ORIGINAL RESEARCH

Theoretical approaches to identify the potent scaffold for humansirtuin1 activator: Bayesian modeling and density functionaltheory

Sugunadevi Sakkiah • Mahreen Arooj •

Keun Woo Lee • Jorge Z. Torres

Received: 29 October 2013 / Accepted: 25 February 2014 / Published online: 9 March 2014

� Springer Science+Business Media New York 2014

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 finger-

prints (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 gen-

erated 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

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00044-014-0983-3) contains supplementarymaterial, which is available to authorized users.

S. Sakkiah � J. Z. Torres (&)

Department of Chemistry and Biochemistry, University of

California, 607 Charles E. Young Drive East, Los Angeles,

CA 90095, USA

e-mail: torres@chem.ucla.edu

M. Arooj � K. W. Lee

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), Jinju, South Korea

J. Z. Torres

Jonsson Comprehensive Cancer Center, University of California,

Los Angeles, CA 90095, USA

J. Z. Torres

Molecular Biology Institute, University of California,

Los Angeles, CA 90095, USA

123

Med Chem Res (2014) 23:3998–4010

DOI 10.1007/s00044-014-0983-3

MEDICINALCHEMISTRYRESEARCH

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

Introduction

Sirtuins (SIRTs), known as silent information regulator 2

(Sir2) proteins, are conserved from E. coli to mammals and

involved in regulating cellular responses to a variety of

stresses (Sakkiah et al., 2009). SIRTs belong to a family of

histone deacetylases (HDACs), which counteract histone

acetyltransferase enzymes (Frye, 2000; Mai et al., 2005).

The HDAC family of proteins is divided into four different

classes based on their cofactors, sequence homology and

functions (Witt et al., 2009): Class I, HDAC1–3 and 8;

Class II, HDAC4–7 and 9, 10; Class III, Sir2 also known as

SIRTs and Class IV, HDAC11. Classes I, II and IV HDACs

are similar in their catalytic domain and utilize a zinc ion to

deacetylate histone and non-histone proteins. In contrast,

SIRTs require NAD? to deacetylate histone and non-his-

tone proteins (Metoyer and Pruitt, 2008).

SIRTs are critical to cellular homeostasis and play an

important role in a myriad of biological process, including

life span regulation (Cohen et al., 2004; Howitz et al.,

2003; Kaeberlein et al., 1999; Tissenbaum and Guarente,

2001), fat metabolism (Picard et al., 2004), insulin secre-

tion (Moynihan et al., 2005), cellular stress responses

(Anderson et al., 2003; Brunet et al., 2004; Cohen et al.,

2004), axonal degeneration (Araki et al., 2004), basal

transcription factor activity, regulation of enzyme activi-

ties, rDNA recombination, and apoptosis (Gertz et al.,

2012). Thus, there has been much interest in uncovering

the chemical and structural details of these fascinating

enzymes (Sakkiah et al., 2013a). Until now, seven human

sirtuins (SIRT1–7) have been identified (Haigis and Gua-

rente, 2006; Leonard, 2007) and all isoforms share a sim-

ilar catalytic domain which consists of *275 amino acids.

With the exception of SIRT1 all other sirtuins (SIRT2–7)

consist of approximately 400 residues, whereas SIRT1

consists of 744 residues. However, SIRT size does not

appear to dictate sirtuin function, as small (SIRT6) and

large (SIRT1) sirtuins have overlapping functions in pro-

cesses like aging and stress response (Gertz et al., 2012).

Among the seven sirtuins, SIRT1 is the most charac-

terized member of the Sir2 family. SIRT1 contains two

major domains: a large domain containing a Rossmann fold

and a small domain, which splits into a zinc-binding

domain and a helical domain (Sakkiah et al., 2009). The

NAD? cofactor binds within the cleft formed by the large

and small domains adjacent to the acetylated protein

(substrate) binding tunnel. The N-terminal residues (G183–

I225) play a critical role in the enhancement of SIRT1

activity (Milne et al., 2007). The tumor suppressor protein

p53 is a transcription factor that regulates the expression of

genes, including p21 and Bax, to initiate cell-cycle arrest

and senescence or apoptosis. Interestingly, SIRT1 deacet-

ylates p53 at lysine 382 and attenuates its transcriptional

activity in response to DNA damage (Brunet et al., 2004;

Dryden et al., 2003; Motta et al., 2004; Rodgers et al.,

2005; Vaziri et al., 2001; Yeung et al., 2004). SIRT1 has

also been implicated as a mediator of caloric restriction, a

low-calorie dietary regimen without malnutrition that

extends the life span of yeast, worms, flies, and mammals

Med Chem Res (2014) 23:3998–4010 3999

123

and decreases the incidence of age-associated disorders

like cardiovascular disease, diabetes, and cancer. There-

fore, SIRT1 is emerging as a crucial drug target for

designing activators and inhibitors for the treatment of

various diseases (Taylor et al., 2008). There are two

putative conceptual modes for activating SIRT1, the direct

and allosteric methods (Westphal et al., 2007). In this

work, we focus on the allosteric method to design activa-

tors for SIRT1 based on the reported activators including

resveratrol (a natural substrate found in red wine which

enhances SIRT1 activity, and thereby extends the lifespan

of mammals Pillarisetti, 2008), SRT1720, SRT2183, and

SRT1460 (Szczepankiewicz and Ng, 2008).

Currently there is no SIRT1 (allosteric site) three-

dimensional (3D) structure available, hence to further our

understanding of SIRT1 activators, we developed a

Bayesian model (BM) based on the reported SIRT1 acti-

vators. An integrated Bayesian and pharmacophore mod-

eling approach was utilized to identify the molecular

fragments and critical chemical features of SIRT1 activa-

tors, respectively (Rogers et al., 2005; Sakkiah et al., 2009,

2010, 2011a, b). The generated 2D and 3D models were

used as a query for virtual screening of a drug-like data-

base. The combined 2D and 3D approaches enabled the

elucidation of the molecular fragments and chemical fea-

tures of small molecules that are important to enhance the

SIRT1 activity. Orbital energies like highest occupied

molecular orbital (HOMO) and lowest unoccupied molec-

ular orbital (LUMO) were calculated for the 44 known

activators and were used as criteria for identifying hit leads

from virtual screens.

