





































ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 
 

IN SILICO STUDIES OF  

COMPOUNDS PRESENT 

IN AZADIRACHTA INDICA 

(NEEM) AND THEIR  

ABILITY TO BIND  

HIV INTEGRASE PROTEIN 
 

ZHONGRUI ZHANG, YIN HEI LAU 

SONIA ARORA (FACULTY ADVISOR) 

 

✵ ABSTRACT 
Azadirachta indica (Neem) is an evergreen 

tree that belongs to the Meliaceae family. It is native 

to the Indian subcontinent and grows worldwide. It 

is also known as the "village pharmacy" in India for 

its wide range of therapeutic and pharmacological 

properties. An in vitro study indicated that A. indica 

showed anti-HIV properties. However, the exact 

mechanism for the supposed anti-HIV properties re-

mains unknown. This study aimed to construct an in-

silico database of the compounds present in A. in-

dica and propose a computational analysis of these 

compounds against HIV integrase. We performed a 

thorough literature search to gather relevant infor-

mation on the plant compounds, including chemical 

structure, location within the plant, extraction 

method, and percent yield of each compound 

found in the plant. We took a comprehensive ap-

proach to closely study the binding pockets of HIV 

integrase and performed molecular docking on A. 

indica compounds using Molecular Operating Envi-

ronment. A deductive analysis of the docking ener-

gies of these compounds revealed thirty potential 

binders against HIV integrase proteins. We further 

validated these binders by comparing the ligand in-

teractions to known inhibitors using Ligplot+, which 

identified the presence of numerous hydrogen 

bonds and hydrophobic interactions at the protein 

binding pocket. In conclusion, we propose an un-

derlying binding potential for several A. indica com-

pounds with HIV integrase, yielding a potential 

mechanism for the anti-HIV activity of A. indica. 

 

KEY TERMS: Neem, anti-HIV, medicinal plant, molecular 

docking 
 

1 INTRODUCTION 
Azadirachta indica (family: Meliaceae), com-

monly known as neem, is a large, fast-growing, trop-

ical evergreen tree that has been widely used in tra-

ditional medicine since prehistoric times (Abdel-

hady et al., 2015; Paul, Prasad, & Sah, 2011; 

Sadeghian & Mortazaienezhad, 2007). A. indica is 

indigenous to the Indian subcontinent and is culti-

vated in at least 30 countries worldwide (Abdelhady 

et al., 2015). In India, A. indica is also known as "the 

village pharmacy," "the wonder tree," "nature's 

drug store," and "the life-giving tree" (Hossain et al., 

2013; Patel et al., 2016; Paul et al., 2011). All parts 

of the tree can be used in disease treatments due to 

the presence of various phytochemicals. 

Medicinal plants play an important role in 

the health of human society. A. indica is a medicinal 

plant with a broad spectrum of therapeutic applica-

tions. Bioactive phytochemicals, such as flavonoids, 

terpenoids, tannins, carbohydrates, and proteins, 

provide A. indica with its healing properties. Many 

unique compounds have been identified and iso-

lated from all parts of A. indica (Sarah et al., 2019). 

Azadirachtin, nimbin, gedunin, and quercetin are 

some of the most studied compounds in A. indica. 

These compounds carry various biological and 

pharmacological properties such as antimicrobial, 

antiviral, antifungal, antimalarial, anti-inflammatory, 

antiulcer, and anticancer properties (Jerobin et al., 

2015 and Paul et al., 2011). Clinical trials have af-

firmed various therapeutic properties of neem. For 

example, neem bark extract was found to control 

gastric hypersecretion as well as gastroesophageal 

and gastroduodenal ulcers (Bandyopadhyay et al., 

2004). A recent randomized controlled trial also 

found the therapeutic potential of neem in prevent-

ing COVID-19 infection (Nesari et al., 2021). There-

fore, A. indica has been used in the treatment of fe-



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

ver, malaria, intestinal infections, inflammation, ar-

thritis, and skin diseases (Abdelhady et al., 2015; 

Anyaehie, 2009). Because of its medicinal proper-

ties in disease prevention and treatment, the United 

Nations proclaimed A. indica as the "Tree of the 21st 

Century” (Hossain et al., 2013). 

The human immunodeficiency virus (HIV) is 

a retrovirus that attacks the human immune system. 

