Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4

  • Léa El Khoury
  • Diogo Santos-Martins
  • Sukanya Sasmal
  • Jérôme Eberhardt
  • Giulia Bianco
  • Francesca Alessandra Ambrosio
  • Leonardo Solis-Vasquez
  • Andreas Koch
  • Stefano ForliEmail author
  • David L. MobleyEmail author


Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson–Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, AutoDock4, to that of MM-GBSA in predicting the binding affinities of a set of \(\beta\)-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: (i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, (ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, (iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding affinity predictions.


Docking MM-GBSA AutoDock Scoring functions 



SS, LEK and DM thank Christopher I. Bayly (OpenEye Scientific Software) for helpful discussions on MM-GBSA calculations. SS, LEK and DM also acknowledge OpenEye Scientific Software for licensing the pieces of software used in this work. The National Institutes of Health supported this work through grants 1R01GM108889-01 (DLM), R01 GM069832 (DSM, JE, SF) and U54-GM103368 (GB). LSV and AK thank the German Academic Exchange Service (DAAD) and the Peruvian National Program for Scholarships and Educational Loans (PRONABEC) for financial aid.

Supplementary material

10822_2019_240_MOESM1_ESM.pdf (91 kb)
Electronic supplementary material 1 (PDF 91 kb)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Pharmaceutical SciencesUniversity of California, IrvineIrvineUSA
  2. 2.Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaUSA
  3. 3.Department of Health Sciences“Magna Græcia” University of CatanzaroCatanzaroItaly
  4. 4.Embedded Systems and Applications GroupTechnische Universität DarmstadtDarmstadtGermany
  5. 5.Department of ChemistryUniversity of California, IrvineIrvineUSA

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