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i Nutraceuticals Based Computational Medicinal Chemistry KAYATHRI RAJARATHINAM Licentiate Thesis - 2013 School of Biotechnology Division of Theoretical Chemistry and Biology Royal Institute of technology Stockholm, Sweden

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Nutraceuticals Based Computational Medicinal Chemistry

KAYATHRI RAJARATHINAM

Licentiate Thesis - 2013

School of Biotechnology

Division of Theoretical Chemistry and Biology

Royal Institute of technology

Stockholm, Sweden

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© Kayathri Rajarathinam, 2013 ISBN 978-91-7501-806-5 ISSN 1654-2312 TRITA-BIO Report 2013:11 Printed by Universitets service US-AB Stockholm, Sweden 2013

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To my lovable family

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Abstract

In recent years, the edible biomedicinal products called nutraceuticals have been becoming more

popular among the pharmaceutical industries and the consumers. In the process of developing

nutraceuticals, in silico approaches play an important role in structural elucidation, receptor-

ligand interactions, drug designing etc., that critically help the laboratory experiments to avoid

biological and financial risk. In this thesis, three nutraceuticals possessing antimicrobial and

anticancer activities have been studied. Firstly, a tertiary structure was elucidated for a coagulant

protein (MO2.1) of Moringa oleifera based on homology modeling and also studied its

oligomerization that is believed to interfere with its medicinal properties. Secondly, the

antimicrobial efficiency of a limonoid from neem tree called ‘azadirachtin’ was studied with a

bacterial (Proteus mirabilis) detoxification agent, glutathione S-transferase, to propose it as a

potent drug candidate for urinary tract infections. Thirdly, sequence specific binding activity was

analyzed for a plant alkaloid called ‘palmatine’ for the purpose of developing intercalators in

cancer therapy. Cumulatively, we have used in silico methods to propose the structure of an

antimicrobial peptide and also to understand the interactions between protein and nucleic acids

with these nutraceuticals.

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List of papers

1. Dimerization of a flocculent protein from Moringa oleifera: experimental evidence and

in silico interpretation

Asalapuram R. Pavankumar, Rajarathinam Kayathri, Natarajan A. Murugan, Qiong Zhang,

Vaibhav Srivastava, Chuka Okoli, Vincent Bulone and Hans Ågren;

Journal of Biomolecular Structure and Dynamics, 2013 (In press)

2. A plant alkaloid, azadirachtin as an inhibitor for glutathione-S-transferase from

Proteus mirabilis

Rajarathinam Kayathri, Natarajan A. Murugan, Premkumar Albert, Hans Ågren;

(Journal of Chemical Information and Modeling - communicated)

3. Palmatine, a nucleotide sequence specific inhibitor for cancer therapy.

Rajarathinam Kayathri, Natarajan A. Murugan, Hans Ågren (in manuscript)

Paper 1: It is teamwork, where I am responsible for the calculations and participated actively in

manuscript preparation.

Paper 2 and Paper 3: In which, I have done the calculations. The manuscript was solely

prepared by me and actively participated in discussions with the co-authors.

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Acknowledgements

First of all, I would like to express my heartfelt and sincere gratitude to Prof. Hans Ågren for

giving me a great space, trusting and accepting me as his student. He has been an inspiration to

me by setting an example of sincerity, activeness and commitment.

I should thank Dr. Arul Murugan for his constant guidance, help and valuable time during my

period of the study. Dr. Yaoquan Tu, for his encouragement and patience to answer all my

thoughtless questions. I am always impressed by Dr. Boris Minaev for his active and sincere

dedication for any kind of work and it was my privilege listening to his lectures.

This stage of mine would not be possible without Prof. Yi Luo, Prof. Faris Gel'mukhanov, Prof.

Olav Vahtras, Dr. Marten Ahlquist and Dr. Zilvinas Rinkevicius, who always found involve in

the scientific discussions which was well encouraging. The energy, the humour sense, and the

extrovert of the faculties gave plenty of liveliness in the working atmosphere. Ms. Nina Bauer

made my task easier for me with all her tireless administrative support..

I cannot stop here without mentioning about my friends and colleagues in the department for

making me to feel better in the work place and fun in the office. Great thanks for Anuar, Y. Ai,

Anand, Weijie, Y. Ai, Lili, Xin Li, F. Qiang, Rocio, S. Duan, Robert, X. Chen, Liqin, Bogdan,

Ce Song, Chunze, Ignat, Irina, Jaime, Jungfeng, Jing, Yongfei, Hongboa, Lijun, Xin Li, Gao Li,

Lu Sun, Miao, Guangjun, Xu, Yan Wang, Wei Hu, Xiao-Fei Li, Yong Ma, Xinrui, Vinicius, Ke-

Yan Lian, Qiang Zhang, Kestutis, Ying Wang, Guanglin especially Xianqiang and Runa, there is

not much we have not discussed! There are lot more in the list to express my gratitude.

Family is something, which helps for any step of one’s development, that’s absolutely true in my

case. I thank all my family members in India, for their moral support, encouragement, love and

understanding throughout my life. Though they do not read this, my love should be expressed.

My parents, without whom my life would not be to this extent. I am very lucky to have my

parents-in-law, who never put me down in front of anybody and treat me more than their own

daughter. The boon of my life is my beloved sister, the heart always that I can trust and think

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only good for me, and also my brother-in-law who gave “do it now” attitude to me. The person

who made meaning in my life is my brilliant nephew, Vamsi, love you!

“Behind every man, there is a woman” in my case, it’s none other than my lovable husband,

Pavan and his help in my life cannot be expressed in words. Another very important person in

my life who actually helps always with tons and tons of energy just by an innocent smile and

touch is my sweet baby girl, Mitti. For these two pillars of my life, I cannot stop with thanks.

Because they gave, giving and will give everything and anything that I need and want in my life,

this thesis is part of their love. No words to express my gratefulness and love to both of you.

Thanks for all your help and encouragement.,

Kayathri

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Abbreviations

AZT Azadirachtin

B3LYP Becke 3 parameter Lee, Yang-Parr functional

DNA Deoxyribonucleic acid

DOF Degrees Of Freedom

GBSA Generalized Born Surface Area

GSH Gluthathione

GST Gluthathione-S-Transferase

HL Human Leukemic

LDL Low-Density Lipoprotein

MD Molecular Dynamics

MM Molecular Mechanics

MM-GBSA Molecular Mechanics- Generalized Born Surface Area

MM-PBSA Molecular Mechanics- Poisson Bolzmann Surface Area

MO Moringa oleifera

MRSA Methicillin-Resistant Staphylococcus aureus

NAB Nucleic Acid Builder

Nmode Normal Mode

NMR Nuclear Magenetic Resonanace

PBSA Poisson Bolzmann Surface Area

PDB Protein Data Bank

PmGST Proteus mirabilis Gluthathione-S-Transferase

RDA Recommended Daily Allowance

RMSD Root Mean Square Displacement

RNA Ribonucleic acid

SASA Solvent Accessible Surface Area

VLDL Very-low-density lipoprotein

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One and three letter abbreviations for amino acids

Alanine Ala A

Arginine Arg R

Asparagine Asn N

Aspartic acid Asp D

Cysteine Cys C

Glutamic acid Glu E

Glutamine Gln Q

Glycine Gly G

Histidine His H

Isoleucine Ile I

Leucine Leu L

Lysine Lys K

Methionine Met M

Phenylalanine Phe F

Proline Pro P

Serine Ser S

Threonine Thr T

Tryptophan Trp W

Tyrosine Tyr Y

Valine Val V

Abbreviations for nucleic acid residues

A Adenine

C Cytosine

G Guanine

T Thymine

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List of contents

1 Introduction

1.1 Natural products for biomedical applications

1.2 Nutraceuticals

1.2.1 Types and importance of nutraceuticals

1.2.2 Plant derived medicinal plants

1.3 In silico study in biomedical applications

1.4 Plant-based medicinal products

1.4.1 Medicinal values of Moringa oleifera

1.4.1.1 Coagulant protein from the seeds of Moringa oleifera

1.4.2 Traditional medicinal plant Azadirachta indica

1.4.2.1 The limonoid, azadirachtin as an antimicrobial compound

1.4.3 Palmatine – structural analogue of berberine

1.5 Objectives

2 Computational methods

2.1 Docking

2.1.1 Introduction

2.1.2 Types of docking

2.1.3 Applications

2.2 Molecular dynamics simulation

2.3 Binding free energy

3 Present investigations

3.1 Structure determination of Moringa oleifera coagulation protein (Paper 1)

3.2 Azadirachtin as an antimicrobial compound (Paper II)

3.3 Palmatine, a nucleotide sequence specific molecule for cancer treatment (Paper 3)

Bibliography

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Chapter 1

Introduction

Over the decades, natural products are considered to be the most productive source for the

development of novel drugs. Serving as the most active ingredients for a wide-range of novel

medicines, natural products are usually analysed through different biochemical techniques to

evaluate their biological competence. Several combinatorial chemistry techniques combined with

in silico methods are applied on natural scaffolds to create ‘drug-like compounds or lead-

molecules’. Such modern combinatorial methods are advent of high-throughput screening and

the post-genomic era to discover more than 80% of drug substances from the natural compounds

of ‘olden times’ [1, 2].

