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Computational insights of 1-Guanidinosuccinimide
and Benzene-ethanamine, 2,5-diuoro-β- 3,4-
trihydroxy-n-methyl with MDM2 as Potential
Anticancer Agent
Bulus Bako
Federal University Wukari
Emmanuel E. Etim
Federal University Wukari
John P. Shinggu
Federal University Wukari
Humphrey S. Samuel
Federal University Wukari
Liberty J. Moses
Federal University Wukari
Research Article
Keywords: Computational & Molecular Docking, In-silico Drug-likeness, Anticancer agent, Bioactive
Compounds; Mouse Double Minute 2
Posted Date: July 31st, 2024
DOI: https://doi.org/10.21203/rs.3.rs-4653936/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Additional Declarations: No competing interests reported.
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Abstract
Cancer remains a signicant challenge in healthcare, spurring ongoing exploration for effective
therapies. Computational methods, emerging as invaluable tools in drug discovery, have garnered
attention for their cost-effectiveness and eciency. In this study, we investigate the anticancer potential
of 1-Guanidinosuccinimide and Benzene-ethanamine, 2,5-diuoro-β, 3,4-trihydroxy-n-methyl, targeting
Mouse double minute 2, a critical protein in cancer pathways. Quantum chemical calculations with
GAUSSIAN 09 (B3LYP; 6-311(d,p)) explored molecular structures across various solvation environments
(Dimethyl Sulfoxide (DMSO) , ethanol, and methanol). Docking analysis using AutoDock Vina revealed
binding to 4ZFI, with anities of -5.9 and -6.6 kcal/mol, indicating diverse interactions. In-silico
pharmacokinetics and ADMET proling underscored favorable drug-like properties. Compound 2
emerged as a promising therapeutic candidate, showing superior binding versatility and strength. Both
compounds adhere to Lipinski's rule, suggesting their potential as viable drug candidates. Further
research and experimental validation are advocated to realize their therapeutic potential and expedite
drug development efforts.
1. INTRODUCTION
Cancer is a complex and devastating disease characterized by the uncontrolled growth and spread of
abnormal cells (Sarkar
et al.
, 2013). The complicated interplay of genetic, environmental, and lifestyle
factors contributes to the heterogeneous nature of cancer, rendering it a diverse and elusive adversary
(Wang
et al.
, 2022). Despite signicant advancements in cancer treatment, it continues to be a leading
cause of morbidity and mortality, imposing an immense burden on healthcare systems and societies at
large (Yabroff
et al.
, 2019). The quest for effective cancer therapies is a dynamic and continually evolving
endeavor, driven by the urgency to mitigate the devastating impact of this disease (Cheville
et al.
, 2021 &
Kaspar, 2019). Therefore, the development of novel and effective anticancer agents is an urgent need.
Natural compounds have served as a rich source of inspiration for drug discovery, giving rise to
numerous anticancer agents derived from plant sources (Cragg & Newman, 2009; Majolo
et al.
, 2019;
Cragg & Newman, 1999; Dzobo, 2022; Cragg
et al.
, 2009; Bako
et al.
, 2023a; Ushie
et al.
, 2022; Bako,
2023; Ushie
et al.
, 2013). Additionally, the signicance of natural compounds extends to the search for
new antimicrobial agents, as they provide a diverse array of chemical structures with potential
applications in combating microbial infections (Khan
et al.
, 2023; Osigbemhe
et al.
, 2023; Bako
et al.
,
2023b; Oko
et al.
, 2020; Bako
et al.
, 2023c). In the pursuit of innovative and effective anticancer agents,
the present study delves into the computational and molecular docking analysis of two promising
compounds: 1-Guanidinosuccinimide and Benzene-ethanamine, 2,5-diuoro-β, 3,4-trihydroxy-n-methyl.
These compounds exhibit intriguing molecular structures and have garnered attention for their potential
anticancer properties (Hanumantharaju
et al.
, 2020; Emelike
et al.
, 2021; de Rodríguez
et al.
, 2017a & b).
Mouse Double Minute 2 (MDM2), a key regulator of the tumor suppressor protein p53, emerges as a
critical target in cancer therapeutics (Munisamy
et al.
, 2021; Shangary & Wang, 2008; Hong e
t al.
, 2014).
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Dysregulation of the p53-MDM2 interaction is a common occurrence in various cancer types, making
MDM2 an attractive focal point for drug development (Nag
et al.
, 2013). The compounds under
investigation, 1-Guanidinosuccinimide and Benzene-ethanamine, 2,5-diuoro-β, 3,4-trihydroxy-n-methyl,
have been chosen due to their structural characteristics that suggest possible interactions with MDM2.
Building upon the foundation of previous research is integral to advancing our understanding. In a
notable study by Lauria
et al.
