Evaluation of Antimicrobial Activity of New MastoparanDerivatives Using QSAR and Computational Mutagenesis

Speranta Avram • Daniel Duda-Seiman •

Florin Borcan • Beatrice Radu •

Corina Duda-Seiman • Dan Mihailescu

Accepted: 13 December 2010 / Published online: 25 December 2010

� Springer Science+Business Media, LLC 2010

Abstract Antimicrobial peptides, also called body defense

peptides, are used against a wide range of pathogens, such as

negative- and positive-gram bacteria, mycobacteria, fungi,

viruses, etc. Contrary to antibiotics, antimicrobial peptides

do not develop resistance. Their wide antimicrobial spec-

trum situates them as important and attractive targets in

research and pharmaceutical industry in order to obtain new

structures using modern drug design techniques. We present

here eleven QSAR models in which antimicrobial activity

expressed as minimal inhibitory concentration values at

Bacillus subtilis of 37 mastoparan analogs was correlated

with different physicochemical parameters like: number of

hydrophobic centers, molecular area and volume, internal

dipole moment, refractivity, RPCG (relative positive char-

ges) and number of donor and acceptor atoms generating by

use of the computational software Sybyl. Significant R2

(0.68–0.72) correlation coefficients and standard error of

prediction SEE (0.199–0.230) were obtained, indicating that

the established equations can be used. Thus, these linear

models allowed us to create a library of 19 derivatives of

mastoparan analogs obtained through computational muta-

genesis. We propose this library of compounds as a source of

possible derivatives with a more potent antimicrobial

activity.

Keywords QSAR � Antimicrobial peptides �Computational mutagenesis

Introduction

Antimicrobial peptides (AMPs) have been demonstrated to

act against Gram negative and positive bacteria, myco-

bacteria, enveloped viruses or fungi. It was also proven

that, unlike the majority of conventional antibiotics, anti-

microbial peptides may also have the ability to enhance

immunity by functioning as immunomodulators. Antimi-

crobial peptides provide potential templates for a new

generation of antimicrobials, as they kill microorganisms

rapidly and directly, and in some cases, activate host innate

immunity. Moreover, some seem to counteract harmful

inflammatory/septic responses induced by bacterial endo-

toxines (Brandenburg et al. 2010), which is extremely

important (Bowdish and Hancock 2005; Periti and Mazzei

1999). AMPs are effective components in innate host

defense mechanisms against infectious pathogens (Zhu

et al. 2007; Zanetti et al. 2002; Zasloff 2002), which do not

target specific receptors of the pathogens, but instead

interact and modulate the permeability of microbial

membranes (Zasloff 2002). AMPs are able to combat the

increasing emergence of drug-resistant bacteria (Hancock

S. Avram (&) � B. Radu � D. Mihailescu

Department of Anatomy, Animal Physiology and Biophysics,

Faculty of Biology, University Bucharest, Splaiul Independentei

91-95, 076201 Bucharest, Romania

e-mail: speranta.avram@gmail.com

D. Duda-Seiman

Clinic of Preventive Cardiology and Cardiovascular

Rehabilitation, University of Medicine and Pharmacy ‘‘Victor

Babes’’ Timisoara, 49th Loga Bvd, 300020 Timisoara, Romania

F. Borcan

Biochemistry Department, University of Medicine and

Pharmacy ‘‘Victor Babes’’ Timisoara, 2nd E. Murgu Sq.,

300041 Timisoara, Romania

C. Duda-Seiman

Chemistry Department, Faculty of Chemistry,

Biology and Geography, West University Timisoara,

16th Pestalozzi Str., 300115 Timisoara, Romania

123

Int J Pept Res Ther (2011) 17:7–17

DOI 10.1007/s10989-010-9235-7

and Scott 2000). Many therapeutic applications to date

have focused on infections of epithelial and topical barri-

ers; however, more recently, pharmaceutical companies are

developing peptides of greater potential for systemic

infections (Maher and McClean 2006).

The peptides can be classified into two types considering

their cell selectivity: those that are toxic for bacterial cells

but not for mammalian cells (e.g. magainins and cecropins),

and those that are toxic to both bacterial and mammalian

cells (e.g. melittin and pardaxin) (Mahalka and Kinnunen

2009; Subasinghage et al. 2010). It is considered that length,

net electric charge, and hydrophobicity of AMPs appear to

be responsible for their selectivity for bacterial and mam-

malian cells. In addition, the self-association of AMPs in

an aqueous environment is also known to be important

to control their selectivity for specific targets (Chen et al.

2005).

Stinging hymenoptera such as honeybees, wasps, bum-

blebees and ants have venom reservoirs that contain venom

consisting of a complex mixture of enzymes, neurotoxins,

low molecular mass compounds and a whole series of

peptides (Mahalka and Kinnunen 2009; Harris et al. 2009).

The main function of the venom is to protect against pre-

dators and to defend nests against intruders. Among the

peptides that have such characteristics, the mastoparan

family of peptides (INLKALAALAKALL, from wasp

venom; INLKALAALAKKIL, from vespula; VDWK

KIGQHILSVL, from Polistes jadwagae) forms an impor-

tant group (Hancock and Scott 2000; Davison et al. 2009).

Antimicrobial peptides selected in this study were identi-

fied as cationic peptides with amphipathic a-helical con-

formation and this feature permits them to interact with the

anionic components of the bacterial membranes. There are

studies which confirmed the superior pharmacological

effects of mastoparan analogs (Cerovsky et al. 2008;

Mendes et al. 2004; dos Santos Cabrera et al. 2008). Their

antimicrobial potency was tested against Bacillus subtilis,

Staphylococcus aureus, Escherichia coli and Pseudomonas

aeruginosa. Minimal inhibitory concentration (MIC) val-

ues for studied bactericidal and fungicidal effects of mas-

toparan analogs are in the micromolar range. All these

peptides have been proved to be quite effective antimi-

crobial agents in the drop diffusion test, which in general

indicates higher potency against B. subtilis than E. coli.

However, the obtained MIC values show that the peptides

have different selectivity towards these bacteria. PDD-A,

as well as PMM, showed negligible hemolytic activity

(50% hemolysis at 100 mM higher concentration), while

PDDB was slightly more hemolytic (Cerovsky et al. 2008).

An objective of this study was to use 2D-QSAR models

to predict the effect of molecular descriptors like molecu-

lar volume and area (Ebalunode et al. 2011), electronic

(Zuo et al. 2005; Snyder and Hendry 2005) and also the

descriptors derived directly from amino acids sequence

(number of hydrophobic centers (Zhao et al. 2010), number

of acceptor and donor atoms (Raevsky and Skvortsov

2005)) on antimicrobial activity of peptides of the mas-

toparan family, characterized recently in the venom of

three wasps Polistes major major, Polistes dorsalis

dorsalis and Mischocyttarus phthisicus, in order to gain

new insights into drug-design models.