The genetic material of the retrovirus is inserted into 

the host genome by the retroviral integrase during 

the process of integration (Komal et al., 2020 and 

Smith & Daniel, 2006). As a result of a weakened im-

mune system, various symptoms — such as fever, 

cough, swollen lymph nodes, mouth ulcers, and 

muscle aches — can develop. If HIV is not treated 

properly, it can lead to severe diseases such as tu-

berculosis, cryptococcal meningitis, cancers, and 

acquired immunodeficiency syndrome (AIDS) 

(World Health Organization [WHO], 2021; Centers 

for Disease Control and Prevention [CDC], 2021). 

Hence, urgent treatment is necessary for HIV pa-

tients. According to the WHO, there were approxi-

mately 37.7 million HIV cases in 2020. However, due 

to the development of resistance to current medica-

tion targeting integrase, the investigation of new in-

tegrase inhibitors is needed (Mesplède et al., 2012). 

In a previous in vitro study conducted by Udeinya et 

al. (2004), a fractionated acetone-water extract pre-

pared from A. indica showed anti-cytoadhesion ac-

tivity, which protects lymphocytes against invasion 

by HIV and suggests the anti-retroviral property of 

this plant. 

In-silico screening, or computer-aided drug 

design (CADD), has become a crucial part of the 

modern drug discovery process. It uses a variety of 

bioinformatics applications and algorithms to effi-

ciently screen for potential drug candidates and sig-

nificantly reduce the time and resources needed in 

the traditional lab-bench-based drug delivery pro-

cess (Rodrigues and Schneider, 2015). In addition, 

the aforementioned algorithms can be used to pre-

dict the pharmacological properties and interac-

tions of molecules. Molecular docking is a type of 

CADD that predicts the protein-ligand interaction 

between the drug target and the drug candidate. It 

runs computer simulations of the potential drug 

candidates (ligands) with different 3D postures in-

teracting with the drug targets (proteins) and 

measures the favorableness of such interactions in 

terms of binding energy. Such predictions could 

therefore be used as the first step of drug candidate 

screening and can eliminate unlikely candidates 

within a relatively short time frame using fewer re-

sources. 

Despite recent findings on A. indica’s anti-

HIV potential, the exact mechanism of action is still 

unknown. Previous studies from our lab have fo-

cused on several anti-HIV targets such as HIV prote-

ase and reverse transcriptase. However, none of 

these studies have led to conclusive data (un-

published observations). Therefore, this study 

aimed to construct an in-silico database of the com-

pounds present in A. indica and propose a compu-

tational analysis of these compounds against HIV in-

tegrase — one of the important proteins in the HIV 

life cycle — to investigate the potential inhibitory ac-

tivity in reducing viral load. 

 

2 METHODS 
LITERATURE REVIEW AND DATABASE BUILDING 

A literature search on A. indica was first con-

ducted to collect common compounds present in 

this plant with readily available structures. For each 

of these compounds, the percent yield and the lo-

cation within the plant containing the highest abun-

dance of compounds were also collected. The 

three-dimensional (3D) structures of the com-

pounds were collected on PubChem (Kim et al., 

2020) (SDF format) or ChemSpider (MOL format). 

For compounds without readily available 3D struc-

tures on these public sources, the 2D structures 

were collected and converted to 3D models using 

Discovery Studio (D.S.) Visualizer (BIOVIA & Das-

sault Systèmes, 2017). Hydrogen atoms were added 

to all 3D compound structures. The geometry of 

each compound was cleaned using the built-in Min-

imize Structure tool in UCSF Chimera (Pettersen et 

al., 2004) to reduce the internal energies. All opti-

mized A. indica compounds were saved in a MOL2 

format and ready for molecular docking. 

 

 



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

PROTEIN VISUALIZATION AND OPTIMIZATION 

 The 3D protein structures of HIV integrase 

were retrieved from the RCSB Protein Data Bank to 

serve as the target model of investigation (Berman 

et al., 2000). The human protein structures with 

higher resolution and known bounded ligands were 

prioritized during the collection process. Minor pro-

tein processing was performed to optimize the pro-

tein model and minimize errors. All selected pro-

teins underwent energy optimization and geometry 

cleaning with the Structural Preparation and Proto-

nate 3D tool in Molecular Operating Environment 

(MOE) ("Molecular Operating Environment (MOE), 

2019.01," 2022), and the processed proteins were 

saved in MOE format. A pre-docking binding 

pocket analysis was also performed using Lig-

plot+(Laskowski & Swindells, 2011) to collect base-

line protein-ligand interactions between the known 

inhibitor compounds and the HIV integrase pro-

teins. 

