Exploring The Predictive Capabilities of AlphaFold Using Adversarial Protein Sequences

Abstract

The ability to obtain 3D structures of proteins is crucial for advancing our understanding of their functionalities, and Alphafold, a machine learning-based system, has demonstrated remarkable success in predicting these structures. However, the adoption of advanced machine learning models and artificial intelligence systems like protein folding neural networks (PFNNs) poses potential security and safety threats. Our investigation of the impact of adversarial protein sequences on the predictions made by PFNNs, including Alphafold, will inform the development of safer and more secure protein folding technologies, advancing our understanding of protein functionalities and contributing to the ongoing exploration of the potential of machine learning-based systems.