Basically, all QSAR methods consider that macroscopic

properties are induced by the molecular structure and every

change into molecular structure leads to modification of

these properties (Akamatsu 2002; Frederic et al. 2005).

A QSAR generally takes the form of a linear equa-

tion: Biological Activity = const ? (c1 * p1) ? (c2 * p2) ?

(c3 * p3) ?…, where the parameters p1 through pn are:

molecular volume, dipole moment, etc. and the coefficients

c1 through cn are calculated by fitting variations in the

parameters and the biological activity.

There are few QSAR studies which correlated the anti-

microbial activity with different macroscopic descriptors

like isoelectric point and hydrophobicity (Mikut 2010),

weighted holistic invariant molecular index (WHIM) of

amino acids (Tong et al. 2008), the contact energy between

neighboring amino acids (Jenssen et al. 2008). Also, the

isotropic surface area (ISA) and electronic charge index

(ECI), reflecting steric character of side chains and local

dipole of amino acids, were successfully used to establish a

series of QSAR models (Collantes and Dunn 1995).

Materials and Methods

Dataset for Analysis

The antimicrobial activity expressed as the Minimum

Inhibitory Concentrations (MIC) of 37 antimicrobial drugs

like mastoparan analogs were compiled from literature

(Cerovsky et al. 2008). MIC activity originally determined

in micromolar values was converted to a pMIC value by

taking Log (1/MIC). The pMIC values were used as the

dependent variable in all QSAR models subsequently

developed in this study. This data set was selected

according to following criteria: (i) range of observed

antimicrobial activities (MIC from 4 to 100 micromolar),

(ii) important variety of amino acids in structure of anti-

microbial peptide, (iii) various bacterial lines that these

peptides do inhibit (e.g. E. coli, B. subtilis). This study

evaluated the antimicrobial activity of mastoparan analogs

at E. coli and B. subtilis lines. Results showed that while

PDD-B and MP analogs are selective for B. subtilis, PMM

analogs are more active on E. coli line. Analysis of toxicity

of these analogs showed that PDD-A have a low hemolytic

activity compared with derivatives PDD-B and MP.

8 Int J Pept Res Ther (2011) 17:7–17

123

The name and amino acids sequence of mastoparan

analogs and also their corresponding observed antimicro-

bial activity values are given in Table 1.

Molecular Modeling of Antimicrobial Peptides Analogs

and Minimum Energy Strategy

Three dimensional structures of studied compounds were

obtained using the Biopolymer module from Sybyl 7

software package (Tripos Inc., St. Louis, USA). In the first

step, 2D structures of mastoparan analogs were obtained

from the template structures (mastoparan) using the module

Mutate Monomers from Sybyl and the additive hydrogen

atoms were performed. Afterwards, secondary structures

were obtained using Biopolymer/Conformation/Find Sec-

ondary structure by garnier_osguthorpe_robson method

(Persson 2000) and than automatically switched to 3D

structures of the antimicrobial peptides, which were saved in

mol2. Sybyl specific files.

In our study, the conformation with minimum potential

energy of the peptide analogs was established using, con-

jugate gradient method routine in Sybyl 7, with Kollman

force-field (Cornell et al. 1995), conjugate-gradient algo-

rithm and convergence 0.05. After energy minimization,

Kollman partial charges of the compounds were loaded on

the chemical structures from the Sybyl 7 dictionary.

Design of New Derivatives of PDD-A and PMM-12

Mastoparan Analogs with Possible Superior

Antimicrobial Activity and Minimum Energy Strategy

An important objective of the study was to determine the

pharmacological characteristics of new derivatives of

mastoparan analogs, obtained by computational mutagen-

esis, starting from the PDD-A and PMM-12 mastoparan

analogs. In our attempt to obtain new antimicrobial pep-

tides, some general rules have been accepted; first, we

generated more positive electrostatic contacts by adding of

positive amino acids and second, we enhanced the number

of hydrophobic contacts of mastoparan analogs PDD-A and

PMM-12 by specific amino acids (see Table 2). We have

chosen to design new PDD-A derivatives because it was

proven that none of these amino acid residue replacements

substantially increase the hemolytic activity of these pep-

tides (EC50 [ 100 mM) and antimicrobial activity of

obtained PDD-A analogs was not improved compared to

that of PDD-A. Also, in our study we have designed PMM-

12 derivatives started from the Cerovsky’s considerations

that the PMM-12 analog presents a good antimicrobial

activity.

Molecular modeling of new 19 PDD-A and PMM-12

derivatives was performed under the above described

protocol. The antimicrobial peptide derivatives were gen-

erated by adding atoms and amino acids from Sybyl data

base. The minimum potential energy for antimicrobial

peptide derivatives was established using the Maxim 2

minimization routine in Sybyl 7, with Kollman force-field,

conjugate-gradient algorithm and convergence 0.05. Dur-

ing energy minimization, the specific amino acids were

allowed free movements. After energy minimization, the

partial charges of the compounds were loaded on the

chemical structures from the Sybyl 7 dictionary. The name

Table 1 Sequence of mastoparan analogs and their observed mini-

mal inhibitory concentration at B. subtilis

Derivative Sequence MIC B. subtilis (lM)