 

DOCKING PARAMETER OPTIMIZATION AND BASELINE  

BUILDING 

The minimized HIV integrase protein files 

were re-docked using MOE Docking. Re-docking 

was performed as a suitability control experiment, 

which involved taking out the originally bounded 

ligands in each protein file and docking them back 

into the protein binding pocket with various binding 

parameters to identify the most optimal conditions 

for the experiment. The default placement method 

of Triangle Matcher with London dG scoring system 

and the refinement method of Rigid Receptor with 

GBVI/WSA dG scoring system were used as the 

docking methods. These methods and scoring sys-

tems are known to give reliable results, and the re-

sults were estimated in terms of free energy re-

ported in kcal/mol (Corbeil et al., 2012; Galli et al., 

2014). However, other parameters such as receptor 

region, docking site, and number of docked poses 

were tested for the most optimized parameter com-

binations for each protein target, which was meas-

ured by the RMSD values between the original lig-

and and the re-docked models. The docking ener-

gies for the most suitable docking parameters (often 

resulted in the lowest RMSD values) were also rec-

orded for baseline purposes. 

 

A. INDICA COMPOUND DOCKING 

A MOE database file in MDB format was cre-

ated with the name and the structure in MOL2 for-

mat of each optimized A. indica compound. The op-

timized docking parameters were used albeit the 

MOE compound database file, which was used as 

the docking ligands. The docked poses with the 

best docking energies (most negative) for each 

compound were recorded. For each A. indica com-

pound, the average docking energies against all 

protein targets were compared with the average 

docking energies in optimization. Since the docking 

energy was measured in free energy, any A. indica 

compounds with more negative average docking 

energies than optimization was more thermody-

namically favorable to bind and therefore identified 

as potential HIV integrase binders. 

 

POST-DOCKING ANALYSIS 

Ligplot+ was used to perform a post-dock-

ing analysis on all binders in complex with their pro-

tein binding pockets. Two of the most common and 

relatively strong protein-ligand interactions — hy-

drogen bonds and hydrophobic interactions — 

were examined to help explain and verify the favor-

able docking energies obtained by the binder com-

pounds. 

 

3 RESULTS 
COMPOUND DATABASE BUILDING 

 A comprehensive database comprised of 

50 compounds present in A. indica was created. FIG-

URE 1 shows the structures of a few compounds pre-

sent in the database. In addition to the 3D structures 

of each compound, the database also collected the 

locations where these compounds are found on the 

plant, a brief categorization of each compound, the 

extraction methods, and the corresponding percent 

yield found in the plant (TABLE 1).  

 

 



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

Azadirachtin Nimbin Gedunin

Nimbolide Nimbolin Salannin

Cycloeucalenol Nimbosterol Nimbolicin
 

FIGURE 1. The 3D structures of nine representative A. indica compounds collected in the database. Grey: carbon; 
white: hydrogen; red: oxygen. 
 

 

 

 

 

 

 
PROTEIN DATA COLLECTION AND BINDING POCKET 

ANALYSIS OF HIV INTEGRASE 

Four HIV integrase proteins (PDB IDs: 

1QS4, 3NF6, 3NF7, 6WC8) were selected 

(Goldgur et al. 1999; Gorman et al. 2020; Peats 

et al. 2010). These PDB files had the highest res-

olution, were derived from human targets, and 

contained at least one known HIV integrase in-

hibitor. Clustal Omega multiple protein se-

quence alignment revealed that these selected 

integrase proteins were at least 95% identical 

between any two proteins (Sievers et al. 2011).  

 

The difference in protein sequence was 

due to the presence of 2-4 unique mutation 

sites in each protein. The number of files used 

in this study was a balance between accuracy 

and resource, as previous studies from our lab 

had demonstrated that using four target files 

was sufficient to generate reliable results. Bind-

ing pocket analysis of each of the four proteins 

revealed the presence of numerous hydrogen 

bonds and hydrophobic interactions between 

the known inhibitor ligands and the integrase 

proteins, as shown in TABLE 2.   