, (2010), the authors investigated the molecular interactions of compounds
structurally analogous to our selected agents with MDM2. Their ndings highlighted the potential of
small-molecule inhibitors of the p53-MDM2 protein-protein interaction for the treatment of cancers
retaining wild-type p53. The p53-MDM2 interaction is a critical pathway in cancer development, and
inhibiting this interaction has the potential to restore p53 tumor suppressor function and halt cancer cell
growth. The most studied chemotypes include cis-imidazolines, benzodiazepines, and spiro-oxindoles.
Cis-imidazolines were the rst class of potent and selective MDM2 inhibitors, and they continue to show
promise for cancer therapy, underscoring the signicance of our current exploration. Additionally, the
work of many researchers (Wang
et al.
, 2006; Agarwal, 2007; Tovar
et al.
, 2002; Katsila et al., 2016), with
recent advancements in MDM2-targeted cancer therapies highlights the signicance of specic
structural motifs in augmenting binding anity, reinforcing the exploration of our research.
Computational methods have become indispensable in drug discovery, offering a cost-effective and
ecient means to predict and analyze molecular interactions (Baldi, 2010). Molecular docking, in
particular, allows for the exploration of the binding modes and anities between small molecules and
target proteins (Guedes et al., 2014; Izuagbe et al., 2022a). In this study, we utilize state-of-the-art
computational tools, specically Density Functional Theory (DFT), to investigate the electronic structure
properties of the selected compounds. HOMO-LUMO analysis offers insights into their reactivity and
stability, while geometry optimization calculations identify the most energetically favorable
conformations (Ejaz et al., 2023; Izuagbe et al., 2022b) Vibrational frequency analyses, focusing on
Infrared (IR) spectra, are conducted to elucidate the dynamic behavior of the compounds (Ushie et al.,
2019; Khan et al., 2021; Etim et al., 2020a; Etim et al., 2018a; Etim et al., 2017a). Additionally, our study
broadens its scope to evaluate how different solvation methods impact the electronic and structural
properties of the compounds. This approach provides a nuanced understanding of their behavior in
diverse chemical environments, shedding light on binding mechanisms and the potential ecacy of
these compounds against Mouse Double Minute 2 (MDM2). The signicance of our work lies in its
potential to contribute to the development of novel anticancer therapeutics. By utilizing computational
methods, molecular docking analysis, and insights from prior studies, we it is aimed to advance the eld
and facilitate the rational design of compounds with improved anticancer properties. The investigations
highlights not only a deeper understanding of the compounds' interactions with Mouse Double Minute 2
(MDM2) but also a nuanced exploration of their behavior in diverse solvation environments, enhancing
the translational potential of these compounds in cancer therapy.
2. COMPUTATIONAL METHODS
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Quantum chemical calculations were performed employing the Gaussian 09 program suite to investigate
the molecular structures of 1-Guanidinosuccinimide and Benzene-ethanamine, 2,5-diuoro-β, 3,4-
trihydroxy-n-methyl (Fig.1). Employing the Becke-3-LeeYangPar (B3LYP) level, a widely recognized
density functional theory (DFT) approach renowned for its accuracy, and utilizing the standard 6-311
(d,p) basis set, computations were executed for geometry optimization, vibrational frequency analysis
(FT-IR), and the determination of crucial electronic properties (Etim et al., 2020b, Etim et al., 2020c; Etim
et al., 2020d; Shinggu et al., 2023). This strategic choice of methodology ensures a comprehensive
exploration of the compounds' molecular and electronic structure, facilitating a profound understanding
of their behavior on different solvation (Dimethyl Sulfoxide (DMSO), ethanol and methanol).
Subsequently, the calculation of key molecular properties, such as the Highest Occupied Molecular
Orbital (HOMO), Lowest Unoccupied Molecular Orbital (LUMO), and other pertinent parameters, provided
invaluable insights into the molecular electronic or orbital characteristics of the compounds. Additionally,
the vibrational frequency analysis contributed crucial information concerning the stability and nature of
the optimized structures, enhancing the overall understanding of these molecules at a quantum
mechanical level (Kavitha et al., 2010; Etim et al., 2021; Etim et al., 2018b; Andrew et al., 2018; Etim et al.,
2018c; Etim et al., 2017b).
2.1 Molecular Docking Study of Compound 1 and 2
The structures of the compounds (designated as compound 1 and 2) were sourced from the PubChem
database in SDF format. These compounds, previously reported in the literature for their anti-
inammatory and anti-cancer properties (Emelike et al., 2021; de Rodríguez, et al., 2017), were utilized in
this study and subsequently treated quantum mechanically through Gaussian 09 at the Becke-3-
LeeYangPar (B3LYP) level, employing the 6-311 (d,p) basis set (Osigbemhe et al., 2022 b & c). To attain a
stable structure with minimal energy, all parameters were meticulously congured. The 3-Dimensional
(3D) Structure (PDB) of each molecule were obtained post-optimization. The structure of Mouse Double
Minute 2 (PDB ID: 4ZFI) were sourced from the Protein Data Bank. Protein preparation adhered to the
standard AutoDock protocol utilizing Molecular Graphics Laboratory (MGL) tools 1.5.6 (Thomsen &
Christensen 2006). For protein preparation, the target protein, Mouse Double Minute 2 protein complex
(4ZFI), was obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank
(RCSB PDB) database and downloaded in Protein Data Bank (PDB) format. The AutoDock tools standard
protocol was applied for preparation, involving the removal of water molecules, addition of only polar
hydrogens, and application of Kollman charges to prevent interference with the docking process
(Thomsen & Christensen 2006; Abdullahi et al., 2022). The prepared protein structures were saved as
Protein Data Bank, Partial Charge (Q), and Atom Type (T) (PDBQT) les, enabling precise identication of
ligand molecule active sites for subsequent docking procedures. AutoDock Vina facilitated the
exploration of optimal docking conformations for the compounds with the protein, considering a
maximum of nine conformers for each ligand during the docking process. Conformations with the
lowest free binding anity were selected for further analysis of interactions, conducted using BIOVIA
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Discovery Studio Visualizer. The molecular docking procedures employed AutoDock Tools (Trott & Olson,
2010).
