Computational structure-activity relationship analysis of small-molecule agonists for human formyl peptide receptors

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

N-Formyl peptide receptors (FPRs) are important in host defense. Because of the potential for FPRs as therapeutic targets, recent efforts have focused on identification of non-peptide agonists for two FPR subtypes, FPR1 and FPR2. Given that a number of specific small-molecule agonists have recently been identified, we hypothesized that computational structure-activity relationship (SAR) analysis of these molecules could provide new information regarding molecular features required for activity. We used a training set of 71 compounds, including 10 FPR1-specific agonists, 36 FPR2-specific agonists, and 25 non-active analogs. A sequence of (1) one-way analysis of variance selection, (2) cluster analysis, (3) linear discriminant analysis, and (4) classification tree analysis led to the derivation of SAR rules with high (95.8%) accuracy for correct classification of compounds. These SAR rules revealed key features distinguishing FPR1 versus FPR2 agonists. To verify predictive ability, we evaluated a test set of 17 additional FPR agonists, and found that the majority of these agonists (>94%) were classified correctly as agonists. This study represents the first successful application of classification tree methodology based on atom pairs to SAR analysis of FPR agonists. Importantly, these SAR rules represent a relatively simple classification approach for virtual screening of FPR1/FPR2 agonists.

Original languageEnglish
Pages (from-to)5406-5419
Number of pages14
JournalEuropean Journal of Medicinal Chemistry
Volume45
Issue number11
DOIs
Publication statusPublished - Nov 2010
Externally publishedYes

Fingerprint

Formyl Peptide Receptor
Structure-Activity Relationship
Molecules
Cluster analysis
Discriminant Analysis
Discriminant analysis
Analysis of variance (ANOVA)
Cluster Analysis
Analysis of Variance
Screening
Atoms

Keywords

  • Atom pairs
  • Formyl peptide receptor (FPR)
  • FPR agonists
  • Molecular descriptors
  • Structure-activity relationship analysis

ASJC Scopus subject areas

  • Drug Discovery
  • Organic Chemistry
  • Pharmacology

Cite this

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title = "Computational structure-activity relationship analysis of small-molecule agonists for human formyl peptide receptors",
abstract = "N-Formyl peptide receptors (FPRs) are important in host defense. Because of the potential for FPRs as therapeutic targets, recent efforts have focused on identification of non-peptide agonists for two FPR subtypes, FPR1 and FPR2. Given that a number of specific small-molecule agonists have recently been identified, we hypothesized that computational structure-activity relationship (SAR) analysis of these molecules could provide new information regarding molecular features required for activity. We used a training set of 71 compounds, including 10 FPR1-specific agonists, 36 FPR2-specific agonists, and 25 non-active analogs. A sequence of (1) one-way analysis of variance selection, (2) cluster analysis, (3) linear discriminant analysis, and (4) classification tree analysis led to the derivation of SAR rules with high (95.8{\%}) accuracy for correct classification of compounds. These SAR rules revealed key features distinguishing FPR1 versus FPR2 agonists. To verify predictive ability, we evaluated a test set of 17 additional FPR agonists, and found that the majority of these agonists (>94{\%}) were classified correctly as agonists. This study represents the first successful application of classification tree methodology based on atom pairs to SAR analysis of FPR agonists. Importantly, these SAR rules represent a relatively simple classification approach for virtual screening of FPR1/FPR2 agonists.",
keywords = "Atom pairs, Formyl peptide receptor (FPR), FPR agonists, Molecular descriptors, Structure-activity relationship analysis",
author = "Khlebnikov, {Andrey Ivanovich} and Schepetkin, {Igor A.} and Quinn, {Mark T.}",
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AU - Schepetkin, Igor A.

AU - Quinn, Mark T.

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AB - N-Formyl peptide receptors (FPRs) are important in host defense. Because of the potential for FPRs as therapeutic targets, recent efforts have focused on identification of non-peptide agonists for two FPR subtypes, FPR1 and FPR2. Given that a number of specific small-molecule agonists have recently been identified, we hypothesized that computational structure-activity relationship (SAR) analysis of these molecules could provide new information regarding molecular features required for activity. We used a training set of 71 compounds, including 10 FPR1-specific agonists, 36 FPR2-specific agonists, and 25 non-active analogs. A sequence of (1) one-way analysis of variance selection, (2) cluster analysis, (3) linear discriminant analysis, and (4) classification tree analysis led to the derivation of SAR rules with high (95.8%) accuracy for correct classification of compounds. These SAR rules revealed key features distinguishing FPR1 versus FPR2 agonists. To verify predictive ability, we evaluated a test set of 17 additional FPR agonists, and found that the majority of these agonists (>94%) were classified correctly as agonists. This study represents the first successful application of classification tree methodology based on atom pairs to SAR analysis of FPR agonists. Importantly, these SAR rules represent a relatively simple classification approach for virtual screening of FPR1/FPR2 agonists.

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