1.1 Natural products for biomedical applications

Traditionally, natural products are being used as dyes, polymers, fibers, oils, glues/waxes,

flavouring agents, perfumes, and drugs like penicillin. There are more than 100,000 low-

molecular-mass natural products, mostly derived from the isoprenoid, phenylpropanoid, alkaloid

or fatty acid/polyketide pathways are being used for a wide variety of biomedical and industrial

purposes. The investigation of natural products, as the source of novel human therapeutics

attained its peak during 1970-1980 and completely revolutionized the area of pharmaceutical

chemistry to develop novel non-synthetic molecules. Based on Pharmaproject database, the

natural products in different stages of drug development from various biological sources are

explained in Table 1. As a result, pharmaceutical industries developed about 63% of 974 small

molecules (new chemical entities) between 1981 and 2006 [2-5]. The empiric recognition of

potent biological activities from numerous natural products has fuelled to develop several novel

biomedicines including antibiotics, insecticides, herbicides, anti-inflammatory and even anti-

cancer drugs. Such promising medicinal advantages triggered the research of natural products to

re-evaluate and identify novel ‘active components or lead molecules’ to address today’s health

problems like cancer, diabetes, neural diseases, antimicrobial resistance, especially in the context

of eco-friendly interactions.

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Table 1: Drugs based on natural products at different stages of development

Development

stages

Plant Bacterial Fungal Animal Semi-

synthetic

Total

Pre-clinical 46 12 7 7 27 99

Phase I 14 5 0 3 8 30

Phase II 41 4 0 10 11 66

Phase III 5 4 0 4 13 26

Pre-registration 2 0 0 0 2 4

Total 108 25 7 24 61 225

Reproduced from Harvey, 2008 [5; Source: Pharmaprojects database, March 2008]

1.2 Nutraceuticals

The term ‘nutraceuticals’ was coined by Dr. Stephen DeFelice in 1989 to define natural food

materials possessing pharmaceutical medicinal activities and nowadays, it is used as standard

acronym in the field of medicinal chemistry [6]. Nutraceuticals can be defined as "a food (or part

of a food) that provides medical or health benefits, including the prevention and/or treatment of a

disease" [7]. Most of the natural medicinal products derived from plants and animals are edible

in one or the other form (Table 2). Hence, in the recent years the focus of pharmaceutical and

natural product researchers have turned to investigate nutraceuticals through several technical

approaches.

1.2.1 Types and importance of nutraceuticals

The nutraceuticals can be broadly classified as (i) dietary supplements (ii) functional foods.

Dietary supplements derived from food products contain nutrients like vitamins, minerals and

amino acids. Functional foods are designed to provide natural enrichment to the consumer, rather

than manufactured dietary supplements in liquid or capsule form. Generally, a process called

‘nutrification’ is followed to provide required amount of body nutrients. Thus, it is vital that the

functional foods of natural origin should be an essential part of our daily diet and thus regulate

the biological processes, resulting in disease control or prevention [8, 9]. Hence, it is proposed

that the herbal remedies may be classified as nutraceuticals because of their perceived risk with

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self-medication (e.g. Digitalis); the functional food consumed as a part of normal diet (e.g.

carotenoid, vitamins, etc.). There are several nutraceuticals available in the market like fortified

cereals (rich in vitamins and minerals (muesli, bran flakes etc), nutrient supplements (vitamin A

(β-Carotene), additional supplements (cod liver, oil, primrose oil, glucosamine, garlic etc.,),

energy drinks/tablets (Tropicana, Minute Maid Pulp, Frooti), cholesterol reducers (Abcor), body

building supplements (protein powders and bars), sports products (Glucon-D, glucose D),

probiotics (lactobacillus containing yoghurt and medicines) [7].

Table 2: Common nutraceuticals and their drug targets

Crop Latin name Drug/natural products Targets

Cassava Manihot esculenta Linamarin, lotaustralin Cancer

Celery Apium graveolens Psoralen, xanthotoxin,

bergapten

Anticoagulant

Broccoli Brassica napa Sulforaphane Cancer

Potato Solanum tuberosum α-solanine, α-chaconine Cancer

Tomato Lycopersicon

esculentum

Tomatine Cancer

Tomato powder

Common bean Phaseolus vulgaris Lectins Cancer, HIV

Soybean Glycine max Genistein, daidzein Breast cancer

(soybean extract)

Lettuce Lactuca sativa Lactucin, lactucopicrin Inflammation,

malaria

Apple (extracts) Malus domestica Vitamin -C Liver cancer

Pomegranate Punica granatum Tetracycline, gentamcin MRSA*

Turmeric Curcuma longa Curcumall Antibacterial

Sarasaparilla Hemidescus indicus Chloramphenicol MRSA*

* MRSA – Methicillin resistant Staphylococcus aureus

Adopted from B. Schmidt et al., 2008 [9].

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However, consumption of fruits, vegetables, oils, seeds, leaves, and other sources of the active

ingredients are also highly important as it serves as an alternative to the ‘drugs forms’. Based on

the current survey it is known that the plant-derived products are more popular to contain

medicinal properties than the animal sources. Hence, studies of phytochemicals are increasing

day-to-day.

1.2.2 Plant-derived medicinal products

The plant-derived products are not only used as edible medicinal food but also raw materials for

many of today’s modern medicines [10]. Generally the plant-derived biomedical products

include amino acids, irregular amino acids and their derivatives, alkaloids, flavonoids, steroidals,

terpinoids, oils, carbohydrates (sugars, sugar amines, sugar alcohols, polysaccharides), food

supplements, etc., are considered to be very important raw materials for industries in food,

chemical and pharmaceutical sectors. Moreover, the plant-derived products serve as a valuable

source for biosynthetic drugs (elliptinium, galantamine, huperzine), semi-synthetic compounds

(tigecycline, everolimus, telithromycin, micafungin, caspofungin) and synthetic drugs (aspirin).

Even the first synthetic drug, aspirin (acetylsalicylic acid), synthesized by Arthur Eichengr n and

Felix Hoffmann in 1897, is also an active ingredient of analgesic herbs. Such discoveries,

especially in biomedical sector ushered ‘novel drug discovery’ programmes in several research

institutes and pharmaceutical industries, worldwide. About 60% and 75% of today’s drugs,

especially those in the area of cancer and infectious diseases are derived from the plants,

respectively [11].

Along with the important chemical properties, the empirical studies of natural products paved the

way to develop several biochemical techniques (separation techniques, spectroscopic

approaches, structure elucidation) and synthetic methodologies are now became the foundation

of modern drug discovery platforms. The heterologous production platforms for recombinant

pharmaceutical proteins are usually produced by large-scale fermentation in bacteria, yeast or

animal cells [12, 13]. However, it has been only recently that biotechnology has been used to

generate plants that produce specific therapeutic proteins, products that are traditionally

synthesized using recombinant microbes or transformed mammalian cells [14]. The

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pharmaceutically important proteins like human serum albumin was first expressed in tobacco

and potato leaves for large-scale production [15]; similarly, monoclonal antibody was expressed

in tobacco leaves [16]. These studies demonstrated that plants could also produce stable and

functional human proteins, leading to the commercial production through recombinant protein

expression [17]. Nowadays, several hundreds of medicinal proteins are being produced in a

variety of plants and plant-based systems. However, many of these compounds now have been

shown to have important adaptive significance in protection against herbivore and microbial

infection, as attractants for pollinators and seed-dispersing animals, and as allelopathic agents

that influence competition among plant species [18]. Cumulatively, the plant-derived products

became the next major commercial development in biotechnology due to advantages like large-

scale production, economy product safety, ease of storage and low-cost drugs and vaccines

production, especially suitable for the developing world [19].