PDD-A INWKKIFQKVKNLV 11.8

PDD-A-1 INWKKIFEKVKNLV 45

PDD-A-2 INWKKIFEKVKDLV 5

PDD-A-3 IDWKKIFEKVKNLV 8.5

PDD-A-4 IDWKKIFEKVKDLV 17.5

PDD-A-5 INWSKIFEKVKNLV 17

PDD-A-6 INWSSIFEKVKNLV 40

PDD-A-7 INWSSIFESVKNLV 87.5

PDD-A-8 INWSSIFESVSNLV 100

PDD-A-9 INWKKIFEKVSNLV 22.5

PDD-A-10 INWKKIFESVKNLV 10

PDD-A-11 INWKSIFEKVKNLV 15

PDD-A-12 NIWKKIFEKVKNLV 20

PDD-B INWLKLGKKILGAL 15.5

PDD-B-1 INWLKLGKKILGAI 25

PDD-B-2 INWLRLGRRILGAL 17.5

PDD-B-3 INFLKLGKKILGAL 22.5

PDD-B-4 INWKKLGKKILGAL 15.5

PDD-B-5 INSLKLGKKILGAL 100

MP INWLKLGKKMMSAL 9

MP-2 INWLKLGKKLLSAL 25

MP-5 INWLKLGKKMMSAI 35

MP-6 SNWLKLGKKMMSAL 40

PMM INWKKIASIGKEVLKAL 25.8

PMM-1 INWKKIASIGKEVLKAI 27.2

PMM-2 NWKKIASIGKEVLKAL 46

PMM-3 WKKIASIGKEVLKAL 125

PMM-4 KKIASIGKEVLKAL 75

PMM-5 KIASIGKEVLKAL 100

PMM-6 INWKKIASIGKEVLKA 70

PMM-7 INWKKIASIGKEVLK 100

PMM-8 INWKKIASIGKEVL 100

PMM-9 INWKKIASIGKEV 100

PMM-10 IWNKIAKSIGKVLEKAL 35

PMM-12 NIWKKIASIAKEVLKAL 10

PMM-13 KNWKKIASIGKEVLKAL 60

PMM-14 SNWKKIASIGKEVLKAL 30

Int J Pept Res Ther (2011) 17:7–17 9

123

and amino acids sequence of derivatives of mastoparan

analogs PDD-A and PMM-12 obtained by computational

mutagenesis are given in Table 2 (in bold are presented the

amino acid mutations).

Descriptors Calculations

Afterwards all compounds were introduced in Sybyl 7 and the

calculations of molecular descriptors of the antimicrobial

peptides were performed. In this study the descriptors were:

van der Walls volume (VOL) and area (AREA), relative

positive charge (RPCG), internal dipole moment (dipole),

refractivity and descriptors derived directly from amino acids

sequence: number of hydrophobic centers (Hyd), number of

donor and acceptor atoms (donor/acceptor).

In Sybyl software descriptors are evaluated in the fol-

lowing manner: (i) descriptors based on dipole moment are

calculated in four columns, containing the total dipole

moment and its X, Y, and Z components, for each of the

molecules referenced by the table as stored in the database;

(ii) the number of hydrophobic, donor and acceptor atoms

are defined in the file sln3d_macros.def. If 3D coordinates

are not supplied, sln_property uses ‘‘pseudo 3D coordi-

nates’’; (iii) van der Walls volume and area are calculated

considering the probe_radius of 1.4 A; (iii) relative posi-

tive charge is computed as QMPA/(P

(?Qi)), QMPA, the

charge on the most positive atom; ?Qi, the charge on the

ith positive atom).

2D-QSAR Methodology

Initially in the 2D-QSAR models, the descriptors included

in this study were considered as individual independent

variable or in various combinations of them to predict the

antimicrobial activity. To avoid the redundancy of inde-

pendent variables and to ensure the absence of chance

correlation, the inter-correlation of the independent vari-

ables matrix were performed follow Pearson correlation

method (Table 3). For the final statistical analysis were

kept only those who met criteria of non-redundancy, but in

a large enough number of descriptors to allow an accurate

validation of QSAR model (R2 higher than 0.65, standard

error of prediction SEE less than 0.20).

In this respect, in our study the relevant QSAR equa-

tions from statistical point of view included:

(i) pMIC = constant ? c1 * Hyd

(ii) pMIC = constant ? c2 * Hyd ? c3 * volume

(iii) pMIC = constant ? c4 * Hyd ? c5 * area

(iv) pMIC = constant ? c6 * Hyd ? c7 * dipole

moment

(v) pMIC = constant ? c8 * Hyd ? c9 * refractivity

(vi) pMIC = constant ? c10 * Hyd ? c11 * acceptor

(vii) pMIC = constant ? c12 * Hyd ? c13 * donor

Table 2 Sequence of design mastoparan derivatives

Derivative Primary structure

PDD-A-derivative 1 INWKKIFEKVKNIV

PDD-A-derivative 2 INWKKIFEKVKNFV

PDD-A-derivtive 3 INWKKIFEKVKNWV

PDD-A-derivative 4 FNWKKIFEKVKNLV

PDD-A-derivative 5 FNWKKFFEKVKNLV

PDD-A-derivative 6 WNWKKIFEKVKNLV

PDD-A-derivative 7 INWKKIFRKVKNLV

PDD-A-derivative 8 INWKKIFHKVKNLV

PDD-A-derivative 9 INWKKIFFKVKNLV

PDD-A-derivative 10 INWKKIFKKVKNLV

PMM-12-derivative 1 NIWKKIASIAKEVIKAL

PMM-12-derivative 2 NIWKKIASIAKEVIKAI

PMM-12-derivative 3 NIWKKIASIAKEVFKAF

PMM-12-derivative 4 NIWKKIASIAKEVWKAW

PMM-12-derivative 5 NFWKKFASFAKEVLKAL

PMM-12-derivative 6 NIWKKIASIAKRVLKAL

PMM-12-derivative 7 NIWKKIASIAKKVLKAL

PMM-12-derivative 8 NIWKKIASIAKHVLKAL

PMM-12-derivative 9 NIWKKIWSIWKEVLKWL

Table 3 The correlation matrix of independent variables

Hyd VOL AREA RPCG Dipole Refractivity Acceptor Donor

Hyd 1.000

VOL 0.636 1.000

AREA 0.402 0.940 1.000

RPCG -0.476 -0.196 -0.019 1.000

Dipole moment -0.066 0.073 0.171 0.095 1.000

Refractivity 0.664 0.677 0.538 -0.142 0.001 1.000

Acceptor 0.128 0.550 0.455 -0.251 -0.385 0.338 1.000

Donor 0.446 0.829 0.728 -0.253 -0.036 0.665 0.725 1.000

10 Int J Pept Res Ther (2011) 17:7–17

123

(viii) pMIC = constant ? c14 * Hyd ? c15 * RPCG ?

c16 * refractivity

(ix) pMIC = constant ? c17 * Hyd ? c18 * RPCG ?

c19 * area

(x) pMIC = constant ? c20 * Hyd ? c21 * RPCG ?

c22 * volume

(xi) pMIC = constant ? c23 * Hyd ? c24 * RPCG ?

c25 * dipole moment

Statistical Calculations

Multiple linear regression analysis (MLRA) was used in

order to generate the correlation models that relate the

structural features to the MIC values and also to calculate

the correlation coefficient R2, standard error of estimate

SEE and Fisher value. A reliable equation for structure

activity relationship should possess a high correlation

coefficient, low standard error of estimate (SEE) and the

least possible number of variables.

