibody components, as revealed by microzone electrophoresis. A procedure for the fractionation of multiple IgG antibody components from such streptococcal antisera is described. Separation is achieved on the basis of differences in relative binding affinities of the antibody components to immunoabsorbent columns. The evidence suggests that the electrophoretic mobility, and thus the net charge of an antibody, bears a reciprocal relationship to its binding affinity for the streptococcal Group C antigens. Furthermore, the relative binding affinity affords another means to assess the functional homogeneity of streptococcal antibodies. A possible relationship between light chain variable-region subclasses and binding affinities of streptococcal antibodies is discussed.

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>

GRAM: A True Null Model for Relative Binding Affinity Predictions

2019 ◽  
Vol 60 (1) ◽  
pp. 11-16
Author(s):  
Guanglei Cui ◽  
Alan P. Graves ◽  
Eric S. Manas
Keyword(s):  
Binding Affinity ◽  
Null Model ◽  
Relative Binding Affinity ◽  
Relative Binding
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