However, the commercialization of plant-derived products is becoming limited by the uncertain

regulatory terrain, particularly for the adaptation of good manufacturing practice regulations to

the field-grown plants. Hence, the plant-derived natural products in drug discovery have been

diminished, which can be supplemented with structure activity–guided organic synthesis,

combinatorial chemistry, and computational (in silico) drug design for better drug targeting.

1.3 In silico study in biomedical applications

The in vitro, in vivo and in situ studies are combined with high risk as these processes are

complicated, expensive and also being done without a restrictive biological outcome. Moreover,

conducting experiments with biological samples including animal models are associated with

very strict ethical procedures and high cost. These disadvantages can be addressed through

computer-aided drug designing by simulating the in situ biological situation or reaction. The

recent technological advances led to renewal of natural products in drug discovery by the

development of new synthetic derivatives. [3]. Since a couple of decades, biochemical systems

theory like ‘computer-aided drug designing’ are helpful to discover that many potent broad-

spectrum antibiotics and anti-cancer drugs available in the market are based on the structural-

activities of the ‘lead compounds’. These activities also helped to even develop dual-activity

compounds based on the chemical characteristics of the natural plant-derived products, which

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have become an integral part of today’s modern organic chemistry and pharmaceutical

industries. Such proactive methods include modification of the active sites of enzymes,

development of inhibitors for a specific protein target, increasing the propensity of bioactive

molecule to improve its binding to the target, identifying a variety of protein domains and

folding motifs are significantly helpful in defining protein–protein or protein-nucleic acid

(DNA/RNA) interactions. As a result, the designed molecules can be more effective in

modulating cellular processes including the immune response, signal transduction, mitosis and

apoptosis, which are considered to be the critical aspects in designing a novel drug molecule. For

example, an enzyme inhibitor must bind to the active site at the interior of the protein, so that it

can involve or restrict or activate few metabolic interactions. Whereas, a small-molecule

disruptor of a protein–protein interaction must bind to the protein surface (rather interior part),

where the tight binding (affinity) of the molecule depends on multiple interactions surface. Such

molecular interactions are based on modifying the binding sites, size, and selective pressure to

bind gene products to initiate a required function. This allows modulating the particular protein–

protein interactions by structural fine-tuning of the medicinal product [20]. Nevertheless, the

relative ease and low cost to offer an affordable solution fuel the modern computer-aided drug

discovery.

1.4 Plant-based medicinal products

Among the several hundreds of medicinal plants including their natural plant-derived products, it

is always interested to study more common edible plants. So considering the wide-importance

and need, the following medicinal plants were studied.

1.4.1 Medicinal values of Moringa oleifera

The medicinal and chemical values of a tropically cultivated ‘magical plant’, Moringa oleifera

(M. oleifera) is a rich source against a broad-spectrum of diseases and infections [21-23]. As a

whole, the plant moringa serves as an effective prophylactic agent in conventional medicine [23].

The most widely reported use of M. oleifera is employment of the seeds for water purification

and clarification [24-26], also exerts its protective effect by decreasing liver lipid peroxides,

antihypertensive [27, 28]. In addition, seed extracts of moringa are reported to remove organic

pollutants [29]. Bark extracts of various moringa tissues have been used as anti-cancer agents

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[30], root extracts as antilithic, rubefacient, vesicant, carminative, antifertility, anti-

inflammatory, stimulant in paralytic afflictions; act as a cardiac/circulatory tonic, used as a

laxative, abortifacient, treating rheumatism, inflammations, articular pains, lower back or kidney

pain and constipation, anti-trypanosomal agents [31 -35], leaves as purgative, applied as poultice

to sores, rubbed on the temples for headaches, used for piles, fevers, sore throat, bronchitis, eye

and ear infections, scurvy and catarrh; leaf juice is believed to control glucose levels, applied to

reduce glandular swelling [36-39], flowers as high medicinal value as a stimulant, aphrodisiac,

abortifacient, cholagogue; used to cure inflammations, muscle diseases, hysteria, tumors, and

enlargement of the spleen; lower the serum cholesterol, phospholipid, triglyceride, VLDL, LDL

cholesterol to phospholipid ratio and atherogenic index; decrease lipid profile of liver, heart and

aorta in hypercholesterolaemic rabbits and increased the excretion of faecal cholesterol [40-45].

Leaf extracts have been shown to regulate thyroid and cholesterol levels [46, 47]. Incorporation

of processed M. oleifera leaves into noodles improves nutritional quality by supplying the

recommended daily allowance (RDA) for a number of vitamins [48]. Moreover, there are several

food varieties, food supplements and medicines are available in the market.

1.4.1.1 Coagulant protein from the seeds of Moringa oleifera

Though a vast research has been done using crude extracts of M. oleifera, there are no evident

reports about the specific proteins responsible for the antimicrobial, anticancer, bioadsorbant and

anticholesterol values. However, a 6.5 kDa protein from crude seed extract of M. oleifera is

reported for its antimicrobial and flocculation activities [49], which is being used a natural water

purifier in several countries. However, the molecular mass of this water soluble protein was

reported between 3.5-14 kDa [50-52]; whereas, recent reports suggested that this protein could

have subunits forming a molecular mass of 17-26 kDa [53] or single cationic protein of 66 kDa

[54]. In order to understand further, it is necessary to know the exact molecular weight of the

protein for the structural elucidation as well as to study its mechanism of action. Such issues can

be addressed through the modern computational techniques.

1.4.2 Traditional medicinal plant Azadirachta indica

The Azadirachta indica (neem tree) is one of the multi-purpose plants, whose products as well as

byproducts are being used for several biomedical applications like insecticidal, antiseptic,

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contraceptive, antipyretic, antiparasitic, fertilizers, etc. [55]. Neem fruits, seeds, oil, leaves, bark

and roots are generally used as antiseptics, antimicrobials, treatment of urinary disorders,

diarrhoea, fever and bronchitis, skin diseases, septic sores, infected burns, hypertension and

inflammatory diseases. Among these triterpenes compounds, limonoids are the most effective

one, which is abundant in neem oil. At least nine limonoids are effective in inhibiting insect

growth, especially some of the most deadly varieties found in human health and agriculture

worldwide. Among these, azadirachitin (AZT) is considered to be the main ingredient for

fighting insects and pests, being about 90% effective. Meliantriol is another feeding inhibitor that

prevents locusts chewing and traditional crop protection. Nimbin and nimbidin, also found in

neem have anti-viral properties and also effective against fungal infections. Gedunin, a lesser

limonoid, is effective in treating malaria through teas and infusion of the leaves.

1.4.2.1 The limonoid, azadirachtin as an antimicrobial compound

AZT is an oxidized tetranortriterpenoid that helps oxygen functionality in variety of carboxylic

esters in order to increase detoxification process. There are at least six isoforms of AZT that have

been identified: AZT-A, AZT-B, AZT-C, AZT-D, AZT-H and AZT-I [56]. The NMR and X-ray

crystal structures have been determined for all the AZT forms [57]. Among them, AZT-A and

AZT-B are the predominant as well as abundant forms, while the rest are available only few

parts compared to hundred parts of AZT-A, which is a well-studied isoform in terms of activity

among other subtypes [58]. The AZT-H has very good fungicidal activity against the

phytopathogenic fungi R. solani and S. rolfsii along with the nematicidal activity [59], as

detoxification process.

Detoxification is one of the major and essential events for any cell, either prokaryotes or

eukaryotes, for their survival against noxious adverse compounds present in their micro

environment. Thus, GST serves as a significant source for contributing resistance to the bacterial

pathogens like Protease mirabilis (P. mirabilis), pathogenic Escherichia coli, Salmonella,

Pseudomonas, etc, by either enhancing their efflux systems or transporting such compounds to

outer surface of the cell [60]. Among these pathogens, the P. mirabilis is a major problem

causing anatomical abnormalities in patients with urolithiasis or a chronic urinary catheter due to

urinary tract infections [61]. The P. mirabilis produce urease, which generates ammonia that

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increases the pH of the urine to >7.2. The elevated calcium and magnesium crystallization in the

urine blocks the catheter lumen and causes bacteriuria, pyelonephritis, bacteremia, and shock.

