Computational Analysis for the Rational Design of Anti-Amyloid Beta (ABeta) Antibodies

TL;DR

This paper demonstrates a computational approach combining docking and free energy calculations to analyze and improve anti-amyloid beta antibodies for Alzheimer's treatment, showing potential for rational drug design.

Contribution

It introduces a computational framework for analyzing and optimizing anti-ABeta antibodies, including rational mutation suggestions to enhance binding affinity.

Findings

Successfully predicted the EFRH epitope emergence

Identified mutations that stabilized antibody binding

Showed potential for computationally guided antibody optimization

Abstract

Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks effective treatment options. Anti-amyloid beta (ABeta) antibodies are the leading drug candidates to treat AD, but the results of clinical trials have been disappointing. Introducing rational mutations into anti-ABeta 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-ABeta antibodies for rational drug design efforts. Our fragment-based docking method successfully predicted the emergence of the common EFRH epitope, MD simulations coupled with MMPBSA binding free energy calculations were used to analyze scenarios described in prior studies, and we introduced rational mutations into PFA1 to improve its calculated binding affinity towards the pE3-ABeta3-8 form of ABeta. Two out of four proposed mutations stabilized binding. Our study demonstrates that a computational approach may lead to an improved drug candidate for AD in the future.