D3R Grand Challenge 4: Ligand Similarity and MM-GBSA-Based Pose Prediction and Affinity Ranking for BACE-1 Inhibitors

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4, which focused on predicting the binding poses and affinity ranking for compounds targeting the beta-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and Dock 6 and found that HYBRID performed better here for pose prediction. We also conducted end-point free energy estimates on protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R Grand Challenge 4 suggest that: i) the generation of the macrocycles conformers is a key step for successful pose prediction, ii) the protonation states of the BACE-1 binding site should be treated carefully, iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.

D3R Grand Challenge 4: Ligand Similarity and MM-GBSA-Based Pose Prediction and Affinity Ranking for BACE-1 Inhibitors

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4, which focused on predicting the binding poses and affinity ranking for compounds targeting the beta-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and Dock 6 and found that HYBRID performed better here for pose prediction. We also conducted end-point free energy estimates on protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R Grand Challenge 4 suggest that: i) the generation of the macrocycles conformers is a key step for successful pose prediction, ii) the protonation states of the BACE-1 binding site should be treated carefully, iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.

D3R Grand Challenge 4: Ligand Similarity and MM-GBSA-Based Pose Prediction and Affinity Ranking for BACE-1 Inhibitors

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4, which focused on predicting the binding poses and affinity ranking for compounds targeting the beta-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and Dock 6 and found that HYBRID performed better here for pose prediction. We also conducted end-point free energy estimates on protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R Grand Challenge 4 suggest that: i) the generation of the macrocycles conformers is a key step for successful pose prediction, ii) the protonation states of the BACE-1 binding site should be treated carefully, iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.