Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures

Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of discussion around their potential role in drug discovery. While there are few preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a model based on low prior structural information. In order to address this, we have developed an AlphaFold2 version where we exclude all structural templates with more than 30% sequence identity from the model -building process. In a previous study, we used those models in conjunction with state-of-the-art free energy perturbation methods and demonstrated that it is possible to obtain quantitatively accurate results. In this work, we focus on using these structures in rigid receptor-ligand docking studies. Our results indicate that using out-of-the-box Alphafold2 models is not an ideal scenario for virtual screening campaigns; in fact, we strongly recommend to include some post-processing modeling to drive the binding site into a more realistic holo model.

Automated summary

Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have had a significant impact on the field of structural biology and sparked discussions about their potential role in drug discovery. However, there have been few studies addressing the use of these models in virtual screening, especially with low prior structural information. In this study, we developed an AlphaFold2 version that excludes structural templates with more than 30% sequence identity, and found that using these structures directly may not be ideal for virtual screening campaigns, suggesting the need for post-processing modeling to generate more realistic binding site models.