Training and Testing Sets

The generation of consistent statistical models depends on

the quality of both training and test sets in terms of struc-

tural diversity and property values distribution. From the

original data set of 37 mastoparan analogs 29 compounds

were selected as members of the training set for model

construction and the other 8 compounds (PDD-A-1, PDD-

B-2, PMM-10, PMM-13, PMM-3, PMM-6, PMM-7, PMM-

9) as members of the test set for external model validation

(Table 6). Prior to divides the mastoparan analogs into two

sets, all compounds were included into the training set and

non-significant statistic parameters (correlation coefficient

R2, standard error of estimate SEE and Fisher value) were

obtained. Consequently, the compounds of the training and

test sets were carefully selected in order to ensure the best

2D-QSAR models on the basis of statistical parameters

mentioned before (correlation coefficient R2 higher 0.66,

standard error of estimate SEE less than 0.20) and to cov-

erage on the entire range of pMIC values. The composition

of both sets was kept unchanged in all 2D-QSAR models.

A statistical cluster analysis confirmed that the composition

of both training and test sets is chose in a representative

manner for whole data set (Table 6).

Results and Discussion

Descriptors Evaluation

The initial stage of our results study was represented by the

evaluation of molecular descriptors presented above.

Mastoparan analogs already tested as antimicrobial agents

and their derivatives obtained by computational mutagen-

esis (Table 4) were evaluated by means of the series of

molecular descriptors.

The analysis of variation of molecular descriptors belong-

ing to the considered mastoparan series showed that: (i)

molecular volume varies significantly with ranges between

the values 3577.62 A´ 3 (PDD-A-8) and 5574.02 A

´ 3 (PMM-

12-derivative 9); (ii) area has also recorded large fluctua-

tions, ranging from 1625.885 A´ 2 (PDD-A-8) to 2391.176 A

´ 2

(PMM-12-derivative 9); (iii) refractivity varies between

max = 582.39 (PMM-12-derivative 9) and min = 332.12

(PMM-5). A large variation of values was recorded in the

internal dipole moment from maximum value 101.85 D

(PMM-13) to minimum value 20.64 D (PDD-A-11). Also, a

large fluctuation between minimum and maximum values of

molecular descriptors derived directly from the peptide

sequence was recorded, (Hyd (8 \ Hyd \ 18); number of

donor atoms (17 \ donor \ 27); number of acceptor atoms

(16 \ acceptor \ 23)). The same observation can not be

done when variation of RPCG (0.3 \ RPCG \ 0.4) descrip-

tor of the mastoparan series was considered.

Statistic Evaluation

In our study we have generated eleven QSAR models, each

of them containing the same number and type of mastoparan

analogs. In Table 5 are presented the statistic parameters

computed from training set (correlation coefficient R2,

standard error of estimate SEE, as well as ANOVA results,

the Fisher F and QSAR regression coefficients).

R2 correlation coefficients and standard errors of pre-

dictions were most statistically significant when following

conditions are simultaneously met: (i) number of hydro-

phobic centers, volume and relative positive charge

(R2 = 0.720, SEE = 0.203); (ii) number of hydrophobic

centers and volume (R2 = 0.719, SEE = 0.199); (iii)

number of hydrophobic centers, area and positive relative

charge (R2 = 0.714, SEE = 0.205);(iv) number of hydro-

phobic centers and donor atoms (R2 = 0.705, SEE =

0.204); (v) number of hydrophobic centers, refractivity and

relative positive charge (R2 = 0.700, SEE = 0.219) were

considered.

The external predictive ability of the 2D-QSAR models

derived using the 29 training set molecules was assessed by

predicting pMIC values for 8 test set molecules which were

not included in the training set for model generation. The

values for predicted pMIC of the mastoparan analogs

belonging to training and test sets and also the residual values

(the differences between pMICobserved and pMICpredicted) just

for three most significant QSAR models were presented

(Table 6).

Int J Pept Res Ther (2011) 17:7–17 11

123

Table 4 Descriptors of mastoparan analogs and their derivatives obtained by computational mutagenesis

