Antibody-enabled structural biology and AI-driven antibody design

TL;DR

Antibodies and AI are transforming structural biology by enabling better study of membrane proteins and accelerating drug discovery.

Contribution

This paper reviews the combined impact of antibody engineering and AI on structural biology and therapeutic development.

Findings

Antibodies stabilize membrane proteins for structural studies.

AI enhances modeling and design of antibody-antigen interactions.

These approaches enable visualization of previously inaccessible complexes.

Abstract

Membrane proteins govern essential cellular processes, including ion transport, signal transduction, and molecular recognition, and collectively represent more than half of all current therapeutic targets. Yet their structural characterization remains challenging due to intrinsic instability, amphipathic surfaces, and conformational heterogeneity. Over the past decade, antibody-based approaches, spanning full-length immunoglobulins, antigen-binding fragments (Fabs), nanobodies, and engineered scaffolds such as designed ankyrin repeat proteins (DARPins), have transformed structural biology by stabilizing dynamic states, augmenting molecular weight for cryo-electron microscopy (cryo-EM), and enabling visualization of previously inaccessible complexes. In parallel, advances in artificial intelligence and machine learning have begun to enhance predictive modeling, accelerate structure determination, and guide rational design of protein-ligand and antibody–antigen interactions. This review examines how antibody engineering and AI-driven computation together are reshaping the landscape of structural biology and therapeutic discovery.

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