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, C. de Graaf

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)1030-44
Number of pages15
JournalJournal of Chemical Information and Modeling
Volume55
Issue number5
DOIs
Publication statusPublished - 26 May 2015

Fingerprint

Histamine H1 Receptors
Histamine
Serotonin
Screening
Fusion reactions
Ligands
Group
Ligand-Gated Ion Channels
Proteins
Histamine Receptors
G-Protein-Coupled Receptors
performance
Membranes
Molecules
Ions

Keywords

  • Combinatorial Chemistry Techniques
  • Consensus
  • Drug Evaluation, Preclinical
  • Ligands
  • Receptors, Histamine H1
  • Receptors, Serotonin, 5-HT3
  • User-Computer Interface
  • Comparative Study
  • Journal Article
  • Research Support, Non-U.S. Gov't

Cite this

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title = "Combinatorial consensus scoring for ligand-based virtual fragment screening: A comparative case study for serotonin 5-HT3A, histamine H1, and Histamine H4 receptors",
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.",
keywords = "Combinatorial Chemistry Techniques, Consensus, Drug Evaluation, Preclinical, Ligands, Receptors, Histamine H1, Receptors, Serotonin, 5-HT3, User-Computer Interface, Comparative Study, Journal Article, Research Support, Non-U.S. Gov't",
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}, C.",
year = "2015",
month = "5",
day = "26",
doi = "10.1021/ci500694c",
language = "English",
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journal = "Journal of Chemical Information and Modeling",
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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.; Nijmeijer, Saskia; Haaksma, Eric E J; Leurs, Rob; De Esch, Iwan J P; de Graaf, C.

In: Journal of Chemical Information and Modeling, Vol. 55, No. 5, 26.05.2015, p. 1030-44.

Research output: Contribution to JournalArticleAcademicpeer-review

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T2 - A comparative case study for serotonin 5-HT3A, histamine H1, and Histamine H4 receptors

AU - Schultes, Sabine

AU - Kooistra, Albert J.

AU - Vischer, Henry F.

AU - Nijmeijer, Saskia

AU - Haaksma, Eric E J

AU - Leurs, Rob

AU - De Esch, Iwan J P

AU - de Graaf, C.

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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.

KW - Combinatorial Chemistry Techniques

KW - Consensus

KW - Drug Evaluation, Preclinical

KW - Ligands

KW - Receptors, Histamine H1

KW - Receptors, Serotonin, 5-HT3

KW - User-Computer Interface

KW - Comparative Study

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

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DO - 10.1021/ci500694c

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SP - 1030

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JO - Journal of Chemical Information and Modeling

JF - Journal of Chemical Information and Modeling

SN - 1549-9596

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ER -