Though P. mirabilis is susceptible to β-lactams, aminoglycosides, fluoroquinolones, and

trimethoprim/sulfamethoxazole, it shows resistance to nitrofurantoin, tetracycline and other

powerful antibiotics [62]. This increased resistance contributes to high morbidity and mortality

of the patients, especially in the hospitals. In this context, using their null mutants [63], reported

that GST protects the bacteria against several toxic chemicals, including antimicrobial drugs.

Hence, the GST protein could be a good target biostructure for treating bacterial infection from

P. mirabilis.

1.4.3 Palmatine – structural analogue of berberine

Palmatine is found in many plant families, which is a close structural analog of berberine.

Palmatine bears the same tetracyclic structure (7, 8, 13, 13atetrahydro-9, 10-dimethoxy-

berberinium) as similar to berberine but differs by the nature of the substituents at positions 2,3

on the benzo ring; being methylene dioxy for berberine and dimethoxy for palmatine. This

alkaloid has been shown to exhibit significant antitumor activity against HL-60 cells and has

antimicrobial properties [64, 65]. However, the binding studies of palmatine to nucleic acid bio-

targets have been scanty and till today no data is available in the literature on the mode,

mechanism and base sequence specificity of binding of palmatine to DNA [66].

Recently, it is identified that DNA as a potential bioreceptor for palmatine and suggested both

intercalation and external stacking of palmatine on complexation with natural DNA having

heterogeneous AT and GC sequences. The noncovalent complexes of several protoberberine

alkaloids including berberine and palmatine with few oligonucleotides showed that the palmatine

has higher binding affinity than berberine to the oligonucleotides and also demonstrate a

remarkable AT base pair specific intercalative mode of DNA binding for palmatine [67]. Hence,

it could be useful if the effect of palmatine is studied with known sequence of DNA templates.

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1.5 Objectives

With the growing demand for natural medicinal products, we are interested to apply

computational tools to address the biological problems to propose novel drug molecules. With

this overall aim, we frame the following objectives:

(i) Though the (M. oleifera) MO2.1 protein is considered to be the most important

substance for coagulation and antimicrobial activity, its structure is not yet known.

Hence, our first objective is to identify a plausible tertiary structure of its

monomer has been elucidated based on homology modeling. With this structure,

we try to investigate the oligomerization property of this protein using protein-

protein docking experiments.

(ii) Considering the increasing incidence of urinary tract infection due to P. mirabilis,

GST responsible for the antimicrobial resistant is targeted. We aim to propose a

potent antimicrobial agent, AZT to disrupt the detoxification process by inhibiting

GST of P. mirabilis. The binding affinity of AZT with GST is studied by binding

free energy calculations.

(iii) Based on the experimental results, we try to investigate the efficiency of

palmatine with the nucleotide sequences. In order to re-evaluate its efficiency,

palmatine is docked and simulated with DNA to observe its specificity with

different nucleotide sequences.

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Chapter 2

Computational methods

Understanding the stable three-dimensional complex structures in the living organisms and to

find the ligand binding affinity to the target is very important in computer-aided drug design.

Both these aspects are vital for any kind of application, especially in the field of biomedicine to

accelerate the speedy identification of a new drug candidate. We have used some tools to

understand the property and the binding affinity of the natural products mentioned in Chapter 1.

The docking studies are used to identify the lead or hit molecule from the high-throughput virtual

screening. Here, it is used to find the orientation of the ligand in the binding site (paper 2 and

paper3) and the stable complex structure (paper 1). The more accurate and rigorous methods are

used further to calculate binding free energy using Molecular Mechanics/Poisson Bolzmann

Surface Area (MM-PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM-

GBSA) approaches [68]. And the missing entropy contributions were calculated by NMode

analysis.

The theoretical methods we used are in the following order (1) Docking (2) Molecular Dynamics

(MD) simulation (3) Binding free energy calculation. The brief introduction of the methods used

is discussed below.

2.1 Docking

2.1.1 Introduction

The aim of finding a drug target is to estimate the binding affinity of a ligand with the

macromolecule, which in turn accelerates the process of developing a potent drug. There are

many computer based methods to study the interaction of the receptor-ligand, which could be a

protein-protein, protein-drug, and nucleic acid-drug. The initial step which gives the idea of the

binding orientation of the ligand with respect to the target molecule to form a stable complex is

docking [69]. The strength of binding affinity of the ligand to the receptor is determined by the

orientation of the ligand [70]. This is also referred as lock and key model, where the receptor and

the ligand are rigid, in some cases the protein changes its conformation to fit the ligand and

called as induced-fit model.

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2.1.2 Types of docking

Depending upon the binding site:

Local docking: the ligand orientation in the binding site is determined where the

binding site of the receptor is known.

Global docking/blind docking: in doing so, the binding site of the receptor is unknown

and the search for the binding site and the orientation of the ligand are determined.

Based on its structure:

Bound docking: Docking is performed to test the known complex, where the receptor and

the ligand are bound to each other.

Unbound docking: Docking is done from the unbound receptor and the ligand.

Depending upon the rigidity of the receptor and the ligand:

Rigid docking: Here, both the receptor and the ligands are kept rigid. This method is

easier due to the less degree of freedom (DOF).

Flexible docking: In this, either the ligand or the receptor or both are kept flexible. This

has more DOF and the difficult to perform.

Depending on the receptor:

Macromolecular docking: In macromolecular docking two or more biological

macromolecules interact to form the quaternary complex structures. The stable

macromolecular structure complex that occurs in the living organism can be found. Eg.

Protein-protein docking or protein-nucleic acids docking.

Protein-ligand/nucleic acids-ligand: This docking is to predict the position, binding

affinity and the orientation of the ligand with the receptor (protein/nucleic acids).

2.1.3 Applications

The different applications of docking which reduce the cost and time to find a potent drug

candidate are listed below:

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(i) To address the protein-protein interaction for biomedical application and

bioremediation.

(ii) In drug designing to identify the molecule which can bind with the receptor by

screening large set of molecules, is called as hit identification.

(iii) To find the most stable orientation of the ligand to bind with the receptor to form the

most stable complex structure, lead optimization.

The main input structure can either be downloaded from the PDB (determined by X-ray and

NMR) or by homology modeling. The good docking is predicted from scoring criteria and search

strategies/algorithms.

Docking scoring criteria:

The scoring function may be based on the molecular mechanics (MM) force-field and the

statistical potential approach. The force field estimates the good hydrogen bonding; the low

energy indicates the stable minimized system. The statistical potential is from the large set of

data (from PDB) and the charge potential which defines the polarity. The scoring function is

more from the geometry match of the large set of molecules [69].

Docking search algorithm:

The different docking search algorithms are systematic search method, simulated method and

random search method [71]. In the simulated method, the receptor is rigid and the ligand takes

different conformations, and the lowest energy conformation is considered. The systematic

search method is the one in which all the possible DOF are explored for the given molecule in all

the possible combinatorial way. CLUSPRO [72] used in paper1 to study the protein-protein

interaction, is the systematic search method of the Fast Fourier Transform correlation technique.

CLUSPRO is the fully automated program for protein docking.

Lastly, in the random method or stochastic method the pose of the ligand is estimated from the

pre-defined probability function. The examples of the random search algorithm are genetic

algorithm, Monte Carlo and Tabu search. The AUTODOCK 1.5 [73] program was based on the

Lamarckian algorithm, improvisation of the genetic algorithm, which is used in paper 2 and

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paper 3. AUTODOCK is used mainly for the receptor (protein, nucleic acids)-ligand interaction.

This has two main programs autogrid and autodock.

The conformational changes due to binding of the small molecule with the protein are not easily

predicted in case of rigid docking. So, the most feasible method is by keeping the receptor rigid

and the ligand flexible with more manageable degrees of freedom. The false positive results are

more common by this scoring function because some of the low affinity ligands can form a

stable complex than the high affinity ligands. This can be overcome by the more rigorous

computational methods like GB/PB methods.

Docking gives the binding orientation of the ligand without the explicit solvent molecules.