Materials and methods

Molecular simulations

The molecular simulation studies were performed using the

Discovery Studio v 3.1. (DS, Accelrys, Inc., San Diego,

USA). Three different approaches, Bayesian modeling,

pharmacophore modeling, and density functional theory

(DFT), were applied to the discovery of chemical features

important for the potency of SIRT1 activators.

BM generation

In chemoinformatics, the Bayesian network (an emerging

new technique in drug discovery) acts as the best alterna-

tive tool for similarity-based virtual screening approaches.

To generate and validate the BMs, a set of 360 SIRT1

activators were collected from various literatures and pat-

ents (Bemis et al., 2009; Mai et al., 2009; Vu et al., 2009)

with a reported biological activity value (EC50). The

collected SIRT1 activators were sketched with the help of

ACD ChemSketch v12 and transformed to 3D using DS.

Based on the EC50 values the collected 360 activators were

divided into 184 highly active activators (EC50 B5 lM)

and 176 least active activators (EC50 [5 lM). The force

field of Chemistry at HARvard Macromolecular Mechan-

ics, CHARMM (Brooks et al., 1983), a flexible and com-

prehensive empirical energy function that is a summation

of many individual energy terms, was applied to all SIRT1

activators. The quality of the BM was directly proportional

to the preeminent of training set compounds, descriptors/

fingerprints, and statistical method (Prathipati et al., 2008).

Preparation of training set based on diversity

The molecular diversity in the training set plays a major role

in finding an important fragment in the generation of BM.

Hence, the Diverse Molecules/Library Analysis was applied

to select the training and test sets compounds based on the

subset of structure diversity with respect to functional class

fingerprints of maximum diameter 4, FCFP_4 (Prathipati

et al., 2008). The curated activators (360 activators) were

divided into training set (20 highly active and 20 least active)

and test set (164 highly active and 156 least active) based on a

maximum dissimilarity approach. DS provides three types of

conformational analysis: FAST, BEST, and CAESAR

quality analysis (Sakkiah et al., 2011b). BEST conformation

analysis ensures the best coverage of the conformational

space, hence it was used to generate the conformations for

the training and test set activators. A maximum number of

255 diverse conformations were generated for each com-

pound in training and test sets using Monte-Carlo-like

algorithm with an energy range of 20 kcal mol-1 together

with the poling algorithm (Smellie et al., 1995).

Selection of molecular descriptors

Descriptors are classified based on the molecular repre-

sentation such as 1D (molecular formula, volume, and

LogP), 2D (molecular connectivity/topology), 3D (molec-

ular geometry/stereochemistry/pharmacophore), and 4D/

5D (conformational ensembles). Here, we focused on the

2D descriptors including ALogP, molecular properties

counts and element counts, surface and volume using 2D

estimation and Estate Keys to generate the BMs. The main

aim in calculating the descriptors is to check how well

these descriptors are correlated with the reported biological

activity of compounds.

Fingerprints selection

Laplacian-modified Bayesian analysis combined with fin-

gerprints is especially useful for high-throughput data

4000 Med Chem Res (2014) 23:3998–4010

123

analysis because it is fast, easily automated and scales

linearly with the number of samples (Rogers et al., 2005).

Hence, we selected 12 different fingerprints including

(i) atom type extended connectivity fingerprints counts

(ECFC), (ii) atom type extended connectivity fingerprints

(ECFP), (iii) atom type connectivity fingerprints counts

(EPFC), (iv) atom type daylight path-based fingerprints

(EPFP), (v) functional class daylight path-based fingerprint

counts (FPFC), (vi) functional class daylight path-based

fingerprints (FPFP), (vii) functional class extended con-

nectivity fingerprints counts (FCFC), (viii) functional class

extended connectivity fingerprints (FCFP), (ix)

ALogP types extended connectivity fingerprint counts

(LCFC), (x) ALogP extended connectivity fingerprint

(LCFP), (xi) ALogP types daylight path-based fingerprint

counts (LPFC), and (xii) ALogP types daylight path-based

fingerprints (LPLP), with a diameters of 4, 6, 8, and 10 to

design BMs. Extended connectivity fingerprints generate

higher-order features with each feature representing the

presence of a structural unit. Atom environment counts

generate higher-order features (Bender et al., 2004) and

hashed atom environment counts use a hashing algorithm

to create an integer representation of the atom environment

counts. The only difference in the generation of a func-

tional class or atom type is the assignment of the initial

atom code for each heavy, non-hydrogen atom of the

molecule. The initial code assigned to an atom type is

based on the number of connections to the atom, element

type, charge, atom mass, and valence. For the functional

class, initial atom code is based on quick estimate of the

functional role the atom plays, this role indicates that the

atom must be a combination of hydrogen-bond acceptor

(HBA), hydrogen-bond donor (HBD), positively ionized or

positively ionizable (PI), aromatic, and halogen. Hence,

these different fingerprints were chosen to find which one

was more appropriate to differentiate the highly active

from least active SIRT1 activators.

Laplace modified naıve Bayesian classifier

In recent studies, Bayesian inference network was intro-

duced as a promising similarity search approach. In this

work, Laplacian-corrected Bayesian classifier algorithm

(Prathipati et al., 2008) was used to generate BMs for SIRT1

activators. Multiple reference structures were used or more

weights assigned to some fragments in molecular structure.

