Combinatorial consensus scoring for ligand-based virtual fragment screening: A comparative case study for serotonin 5-HT3A, histamine H1, and Histamine H4 receptors

Sabine Schultes; Albert J. Kooistra; Henry F. Vischer; Saskia Nijmeijer; Eric E.J. Haaksma; Rob Leurs; Iwan J.P. De Esch; Chris De Graaf

doi:10.1021/ci500694c

Combinatorial consensus scoring for ligand-based virtual fragment screening: A comparative case study for serotonin 5-HT₃A, histamine H₁, and Histamine H₄ receptors

Sabine Schultes, Albert J. Kooistra, Henry F. Vischer, Saskia Nijmeijer, Eric E.J. Haaksma, Rob Leurs, Iwan J.P. De Esch, Chris De Graaf^*

^*Corresponding author af dette arbejde

13 Citationer (Scopus)

Abstract

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H₁ receptor (H₁R) and the histamine H₄ receptor (H₄R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT₃AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

Originalsprog	Engelsk
Tidsskrift	Journal of Chemical Information and Modeling
Vol/bind	55
Udgave nummer	5
Sider (fra-til)	1030-1034
Antal sider	5
ISSN	1549-9596
DOI	https://doi.org/10.1021/ci500694c
Status	Udgivet - 26 maj 2015
Udgivet eksternt	Ja

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10.1021/ci500694c

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Link to publication in Scopus

Citationsformater

Schultes, S., Kooistra, A. J., Vischer, H. F., Nijmeijer, S., Haaksma, E. E. J., Leurs, R., De Esch, I. J. P., & De Graaf, C. (2015). Combinatorial consensus scoring for ligand-based virtual fragment screening: A comparative case study for serotonin 5-HT₃A, histamine H₁, and Histamine H₄ receptors. Journal of Chemical Information and Modeling, 55(5), 1030-1034. https://doi.org/10.1021/ci500694c

Combinatorial consensus scoring for ligand-based virtual fragment screening: A comparative case study for serotonin 5-HT₃A, histamine H₁, and Histamine H₄ receptors. / Schultes, Sabine; Kooistra, Albert J.; Vischer, Henry F. et al.
I: Journal of Chemical Information and Modeling, Bind 55, Nr. 5, 26.05.2015, s. 1030-1034.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

Schultes, S, Kooistra, AJ, Vischer, HF, Nijmeijer, S, Haaksma, EEJ, Leurs, R, De Esch, IJP & De Graaf, C 2015, 'Combinatorial consensus scoring for ligand-based virtual fragment screening: A comparative case study for serotonin 5-HT₃A, histamine H₁, and Histamine H₄ receptors', Journal of Chemical Information and Modeling, bind 55, nr. 5, s. 1030-1034. https://doi.org/10.1021/ci500694c

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abstract = "In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.",

author = "Sabine Schultes and Kooistra, {Albert J.} and Vischer, {Henry F.} and Saskia Nijmeijer and Haaksma, {Eric E.J.} and Rob Leurs and {De Esch}, {Iwan J.P.} and {De Graaf}, Chris",

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AU - Leurs, Rob

AU - De Esch, Iwan J.P.

AU - De Graaf, Chris

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N2 - In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

AB - In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

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