Exploring AlphaFold 3 for CD47 Antibody-Antigen Binding Affinity: An Unexpected Discovery of Reverse docking

TL;DR

This study evaluates AlphaFold 3's ability to predict antibody-antigen structures and binding affinities, revealing a surprising reverse docking phenomenon linked to its AI architecture, with implications for drug discovery.

Contribution

First application of AF3 to antibody-antigen binding affinity prediction, uncovering reverse docking phenomena and insights into AI architecture impacts on structural predictions.

Findings

AF3 accurately predicts antibody-antigen structures and binding energies.

Reverse docking phenomenon observed, linked to AI architecture.

AF3 shows promise for pre-screening drug candidates.

Abstract

AlphaFold 3 (AF3) is a powerful biomolecular structure-predicting tool based on the latest deep learning algorithms and revolutionized AI model architectures. A few of papers have already investigated its accuracy in predicting different biomolecular structures. However, the potential applications of AF3 beyond basic structure prediction have not been fully explored. In our study, we firstly focused on structure predictions of antibody-antigen (CD47) complexes, which is believed to be challenge for AF3 due to limited resolved cognate crystallographic structures. Furtherly, we aimed to the potentiality of AF3 in performing pre-screening for potent antibody candidates as an auxiliary work through binding affinity analysis compared to other molecular docking modules of commercial software, which would greatly benefit the lead identification or optimization process in the drug development. In essence, this is not limited to antibody-antigen binding affinity, but many other chemical or physical properties of any drug candidate based on AF3's accurate predicting structures that are extremely close to the reality. According to our experimental results, AF3 is a very promising competitor, which can efficiently produce highly reliable molecular structures and subsequent binding energy predictions for most subjects. Surprisingly, an unexpected and nonrandom phenomenon "reverse docking" was observed for two of our antibody subjects, suggesting new issues arising from the architectural revolution of AF3. Our analysis and error correction experiments show that this phenomenon is likely to be caused by revolutionized AI model architectures, which provides important experience and reminders for the optimization and design direction of AI for structural prediction. All software copyrights belong to the China Pharmaceutical University (CPU) and its affiliated School of Pharmacy and School of Science.