This implementation of Bayesian statistics used information

from both highly active and least active SIRT1 activators in

the training set and removed features from the model which

were deemed to be less important. The following steps were

taken for BM generation: (i) features of the sample were

generated for each compound, (ii) the weight was calculated

for each feature using a Laplacian-adjusted probability

estimate, (iii) weights were summed to provide a probability

estimate which is a relative predictor of the likelihood of that

sample being from the good subset, and (iv) Laplacian-cor-

rected estimator was used to adjust the uncorrected proba-

bility estimate of a feature to account for different sampling

frequencies of different features. The value of ‘‘1’’ was

assigned to highly active activators and ‘‘0’’ for least active

activators. The BM was built based on Bayes’ theorem:

Pðh=dÞ ¼ Pðd=hÞPðhÞ=PðdÞ;

where h denotes the model. D is the observed data,

P(h) indicates the prior belief (probability of pharmaco-

phore model h before observing any data), P(d) is the data

evidence (marginal probability of the data), P(d/h) is the

likelihood (probability of data d if pharmacophore model

h is true), and P(h/d) is the posterior probability (proba-

bility of pharmacophore model h being true given the

observed data d; Sakkiah et al., 2013b). The modeling

process creates a predictive model from the training set that

can then be applied to score samples in the test set, and the

score can be used to prioritize samples for screening.

Qualitative pharmacophore model generation

Pharmacophore modeling is one the most potent techniques

used to identify the critical chemical features from known

inhibitors/activators of a particular target. The ligand-based

approach is one of the most powerful tools in the rational

drug design process. Qualitative hypothesis (Hypo) using

the Hip-Hop algorithm was utilized to generate a Hypo for

SIRT1 activators.

Training set preparation

Initially 45 good SIRT1 activators were collected from the

literature. The diversity of the training set was directly

proportional to the quality of the generated Hypo. Hence

Cluster Ligands protocol was used to select 6 highly

diverse SIRT1 activators from the 45 activators. Cluster

Ligands protocol recognized the common pattern or

chemical features present in the SIRT1 activators and

assigned a set of molecules into subsets or clusters based

on the root mean square difference in the Tanimoto dis-

tance for fingerprints, such that molecules with similar

properties clustered. The cluster analysis was performed by

a relocation method based on maximal dissimilarity por-

tioning. Initially, it randomly chose the data set as the first

cluster center and based on the distance record from the

first center, it selected the next cluster center. The process

was repeated until a sufficient number of cluster centers

were achieved. Six clusters were obtained through this

process and finally six SIRT2 activators were selected as a

training set (one SIRT1 activator from each cluster).

Med Chem Res (2014) 23:3998–4010 4001

123

Pharmacophore model generation

DS provides a dictionary of chemical features important in

drug-enzyme/receptor interactions such as HBA, HBD,

hydrophobic (Hy), Hy aliphatic (HAli), Hy aromatic

(HAro), ring aromatic (RA), and PI and negative ionizable

(NI) chemical groups. Sakkiah et al. (2009) reported that

the HBA, HBD, RA, PI, and Hy chemical features were

important for a molecule to enhance SIRT1 activity. Hence

these five chemical features were selected to generate a

qualitative Hypo models.

The qualitative pharmacophore generation for SIRT1

activators was performed in three-steps (Sakkiah et al.,

2011a; Ferreira da Silva et al., 2004): (i) generation of the

conformation for each molecule in the training set, (ii) each

conformer is examined for the presence of certain chemical

features, and (iii) a 3D configuration of chemical features

common to the input molecules is determined. In the Hypo

generation methodology, the highest weight value of ‘‘2’’

was assigned for all compounds which ensures that all the

chemical features present in the compound will be con-

sidered in building Hypo space and ‘‘0’’ for the principal

and maximum omitting features columns (Arooj et al.,

2013). The ranking score for each individual Hypo was

calculated based on a ranking formula and the default

definition of the ‘‘FIT’’ of a molecule to the Hypo, in order

to determine the probability that a selected Hypo mapped

with the training set molecule by a chance correlation. The

top 10 common feature Hypos were generated with the best

ranking scores. The quality of the Hypo was predicted by

calculating the ‘‘fit-value’’ and this value was defined as the

weight (f) 9 [1 - SSE(f)], where f is the mapping features,

SSE(f) is the sum over location constraints c on f of [D(c)/

T(c)] 2, D is the displacement of the feature from the center

of the location constraint, and T (tolerance) is the radius of

the location constraint sphere for the feature.

Pharmacophore model validation

The test set was prepared to identify the best pharmaco-

phore model, as well as to check how accurately it was able

to differentiate the highly active from moderately/least

active SIRT1 activators. The test set contained 130 acti-

vators collected from the literature and classified into three

sets based on their activity values: highly active (EC50

\5 lM), moderately active (5 B EC50 B 50 lM), and

least active (EC50[50 lM). All the activators were energy

minimized by applying CHARMM force field and 255

conformations were generated for each compound based on

the energy values. To select a best Hypo, Ligand Phar-

macophore mapping module was used to screen the test set

to find which Hypo was able to pick a reliable number of

highly active SIRT1 activators.

Preparation of drug-like database

Chemists typically prioritize screening hits on the basis of

‘‘drug-like properties,’’ synthetic accessibility, intellectual

property potential, and potency. Hence, a drug-like data-

base was generated by removing the non-drug-like com-

pounds with a high probability of having undesirable

molecular features that would hamper their development.

Initially, a simple Rule of 5 was applied to filter Maybridge

(60,000), Chembridge (50,000), NCI (*200,000), and

ChemDiv (*0.7 million) databases on the basis of their

likelihood to be orally availability. Rule of 5 (Lipinski,

2000) states that the molecular weight should be less than

or equal to 500 kDa, LogP less than or equal to 5, HBA (O

and N) less than or equal to 10, and HBD (OH and NH) less

than or equal to 5. Furthermore, absorption, distribution,

metabolism, excretion, and toxicity, ADMET (Sakkiah

et al., 2010, 2011b) was applied to select the compounds

having less toxicity, should not cross the blood–brain

barrier (BBB), good solubility, and absorption. The AD-

MET functionality estimates the values of BBB penetra-

tion, solubility, cytochrome P450 (CYP450) 2D6

inhibition, hepatotoxicity, human intestinal adsorption

(HIA), plasma protein binding (PPB) and access a broad

range of ligand toxicity measures. This approach is based

on the assumption that compounds resembling known

drugs are more likely to possess desirable biological

properties such as low toxicity, high oral absorption and

permeability, resistance to metabolic degradation, and the

absence of rapid excretion. Finally, Prepare ligand module

was used to eliminate the duplicate structures, as well as to

generate the possible tautomer’s and isomers.

