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Evaluating the performance of MM/PBSA for binding affinity prediction using class A GPCR crystal structures

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Abstract

The recent expansion of GPCR crystal structures provides the opportunity to assess the performance of structure-based drug design methods for the GPCR superfamily. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA)-based methods are commonly used for binding affinity prediction, as they provide an intermediate compromise of speed and accuracy between the empirical scoring functions used in docking and more robust free energy perturbation methods. In this study, we systematically assessed the performance of MM/PBSA in predicting experimental binding free energies using twenty Class A GPCR crystal structures and 934 known ligands. Correlations between predicted and experimental binding free energies varied significantly between individual targets, ranging from r = − 0.334 in the inactive-state CB1 cannabinoid receptor to r = 0.781 in the active-state CB1 cannabinoid receptor, while average correlation across all twenty targets was relatively poor (r = 0.183). MM/PBSA provided better predictions of binding free energies compared to docking scores in eight out of the twenty GPCR targets while performing worse for four targets. MM/PBSA binding affinity predictions calculated using a single, energy minimized structure provided comparable predictions to sampling from molecular dynamics simulations and may be more efficient when computational cost becomes restrictive. Additionally, we observed that restricting MM/PBSA calculations to ligands with a high degree of structural similarity to the crystal structure ligands improved performance in several cases. In conclusion, while MM/PBSA remains a valuable tool for GPCR structure-based drug design, its performance in predicting the binding free energies of GPCR ligands remains highly system-specific as demonstrated in a subset of twenty Class A GPCRs, and validation of MM/PBSA-based methods for each individual case is recommended before prospective use.

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Abbreviations

5-HT2B :

5-Hydroxytryptamine 2B receptor

A2AAR:

Adenosine A2A receptor

M1R:

Muscarinic acetylcholine 1 receptor

M3R:

Muscarinic acetylcholine 3 receptor

M4R:

Muscarinic acetylcholine 4 receptor

β1AR:

Beta-1 receptor

β2AR:

Beta-2 receptor

CB1 :

Cannabinoid 1 receptor

D3R:

Dopamine 3 receptor

D4R:

Dopamine 4 receptor

EM:

Energy minimized

GPCR:

G protein-coupled receptor

H1R:

Histamine 1 receptor

Ki :

Inhibitory constant

MD:

Molecular dynamics

MM/PBSA:

Molecular Mechanics/Poisson Boltzmann Surface Area

δ-OR:

Delta opioid receptor

μ-OR:

mu opioid receptor

N/OFQ-OR:

Nociception/orphanin FQ receptor

P2Y12 :

Purinergic receptor

PDB:

Protein Data Bank

r :

Pearson correlation coefficient

SBDD:

Structure-based drug design

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Funding

This research was supported by Taylor’s University through its Taylor’s University Flagship Research Grant Scheme under grant number TUFR/2017/002/10 and Taylor’s PhD Scholarship Program.

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Authors and Affiliations

  1. School of Pharmacy, Faculty of Health and Medical Sciences, Taylor’s University, No. 1 Jalan Taylor’s, 47500, Subang Jaya, Selangor, Malaysia

    Mei Qian Yau, Nathaniel J. Y. Chan & Jason S. E. Loo

  2. School of Pharmacy, The University of Nottingham Malaysia Campus, Jalan Broga, 43500, Semenyih, Selangor, Malaysia

    Abigail L. Emtage

  3. RCSI and UCD Malaysia Campus, No. 4 Jalan Sepoy Lines, 10450, George Town, Penang, Malaysia

    Stephen W. Doughty

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  1. Mei Qian Yau
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  2. Abigail L. Emtage
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  5. Jason S. E. Loo
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Supplementary material 1. The full list of ligands used in the datasets is available as supporting information (PDF 293 kb)

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Yau, M.Q., Emtage, A.L., Chan, N.J.Y. et al. Evaluating the performance of MM/PBSA for binding affinity prediction using class A GPCR crystal structures. J Comput Aided Mol Des 33, 487–496 (2019). https://doi.org/10.1007/s10822-019-00201-3

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