Mastoparan analogs and derivatives Hyd VOL (A´ 3) AREA (A

´ 2) Dipole (D) RPCG Refractivity Acceptor Donor

MP 11 3974.45 1755.28 70.55 0.03 409.82 17 20

MP-2 11 3942.52 1761.13 71.42 0.03 402.50 17 20

MP-5 11 3913.54 1724.99 69.57 0.03 409.74 17 20

MP-6 10 3824.70 1692.42 68.65 0.03 397.76 18 21

PDD-A 12 4143.15 1830.55 37.31 0.03 441.18 18 22

PDD-A-1 12 4149.89 1787.19 38.55 0.03 436.89 19 21

PDD-A-10 11 4067.01 1761.28 53.39 0.03 422.95 20 21

PDD-A-11 11 4033.47 1785.92 20.64 0.03 422.95 20 21

PDD-A-12 12 4099.29 1813.93 41.48 0.03 436.89 19 21

PDD-A-2 12 4131.01 1784.19 60.17 0.03 432.62 20 20

PDD-A-3 12 4127.58 1807.82 23.05 0.02 432.61 20 20

PDD-A-4 12 4116.06 1760.84 27.79 0.03 428.34 21 19

PDD-A-5 11 3987.39 1751.27 70.31 0.03 422.02 19 21

PDD-A-6 10 3906.46 1710.80 37.29 0.03 409.00 21 21

PDD-A-7 9 3731.77 1658.89 33.73 0.03 395.05 22 21

PDD-A-8 8 3577.62 1625.89 36.82 0.03 381.11 23 21

PDD-A-9 11 4026.53 1780.69 92.44 0.02 422.92 20 21

PDD-A-derivative 1 12 4147.29 1820.87 44.74 0.03 436.82 19 21

PDD-A-derivative 10 13 4297.09 1914.64 42.61 0.03 444.92 17 22

PDD-A-derivative 2 12 4186.99 1825.13 45.8 0.03 447.84 19 21

PDD-A-derivative 3 13 4248.24 1859.38 42.47 0.03 458.94 19 22

PDD-A-derivative 4 12 4154.07 1833.24 46.06 0.03 459.30 19 21

PDD-A-derivative 5 12 4172.23 1813.51 45.52 0.03 456.07 19 21

PDD-A-derivative 6 13 4247.79 1831.11 47.76 0.03 448.63 19 22

PDD-A-derivative 7 12 4220.89 1853.44 57.78 0.03 454.05 17 24

PDD-A-derivative 8 13 4170.05 1835.43 44.12 0.03 396.38 19 23

PDD-A-derivative 9 13 4217.95 1862.29 42.89 0.03 396.05 17 21

PDD-B 11 3887.07 1755.94 69.53 0.03 429.82 16 19

PDD-B-1 11 3867.53 1738.69 68.92 0.03 384.99 16 19

PDD-B-2 8 4025.77 1729.38 64.55 0.03 398.20 16 25

PDD-B-3 10 3829.11 1744.16 69.43 0.03 361.92 16 18

PDD-B-4 11 3954.41 1825.28 76.63 0.03 469.89 16 20

PDD-B-5 9 3738.25 1740.35 70.36 0.04 469.81 17 19

PMM 12 4943.09 2240.78 68.79 0.03 469.89 22 24

PMM-1 12 4929.18 2235.27 66.65 0.03 469.81 22 24

PMM-10 12 4922.71 2218.64 83.06 0.03 469.89 22 24

PMM-12 12 4833.69 2236.73 60.99 0.03 474.38 22 24

PMM-12-derivative 1 12 4862.89 2221.73 60.68 0.03 474.30 22 24

PMM-12-derivative 9 18 5574.03 2391.18 58.13 0.02 582.39 22 27

PMM-12-derivative 2 12 4843.11 2220.31 60.69 0.03 474.23 22 24

PMM-12-derivative 3 12 5018.47 2275.36 58.72 0.03 496.27 22 24

PMM-12-derivative 4 14 5201.25 2330.50 61.62 0.03 519.04 22 26

PMM-12-derivative 5 12 5021.75 2305.53 68.16 0.03 507.45 22 24

PMM-12-derivative 6 12 4988.44 2253.55 57.09 0.03 493.56 20 27

PMM-12-derivative 7 13 4940.17 2262.68 62.22 0.03 482.41 20 25

PMM-12-derivative 8 13 4899.63 2279.31 51.3 0.03 486.21 22 26

PMM-13 12 4941.25 2276.02 101.85 0.03 471.78 22 25

PMM-14 11 4812.52 2207.10 64.72 0.03 457.83 23 25

12 Int J Pept Res Ther (2011) 17:7–17

123

Table 4 continued

Mastoparan analogs and derivatives Hyd VOL (A´ 3) AREA (A

´ 2) Dipole (D) RPCG Refractivity Acceptor Donor

PMM-2 11 4627.27 2164.31 64.88 0.03 439.86 21 23

PMM-3 11 4387.48 2100.72 59.81 0.03 416.45 19 21

PMM-4 9 4007.00 1976.66 60.67 0.03 364.03 18 19

PMM-5 8 3640.91 1810.29 64.13 0.04 332.12 17 17

PMM-6 11 4551.67 2047.86 65.66 0.03 439.80 21 23

PMM-7 11 4299.16 1952.26 66.10 0.03 423.36 20 22

PMM-8 10 3926.06 1777.26 92.70 0.03 391.45 19 20

PMM-9 9 3582.14 1672.89 91.38 0.03 361.35 18 19

Table 5 Statistic results of the 2D-QSAR models

Descriptors R2 SEE ANOVA results QSAR coefficients

Hyd 0.655 0.217 df SS MS F Constant = 1.946

Regression 1 2.411 2.553 51.317

Residual 27 1.268 0.047 c1 = 0.246

Total 28 3.679 2.600

Hyd volume 0.719 0.199 df SS MS F

Regression 2 2.635 1.317 33.315 Constant = 2.583

Residual 26 1.028 0.040 c2 = 0.310

Total 28 3.663 1.357 c3 = -0.001

Hyd area 0.714 0.201 df SS MS F

Regression 2 2.614 1.307 32.392 Constant = 2.576

Residual 26 1.049 0.040 c4 = 0.278

Total 28 3.663 1.347 c5 = -0.001

Hyd dipole moment 0.663 0.218 df SS MS F

Regression 2 2.430 1.215 25.614 Constant = 2.092

Residual 26 1.233 0.047 c6 = 0.243

Total 28 3.663 1.262 c7 = -0.002

Hyd refractivity 0.673 0.215 df SS MS F

Regression 2 2.465 1.233 26.758 Constant = 2.355

Residual 26 1.198 0.046 c8 = 0.284

Total 28 3.663 1.279 c9 = -0.002

Hyd acceptor 0.680 0.213 df SS MS F

Regression 2 2.502 1.251 27.649 Constant = 2.370

Residual 26 1.177 0.045 c10 = 0.252

Total 28 3.679 1.296 c11 = -0.026

Hyd donor 0.705 0.204 df SS MS F

Regression 2 2.595 1.298 31.142 Constant = 2.589

Residual 26 1.083 0.042 c12 = 0.280

Total 28 3.678 1.340 c13 = -0.049

Hyd RPCG refractivity 0.700 0.219 df SS MS F Constant = 2.377

Regression 2 2.465 0.822 17.154 c14 = 0.283

Residual 26 1.198 0.048 c15 = -0.377

Total 28 3.663 0.870 c16= -0.002

Int J Pept Res Ther (2011) 17:7–17 13

123

Data presented in Table 6 are supported by the corre-

lation between observed and predicted antimicrobial

activities when the contribution of number of hydrophobic

centers, refractivity and relative positive charge (Fig. 1a)

and number of hydrophobic centers and volume (Fig. 1b)

are considered.

Best correlations between observed and calculated pMIC

for the mastoparan analogs included in 2D-QSAR model in

which the considered descriptors simultaneously share

number of hydrophobic centers and volume seem to be

for MP (pMICobserved - pMICpredicted = -0.56), PDD-A-10

(pMICobserved - pMICpredicted = -0.59) and PDD-A-2

Table 5 continued

Descriptors R2 SEE ANOVA results QSAR coefficients

Hyd RPCG area 0.718 0.205 df SS MS F Constant = 2.616

Regression 2 2.614 0.871 20.770 c17 = 0.276

Residual 26 1.049 0.042 c18 = -0.721

Total 28 3.663 0.913 c19 = -0.001

Hyd RPCG volume 0.720 0.203 df SS MS F Constant = 2.712

Regression 2 2.637 0.879 21.420 c20 = 0.306

Residual 26 1.026 0.041 c21 = -2.259

Total 28 3.663 0.920 c22 = -0.001

Hyd RPCG dipole moment 0.664 0.222 df SS MS F Constant = 2.009

Regression 2 2.431 0.810 16.441 c23 = 0.246

Residual 26 1.232 0.049 c24 = 1.582

Total 28 3.663 0.859 c25 = -0.002

Table 6 Observed and predicted pMIC of mastoparan analogs at B. subtilis and residual values (in brackets) when combinations of (a) Hyd,

volume, (b) Hyd, RPCG, area, (c) Hyd, RPCG, refractivity and (d) Hyd and number of donor atom were considered

Peptides pMICobserved (pMICHyd,volume)pred (pMICHyd,RPCG,area)pred (pMICHyd,RPCG,refractivity)pred (pMICHyd,donor)pred

MP 5.04 5.60 (-0.56) 3.87 (1.17) 4.66 (0.38) 4.69 (0.35)