Virtual screening using BM and pharmacophore models

Initially, the best BM was used as an input for virtual

screening to select compounds from the drug-like database.

BM screening retrieved compounds from the drug-like

database with similar molecular fragments present in

highly active SIRT1 activators. The screened compounds

which had the similar molecular fragment were further

validated by mapping to best qualitative pharmacophore

model to identify whether these compounds had the

important chemical features found in highly active SIRT1

activators.

Density functional theory

There is growing evidence that DFT provides an accurate

description of the electronic and structural properties of

small molecules by computing the electronic structure of

matter. The energy of SIRT1 activators was calculated

using Calculate Energy module by combining the quantum

4002 Med Chem Res (2014) 23:3998–4010

123

mechanics (QM) and molecular mechanics (MM) force

field (Sherwood et al., 2003). DFT calculated the QM–MM

single point energies and geometry optimization minimi-

zations using Dmol3 as the quantum server with

CHARMM force field (Momany and Rone, 1992). This

protocol simulated the systems by dividing the input into

two regions, central and outer regions, which were treated

by QM and MM methods. It also calculated the electronic

orbital properties for a molecule including the highest

occupied atomic orbital (HOMO) and lowest occupied

atomic orbital (LUMO). Bayesian training set molecules

were optimized by applying the QM–MM at Becke

exchange plus Lee–Yang–Parr correlation (BLYP) hybrid

DFT. The optimized molecules were used to calculate the

HOMO and LUMO energy values.

Results and discussion

The major aims of this study were to derive a BM and a

pharmacophore model that would be able to select the

beneficial chemical fragments and chemical features, with

suitable geometric constraints to discriminate between

good and weak SIRT1 activators.

Bayesian classifier model

A BM is a type of classification model that captures a

simple two-class relationship (active and inactive) to dis-

tinguish between ‘‘good’’ and ‘‘bad’’ fragments. The main

advantages of Bayesian categorization are: (i) it can handle

large amounts of data, (ii) it learns fast, (iii) it is tolerant of

random noise, and (iv) it is not prone to over fitting.

Development and validation of BMs

The training set contained 40 (20 active and 20 inactive)

SIRT1 activators, which were selected based on molecular

diversity. A total of 48 BMs were generated from 12 dif-

ferent fingerprints with diameters of 4, 6, 8, and 10 and

validated using the 10-fold cross validation (Table S1). In

this validation process, one tenth of the samples were left

out and the remaining samples were used to build the

models. The generated models were used to predict the

scores for the left out samples. This process was repeated

until all samples had prediction values. The samples, sorted

out by decreasing the score value, were used to estimate the

predictive power of the resultant BMs based on several

statistical parameters like receiver operating characteristic

(ROC) value, total accuracy, and enrichment values. The

ROC value indicates that the classification power of the

models by representing how often the model correctly

identifies the true positive (TP) and true negative (TN) in

the dataset. In addition, ROC values between 0.50–0.60,

0.60–0.70, 0.70–0.80, 0.80–0.90, and 0.90–1 indicate the

accuracy of the model as fail, poor, fair, good, and excel-

lent, respectively. In our study, out of 48, 9 models showed

a ROC value between 0.9 and 1 which indicates that these

models are excellent when compared with the remaining

models.

The nine excellent BMs were obtained from three fin-

gerprints (EPFC, FPFC, and LPFC) with a diameter of 10,

8, and 6. Table S2 summarizes the key statistical dis-

criminative parameters of these models in comparison with

the performance of nine derived models. The EPFC_10

Bayesian model (BM1) was chosen as a best model, among

the nine derived models, based on the highest average

prediction accuracy for highly active activators (positive

recalls 19) and the highest total prediction accuracy (total

accuracy 0.98; Table S3). The ROC plot revealed that the

BM1 had an excellent prediction accuracy for training set

(ROC 0.96; Fig. 1), as well as for the test set (ROC 0.94).

Enrichment plots display the percentage of true category

members captured at a particular percentage cutoff. The

BM1 enrichment plot indicated very high and reasonable

enrichment values for the training and test set, respectively.

The best split is a statistical value used to evaluate the

quality of the BM. The spilt minimized the sum of percent

misclassified for category members and nonmembers,

using the cross-validated score for each sample. Based on

the split, a contingency table was constructed containing

the number of TP, false negative (FN), false positive (FP)

and TN (Alcendor et al., 2004; Yang et al., 2013). The total

accuracy, negative recall, precision, EF, Mathew’s corre-

lation coefficient (MCC), sensitivity (positive recall) and

specificity were calculated based on TP, TN, FP and FN

(Table S2). Finally, a distribution curve for the training set

suggested that the selected BM1 model can segregate the

highly active from least active activators. While the La-

placian-modified Bayesian score for the highly active

Fig. 1 ROC plot between true positive (TP) and false positive (FP)

for Bayesian BM1

Med Chem Res (2014) 23:3998–4010 4003

123

activators was mostly in the range of 0.598–0.551, the least

active activators were predominantly between -2.129 and

-1.655. In summary, all statistical parameters suggested

that the BM1 model had a higher predictive ability than

other BMs. The list of good and bad substructures prefer-

entially present in highly and least active activators can

serve as a look-up guide for chemists/computational

chemists during hit-to-lead or lead optimization campaigns

(Figs. 2, 3).