However, the flexibility of the receptor and the ligand in presence of the explicit solvent can be

well studied by MD simulation. The exact bonding affinity of the ligand to that of the receptor

can be evaluated by binding free energy calculations [74, 75].

2.2 Molecular dynamics simulation

MM approach was developed to study the bigger system consisting of thousands to millions of

atoms. Newton’s second law of motion (Eq. 1) paved the way to study the larger systems by MM

approach. By representation of classical spheres for the atoms, the motion of the nuclei is

modeled without including the electrons explicitly in MM approach. This is the basic principle of

MD simulation of the atoms and molecules of the larger system.

(1)

where, F is the force exerted on the particle, m is the mass and a is the acceleration.

The acceleration of each atom in the system is determined by the force acting on each atom. The

trajectory gives the average value of properties and it is the integration of the equation of motion

to define the velocities, acceleration and the position of the particles. From the velocity and

position of each atom, the state of the system can be predicted with respect to time.

Thus,

(2)

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(3)

which explains the movement of the particle mass m along the direction x.

(4)

The accuracy of the calculation depends much on the force from the force field which in turn

explains the potential energy of the system. The potential energy is explained in terms of

covalent and non-covalent interactions by the force-field.

+ (5)

where, and interactions are defined as Eq. 6 and Eq. 7, respectively.

(6)

(7)

Each of the bonded and the non-bonded interaction terms are expressed as below:

(8)

(9)

[ ] (10)

(11)

[(

)

(

)

] (12)

(13)

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Molecular dynamics simulations were carried out for all the three systems (paper 1-3) were

carried out using the Amber 11 software [76].

In paper 1:

The MO2.1 polypeptide sequence, QGPGRQPDFQRCGQQLRNISPPQRCPSLRQAVQL

THQQQGQVGPQQVRQMYRVASNIPST (UniProt ID: P24303) was BLAST (Basic Local

Alignment Search Tool) against the PDB database to identify a structural homologue. The

homologue structure after docking was used as the starting structure for the simulation.

In paper 2:

The crystal structure of GST-GSH complex from 1PMT was downloaded from the protein data

bank and the stable GST-AZT complex from docking were used as the initial configuration for

the MD simulation.

In paper 3:

The B-DNA structure of four different sequences of poly AA, poly AT, poly GG, poly GC were

generated by the NAB program in AMBER 11 package. The equilibrated structure was used for

docking; the stable complex is used further for the MD simulation.

The ESP (electrostatic potential) charges of the small molecules (paper 2 and 3) were calculated

using the B3LYP/6-31+G(d) basis set in Gaussian 09 package [77]. The generalized Amber

Force-Field (GAFF) was used for all the ligands, GSH, AZT and palamtine; whereas, PARM99

was used for all the receptors (paper 1-3). The systems were neutralized by Na+ or Cl

- ions and

solvated by TIP3P water model. Equilibration was carried out for the solvated complexes after

running a minimization of 500 steps in each case. The isothermal-isobaric ensemble of

temperature 298K and 1 atmospheric pressure was maintained throughout the Langevin

dynamics with weak restraints under periodic boundary condition. The SHAKE algorithm [78]

was used to constrain the hydrogen bond lengths with a non-bonded cut-off of 8Å for the van der

Waals and electrostatic interactions. The production run was performed until the convergence of

structural quantities like density and total energies of the complex. The trajectories of the MD

were used for the binding free energy calculation.

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Root mean square displacement (RMSD) was assessed for the ligand to know the mobility of the

ligands during the simulation by Eq. 13.

√∑

(14)

2.3 Binding free energy

All the reactions or changes in the biological reaction take place in the presence of solvent

molecules. Thus, it is important to study the dissolvation of the solute in a solvent that depends

on the free energy change. There are different types of interactions which is of short and long

range effects like hydrogen bonding, ion-dipole/electrostatic interactions, Van der Waals/dipole-

dipole, ion-ion interactions. The favourable solvation process is based on the thermodynamic

properties and associated with the decrease of Gibbs free energy. If Gibbs free energy is

negative, it indicates the difference of the enthalpy and the entropy should be a negative value.

The two major types of commonly used solvent models are the explicit solvent model (Fig. 2.1)

and the implicit solvent model (Fig. 2.2). The explicit model is where all the individual water

molecules are included explicitly. Thought, it is a precise method but the calculation of the free

energy of solvation by the simulation methods is time consuming even in the classical approach.

In the implicit model the solvent is treated as the polarizable continuum with a dielectric

constant. The charge distribution of the solute polarizes the solvent by creating a reaction

potential, this in turn changes the solute’s reaction potential. This interaction is introduced into

the Hamiltonian is represented by solvent reaction potential and computed self-consistently.

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Figure 2.1: Explicit water model Figure 2.2: Implicit water model

The widely used implicit model is generalized Born/surface area model [79-81] and Poisson

Boltzmann/surface area model. These models combined with molecular mechanics [82] are MM-

GBSA/MM-PBSA method which is widely used to study the receptor (biomolecules)-ligand

binding free energy is an endpoint method [73, 83].

In the MM-PB(GB)SA, the difference of the binding free energy of a ligand (L) to a receptor (R)

to form the complex (RL) [84] is represented as:

(15)

The free energy of the receptor, ligand and the complex is written as:

(16)

- the total molecular mechanics energy of molecular system X in the gas phase

- solvation free energy for X in solvent

S - entropy of X.

The terms appearing in Eq. 16 are explained below:

(17)

The energy contributions for the bound and unbound state are calculated using the molecular

mechanics or the force-field method.

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(18)

The solvation free energy is the sum of polar and non-polar contributions. The polar terms

are by the generalized Born, Poisson, or Poisson-Boltzmann model and the non-polar terms are

by solvent-accessible surface area (SASA).

Where, (19)

and depends on the solvation model and the method (PBSA or GBSA) [85].

Increase in the SASA in turn increases the solvation free energy, which is unfavourable and

excludes the nonpolar terms in the solute. The polar contribution ( ) is the solvation free

energy contribution to the electrostatic interactions of the solute surrounded by the solvent. In PB

and GB models, the solvent is treated as a high dielectric medium and the solute as low dielectric

medium.

(20)

The

, is the interaction between the charge i and j is determined using the GB model by

an analytical expression form [86, 87] or by the numerical equation of PB model.

(

) [

(

)]

(21)

- distance from the solute-solvent dielectric boundary of atoms i

ε - solvent dielectric constant

- distance between i and j

The constants n and A were set to 2 and 4, respectively by Still and coworkers (84).

The difference between the PB and GB model is the solute dielectric constant ( ) term. In PB,

varies the

in Eq. 20. The is excluded in GB model (Eq. 21) due to the

approximations for the analytical expression. Still can be expressed other than 1 and in Eq.

21 it becomes( ⁄ ⁄ ).

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The Eq. 20 is applied on the receptor, ligand and complex which the expresses the equation

below:

(22)

The electrostatic free energy in Eq. 22 can be divided as the desolvation and the interaction

components [16, 17] due to the summation of atomic charges in Eq. 20.

[

] [

] (23)

There are three terms on the right hand side of the Eq. 23, the first term is the interaction term

from the receptor and ligand charges which are the contributions from the specific residues of the

receptor [88, 89]. Therefore, the interaction term can be written for the specific residue T as,

(24)

The last two terms on the right hand side of the Eq. 23 are the desolvation terms corresponding

to the receptor and ligand, respectively. The desolvation contribution is due to the change in the

charge distribution and by replacing the high dielectric solvent medium by the low dielectric

medium (paper).

The equilibrium conformations after discarding the explicit solvent for the receptor, ligands, and

the complex are used to calculate the MM-GB(PB)SA, where the solvent is expressed as

continuum dielectric. These models are implemented in packages of Amber [90], Delphi [91]

and Schrödinger [92].

The last term TS in Eq. 16, is estimated by the normal mode analysis of harmonic or

quasiharmonic method [93-95]. In the MM-PB(GB)SA methods, the entropy change with the

ligand is estimated by normal mode analysis [96, 97]. The entropy S is the sum of the

translational, rotational and vibrational contributions. For the optimized configurations the

Nmode analysis was performed to obtain the 3N modes (including the 3N-6 vibrational, 3

translational and 3 rotational modes). All the three contributions are calculated by the current

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software implementations. The entropy contribution is necessary for calculating the absolute

binding free energy, but it can be neglected in case of relative binding free energy due to its high

computational cost.