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

TABLE 1: In-silico compound database for A. indica. All data was collected from publicly available journal sources as 
indicated in Reference column. The percent yields corresponded to the extraction method as listed. N/A: Data not 
available. 
 

 Compound Source Extraction 
method 

Percent 
Yield 

Reference 

1 Azadirachtin Flower, 
fruit, leaf, 
and seed 

Aqueous 0.1-0.3% 
in seed 

(Biswas et al, 2002; Kaushik, 2021; 
Morgan, 2009; Paul et al., 2011; 

Ponnusamy et al., 2015; Sadeghian 
& Mortazaienezhad, 2007; Singh et 

al., 2017) 

2 Isomargolonone Bark N/A N/A (Biswas et al., 2002; Singh et al., 
2017) 

3 Azadiradione Fruit, leaf, 
seed 

Aqueous 0.3% in 
leaf 

(Paul et al., 2011; Ponnusamy et al., 
2015; Sadeghian & Mor-

tazaienezhad, 2007) 

4 Epicatechin Bark N/A N/A (Biswas et al., 2002; Singh et al., 
2017) 

5 Mahmoodin Seed oil N/A N/A (Biswas et al., 2002) 

6 Azadirone Fruit, leaf, 
seed oil 

Aqueous 2.46% in 
leaf 

(Paul et al., 2011; Ponnusamy et al., 
2015; Sadeghian & Mor-

tazaienezhad, 2007) 

7 Flavanone Flower N/A N/A (Nakahara et al., 2003) 

8 Margolone Bark N/A N/A (Biswas et al., 2002; Singh et al., 
2017) 

9 Catechin Bark N/A N/A (Biswas et al., 2002; Singh et al., 
2017) 

10 Nimbin Leaf, seed 
oil, trunk 
and root 

bark 

Aqueous 2.6% in 
leaf 

(Biswas et al., 2002; Kaushik et al., 
2021; Paul et al., 2011; Ponnusamy 

et al., 2015; Sadeghian & Mor-
tazaienezhad, 2007; Singh et al., 

2017) 

11 Gedunin Leaf, Seed 
oil 

N/A N/A (Anand, 2017; Biswas et al., 2002; 
Paul et al., 2011; Ponnusamy et al., 

2015; Sadeghian & Mor-
tazaienezhad, 2007; Singh et al., 

2017) 

12 Nimbinin Leaf, seed 
oil, trunk 
and root 

bark 

N/A N/A (Koul, Isman, & Ketkar, 1990; Paul et 
al., 2011) 

13 Nimbolide Leaf, Seed 
oil 

Aqueous 2.20% leaf (Biswas et al., 2002; Kaushik et al., 
2021; Sadeghian & Mor-

tazaienezhad, 2007; Singh et al., 
2017) 

14 Nimbidin Leaf, Seed N/A N/A (Biswas et al., 2002; Koul et al., 
1990; Singh et al., 2017) 

15 Nimbolin A Trunk 
wood 

N/A N/A (Paul et al., 2011) 

16 Nimbolin B Trunk 
wood 

N/A N/A (Paul et al., 2011) 

17 Quercetin Flower and 
leaf 

N/A N/A (Kaushik et al., 2021; Paul et al., 
2011) 

18 Salannin Leaf, seed 
oil 

Aqueous 5.6% in 
leaf 

(Paul et al., 2011; Ponnusamy et al., 
2015; Sadeghian & Mor-

tazaienezhad, 2007) 

19 Nimbidol Leaf N/A N/A (Anand, 2017) 

20 Cycloeucalenol Wood oil N/A N/A (Paul et al., 2011) 



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

21 Nimbosterol (beta-si-
tosterol) 

Leaf, wood 
oil 

N/A N/A (Kaushik et al., 2021) 

22 Nimbinone Bark N/A N/A (Ara, Siddiqui, Faizi, & Siddiqui, 
1988) 

23 Nimbolicin Bark N/A N/A (Read & French, 1993) 

24 Margocin Root bark N/A N/A (Ara et al., 1990) 

25 Gallic acid Bark N/A N/A (Biswas et al., 2002; Singh et al., 
2017) 

26 2-methyl-5-ethylfuran Leaf Butanol 4.8273% (Hossain et al., 2013) 

