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Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA

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Abstract

The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF1% and EF5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF1% and EF5% levels. In comparison, only 14–22% and 6–11% of combinations of classical scoring functions produced improvements at EF1% and EF5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

5HT1B:

5-Hydroxytrptamine 1B receptor

5HT2B:

5-Hydroxytrptamine 2B receptor

AA2AR:

Adenosine A2A receptor

ACM2:

Muscarinic acetylcholine 2 receptor

ACM3:

Muscarinic acetylcholine 3 receptor

ADRB1:

Beta-1 adrenergic receptor

ADRB2:

Beta-2 adrenergic receptor

BCa:

Bias-corrected and accelerated

BEDROC:

Boltzmann-enhanced discrimination of receiver operating characteristic

CASF:

Comparative assessment of scoring functions

CCR5:

C–C chemokine receptor type 5

CRFR1:

Corticotropin releasing factor receptor 1

CXCR4:

C-X-C chemokine receptor type 4

DRD3:

Dopamine 3 receptor

DSX:

DrugScore eXtended

DUD-E:

Directory of useful decoys-enhanced

GPCR:

G protein-coupled receptor

GPR40:

Free fatty acid receptor 1

EF:

Enrichment factor

HRH1:

Histamine 1 receptor

MGLUR1:

Metabotropic glutamate receptor 1

MGLUR5:

Metabotropic glutamate receptor 5

MM/GBSA:

Molecular Mechanics/Generalized Born surface area

MM/PBSA:

Molecular Mechanics/Poisson–Boltzmann surface area

OPRD:

Delta opioid receptor

OPRK:

Kappa opioid receptor

OPRM:

Mu opioid receptor

OPRX:

Nociception receptor

OX2R:

Orexin receptor type 2

PAR1:

Proteinase-activated receptor 1

P2Y12:

P2Y purinoceptor 12

S1PR1:

Sphingosine 1-phosphate receptor 1

SMO:

Smoothened receptor

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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 University PhD Scholarship Program.

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Yau, M.Q., Loo, J.S.E. Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA. J Comput Aided Mol Des 36, 427–441 (2022). https://doi.org/10.1007/s10822-022-00456-3

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