Qualitative pharmacophore model generation

Pharmacophore approaches have attempted to create 3D

models with the necessary key chemical features that

interact with the protein by common substructure features

present in known activators or inhibitors. Pharmacophore

models are used to identify structure–activity relationships

(SARs) as well as to develop new lead compounds which

can enhance the activity and selectivity for a particular

target (Sakkiah et al., 2011b). A total of 45 SIRT1 acti-

vators were collected from the literatures and 6 chemical

clusters were produced based on the FCFP_6 fingerprints

using Cluster Ligand module. One molecule from each

cluster (to achieve the maximum diversity of the activators)

with good structure diversity was used to generate the top

10 qualitative Hypos, which were selected as a training set

(Fig. 4). The top 10 Hypos were generated with their

ranking score values between 83.40 and 74.43 bits. Direct

hit, partial hit mask value of ‘‘1’’ and ‘‘0’’ for all Hypos

indicated that all the molecules of the dataset mapped all

features of the Hypo and there was no partial mapping or

missing features in the training set molecules, respectively

(Table S4). Among these 10 hypotheses, Hypo1 and Hypo2

had similar chemical features and showed the highest-

ranking score. The high-ranking scores indicated that these

Hypos could select compounds with similar chemical fea-

tures from any database. However, an external validation

test set was used to validate these Hypos; how well they

were able to pick the active activators from moderately or

least active activators other than the training set

compounds.

Qualitative pharmacophore models validation

The test set, containing 130 compounds that were not

present in the training set, was used to evaluate the effec-

tiveness of the top 10 Hypos. These top 10 Hypos were

divided into two groups contingent on the number of

chemical features: Group I, contained four hypotheses

(Hypo1: 2-RA, 2-Hy, 2-HBA; Hypo2: 2-RA, 2-Hy,

2-HBA; Hypo3: 1-RA, 3-Hy, 2-HBA and Hypo4: 1-RA,

3-Hy, 2-HBA) with six pharmacophore features and Group

II, consisted of six hypotheses (Hypo5: 2-RA, 1-Hy,

2-HBA; Hypo6: 2-RA, 1-Hy, 2-HBA; Hypo7: 2-RA, 1-Hy,

Fig. 2 Fingerprints predicted to be crucial for enhancing SIRT1 activity keyed out from Bayesian theory

4004 Med Chem Res (2014) 23:3998–4010

123

2-HBA; Hypo8: 2-RA, 1-Hy, 2-HBA; Hypo9: 2-RA, 1-Hy,

2-HBA and Hypo10: 2-RA, 1-Hy, 2-HBA) with five

pharmacophore features. All of the pharmacophore models

in Group I were able to retrieve a good number of active

compounds (greater than 90 % from the test set), whereas

Group II Hypos selected less than 60 % of active com-

pounds from the test set (Table S5). Thus, we focused on

Group I Hypos, among the four hypotheses, Hypo1 was

Fig. 3 Unfavorable fingerprints identified by the Bayesian theory

Fig. 4 Training set compounds

used for generating quantitative

hypotheses. Their EC50 values

in nM are indicated in brackets

Med Chem Res (2014) 23:3998–4010 4005

123

able to screen only 94 % of the active compounds, which

was less compared to the other remaining hypotheses

(Hypo2: 100 %, Hypo3: 96 % and Hypo4: 96 %). Hypo1

and Hypo2 have similar chemical features, however, due to

the geometric constraints of the chemical features Hypo1

retrieved a lower percentage of activators from the test set.

On the other hand, Hypo2 picked the least number of

compounds from the moderately and least active activators

compared with Hypo3 and Hypo4 (Table S6). Hypo2,

which shows a good ranking score and predictive ability,

consists of 2-RA, 2-Hy, and 2-HBA and its corresponding

geometric constrains are summarized in Table S4. Thus,

Hypo2 was selected as the best Hypo from the qualitative

Hypos (Fig. 5). The best and least fit compounds by Hypo2

are shown in Fig. 6. Therefore, we suggest that the

chemical features of Hypo2 reflect the characteristics of the

atoms present in SIRT1 activators.

Virtual screening using best BM and pharmacophore

models

Many virtual screening techniques such as substructure and

similarity searches, molecular docking, and quantitative

SARs (QSAR) have been the focus of medicinal chemists

to reduce the time, cost, and man power of identifying

potent activators and inhibitors. Virtual screening of

chemical databases can serve two main purposes: (i) to

validate the quality of a generated Hypo by selective

detection of compounds with known inhibitory activity and

(ii) to find novel potential leads which are suitable for

further characterization.

The best BM and quantitative pharmacophore models

were used to screen a generated drug-like database. First,

BM1, best BM, was used to check whether the important

chemical fragments were present in the drugs-like mole-

cules or not. The BM1 picked nearly 40,000 compounds

from the drug-like database, and these compounds were

subsequently screened using Hypo2 to select the ones with

suitable chemical features and with geometrical constrains

to enhance the activity of SIRT1. Interestingly, 196 com-

pounds contained all the chemical features in Hypo2 with a

suitable geometric constrain. The reactivity of 196 com-

pounds was predicted by calculating the energy gap value

using DFT.

Density functional theory

DFT is a popular and successful tool for computing the

electronic structure of matter. DFT has been used to predict

the stability of charge transfers and to analyze the reaction

mechanisms responsible for antioxidant activity (Estevez

and Mosquera, 2008; Ferreira da Silva et al., 2004; Leo-

poldini et al., 2010). Thus, we utilized the DFT approach to

theoretically characterize the electronic properties and

structure properties that correlate with SIRT1 activators.

DFT predicts a great variety of molecular properties such

as molecular structures, vibration frequencies, atomization

Fig. 5 Hypo2 chemical features and spatial arrangement. HBA

hydrogen-bond acceptor (green), RA ring aromatic (brown), and Hy

hydrophobic (cyan) (Color figure online)

Fig. 6 Best (a) and least (b) fit

training set molecules in Hypo2.

