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Structural prediction of antibody-APRIL complexes by computational docking constrained by antigen saturation
Andrew M Wollacott1, Luke N Robinson1, Boopathy Ramakrishnan1
1Visterra, Inc., Waltham, MA, USA.
Journal of Molecular Recognition : JMR
|February 15, 2019
Summary
This study presents a novel experimental-computational method to predict antibody-antigen structures, aiding in the engineering of therapeutics for IgA nephropathy (IgAN) by targeting the cytokine APRIL.
Area of Science:
- Immunology
- Structural Biology
- Computational Biology
Background:
- IgA nephropathy (IgAN) is a leading cause of glomerular disease.
- The cytokine APRIL (A Proliferation-Inducing Ligand) is implicated in IgAN pathogenesis.
- Understanding antibody-APRIL interactions is crucial for therapeutic development.
Purpose of the Study:
- To define the structural mode of engagement for anti-APRIL antibodies.
- To enable rational antibody engineering for IgAN therapeutics.
- To overcome limitations of traditional structural biology methods.
Main Methods:
- Developed an integrated experimental-computational workflow.
- Utilized yeast surface display for site-saturation mutagenesis of APRIL.
- Applied computational docking with experimental binding data as constraints.
- Validated models with X-ray co-crystallography.
Main Results:
- Generated robust structural models of antibody-APRIL complexes.
- Experimental data accurately constrained computational predictions.
- Validated models showed strong agreement with co-crystal structure.
- Successfully engineered an antibody for cross-species APRIL binding.
Conclusions:
- The integrated approach rapidly and accurately predicts antibody-antigen structures.
- This method aids rational antibody engineering and understanding molecular mechanisms.
- The workflow provides insights into IgAN pathogenesis and therapeutic strategies.
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