27 Arabinose Bark N/A N/A (Kumar et al., 2017) 

28 m-toluylaldehyde Leaf Methanol 22.7669% (Hossain et al., 2013) 

29 2-methyl-benzalde-
hyde 

Leaf Butanol 11.8674% (Hossain et al., 2013) 

30 Levoglucosenone Leaf Butanol 7.1217% (Hossain et al., 2013) 

31 Methyl isoheptade-
canoate 

Leaf Hexane 
Chloroform 
Methanol 

2.1921% 
11.6299% 
12.2749% 

(Hossain et al., 2013) 

32 Methyl petroselinate Leaf Hexane 11.2380% (Hossain et al., 2013) 

33 Phytol Leaf Hexane 
Ethyl Acetate 
Chloroform 

2.6170% 
61.2401% 
10.0515% 

(Hossain et al., 2013) 

34 Butyl palmitate Leaf Hexane 6.6981% (Hossain et al., 2013) 

35 Isobutyl stearate Leaf Hexane 4.2521 % (Hossain et al., 2013) 

36 Oxalic acid Leaf Hexane 13.7094% (Hossain et al., 2013) 

37 Methyl 14-methylpen-
tadecanoate 

Leaf Methanol 
Ethyl Acetate 
Chloroform 

Butanol 

38.1251% 
6.4278% 

31.8674% 
13.4471% 

(Hossain et al., 2013) 

38 Hexahydrofarnesyl ac-
etone 

Leaf Ethyl Acetate 2.5888% (Hossain et al., 2013) 

39 Lineoleoyl chloride Leaf Methanol 
Chloroform 

Butanol 

26.8329% 
11.3587% 
13.6057% 

(Hossain et al., 2013) 

40 Nonacosane Leaf Chloroform 
Butanol 

20.6575% 
12.8752% 

(Hossain et al., 2013) 

41 Stearic acid Kernel Oil N/A 18% (Do et al., 2022 ) 

42 Palmitic acid Kernel Oil N/A 16.9% Do et al., 2022 ) 

43 Oleic acid Kernel Oil N/A 45.9% (Do et al., 2022 ) 

44 Linoleic acid Kernel Oil N/A 15.69% (Do et al., 2022 ) 

45 Pyroligneous acid Heartwood N/A 38.4% (Kumar et al., 2017) 

46 Hentriacontane Leaf Butanol 13.9887 (Hossain et al., 2013) 

47 Heptacosane Leaf Hexane 8.1010% (Hossain et al., 2013) 

48 Octacosane Leaf Hexane 7.0926 (Hossain et al., 2013) 

49 Eicosane Leaf Hexane 10.0136 (Hossain et al., 2013) 

50 Nonadecane Leaf Hexane 3.7587% (Hossain et al., 2013) 



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

TABLE 2: Binding pocket analysis of the four selected HIV integrase proteins. The hydrogen bonds and hydrophobic 
interactions were identified using Ligplot+. All ligands present in these proteins had shown existing binding activity 
toward HIV integrase. 
 
100: 1-(5-chloroindol-3-yl)-3-hydroxy-3-(2h-tetrazol-5-yl)-propenone 

IMV: 5-[(2-oxo-2,3-dihydro-1H-indol-1-yl)methyl]-1,3-benzodioxole-4-carboxylic acid 

CIW: 5-[(5-chloro-2-oxo-2,3-dihydro-1H-indol-1-yl)methyl]-1,3-benzodioxole-4-carboxylic acid 

TQM: {5-(3-fluorophenyl)-2-[(thiophen-2-yl)ethynyl]-1-benzofuran-3-yl}acetic acid 

 
PDB # Ligand Hydrogen Bond Hydrophobic 

1QS4 100                
(5ClTEP) 

Thr66 Asp64 Asn155   

Lys159 Gln148 Lys156   

  Ile151    

  Glu152     

3NF6 IMV Glu170 Gln95 Ala128 Lys173 

His171 Tyr99 Ala129 Met178 

Thr174 Leu102 Trp132   

  Thr125 Ala169   

3NF7 CIW   Val77 Val150 Leu158 

  Val79 Ser153 His183 

  Gly82 Met154   

  Ile84 Glu157   

6WC8 TQM  Gln95 Leu102   

 Glu96 Ala128   

 Ala98 Ala129   

  Tyr99 Trp132   

DOCKING PARAMETER OPTIMIZATION AND BASELINE 

BUILDING 

Docking optimization was performed 

on each of the four HIV integrase targets. The re-

docked models were compared with the origi-

nal ligands to identify the best docking parame-

ters to use for A. indica compounds. After the 

optimization process, the protein atoms without 

the surrounding solvent were set as the docking 

receptor. The protein residues within the 5Å 

space of the original ligand were defined as the 

protein active sites for docking. Thirty place-

ment poses and five refinement poses were 

deemed the best parameters for later studies. 