HBA hydrogen-bond acceptor

(green), RA ring aromatic

(brown), and Hy hydrophobic

(cyan) (Color figure online)

4006 Med Chem Res (2014) 23:3998–4010

123

energies, ionization energies, electric and magnetic prop-

erties, reaction paths, etc. Initially the orbital energies,

HOMO and LUMO, were calculated for 44 SIRT1 acti-

vators by applying the BLYP, which are responsible for the

charge transfer in a chemical reaction (Sakkiah et al.,

2009). The geometry obtained from BLYP was used to

calculate the orbital energy values. The reaction occurs

when the HOMO of the nucleophile overlaps with the

LUMO of the electrophilic species. The orbital energies of

frontier orbitals are small, ranging between -0.20 and

-0.12 au (-5.297 to -2.212 eV) for HOMO and -0.11 to

-0.06 au (-2.598 to -1.413 eV) for LUMO (Table S7).

Along the reaction coordinates, the HOMO–LUMO gap

becomes narrower and favors reactivity. The small values

of orbital energies indicate that the molecules are more

reactive and rapid electron transfer and exchanges are

equally possible. The molecules that have small energy

gaps, signifies stability, are likely to undergo changes in the

charge distribution through rapid electron transfer between

HOMO and LUMO. The higher HOMO value implies that

the molecule has good electron donating ability. On the

other hand, lower HOMO values imply a weak electron

donating ability. In most of the reported SIRT1 activators,

the peptide bond was perfectly mapped by HBA and was

also confirmed by the HOMO and LUMO values (Fig. 7).

The electron density of the HOMO at an atom is a measure

of relative reactivity of the HOMO at the atom within a

single molecule, while the energy level of the HOMO

reflects the reactivity of different molecule, thus molecules

with smaller ionization (-HOMO) are expected to be more

reactive as nucleophiles.

The smaller energy gap between the LUMO and HOMO

energies illustrates the high reactivity of the molecules.

The wide gap is unfavorable for the electron to be excited

from HOMO to LUMO, which consequently leads to the

weak affinity of the SIRT1 activators. The orbital energy

gap values for all the reported activators are in the range of

0.02–0.13 au. Considering the above energy gap value

(0.13 au) as a reference, the orbital energy values were

calculated for the 196 hit leads from the virtual screening

process. Among the 196 compounds only 16 compounds

showed an energy gap value less than 0.13 au. Finally,

these 16 molecules were selected as the best leads, which

can rapidly transfer electrons, by applying the cutoff value

of less than 1 (Table S8). Thus, these protocols are useful

for improving the activity of the molecules, as well as to

identify the novel structures that share the similar chemical

features and may thus elicit the desired biological response.

Fig. 7 Identification of SIRT1 activators. (a) Chemical structures of

representative SIRT1 activators. (b) Molecules that fit in Hypo2

hypothesis. (c) Highest occupied molecular orbital (HOMO)

distribution. (d) Lowest unoccupied molecular orbital (LUMO)

distribution. HBA hydrogen-bond acceptor (green), RA ring aromatic

(brown), and Hy hydrophobic (cyan) (Color figure online)

Med Chem Res (2014) 23:3998–4010 4007

123

Application of the BM and pharmacophore models

The BM and pharmacophore models were well defined for

virtual screening. As an illustration, the selected Bayesian

BM1 model was used as a 3D query to screen the drug-like

database. In addition, the results of the model will be useful

for designing and optimizing compounds with SIRT1

activity, either by replacing the inactive substructures with

active ones, removing inactive substructures altogether or

by adding active substructures to small fragments with

promising cellular activity. Such strategies are especially

useful in lead optimization campaigns with compounds

identified in cellular screens, where SARs are often diffi-

cult to interpret and maintaining cellular activity can be a

challenge. The 3D QSAR pharmacophore model, Hypo2,

could be useful to medical chemists to determine whether

lead compounds contain the suitable chemical features.

The identified compounds were subjected to a novelty

study using PubChem structure search tools and Scifinder

Scholar. This study confirmed that the identified hits were

not previously reported elsewhere for the activation of

SIRT1. These compounds remain virtually identified and

the experimental verification of these compounds will be

required to confirm their activation profiles.

Conclusions and future directions

SIRT1 is an emerging chemotherapeutic drug target. To

date, there is lack of 3D structural detail; hence ligand-

based approaches have been applied to define the critical

chemical fragments and pharmacophoric features for

SIRT1 activators. BMs, derived based on the 2D molecular

properties and fingerprints, were found to effectively dis-

criminate highly active from least active activators. Since

the training set design and choice of fingerprints critically

affects the predictive ability of QSAR models, model

development and validation involved the exploration of 12

different fingerprint types with diameters of 4, 6, 8, and 10

which resulted in a total of 48 BMs. The best Bayesian

model BM1, prioritized using average total accuracy and

positive recall, was derived using ECFC_10 and a few

global descriptors on a training set. This model demon-

strated good discriminant ability in general; with excellent

discriminant statistics for the training set (total accuracy

and positive recall) and a good predictive ability of test set

(total accuracy and positive recall). With such encouraging

results, the model was used to screen a drug-like database

and several compounds were highlighted as potentially

potent SIRT1 activators. To confirm these 2D conforma-

tions present in the BM, a 3D qualitative pharmacophore

model was used to confirm the existence of critical

chemical features that would enhance the SIRT1 activator

activity of lead compounds. Ten quantitative Hypos were

generated based on six known activators, and Hypo2 was

selected as the best Hypo based on its ability to discrimi-

nate between active from moderately/least active activators

of SIRT1. The BM1 (Bayesian model) and the Hypo2

(qualitative pharmacophore model) were used as query in

virtual screening of a drug-like database. A total of 196

compounds from drug-like databases contained similar

chemical fragments and features present in BM1 and

Hypo2. These 196 compounds were subjected to a DFT

study in order to determine the reactivity of the com-

pounds. Finally 16 compounds were selected as lead acti-

vators of SIRT1. The knowledge of 2D and 3D structural

relationships has the potential to accelerate the discovery of

SIRT1 activators. The approach described in this study can

be used to determine the functional characteristics of

SIRT1 activators. Finally, we suggest that the generated

BM and pharmacophore models offer an alternative way to

screen the various chemical databases and to design novel

scaffolds for SIRT1 activators.

