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Medicinal Chemistry Research

, Volume 23, Issue 9, pp 3998–4010 | Cite as

Theoretical approaches to identify the potent scaffold for human sirtuin1 activator: Bayesian modeling and density functional theory

  • Sugunadevi Sakkiah
  • Mahreen Arooj
  • Keun Woo Lee
  • Jorge Z. TorresEmail author
Original Research

Abstract

Bayesian and pharmacophore modeling approaches were utilized to identify the fragments and critical chemical features of small molecules that enhance sirtuin1 (SIRT1) activity. Initially, 48 Bayesian models (BMs) were developed by exploring 12 different fingerprints (ECFC, ECFP, EPFC, EPFP, FPFC, FPFP, FCFC, FCFP, LCFC, LCFP, LPFC, and LPLP) with diameters of 4, 6, 8, and 10. Among them the BM1 model was selected as the best model based on its good statistical parameters including total accuracy: 0.98 and positive recalls: 0.95. Additionally, BM1 showed good predictive power for the test set (total accuracy: 0.87 and positive recall: 0.87). In addition, 10 qualitative pharmacophore models were generated using 6 well-known SIRT1 activators. Hypothesis2 (Hypo2) was selected as best hypothesis, among 10 Hypos, based on its discriminant ability between the highly active and least/moderately active SIRT1 activators. The best models, BM1 and Hypo2 were used as a query in virtual screens of a drug-like database and the hit molecules were sorted based on Bayesian score and fit value, respectively. In addition, the highest occupied molecular orbital, lowest unoccupied molecular orbital, and energy gap values were calculated for the selected virtual screening hits using density functional theory. Finally, 16 compounds were selected as leads based on their energy gap values, which represent the high reactivity of molecules. Thus, our results indicated that the combination of two-dimensional (2D) and 3D approaches are useful for the discovery and development of specific and potent SIRT1 activators, and will benefit medicinal chemists focused on designing novel lead compounds that activate SIRT1.

Keywords

Sirtuin Bayesian model Density functional theory Ligand-based pharmacophore model Activator 

Abbreviations

ADMET

Absorption, distribution, metabolism, excretion and toxicity

BLYP

Becke exchange plus Lee–Yang–Parr correlation

BM

Bayesian model

BBB

Blood–brain barrier

CHARMM

Chemistry at HARvard Macromolecular Mechanics

CYP450

Cytochrome P450

DFT

Density functional theory

DS

Discovery Studio v3.1

ECFC

Atom type extended connectivity fingerprints counts

ECFP

Atom type connectivity fingerprints counts

ECFP

Atom type extended connectivity fingerprints

EPFC

Functional class daylight path-based fingerprints

FCFC

Functional class extended connectivity fingerprints counts

FCFP

Functional class extended connectivity fingerprints

FN

False negative

FP

False positive

FPFC

Functional class daylight path-based fingerprint counts

FPFP

Functional class daylight path-based fingerprints

HAli

Hydrophobic aliphatic

HAro

Hydrophobic aromatic

HBA

Hydrogen-bond acceptor

HBD

Hydrogen-bond donor

HDAC

Histone deacetylase

HIA

Human intestinal adsorption

HOMO

Highest occupied molecular orbital

Hy

Hydrophobic

Hypo

Hypothesis

LCFC

ALogP types extended connectivity fingerprint counts

LCFP

ALogP extended connectivity fingerprint

LPFC

ALogP types daylight path-based fingerprint counts

LPLP

ALogP types daylight path-based fingerprints

LUMO

Lowest unoccupied molecular orbital

MCC

Mathew’s correlation coefficient

MM

Molecular mechanics

NI

Negative ionizable

PI

Positively ionized or positively ionizable

PPB

Plasma protein binding

QM

Quantum mechanics

QSAR

Quantitative structure–activity relationships

RA

Ring aromatic

ROC

Receiver operating characteristic

SIRT1

Sirtuin1

TN

True negatives

TP

True positives

Notes

Acknowledgments

This work was supported by the Basic Science Research Program (2012R1A1A4A01013657), Pioneer Research Center Program (2009-0081539), the Management of Climate Change Program (2010-0029084) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, and the Next Generation BioGreen 21 Program (PJ009486) from Rural Development Administration (RDA) of Republic of Korea.

Conflict of interest

The authors declare no competing financial interests.

Supplementary material

44_2014_983_MOESM1_ESM.docx (86 kb)
Supplementary material 1 (DOCX 85 kb)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Sugunadevi Sakkiah
    • 1
  • Mahreen Arooj
    • 2
  • Keun Woo Lee
    • 2
  • Jorge Z. Torres
    • 1
    • 3
    • 4
    Email author
  1. 1.Department of Chemistry and BiochemistryUniversity of CaliforniaLos AngelesUSA
  2. 2.Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS)Gyeongsang National University (GNU)JinjuSouth Korea
  3. 3.Jonsson Comprehensive Cancer CenterUniversity of CaliforniaLos AngelesUSA
  4. 4.Molecular Biology InstituteUniversity of CaliforniaLos AngelesUSA

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