For each of the four protein targets, these pa-

rameters yielded RMSD values of 1.99, 0.35, 

0.73, 1.63, respectively, which were low enough 

to generate accurate docked results (FIGURE 2). 

The average docking energy of redocked lig-

ands was -5.6358 kcal/mol. 

 

MOLECULAR DOCKING OF A. INDICA COMPOUNDS 

INTO HIV INTEGRASE BINDING POCKET 

Molecular docking was performed on 

all 50 A. indica compounds against each of the 

four HIV integrase proteins using the optimized 

docking parameters. After a comparison be-

tween the average docking energy for the plant 

compounds and the redocked ligands, 30 A. in-

dica compounds were predicted to have a more 

favorable binding energy and were identified as 

potential HIV integrase binders (TABLE 3).  



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

 

FIGURE 2: Ligand-bounded HIV integrase binding pocket (PDB# 3NF7). (A) Docking optimization with original ligand  
(green) and redocked ligand (pink). (B) A. indica compound salannin (blue) and gedunin (orange).

 

TABLE 3: The docking energy for each of the A. indica compounds and the redocked ligands. Each of these energy 
measures was the average docking energy of each compound against all four HIV integrase proteins (PDB# 1QS4, 
3NF6, 3NF7, 6WC8). Highlighted compounds were identified as binders. 
 

* Redocked ligands from docking optimization. 

 

  Compound Average Docking 
Energy (kcal/mol) 

    Compound Average Docking 
Energy (kcal/mol) 

0 Redocked* -5.6358 26 2-methyl-5-ethylfuran -4.1258 

1 Azadirachtin -6.3591 27 Arabinose -4.1509 

2 Isomargolonone -5.3900 28 m-toluylaldehyde -4.1068 

3 Azadiradione -5.4921 29 2-methyl-benzaldehyde -3.9820 

4 Epicatechin -5.2782 30 Levoglucosenone -3.5953 

5 Mahmoodin -5.9337 31 Methyl isoheptadecanoate -6.2995 

6 Azadirone -5.6070 32 Methyl petroselinate -6.4152 

7 Flavanone -5.0024 33 Phytol -6.4347 

8 Margolone -5.3343 34 Butyl palmitate -6.5009 

9 Catechin -5.2375 35 Isobutyl stearate -6.7462 

10 Nimbin -6.0113 36 Oxalic acid -3.0358 

11 Gedunin -5.8379 37 Methyl 14-methylpentadecanoate -6.3077 

12 Nimbinin -5.6339 38 Hexahydrofarnesyl acetone -6.0264 

13 Nimbolide -5.6445 39 Lineoleoyl Chloride -6.2090 

14 Nimbidin -5.4625 40 Nonacosane -7.4238 

15 Nimbolin A -6.6793 41 Stearic acid -6.3773 

16 Nimbolin B -6.3832 42 Palmitic acid -6.1378 

17 Quercetin -4.9746 43 Oleic acid -6.2499 

18 Salannin -5.9750 44 Linoleic acid -6.3096 

19 Nimbidol -5.1154 45 Pyroligneous acid -3.3098 

20 Cycloeucalenol -6.0074 46 Hentriacontane -7.3965 

21 Nimbosterol -6.3417 47 Heptacosane -7.0865 

22 Nimbinone -5.1792 48 Octacosane -7.3098 

23 Nimbolicin -6.4237 49 Eicosane -6.4206 

24 Margocin -5.4672 50 Nonadecane -6.1945 

25 Gallic acid -4.2497       

FIGURE 2A FIGURE 2B 



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

POST-DOCKING ANALYSIS 

A post-docking analysis was performed 

on all thirty A. indica binders against each of the 

four protein targets. The Ligplot+ images re-

vealed the intermolecular interactions of these 

binders in complex with the HIV integrase bind-

ing pockets (FIGURE 3). Most of the binders were 

 

shown to be surrounded by large, hydrophobic 

clusters. Some hydrogen bonds were also ob-

served with some A. indica compounds. The 

common interacting residues of a few repre-

sentative compounds are shown in TABLE 4.  