Associated content

Supporting information

Supporting tables with ROC scores and the best split values

of the 48 BMs, the statistical values for the 9 best BMs, the

statistical values of Bayesian BM1 model, the details of the

top 10 Hypos generated using Hip-Hop, the percentage

comparison for the number of molecules screened from

each Hypo, the distance between every two pharmacophore

features present in Hypo2, the orbital energy values of 44

SIRT1 activators and the orbital energy values of 16 hit

lead compounds.

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 Develop-

ment Administration (RDA) of Republic of Korea.

Conflict of interest The authors declare no competing financial

interests.

References

Alcendor RR, Kirshenbaum LA, Imai S-I, Vatner SF, Sadoshima J

(2004) Silent information regulator 2a, a longevity factor and

class III histone deacetylase, is an essential endogenous apop-

tosis inhibitor in cardiac myocytes. Circ Res 95:971–980

4008 Med Chem Res (2014) 23:3998–4010

123

Anderson RM, Bitterman KJ, Wood JG, Medvedik O, Sinclair DA

(2003) Nicotinamide and PNC1 govern lifespan extension by

calorie restriction in Saccharomyces cerevisiae. Nature

423:181–185

Araki T, Sasaki Y, Milbrandt J (2004) Increased nuclear NAD

biosynthesis and SIRT1 activation prevent axonal degeneration.

Science 305:1010–1013

Arooj M, Sakkiah S, Cao Gp, Lee KW (2013) An innovative strategy

for dual inhibitor design and its application in dual inhibition of

human thymidylate synthase and dihydrofolate reductase

enzymes. PLoS ONE 8:e60470

Bemis JE, Vu CB, Xie R, Nunes JJ, Ng PY, Disch JS, Milne JC,

Carney DP, Lynch AV, Jin L et al (2009) Discovery of

oxazolo[4,5-b]pyridines and related heterocyclic analogs as

novel SIRT1 activators. Bioorg Med Chem Lett 19:2350–2353

Bender A, Mussa HY, Glen RC, Reiling S (2004) Similarity searching

of chemical databases using atom environment descriptors

(MOLPRINT 2D): evaluation of performance. J Chem Inf

Comput Sci 44:1708–1718

Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S,

Karplus M (1983) CHARMM: a program for macromolecular

energy, minimization, and dynamics calculations. J Comput

Chem 4:187–217

Brunet A, Sweeney LB, Sturgill JF, Chua KF, Greer PL, Lin Y, Tran

H, Ross SE, Mostoslavsky R, Cohen HY et al (2004) Stress-

dependent regulation of FOXO transcription factors by the

SIRT1 deacetylase. Science 303:2011–2015

Cohen HY, Miller C, Bitterman KJ, Wall NR, Hekking B, Kessler B,

Howitz KT, Gorospe M, de Cabo R, Sinclair DA (2004) Calorie

restriction promotes mammalian cell survival by inducing the

SIRT1 deacetylase. Science 305:390–392

Dryden SC, Nahhas FA, Nowak JE, Goustin A-S, Tainsky MA (2003)

Role for human SIRT2 NAD-dependent deacetylase activity in

control of mitotic exit in the cell cycle. Mol Cell Biol

23:3173–3185

Estevez L, Mosquera RA (2008) Molecular structure and antioxidant

properties of delphinidin. J Phys Chem A 112:10614–10623

Ferreira da Silva P, Lima JC, Quina FH, Macanita AL (2004) Excited-

state electron transfer in anthocyanins and related flavylium

salts. J Phys Chem A 108:10133–10140

Frye RA (2000) Phylogenetic classification of prokaryotic and

eukaryotic Sir2-like proteins. Biochem Biophys Res Commun

273:793–798

Gertz M, Nguyen GTT, Fischer F, Suenkel B, Schlicker C, Franzel B,

Tomaschewski J, Aladini F, Becker C, Wolters D et al (2012) A

molecular mechanism for direct sirtuin activation by resveratrol.

PLoS ONE 7:e49761

Haigis MC, Guarente LP (2006) Mammalian sirtuins—emerging roles

in physiology, aging, and calorie restriction. Genes Dev

20:2913–2921

Howitz KT, Bitterman KJ, Cohen HY, Lamming DW, Lavu S, Wood

JG, Zipkin RE, Chung P, Kisielewski A, Zhang L-L et al (2003)

Small molecule activators of sirtuins extend Saccharomyces

cerevisiae lifespan. Nature 425:191–196

Kaeberlein M, McVey M, Guarente L (1999) The SIR2/3/4 complex

and SIR2 alone promote longevity in Saccharomyces cerevisiae

by two different mechanisms. Genes Dev 13:2570–2580

Leonard G (2007) Sirtuins in aging and disease. Cold Spring Harb

Symp Quant Biol 72:483–488

Leopoldini M, Rondinelli F, Russo N, Toscano M (2010) Pyranoan-

thocyanins: a theoretical investigation on their antioxidant

activity. J Agric Food Chem 58:8862–8871

Lipinski CA (2000) Drug-like properties and the causes of poor

solubility and poor permeability. J Pharmacol Toxicol Methods

44:235–249

Mai A, Massa S, Lavu S, Pezzi R, Simeoni S, Ragno R, Mariotti FR,

Chiani F, Camilloni G, Sinclair DA (2005) Design, synthesis,

and biological evaluation of sirtinol analogues as class III

histone/protein deacetylase (sirtuin) inhibitors. J Med Chem

48:7789–7795

Mai A, Valente S, Meade S, Carafa V, Tardugno M, Nebbioso A,

Galmozzi A, Mitro N, De Fabiani E, Altucci L et al (2009) Study

of 1,4-dihydropyridine structural scaffold: discovery of novel

sirtuin activators and inhibitors. J Med Chem 52:5496–5504

Metoyer CF, Pruitt K (2008) The role of sirtuin proteins in obesity.