In this thesis, the MM-PB(GB)SA and normal mode analysis were computed form MMPBSA.py

module in Amber tools 1.5. The MM-PB(GB)SA was computed for several 100 snapshots and

Nmode for very few snapshots because of the computational cost.

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Chapter 3

Present investigations

In this thesis, a few nutraceuticals were studied either to propose it as an active drug molecule or

to investigate their binding affinity with protein or nucleic acid targets. A multi-function seed

protein, MO2.1 from M. oleifera, was investigated in paper 1 to propose it as an efficient

antimicrobial target along with its coagulation property. In the second paper, the antimicrobial

efficiency of a plant limonoid called AZT of Azadirachta indica was investigated against a

urinary tract pathogen, P. mirabilis. Similarly, a structural analogue of berberine called

palmatine, was investigated to find its sequence specificity with different nucleotide sequences to

propose it as an anticancer agent against DNA target. The work presented in each of these papers

is described below:

3.1 Structure determination of Moringa oleifera coagulation protein (Paper 1)

The coagulant protein (MO2.1) from the seeds of an ancient medicinal plant, M. oleifera is

considered to be a water purification agent in several tropical countries [23]. Though there are

several research studies to support its water purification and antimicrobial activities, researchers

have reported different molecular weights for this protein ranging from 3.5 kDa to 6.6 kDa. In

this regard, a 6.5 kDa protein was designated for the antimicrobial and coagulant activities by a

couple of studies with recombinant or synthetic protein experiments [98, 99]. Having no crystal

structure elucidated, in 2003, Suarez et al. proposed a homology model for this 6.5 kDa protein,

which was 70% homologous to another plant protein called napin (a homologous 2S-albumin),

but they have not reported its amino acid sequence. This sequence is not effective to study its

interactions with other molecules (ligands). So, we wanted to establish a secondary structure

based on homology modeling, considering its amino acid sequence that can be used for

computational analysis to study the protein’s interaction to address the pharmaceutical

advantages.

In this study, homology modeling for the polypeptide sequence of MO2.1 was performed with

MabinlinB protein (Fig. 3.1(A)) and obtained 55 residues homologous sequence of MO2.1 instead

of 60 residues. So, we supplemented the remaining 5 residues (QGPGR) at the N terminal to

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establish an accurate model for MO2.1. The final sequence revealed a 100% sequence similarity

with the sequence reported by Broin et al. (2002) [98], while 97% with the polypeptide sequence

of MO2.1 (P24303). In comparison with the sequence of P24303, the model peptide showed

differences at 13th and 23rd

residues. The proposed tertiary structure of the monomeric form of

MO2.1 was depicted in Fig.3.1 (B), where the region of five residues (QGPGR) that were not

obtained from homology modeling with MabinlinB was highlighted in red.

Figure 3.1. Homology modeling of MO2.1.

(A) Predicted structure for the monomer MO2.1, red coloured region indicates the five amino acid

residues (QGPGR) that were supplemented at N-terminal. The remaining cyan coloured helices

and loops were obtained from homology modeling. (B) The minimum energy structures of dimer

of MO2.1 protein. The Gly of monomer 1 (40th residue) and Val (42nd residue) shown as ball

and stick model in cyan make contacts of distance less than 4 Å, whereas the residues making

contacts within a distance range 4–5 Å were depicted in yellow and colors. The blue and red

distinguish the two monomers with its respective residues.

The model peptide had only 10 three helices and loop regions when compared to four helical

structures of MabinlinB. To understand the dimer formation of MO2.1 protein, a protein–protein-

docking study was carried out with two monomers using CLUSPRO software. As can be seen

from Fig.3.1 (B), the dimer was formed by both loop-loop and helix-helix contacts of the

monomers, while the binding free energy calculations revealed a stable dimerization property.

Our collaborators have evaluated this phenomenon through their experiments with recombinant

protein of MO2.1 and mass spectrometric analysis. Conclusively, we have developed a tertiary

structure for MO2.1 by homology modeling that can interact with other proteins and ligands.

(A) (B)

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3.2 Azadirachtin as an antimicrobial compound (Paper II)

In the recent years, the incidence of urinary tract infections due to P. mirabilis is considerably

increasing worldwide. This pathogen not only affects the kidneys, but also cause bacteriuria,

pyelonephritis, bacteremia, and shock, which are very serious problems in clinical world. In this

context, the GST contributes to resistance by either enhancing their efflux systems or

transporting such compounds to outer surface of the cell. In order to address this problem, we

chose to analyze the limonoid, AZT (Fig.3.2), obtained from neem tree (Azadirachta indica).

Either the whole parts or their crude extracts of different parts of neem tree are used as an

antimicrobial agent from the ancient days, traditionally. The liminoid, AZT has significant

properties to be treated as an antimicrobial agent, because of the oxygen functionality in various

carboxylic esters that are responsible for the detoxification process. Among several

pharmaceutically important compounds of neem tree, we proposed AZT as a better inhibitor to

overcome the antimicrobial resistant property for P. mirabilis. In this context, we investigate the

interactions between GST and AZT because the GST detoxifies toxic chemicals and

antimicrobial drugs to protect P. mirabilis [63]. The detoxification process involves the binding

of GSH to G-site of GST reducing the pKa value of the thiols from ~9.2 to ~6.5 pH units [100].

This led us to develop an inhibitor for GST that can bind efficiently to GST than GSH, as a

whole.

Figure 3.2: Optimized Structure of AZT by B3LYP/6-31+G(d) method using Gaussian09

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Our preliminary analysis revealed an efficient binding AZT and GST structures, as shown in

Fig.3.3. We also analyzed the stability of AZT-GST complex and the relative binding strength of

GSH and AZT with GST. The binding free energy of GST-GSH and GST-AZT complexes by

MM-PB(GB)SA method are -14.43/-17.59 kcal/mol and -26.24/-29.90 kcal/mol. From both these

results, we predict that GST-AZT complex is more stable than GST-GSH complex around 15-10

kcal/mol.

Furthermore, the Nmode analysis were performed to calculate the entropy contributions of the

complexes and unbound individual components. The ΔS value obtained of GST-GSH and GST-

AZT complexes are not to be the real binding free energy; but the values of GST-AZT complex -

21.92 kcal/mol and favorable than GST-GSH complex -20.20 kcal/mol. Analysis of this study

concludes that the AZT can bind instead of GSH to GST to initiate the detoxification process on

the bacterial cell and stop the growth. Hence the AZT, a phytochemical can be considered as a

pharmaceutically advantageous antimicrobial agent for the treatment of bacterial infections,

especially P. mirabilis. However, further in vivo and in situ studies are necessary for

pharmological assessment of AZT to describe its antimicrobial property.

Figure 3.3: Docked structure of the GST-AZT complex by Autodock

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3.3 Palmatine, a nucleotide sequence specific molecule for cancer treatment (Paper III)

The protoberberine alkaloid, palmatine is a found in several plants like Phellodendron amurense,

Rhizoma coptidis/Coptis Chinensis and Corydalis yanhusuo and this compound is also useful in

the treatment of jaundice, dysentery, hypertension, inflammation, liver diseases and viral

infections. The tetracyclic structure of palmatine (7, 8, 13, 13atetrahydro-9, 10-dimethoxy-

berberinium) is reported to have anticancer and antimicrobial activities, especially by binding

with various polynucleotides that were sequence dependent. As the pharmaceuticals are

enthusiastic in developing dual-activity molecules especially that act on specific nucleotide-rich

sequences, we are proposing palmatine as one of such candidates, based on the available

laboratory reports.