 

 
 
FIGURE 3: Representative figure of the post-docking analysis on A. indica compound bounded HIV integrase proteins. 
Semi-circles indicate protein residue involved in hydrophobic interactions. Arrow-pointed orange compounds indi-
cate protein residue involved in hydrogen bonds. The purple compounds are A. indica compounds of interest. 
  



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

TABLE 4: The common interacting HIV integrase residues of a few representative A. indica binders and the original 

redocked ligand via either hydrophobic interactions or hydrogen bonds. 

 

*Residues involved in hydrogen bonds. 

 
Compound Average Docking  

Energy (kcal/mol) 
Common Interacting Residues 

Re-docked -5.6358 Val77, Val79, Gly82, Ile84, Val150, Ser153, Met154, 
Glu157, Leu158 

Azadirachitin -6.3591 Val79, Gly82, Val150, Ser153, Met154, Glu157, His183, 
Lys188*, Arg199* 

Isobutyl Stearate -6.7462 Val77, Val79, Gly82, Val150, Met154, Glu157, His183, 
Lys186, Lys188  

Nimbolicin -6.4237 Val77, Val79, Ala80*, Gly82, Val150, Met154, Glu157, 
His183, Lys186, Lys188, Arg199 

Nimbolide -5.6445 Ser81, Gly82, Val150, Ser153, Met154, Glu157, His183, 
Lys188*, Arg199* 

Nimbolin A -6.6793 Val77, Val79, Ala80, Gly82, Val150, Ser153, Met154, 
Glu157, His183, Lys188, Arg199 

Nimbosterol -6.3417 Val77, Val79, Gly82, Val150, Met154, Glu157, His183, 
Lys186, Lys188 

Octacosane -7.3098 Val77, Gly82, Val150, Ser153, Met154, Glu157, Lys188 

Salannin -5.975 Ser153, Met154, Glu157, His183, Lys186*, Lys188*, 
Arg199 

4 DISCUSSION & CONCLUSION 
A comprehensive in-silico database of 

A. indica compounds was created, providing 

detailed information on many compounds re-

garding their sources, extraction methods, and 

percent yields extracted from the plant. Alt-

hough the exact percent yield of these com-

pounds varies greatly depending on the extrac-

tion method, this information could provide val-

uable insights for later drug discovery stages. 

The 3D structures collected for each of these 

compounds were also extensively used in the 

molecular docking studies against HIV inte-

grase.  

In the docking optimization process, the 

original ligands in each protein file were docked 

back into the protein binding pockets; the re-

sulting model was referred to as re-docked lig-

ands. The relative position and identity of the re-

docked ligands were visually compared with the 

original ligands to determine the reliability of 

the docking methods. The re-docked ligands 

were shown to occupy a highly similar 3D space 

with the original ligand. This was also quantified 

via the RMSD values, which measured the aver-

age distance between the atoms of the original 

ligand and the re-docked ligand. Therefore, the 

low RMSD values also reflected highly similar 

postures between the predicted model and the 

original ligand. Both verification methods indi-

cated that the optimized docking parameters 

and algorithm were highly accurate in predict-

ing the binding affinity of the A. indica com-

pounds.  



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

Meanwhile, the average docking en-

ergy in optimization was also an important base-

line for identifying the potential A. indica bind-

ers. A closer look at the 30 identified A. indica 

binders revealed that they were bound at the 

same binding pocket as the original ligands, 

suggesting a potentially similar allosteric effect. 

Interestingly, the average docking energies of 

six compounds (nonacosane, hentriacontane, 

octacosane, heptacosane, isobutyl stearate, and 

nimbolin A) were one standard deviation more 

favorable than the redocked ligands, indicating 

more efficient binding activity than the original 

ligands.  

Post-docking analysis was performed to 

explain and validate the favorable docking en-

ergies predicted by the docking algorithm. The 

large hydrophobic clusters surrounding most A. 

indica binders and the presence of hydrogen 

bonds with some binders were both excellent 

indicators of strong intermolecular interactions. 