Pathophysiology 15:103–108

Milne JC, Lambert PD, Schenk S, Carney DP, Smith JJ, Gagne DJ,

Jin L, Boss O, Perni RB, Vu CB et al (2007) Small molecule

activators of SIRT1 as therapeutics for the treatment of type 2

diabetes. Nature 450:712–716

Momany FA, Rone R (1992) Validation of the general purpose

QUANTA �3.2/CHARMm� force field. J Comput Chem

13:888–900

Motta MC, Divecha N, Lemieux M, Kamel C, Chen D, Gu W,

Bultsma Y, McBurney M, Guarente L (2004) Mammalian SIRT1

represses forkhead transcription factors. Cell 116:551–563

Moynihan KA, Grimm AA, Plueger MM, Bernal-Mizrachi E, Ford E,

Cras-Meneur C, Permutt MA, Imai S-I (2005) Increased dosage

of mammalian Sir2 in pancreatic b cells enhances glucose-

stimulated insulin secretion in mice. Cell Metab 2:105–117

Picard F, Kurtev M, Chung N, Topark-Ngarm A, Senawong T,

Machado de Oliveira R, Leid M, McBurney MW, Guarente L

(2004) Sirt1 promotes fat mobilization in white adipocytes by

repressing PPAR-[gamma]. Nature 429:771–776

Pillarisetti S (2008) A review of Sirt1 and Sirt1 modulators in

cardiovascular and metabolic diseases. Recent Pat Cardiovasc

Drug Discov 3:156–164

Prathipati P, Ma NL, Keller TH (2008) Global Bayesian models for

the prioritization of antitubercular agents. J Chem Inf Model

48:2362–2370

Rodgers JT, Lerin C, Haas W, Gygi SP, Spiegelman BM, Puigserver

P (2005) Nutrient control of glucose homeostasis through a

complex of PGC-1alpha and SIRT1. Nature 434:113–118

Rogers D, Brown RD, Hahn M (2005) Using extended-connectivity

fingerprints with Laplacian-modified Bayesian analysis in high-

throughput screening follow-up. J Biomol Screen 10:682–686

Sakkiah S, Krishnamoorthy N, Gajendrarao P, Thangapandian S, Lee

Y-O, Kim S-M, Suh J-K, Kim H-H, Lee K-W (2009) Pharma-

cophore mapping and virtual screening for SIRT1 activators.

Bull Korean Chem Soc 30:1152–1156

Sakkiah S, Thangapandian S, John S, Kwon YJ, Lee KW (2010) 3D

QSAR pharmacophore based virtual screening and molecular

docking for identification of potential HSP90 inhibitors. Eur J

Med Chem 45:2132–2140

Sakkiah S, Thangapandian S, John S, Lee KW (2011a) Identification

of critical chemical features for Aurora kinase-B inhibitors using

Hip-Hop, virtual screening and molecular docking. J Mol Struct

985:14–26

Sakkiah S, Thangapandian S, John S, Lee KW (2011b) Pharmaco-

phore based virtual screening, molecular docking studies to

design potent heat shock protein 90 inhibitors. Eur J Med Chem

46:2937–2947

Sakkiah S, Arooj M, Kumar MR, Eom SH, Lee KW (2013a) Identifi-

cation of inhibitor binding site in human sirtuin 2 using molecular

docking and dynamics simulations. PLoS ONE 8:e51429

Sakkiah S, Arullaperumal V, Hwang S, Lee KW (2013b) Ligand-

based pharmacophore modeling and Bayesian approaches to

identify c-Src inhibitors. J Enzym Inhib Med Chem 29:69–80

Sherwood P, de Vries AH, Guest MF, Schreckenbach G, Catlow

CRA, French SA, Sokol AA, Bromley ST, Thiel W, Turner AJ

Med Chem Res (2014) 23:3998–4010 4009

123

et al (2003) QUASI: a general purpose implementation of the

QM/MM approach and its application to problems in catalysis.

J Mol Struct THEOCHEM 632:1–28

Smellie A, Teig SL, Towbin P (1995) Poling: promoting conforma-

tional variation. J Comput Chem 16:171–187

Szczepankiewicz BG, Ng PY (2008) Sirtuin modulators: targets for

metabolic diseases and beyond. Curr Top Med Chem

8:1533–1544

Taylor DM, Maxwell MM, Luthi-Carter R, Kazantsev AG (2008)

Biological and potential therapeutic roles of sirtuin deacetylases.

Cell Mol Life Sci 65:4000–4018

Tissenbaum HA, Guarente L (2001) Increased dosage of a sir-2 gene

extends lifespan in Caenorhabditis elegans. Nature 410:227–230

Vaziri H, Dessain SK, Eaton EN, Imai S-I, Frye RA, Pandita TK,

Guarente L, Weinberg RA (2001) hSIR2SIRT1 functions as an

NAD-dependent p53 deacetylase. Cell 107:149–159

Vu CB, Bemis JE, Disch JS, Ng PY, Nunes JJ, Milne JC, Carney DP,

Lynch AV, Smith JJ, Lavu S et al (2009) Discovery of

imidazo[1,2-b]thiazole derivatives as novel SIRT1 activators.

J Med Chem 52:1275–1283

Westphal CH, Dipp MA, Guarente L (2007) A therapeutic role for

sirtuins in diseases of aging? Trends Biochem Sci 32:555–560

Witt O, Deubzer HE, Milde T, Oehme I (2009) HDAC family: what

are the cancer relevant targets? Cancer Lett 277:8–21

Yang Y, Zhang W, Cheng J, Tang Y, Peng Y, Li Z (2013)

Pharmacophore, 3D-QSAR, and Bayesian model analysis for

ligands binding at the benzodiazepine site of GABAA receptors:

the key roles of amino group and hydrophobic sites. Chem Biol

Drug Des 81:583–590

Yeung F, Hoberg JE, Ramsey CS, Keller MD, Jones DR, Frye RA,

Mayo MW (2004) Modulation of NF-kappaB-dependent tran-

scription and cell survival by the SIRT1 deacetylase. EMBO J

23:2369–2380

4010 Med Chem Res (2014) 23:3998–4010

123