Among several reports, the experiments of Bhadra et al., [67] Fig. 3.4 prompted us to investigate

in this direction. It is reported that the base dependent binding of the palmatine to four synthetic

polynucleotides, poly(dA).poly(dT), poly(dA–dT).poly(dA–dT), poly(dG).poly(dC) and

poly(dG–dC).poly(dG–dC) of a DNA molecule via intercalation and external stacking properties

with heterogeneous AT and GC sequences. The absorbance measurements of intrinsic binding

constants decreased in the order poly(dA).poly(dT)>poly(dA–dT).poly(dA–dT)>poly(dG–

dC).poly(dG–dC)>poly(dG).poly(dC) and this could be attributed to the different helical

parameters of the DNA helix. The noncovalent complexes of this cytotoxic alkaloid exhibited the

highest binding affinity (much higher than berberine) to the AT rich oligonucleotides and

demonstrated a remarkable base pair specific DNA binding. This specific activity prompted us to

think about utilizing palmatine against sequence-specific DNA molecules, especially during the

uncontrolled cancer conditions. Our idea was to propose palmatine as a lead molecule for either

sequence specific or mutation specific DNA target that could have been aroused from either

cancer or other form of horizontal mutations.

In this study, we designed four polynucleotides of poly(dA).poly(dT), poly(dA–dT).poly(dA–

dT), poly(dG).poly(dC) and poly(dG–dC).poly(dG–dC), which aimed to understand their

molecular mechanisms to check if they form stable structures. As depicted in the Fig. 3.5, the

equilibrated structures from the above sequences were generated and that was used for further

studies to analyze the interactions with the ligand (palmatine). Binding free energies of these

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DNA structures indicated that the ligand is more specific to AT-rich sequence [(-45.48 kcal/mol

(GBSA) & -38.12 kcal/mol (PBSA)] and weak binding to GC-rich sequence [(-26.47 kcal/mol

(GBSA) & -21.13 kcal/mol (PBSA)], indicating the efficacy of this nucleotide-specific target,

which agrees with the experimental data. These results suggest that the palmatine is a sequence

specific inhibitor molecule for DNA based therapeutic agents, especially in cancer treatment.

Figure 3.4: The concentration of palmatine bound to different nucleic acid sequences

(Adopted from Bhadra et al., 2007 [67]).

Figure 3.5: Molecular simulations of sequence specific oligonucleotides with palmatine.

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Future study

These results can be used for further studies to investigate the helical parameters of such DNA

sequences to understand the binding interactions of palmatine. Such approaches will not only

explain the intercalating properties of palmatine, but would certainly be helpful to understand

similar phenomena of other DNA intercalators.

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Paper I

Dimerization of a flocculent protein from Moringa oleifera: experimental evidence and

in silico interpretation

Asalapuram R. Pavankumar, Rajarathinam Kayathri, Natarajan A. Murugan, Qiong Zhang,

Vaibhav Srivastava, Chuka Okoli, Vincent Bulone and Hans Ågren

Journal of Biomolecular Structure and Dynamics, 2013 (In press)

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Paper II

A plant alkaloid azadirachtin as an inhibitor for glutathione-S-transferase from

Proteus mirabilis

Rajarathinam Kayathri, Natarajan A. Murugan, Premkumar Albert, Hans Ågren

(Journal of Chemical Information and Modeling - communicated)

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Paper III

Palmatine, a nucleotide sequence specific inhibitor for cancer therapy

Rajarathinam Kayathri, Natarajan A. Murugan, Hans Ågren

(Manuscript)

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Palmatine, a nucleotide sequence specific inhibitor for cancer therapy

Rajarathinam Kayathri1, Natarajan A. Murugan

1, Hans Ågren

1

1Division of Theoretical Chemistry and Biology, School of Biotechnology, Royal Institute of

Technology (KTH), 10691 Stockholm, Sweden.

Key words: natural plant alkaloids; palmatine; sequence-specific DNA intercalator; helical

parameters; anticancer.

------------------------------------

#Corresponding author: Prof. Hans Ågren, Division of Theoretical Chemistry and Biology,

School of Biotechnology, Royal Institute of Technology (KTH), 10691 Stockholm, Sweden.

Email: [email protected]; Tel: +46 8 5537 8416.

Introduction

The palmatine is a protoberberine alkaloid, which is found in several plants like Phellodendron

amurense, Rhizoma coptidis/Coptis Chinensis and Corydalis yanhusuo. This plant alkaloid,

palmatine is being used as a natural medicinal compound in the treatment of jaundice, dysentery,

hypertension, inflammation, liver diseases and viral infections. Palmatine bears the same

tetracyclic structure (7, 8, 13, 13atetrahydro-9, 10-dimethoxy-berberinium) as similar to

berberine but differs by the nature of the substituents at positions 2, 3 on the benzo ring; being

methylene dioxy for berberine and dimethoxy for palmatine. However, the binding studies of

palmatine to nucleic acid bio-targets have been scanty and till today no data is available in the

literature on the mode, mechanism and base sequence specificity of binding of palmatine to

DNA (1).

The experiments of Kluza et al. (2003) (1) identified that DNA as a potential bioreceptor for

palmatine and suggested both intercalation and external stacking of palmatine on complexation

with natural DNA having heterogeneous AT and GC sequences. The noncovalent complexes of

several protoberberine alkaloids including berberine and palmatine with few oligonucleotides

showed that the palmatine exhibited the highest binding much higher than berberine to the

oligonucleotides studied and demonstrate a remarkable AT base pair specific intercalative mode

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of DNA binding for palmatine (2). This alkaloid has been shown to exhibit significant antitumor

activity against HL-60 cells and has antimicrobial properties (3, 4). Hence, it could be very

useful if the effect of palmatine is studied with a known sequence of DNA templates, which

could be used against nucleotide-sequence specific sequences, like cancer. Apart from cancer,

several genetic disorders or diseases like cancer, ADHD disorders including Alzheimer's,

remains under functional characterization in the physiological context of disease. In spite of

high-end molecular techniques, in vivo determinations of such genes are significantly

challenging. Hence the modern computational analysis could offer reliable solution to understand

the gene functionality and also to develop novel drugs. As the pharmaceuticals are interested to

develop dual-activity molecules, especially which act on specific nucleotide-rich sequences, we

wanted to propose palmatine as one of such candidates based on the available laboratory reports.

In this scenario, the experiments of Badhra et al. (2007) (2) inspired us to investigate the

intercalative binding abilities of palmatine against small sequence-specific DNA nucleotides.

They have reported that the base dependent binding of the palmatine to four synthetic

polynucleotides, poly(dA).poly(dT), poly(dA–dT).poly(dA–dT), poly(dG).poly(dC) and

poly(dG–dC).poly(dG–dC) of a DNA molecule via intercalation and external stacking properties

with heterogeneous AT and GC sequences. The absorbance measurements of intrinsic binding

constants decreased in the order poly(dA).poly(dT)>poly(dA–dT).poly(dA–dT)>poly(dG–

dC).poly(dG–dC)>poly(dG). poly(dC) and this could be attributed to the different helical

parameters of the DNA helix. The noncovalent complexes of this cytotoxic alkaloid exhibited the

highest binding efficiency (much higher than berberine) to the AT rich oligonucleotides and

demonstrated a remarkable base pair specific intercalative mode of DNA binding. So, we

thought to explore the sequence specific binding affinity of palmatine against DNA on the minor

groove-bound state (MNS).

In this study, we designed four polynucleotides of poly(dA).poly(dT), poly(dA–dT).poly(dA–

dT), poly(dG).poly(dC) and poly(dG–dC).poly(dG–dC), and docked them with palmatine. It is

not straightforward to study the intercalation process by the computational methods. The MNS

complex is used to study the binding affinity of palmatine on the different nucleotide sequences.

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Methods

Molecular docking of sequence-specific DNA and palmatine

Here, we are most likely interested on the specificity of a ligand on different polynucleotide

sequences. The nucleic acid builder (NAB) program (5) was used to generate 18 base pair DNA

sequences of poly d(A).poly d(T), poly d(G).poly d(C), poly(dA-dT).poly(dA-dT), poly(dG-

dC).poly(dG-dC). The ligand structure palmatine was optimized by B3LYP/6-31G basis set

using Gaussian 09 (6). The equilibrated structure of DNA was considered for the docking

studies. Autodock 1.5 (7) was used to create the minor groove binding of the ligand to the

different sequences. The docked structures were used further to study the binding specificity of

the ligand. The AMBER charges of the DNA sequences and the ESP charge of palmatine was

mainatained during the docking procedure. During the docking the DNA sequences were kept

rigid and the palmatine was kept flexible. The grid box of 60x60x60 was generated with a

spacing of 0.375A by including the polar hydrogens. The LMA was used for all the runs of

population size of 150 includes 2.5x106 energy evaluations. The best-docked conformation was

chosen from the 10 docked structures based on the lowest binding energy. The intermolecular

and intramolecular energies contribute to the binding energy. The various interactions like van

der Waals, electrostatic, hydrogen bonding and desolvation energy between the receptor and the

ligand contributes to the decomposition energy of the total intermolecular energy. From the

docking studies we could infer the binding affinity, the interaction nature and the initial mode of

binding of the ligand and DNA. In this case we consider the MNS binding of palmatine with the

sequence to know its specificity to the sequences.