MP-2 4.60 5.60 (-1.00) 3.87 (0.73) 4.67 (-0.07) 4.69 (-0.09)

MP-5 4.45 5.60 (-1.15) 3.90 (0.55) 4.66 (-0.21) 4.69 (-0.24)

MP-6 4.39 5.30 (-0.91) 3.66 (0.73) 4.40 (-0.01) 4.36 (0.03)

PDD-A 4.92 5.89 (-0.97) 4.08 (0.84) 4.88 (0.04) 4.87 (0.05)

PDD-A-10 5.00 5.59 (-0.59) 3.87 (1.13) 4.63 (0.37) 4.64 (0.36)

PDD-A-11 4.82 5.59 (-0.77) 3.84 (0.98) 4.63 (0.19) 4.64 (0.18)

PDD-A-12 4.69 5.89 (-1.20) 4.09 (0.60) 4.89 (-0.20) 4.92 (-0.23)

PDD-A-2 5.30 5.89 (-0.59) 4.12 (1.18) 4.90 (0.40) 4.97 (0.33)

PDD-A-3 5.07 5.89 (-0.82) 4.11 (0.96) 4.90 (0.17) 4.97 (0.10)

PDD-A-4 4.75 5.89 (-1.14) 4.15 (0.60) 4.91 (-0.16) 5.02 (-0.27)

PDD-A-5 4.76 5.59 (-0.83) 3.88 (0.88) 4.64 (0.12) 4.64 (0.12)

PDD-A-6 4.39 5.29 (-0.90) 3.64 (0.75) 4.38 (0.01) 4.36 (0.03)

PDD-A-7 4.05 5.00 (-0.95) 3.42 (0.63) 4.12 (-0.07) 4.08 (-0.03)

PDD-A-8 4.00 4.71 (-0.71) 3.17 (0.83) 3.87 (0.13) 3.80 (0.20)

PDD-A-9 4.64 5.59 (-0.95) 3.86 (0.78) 4.64 (0.00) 4.64 (0.00)

PDD-B 4.80 5.60 (-0.80) 3.87 (0.93) 4.62 (0.18) 4.74 (0.06)

PDD-B-1 4.60 5.61 (-1.01) 3.89 (0.71) 4.71 (-0.11) 4.74 (-0.14)

PDD-B-3 4.64 5.30 (-0.66) 3.61 (1.03) 4.47 (0.17) 4.51 (0.13)

PDD-B-4 4.80 5.60 (-0.80) 3.80 (1.00) 4.54 (0.26) 4.69 (0.11)

PDD-B-5 4.00 5.00 (-1.00) 3.33 (0.67) 3.97 (0.03) 4.18 (-0.18)

PMM 4.58 5.81 (-1.23) 3.67 (0.91) 4.82 (-0.24) 4.77 (-0.19)

PMM-1 4.56 5.81 (-1.25) 3.67 (0.89) 4.82 (-0.26) 4.77 (-0.21)

14 Int J Pept Res Ther (2011) 17:7–17

123

(pMICobserved - pMICpredicted = -0.59). Unsuitable

pMICresidual values were obtained for PMM-8 (-1.29),

PMM-1 (-1.25) and PMM (-1.23). Our results are

encouraged by experimental results (Cerovsky et al. 2008):

PDD-A 2 has an enhanced antimicrobial activity against B.

subtilis.

In the 2D-QSAR model, when the contributions of number

of hydrophobic centers, area and RPCG were considered,

good correlations between observed and predicted pMIC were

obtained for MP-5 (pMICobserved - pMICpredicted = 0.55),

PMM-8 (pMICobserved - pMICpredicted = 0.42), PDD-A-12

(pMICobserved - pMICpredicted = 0.60) and PDD-A-4

(pMICobserved - pMICpredicted = 0.60). Poor correlations were

noticed for PMM-12 (pMICobserved - pMICpredicted = 1.33)

and PDD-A-2 (pMICobserved - pMICpredicted = 1.33).

When descriptors like number of hydrophobic cen-

ters, refractivity and RPCG are taken in account, the

best correlations between observed and calculated pMIC

for the mastoparan analogs were noticed for PDD-

A-9 (pMICobserved - pMICpredicted = 0.00), PDD-A-6

(pMICobserved - pMICpredicted = 0.01) and MP-6

(pMICobserved - pMICpredicted = 0.01).

In the 2D-QSAR model, when the contributions of number

of hydrophobic centers and donor atoms were considered,

good correlations between observed and predicted pMIC

were obtained for PDD-A-9 (pMICobserved - pMICpredicted =

0.00), PMM-5 (pMICobserved - pMICpredicted = 0.00) and

PDD-A-6 (pMICobserved - pMICpredicted = 0.03).

Also, the prediction power of 2D-QSAR models pro-

posed in our study is confirmed by the correlation coeffi-

cient, R (0.81–0.83) between the predicted and observed

pMIC of mastoparan analogs.

Table 6 continued

Peptides pMICobserved (pMICHyd,volume)pred (pMICHyd,RPCG,area)pred (pMICHyd,RPCG,refractivity)pred (pMICHyd,donor)pred

PMM-12 5.00 5.82 (-0.82) 3.67 (1.33) 4.81 (0.19) 4.77 (0.23)

PMM-14 4.52 5.51 (-0.99) 3.43 (1.09) 4.56 (-0.04) 4.44 (0.08)

PMM-2 4.34 5.53 (-1.19) 3.47 (0.87) 4.60 (-0.26) 4.54 (-0.20)

PMM-4 4.12 4.97 (-0.85) 3.10 (1.02) 4.18 (-0.06) 4.18 (-0.06)

PMM-5 4.00 4.70 (-0.70) 2.99 (1.01) 3.96 (0.04) 4.00 (0.00)

PMM-8 4.00 5.29 (-1.29) 3.58 (0.42) 4.41 (-0.41) 4.46 (-0.46)

Test set

PDD-A-1 4.35 5.89 (-1.54) 3.58 (0.22) 4.41 (-0.54) 4.92 (-0.57)

PDD-B-2 4.76 4.66 (0.10) 4.12 (1.68) 4.89 (0.92) 3.60 (1.16)

PMM-10 4.46 5.81 (-1.35) 3.07 (0.77) 3.83 (-0.37) 4.77 (-0.31)

PMM-13 4.22 5.81 (-1.59) 3.69 (0.59) 4.82 (-0.60) 4.72 (-0.50)

PMM-3 3.90 5.55 (-1.65) 3.63 (0.37) 4.82 (-0.74) 4.64 (-0.74)

PMM-6 4.15 5.54 (-1.38) 3.53 (0.57) 4.65 (-0.44) 4.54 (-0.39)