Some interacting residues in the original lig-

ands, such as Val77, Val79, Gly82, Val150, 

Ser153, Met154, Glu157, and His183, were 

commonly retained across many A. indica bind-

ers. In addition to the retained interactions, 

most binders also gained new hydrophobic in-

teractions; some of these binders, such as aza-

dirachitin and nimbolide, also gained new hy-

drogen bonds. The A. indica binders that 

gained new interactions suggest a more potent 

binding ability to the target proteins compared 

to the corresponding binding ability of the orig-

inal ligands. Therefore, the intermolecular inter-

actions present in these new protein-ligand 

complexes validated the favorable binding en-

ergies predicted by the docking algorithms, 

which supported the identified A. indica binders 

against HIV integrase. 

Overall, we have identified 30 out of 50 

A. indica compounds as binders of HIV inte-

grase proteins. The large proportion of the 

binder compounds present in this plant pro-

vides a feasible explanation of this plant's HIV 

viral reducing potential. Therefore, we propose 

a potential mechanism for the anti-HIV activity 

for A. indica which could offer insights into a 

novel HIV treatment candidate. However, the 

fact that the docking energies of the existing 

known binders were used as the cut-off point in 

A. indica binder identification may be a poten-

tial limitation this study. The possibility of the A. 

indica compounds with less-than-ideal docking 

energies binding to the targets still exists. This 

study only provides a computational screening 

of the potential HIV drug candidates; further 

bench testing on promising candidates is still 

necessary to validate the results. Therefore, fu-

ture goals include further testing these com-

pounds in a wet lab setting to validate the po-

tential inhibitory potential against HIV integrase. 

The database created in this study may play an 

important role in future studies of this plant 

compared to other biological targets, which in 

turn enables exploration of other therapeutic 

targets∎ 

 

5 ACKNOWLEDGEMENTS 
We would like to express our sincere 

gratitude to our mentor and research advisor 

Dr. Sonia Arora for her continuous support 

throughout the duration of the project. This re-

search work would not be possible without her 

enthusiasm and knowledge to the topic. Her 

guidance was always inspiring and this project 

was a great learning opportunity on in-silico ap-

proaches, which could have endless applica-

tions in drug discovery works. 

 

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Zhongrui Zhang is a recent graduate from Rutgers University-New 

Brunswick. He has a B.S. in Biotechnology, bioinformatics from the 

School of Environmental and Biological Science. He had been con-

ducting research in Dr. Sonia Arora’s lab on two projects over the 

course of two years. One was to utilize in-silico techniques to study 

the compounds present in Ocimum sanctum against inflammatory 

pathways. The other was to investigate the potential anti-HIV activity 

of Azadirachta indica in reducing viral loads. He also assisted PhD stu-

dents in Dr. James Simon's lab in conducting synthetic and analytical 

organic chemistry work, where he gained hands-on experience work-

ing with plant compounds. These research experiences sparked his 

interest in drug discovery, and he is currently working at Bristol Myers 

Squibb within the Biologics Department. In the future, Zhongrui 

would like to pursue graduate studies in the drug development-re-

lated field.  

 

Zhongrui can be reached at zhongrui.zhang@rutgers.edu. 

 

 

Yin Hei Lau is a graduate of Rutgers University. She has a B.S. degree 

in Biotechnology – Bioinformatics from the School of Environmental 

and Biological Science. She has a broad interest in health and medi-

cine and would like to conduct further research in the medical field. 

Her research, under the guidance of Dr. Sonia Arora, investigated the 

anti-HIV properties of compounds in A. indica through in-silico ap-

proach. She also worked in Dr. Judith Storch's research lab for more 

than two years. She assisted a PhD student in functional analysis of 

enterocyte fatty acid binding proteins (FABP). For independent pro-

jects, she studied the hepatic lipid metabolism in the intestine-spe-

cific liver FABP (LFABP) knockout mice and the intestinal lipid metab-

olism in the liver-specific LFABP knockout mice.  

 

Yin Hei can be contacted at: yinhei.lau@rutgers.edu. 

https://www.who.int/news-room/fact-sheets/detail/hiv-aids
https://www.who.int/news-room/fact-sheets/detail/hiv-aids
mailto:zhongrui.zhang@rutgers.edu
mailto:yinhei.lau@rutgers.edu