Molecular dynamic simulations oligonucleotides and palmatine

Amber 11 software (8) used for the MD simulation of MNS complex of DNA-palmatine. The

PARM99 was used for all the four different sequences and GAFF for the ligand, palmatine. The

MNS complex was solvated by TIP3P water molecules in a cubic box size of 15 and neutralized

by adding 22NA ions. The final solvated complex was minimized for the time step of 500 steps

followed by heating upto 300K by Berendesen thermostat for 50ps. The energy minimized

structure was further used for the Langevin dynamics with weak restraints with the maintenance

of the pressure until the convergence of the density and the total energy. The production run of

30ns was performed to extract the trajectories for the binding free energy calculation using the

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SHAKE algorithm (9). The electrostatic and VDW was considered with the non-bonded cutoff of

8. The PBC was continued throughout the simulation in an isothermal-isobaric ensemble, of

300K and 1 atmospheric pressure.

Binding free energy calculations

To know the binding free energy of palmatine with the different DNA sequences in presence of

the solvent is studied by a more rigorous method like molecular mechanics Poisson-Boltzmann

surface area and the generalized-Born surface area methods (MM-PBSA, MM-GBSA).

MMPBSA.PY module in Amber tools 1.5 (10, 11) in which the implicit continuum solvent

model was used to calculate the binding free energy. The MM-PBSA and MM-GBSA models

differ from the electrostatic interaction between the solvent and the solute. The binding free

energy was calculated from the 500 snapshots has been extracted from the last 3ns from the 30ns

simulation of the production run. The formula to explain the binding free energy by:

ΔG = Gcomplex – Greceptor – Gligand

where,

Gcomplex - Binding energy of DNA/palmatine complexes

Greceptor - Binding energy of DNA (poly aa and poly gg sequences)

Gligand – Binding energy of palmatine

The binding free energy of the system is expressed by:

G= EMM + GPB/GGB + Gnon-polar – TS

EMM - Total molecular mechanics energy of the system in the gas phase

S – Entropy of the system

The solvent accessible surface area (SASA) and polar/non-polar terms are calculated using the

PB/GB model. The molecular mechanical energy, the polar and non-polar solvation free energy

and the entropy terms contributes the binding free energy. The free energy average of the

trajectories of the receptor, ligand and the complex were calculated. The more stable complex is

determined by the large value of the binding free energy and the negative value shows the

favorable complex formation.

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Results and discussion

In order to address this problem, we designed sequence specific 18 base pair nucleotides of

poly(dA).poly(dT), poly(dA–dT).poly(dA–dT), poly(dG).poly(dC) and poly(dG–dC).poly(dG–

dC) through NAB programme. Only these sequences alone were simulated to check their

stability (Figure. 1).

As depicted in the figure 1, the equilibrated structures for the above sequences were generated

and that was used for further studies to analyze the interactions with the ligand (palmatine).

These differences in the structure of the DNA helices can be attributed to the change in helical

parameters. The DNA helix gains absolute and relative conformations through the helical

parameters, grouped in two categories: base-pair and base-step parameters, which defines the

point of distortion of a wobble base pair, the distance of helix axis, and non-linear DNA helical

axis. This can be explained by the figure 2.

Figure 1: The longitudinal view of the sequence was captured and depicted, to describe the

change in the bonding angles, and variations in the positions of nucleotides between normal and

simulated sequence.

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Figure 2: The different helical parameters of DNA.

Figure 3: Molecular simulations of sequence specific oligo nuclucleotides with palmatine

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Table 1: Poisson Boltzmann and Generalized Born model calculations for palmatine with AT

rich sequence.

Poisson Boltzmann

Contributions Complex Receptor Ligand Delta

Mean Mean Mean Mean σ

vdW -670.78 -622.92 -2.33 -45.54 0.45

Eel 5644.86 6365.14 -21.79 -698.50 1.62

Epb -11134.44 -11797.81 -45.28 708.65 1.56

Ecavity 37.82 38.13 2.43 -2.73 0.02

Ggas 4974.08 5742.23 -24.11 -744.04 1.99

Gsol -11096.62 -11759.69 -42.85 705.92 1.54

Gtotal -6122.54 -6017.46 -66.97 -38.12 0.56

Generalized Born

Contributions Complex Receptor Ligand Delta

Mean Mean Mean Mean σ

vdW -670.78 -622.92 -2.33 -45.54 0.45

Eel 5644.86 6365.14 -21.79 -698.50 1.62

Epb -10708.97 -11369.11 -43.27 703.40 1.56

Ecavity 51.74 52.13 4.46 -4.85 0.02

Ggas 4974.08 5742.23 -24.11 -744.04 1.99

Gsol -10657.23 -11316.98 -38.80 698.56 1.54

Gtotal -5683.15 -5574.75 -62.92 -45.48 0.56

vdW – van der Waals from MM force field; Eel – electrostatic energy from MM force field;

Epb/Egb – solvation free energy from the electrostatic contribution by PB/GB ; Ecavity/Esurf –

solvation free energy from the non-polar contribution calculated by PB/GB, respectively. Ggas –

total gas phase energy; Gsol - contributions to solvation; Gtotal – the final total binding free energy

(kcal/mol)

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Table 2: Poisson Boltzmann and Generalized Born model calculations for palmatine with GC

rich sequence.

Poisson Boltzmann

Contributions Complex Receptor Ligand Delta

Mean Mean Mean Mean σ

vdW -636.75 -606.94 -2.23 -27.58 0.38

Eel 8046.33 8673.59 -14.55 -612.70 1.52

Epb -11177.00 -11753.47 -44.48 620.95 1.61

Ecavity 38.41 37.75 2.46 -1.80 0.03

Ggas 7409.58 8066.64 -16.78 -640.28 1.78

Gsol -11138.59 -11715.72 -42.01 619.15 1.59

Gtotal -3729.01 -3649.08 -58.80 -21.13 0.34

Generalized Born

Contributions Complex Receptor Ligand Delta

Mean Mean Mean Mean σ

vdW -636.75 -606.94 -2.23 -27.58 0.38

Eel 8046.33 8673.59 -14.55 -612.70 1.52

Epb -10754.11 -11328.83 -42.63 617.35 1.54

Ecavity 52.58 51.66 4.46 -3.54 0.04

Ggas 7409.58 8066.64 -16.78 -640.28 1.78

Gsol -10701.53 -11277.17 -38.16 613.81 1.51

Gtotal -3291.95 -3210.52 -54.95 -26.47 0.38

The binding affinity of the complex is explained in terms of electrostatic, van der Waals, polar

and non-polar solvation energy contribution. The contributions of these terms for palmatine with

poly(dA-dT).poly(dA-dT) and poly(dG-dC).poly(dG-dC), are shown in Table 1 and 2. The

binding specifity of palmatine with poly(dA-dT).poly(dA-dT) is due to the large contribution of

van der Waals (-45.54 kcal/mol) and non-bonded interaction terms (-744.04 kcal/mol) (Table 1)

and in case of poly(dG-dC).poly(dG-dC)-palmatine -27.58 kcal/mol and -640.28 kcal/mol. The

difference in the above contributions are well observed in the complex. The binding free energies

of these DNA structures indicate that the ligand is more specific to AT-rich sequence [(-45.48

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kcal/mol (GBSA) & -38.12 kcal/mol (PBSA)] and weak binding to GC-rich sequence [(-26.47

kcal/mol (GBSA) & -21.13 kcal/mol (PBSA)], indicating the efficacy of this nucleotide-specific

target, which explains the AT- rich sequence specificity from the experiments (2). The binding

affinity of palmatine reported here is based on the MNS, because the direct intercalation for

small ligands is milliseconds and it is much more challenging to study by computational

methods. (12) These results suggest about the ligands sequence specificity for developing DNA

based therapeutic agents, especially in cancer treatment.

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