PMM-7 4.00 5.56 (-1.56) 3.58 (0.32) 4.60 (-0.63) 4.59 (-0.59)

PMM-9 4.00 5.01 (-1.01) 3.68 (0.60) 4.63 (-0.19) 4.18 (-0.18)

Fig. 1 a Correlation between observed and predicted pMIC of

mastoparan analogs when the contribution of number of hydrophobic

centers, refractivity and relative positive charge are considered

(R2 = 0.700, SEE = 0.219); b correlation between observed and

predicted pMIC of mastoparan analogs when the contribution of

number of hydrophobic centers and volume are considered

(R2 = 0.719, SEE = 0.199) (in square, the compounds belong to

test set)

Int J Pept Res Ther (2011) 17:7–17 15

123

Due to the before-mentioned high importance of mas-

toparan analogs as antimicrobial peptides, a set of new 19

derivatives of PDD-A and PMM-12 mastoparan analogs

was created in order to predict a higher pMIC. The pre-

dictive ability of the 2D-QSAR models presented in

Table 5 was used to evaluate the predicted antimicrobial

activity of these new derivatives of PDD-A and PMM-12

mastoparan analogs and also, the residual values (the dif-

ference predicted pMIC derivative vs. observed pMIC of

parent analogs). In Table 7 there are presented the pre-

dicted pMIC of new of PDD-A and PMM-12 derivatives

when descriptors Hyd, volume, area, refractivity and

RPCG are considered.

Following our design strategy presented in the

‘‘2D-QSAR Methodology’’ section, when simultaneously

number of hydrophobic centers and volume are considered,

three derivatives of the PDD-A analog (PDD-A-derivative 3

(INWKKIFEKVKNWV, residual value = 1.27); PDD-

A-derivative 6 (WNWKKIFEKVKNLV, residual value =

1.27) and PDD-A-derivative 8 (INWKKIFHKVKNLV,

residual value = 1.28) recorded a pMIC values in accord

with our intention. In the same QSAR model, four deriv-

atives of the PMM-12 analog (PMM-12-derivative 9

(NIWKKIWSIWKEVLKWL, residual value = 2.91), PMM-

12-derivative 4 (NIWKKIASIAKEVWKAW, residual

value = 1.70), PMM-12-derivative 7 (NIWKKIASIAKKV

LKAL, residual value = 1.42) and PMM-12-derivative 8

(NIWKKIASIAKHVLKAL residual value = 1.42) had a

good outcome. In contrast, not significant increases of pMIC

in PDD-A and PMM-12 derivatives of mastoparan analogs

were recorded in the second 2D-QSAR model (number of

hydrophobic centers, area and RPCG descriptors taken in

account). In the last 2D-QSAR model discussed in our study

(number of hydrophobic centers, refractivity and RPCG

descriptors taken in account), two derivatives of PDD-A

analog (PDD-A-derivative 8, residual value = 0.33 and

PDD-A-derivative 9, residual value = 0.33) were recorded

to show a slightly increased antimicrobial activity. In the

PMM-12 derivatives series, only PMM-12-derivative 9,

residual value = 1.60 has an increased pMIC value. We

notice that double or triple mutations in tryptophan residues

could possibly induce an increased antimicrobial potency in

PMM-12 analog series, if the number of hydrophobic cen-

ters and molecular volume are considered.

Concluding Remarks

Here we have reported 2D-QSAR studies upon a series of

37 mastoparan analogs already tested as antimicrobial

peptides at B. subtilis and also upon 19 new derivatives of

mastoparan analogs. Mastoparan analogs were used to

elucidate the most important physicochemical properties

which might be responsible for the antimicrobial activity.

Table 7 Predicted pMIC of PDD-A and PMM-12 derivatives and their residual values (the difference predicted pMIC vs. observed pMIC of

parent analogs (in bracket)) obtained when descriptors are in combinations: (a) Hyd, volume; (b) Hyd, RPCG, area; (c) Hyd, RPCG, refractivity

Derivatives of mastoparan analogs

obtained by computational mutagenesis

(pMICHyd,volume)pred (pMICHyd,RPCG,area)pred (pMICHyd,RPCG,refractivity)pred

PDD-A-derivative 1 5.89 (0.97) 4.09 (-0.83) 4.89 (-0.03)

PDD-A-derivative 10 6.18 (1.26) 4.27 (-0.65) 5.16 (0.24)

PDD-A-derivative 2 5.88 (0.96) 4.08 (-0.84) 4.87 (-0.05)

PDD-A-derivative 3 6.19 (1.27) 4.32 (-0.60) 5.13 (0.21)

PDD-A-derivative 4 5.89 (0.97) 4.07 (-0.85) 4.84 (-0.08)

PDD-A-derivative 5 5.89 (0.97) 4.10 (-0.82) 4.85 (-0.07)

PDD-A-derivative 6 6.19 (1.27) 4.35 (-0.57) 5.15 (0.23)

PDD-A-derivative 7 5.88 (0.96) 4.06 (-0.86) 4.85 (-0.07)

PDD-A-derivative 8 6.20 (1.28) 4.35 (-0.57) 5.25 (0.33)

PDD-A-derivative 9 6.19 (1.27) 4.32 (-0.60) 5.25 (0.33)

PMM-12-derivative 1 5.82 (1.12) 3.69 (-1.01) 4.81 (0.11)

PMM-12-derivative 9 7.61 (2.91) 5.18 (0.48) 6.30 (1.60)

PMM-12-derivative 2 5.82 (1.12) 3.69 (-1.01) 4.81 (0.11)

PMM-12-derivative 3 5.80 (1.10) 3.63 (-1.07) 4.77 (0.07)

PMM-12-derivative 4 6.40 (1.70) 4.13 (-0.57) 5.29 (0.59)

PMM-12-derivative 5 5.80 (1.10) 3.60 (-1.10) 4.75 (0.05)

PMM-12-derivative 6 5.80 (1.10) 3.66 (-1.04) 4.78 (0.08)

PMM-12-derivative 7 6.12 (1.42) 3.92 (-0.78) 5.08 (0.38)

PMM-12-derivative 8 6.12 (1.42) 4.09 (-0.83) 4.89 (-0.03)

16 Int J Pept Res Ther (2011) 17:7–17

123

In our study number of hydrophobic centers, donor and

acceptor atoms, RPCG supplied by molecular area and

volume, dipole moment and refractivity were considered.

Significant correlation coefficients R2 and standard error of

prediction results were obtained for simultaneous presence

of: (i) number of hydrophobic centers and volume; (ii)

number of hydrophobic centers, volume and RPCG; (iii)

number of hydrophobic centers, area and RPCG;

(iv) number of hydrophobic centers and donor atoms; (v)

number of hydrophobic centers, refractivity and RPCG.

