2 research outputs found
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Computational Analysis for the Rational Design of Anti-Amyloid Beta (Aβ) Antibodies
Alzheimer’s
disease (AD) is a neurodegenerative disorder
that lacks effective treatment options. Anti-amyloid beta (Aβ)
antibodies are the leading drug candidates to treat AD, but the results
of clinical trials have been disappointing. Introducing rational mutations
into anti-Aβ antibodies to increase their effectiveness is a
way forward, but the path to take is unclear. In this study, we demonstrate
the use of computational fragment-based docking and MMPBSA binding
free energy calculations in the analysis of anti-Aβ antibodies
for rational drug design efforts. Our fragment-based docking method
successfully predicts the emergence of the common EFRH epitope. MD
simulations coupled with MMPBSA binding free energy calculations are
used to analyze scenarios described in prior studies, and we computationally
introduce rational mutations into PFA1 to predict mutations that can
improve its binding affinity toward the pE3-Aβ<sub>3–8</sub> form of Aβ. Two out of our four proposed mutations are predicted
to stabilize binding. Our study demonstrates that a computational
approach may lead to an improved drug candidate for AD in the future
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Modeling Membrane Protein–Ligand Binding Interactions: The Human Purinergic Platelet Receptor
Membrane proteins, due to their roles
as cell receptors and signaling
mediators, make prime candidates for drug targets. The computational
analysis of protein–ligand binding affinities has been widely
employed as a tool in rational drug design efforts. Although efficient
implicit solvent-based methods for modeling globular protein–ligand
binding have been around for many years, the extension of such methods
to membrane protein–ligand binding is still in its infancy.
In this study, we extended the widely used Amber/MMPBSA method to
model membrane protein–ligand systems, and we used it to analyze
protein–ligand binding for the human purinergic platelet receptor
(P2Y<sub>12</sub>R), a prominent drug target in the inhibition of
platelet aggregation for the prevention of myocardial infarction and
stroke. The binding affinities, computed by the Amber/MMPBSA method
using standard parameters, correlate well with experiment. A detailed
investigation of these parameters was conducted to assess their impact
on the accuracy of the method. These analyses show the importance
of properly treating the nonpolar solvation interactions and the electrostatic
polarization in the binding of nucleotide agonists and non-nucleotide
antagonists to P2Y<sub>12</sub>R. On the basis of the crystal structures
and the experimental conditions in the binding assay, we further hypothesized
that the nucleotide agonists lose their bound magnesium ion upon binding
to P2Y<sub>12</sub>R, and our computational study supports this hypothesis.
Ultimately, this work illustrates the value of computational analysis
in the interpretation of experimental binding reactions