The judicious modulation of physicochemical properties

may be useful in designing new antimicrobial peptide

antibiotic drugs. Considering the established QSAR equa-

tions obtained in this study, a set of 19 new derivatives of

mastoparan analogs was proposed and used to predict the

antimicrobial activity. It was noticed that the single or

multiple mutations with tryptophan residue and also the

presence of lysine and histidine significantly increase the

predicted pMIC of antimicrobial peptide derivatives.

Acknowledgments We acknowledge the financial support of

CNMP PNII 61-016/2007 and CNMP PNII 62-061/2008.

References

Akamatsu M (2002) Current state and perspectives of 3D-QSAR.

Curr Top Med Chem 2:1381–1394

Bowdish DM, Hancock RE (2005) Anti-endotoxin properties of

cationic host defence peptides and proteins. J Endotoxin Res

11:230–236

Brandenburg K, Schromm AB, Gutsmann T (2010) Endotoxins:

relationship between structure, function and activity. Subcell

Biochem 53:53–67

Cerovsky V, Slaninova J, Fucik V, Hulacova H, Borovickova L,

Jezek R, Bednarova L (2008) New potent antimicrobial peptides

from the venom of Polistinae wasps and their analogs. Peptides

29:992–1003

Chen Y, Mant CT, Farmer SW, Hancock RE, Vasil ML, Hodges RS

(2005) Rational design of a-helical antimicrobial peptides with

enhanced activities and specificity/therapeutic index. J Biol

Chem 280:12316–12329

Collantes ER, Dunn WJ (1995) Amino acid side chain descriptors for

quantitative structure activity relationship studies of peptide

analogues. J Med Chem 38:2705–2713

Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM,

Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) A

second generation force field for the simulation of proteins, nucleic

acids, and organic molecules. J Am Chem Soc 117:5179–5197

Davison G, Allgrove J, Gleeson M (2009) Salivary antimicrobial

peptides (LL-37 and alpha-defensins HNP1-3), antimicrobial and

IgA responses to prolonged exercise. Eur J Appl Physiol 106:

277–284

dos Santos Cabrera MP, Costa ST, de Souza BM, Palma MS, Ruggiero

JR, Ruggiero Neto J (2008) Selectivity in the mechanism of

action of antimicrobial mastoparan peptide Polybia-MP1. Eur

Biophys J 37:879–891

Ebalunode JO, Zheng W, Tropsha A (2011) Application of QSAR and

shape pharmacophore modeling approaches for targeted chem-

ical library design. Methods Mol Biol 685:111–133

Frederic C, Cristine AC, Erwan A, David A, Cecile D, Michael F,

Jean-Francois H, Martinez J, Solveig LJ, Philippe M, Andrea P,

Agnes P, Christopher P, Philippe R, Peter SG, Sherwin W (2005)

JLK inhibitors: isocoumarin compounds as putative probes to

selectively target the gamma-secretase pathway. Curr Alzheimer

Res 2:327–334

Hancock EW, Scott MG (2000) The role of antimicrobial peptides in

animal defenses. Proc Natl Acad Sci USA 97:8856–8861

Harris F, Dennison SR, Phoenix DA (2009) Anionic antimicrobial

peptides from eukaryotic organisms. Curr Protein Pept Sci 10:

585–606

Jenssen H, Fjell CD, Cherkasov A, Hancock RE (2008) QSAR

modeling and computer-aided design of antimicrobial peptides.

J Pept Sci 14:110–114

Mahalka AK, Kinnunen PK (2009) Binding of amphipathic alpha-

helical antimicrobial peptides to lipid membranes: lessons from

temporins B and L. Biochim Biophys Acta 1788:1600–1609

Maher S, McClean S (2006) Investigation of the cytotoxicity of

eukaryotic and prokaryotic antimicrobial peptides in intestinal

epithelial cells in vitro. Biochem Pharmacol 71:1289–1298

Mendes MA, De Sousa BM, Marques MR, Palma MS (2004)

Structural and biological characterization of two novel peptides

from the venom of the neotropical social wasp Agelaia pallipespalipes. Toxicon 44:67–74

Mikut R (2010) Computer-based analysis, visualization, and inter-

pretation of antimicrobial peptide activities. Methods Mol Biol

618:287–299

Periti P, Mazzei T (1999) New criteria for selecting the proper

antimicrobial chemotherapy for severe sepsis and septic shock.

Int J Antimicrob Agents 12:97–105

Persson B (2000) Bioinformatics in protein analysis. EXS 88:215–231

Raevsky OA, Skvortsov VS (2005) Quantifying hydrogen bonding in

QSAR and molecular modeling. SAR QSAR Environ Res 16:

287–300

Snyder RD, Hendry LB (2005) Toward a greater appreciation of

noncovalent chemical/DNA interactions: application of biolog-

ical and computational approaches. Environ Mol Mutagen 45:

100–105

Subasinghage AP, Conlon JM, Hewage CM (2010) Development of

potent anti-infective agents from Silurana tropicalis: conforma-

tional analysis of the amphipathic, alpha-helical antimicrobial

peptide XT-7 and its non-haemolytic analogue [G4K]XT-7.

Biochim Biophys Acta 1804:1020–1028

Tong J, Liu S, Zhou P, Wu B, Li Z (2008) A novel descriptor of

amino acids and its application in peptide. QSAR Theor Biol

253:90–97

Zanetti M, Gennaro R, Skerlavaj B, Tomasinsig L, Circo R (2002)

Cathelicidin peptides as candidates for a novel class of

antimicrobials. Curr Pharm Des 8:779–793

Zasloff M (2002) Antimicrobial peptides of multicellular organisms.

N Engl J Med 347:1199–1200

Zhao X, Yuan M, Huang B, Ji H, Zhu L (2010) Ligand-based

pharmacophore model of N-aryl and n-heteroaryl piperazine

alpha(1A)-adrenoceptors antagonists using GALAHAD. J Mol

Graph Model 29:126–136

Zhu WL, Song YM, Park Y, Park KH, Yang ST, Kim JI, Park IS, Hahm

KS, Shin SY (2007) Substitution of the leucine zipper sequence

in melittin with peptoid residues affects self-association, cell

selectivity and mode of action. Biochim Biophys Acta 1768:

1506–1517

Zuo Z, Luo X, Zhu W, Shen J, Shen X, Jiang H, Chen K (2005)

Molecular docking and 3D-QSAR studies on the binding

mechanism of statine-based peptidomimetics with beta-secretase.

Bioorg Med Chem 13:2121–2131

Int J Pept Res Ther (2011) 17:7–17 17

123