2.2 In-silico Pharmacokinetics and the ADMET Prole of the Compounds
In-silico pharmacokinetics and Absorption, Distribution, Metabolism, Excretion, and Toxicity. (ADMET)
proling have emerged as essential tools in drug discovery, providing a cost-effective, time-ecient, and
ethical approach to evaluating the potential ecacy and safety of drug candidates (Maharao et al.,
2020). By utilizing in-silico methods, researchers can accelerate the drug discovery process, identify
promising compounds for further development, and ultimately contribute to the development of safer
and more effective therapies (Brogi et al., 2020). The molecular structures of these compounds
underwent conversion to their canonical Simplied Molecular-Input Line-Entry System (SMILES) format
and were then processed through the Swiss Institute of Bioinformatics Absorption, Distribution,
Metabolism, and Excretion (SwissADME) tool for the estimation of in-silico pharmacokinetic parameters.
Utilizing the Swiss Institute of Bioinformatics Absorption, Distribution, Metabolism, and Excretion
(SwissADME) predictor, information pertaining to the count of hydrogen donors, hydrogen acceptors,
rotatable bonds, and the total polar surface area of the compounds was obtained. The ligands
(compounds) additionally underwent screening by Pharmacokinetics of Cytochrome P450 Substrate and
Mutations (pkCSM) predictors for Absorption, Distribution, Excretion, Toxicity (ADMET) prediction prole
(Abdullahi et al., 2022; Abdullahi et al., 2023).
3. RESULTS AND DISCUSSIONS
3.1 Optimized Geometry
The optimized conguration of compound 1 and 2 is of paramount signicance as it unveils crucial
insights into their molecular arrangement and spatial conformation. The geometry not only provides a
visual representation of the structural features (Fig.2) but also offers valuable information about
intermolecular interactions and overall three-dimensional characteristics (Shinggu et al., 2023b).
Optimization helps in understanding the selected compounds' properties, including stability, reactivity,
and binding interactions (Venkatesh et al., 2000; Onen et al., 2017). In essence, the optimized geometry
serves as a key to unraveling the functional aspects of compound 1 and 2, contributing to a
comprehensive comprehension of their molecular nature and potential applications.
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Table 1
Optimized Geometry of Compounds 1, & 2 using Solvents of DMSO, Ethanol and Methanol
Bond Lengths Compound 1 Bond Lengths Compound 2
DMSO Ethanol Methanol DMSO Ethanol Methanol
R(1–8) 1.236 1.236 1.240 R(1–10) 1.367 1.367 1.367
R(2–9) 1.237 1.237 1.242 R(2–13) 1.359 1.359 1.362
R(3–8) 1.409 1.409 1.403 R(3–7) 1.426 1.425 1.432
R(3–9) 1.408 1.407 1.403 R(3–21) 0.974 0.974 0.973
R(3–10) 1.448 1.448 1.449 R(4–12) 1.363 1.363 1.363
R(4–10) 1.361 1.362 1.360 R(4–25) 0.966 0.966 0.968
R(4–15) 1.007 1.007 1.007 R(5–14) 1.359 1.359 1.361
R(4–16) 1.006 1.006 1.006 R(5–26) 0.968 0.968 0.969
R(5–10) 1.288 1.287 1.287 R(6–9) 1.464 1.464 1.465
R(5–17) 1.024 1.024 1.024 R(6–15) 1.466 1.466 1.466
R(6–7) 1.536 1.536 1.537 R(6–20) 1.016 1.016 1.017
R(6–8) 1.513 1.513 1.509 R(7–8) 1.513 1.513 1.512
R(6–11) 1.089 1.089 1.090 R(7–9) 1.540 1.541 1.536
R(6–12) 1.090 1.090 1.090 R(7–16) 1.098 1.098 1.096
R(7–9) 1.512 1.512 1.508 R(8–10) 1.388 1.388 1.389
R(7–13) 1.089 1.089 1.090 R(8–11) 1.398 1.398 1.399
R(7–14) 1.090 1.090 1.090 R(9–17) 1.094 1.094 1.094
- - - - R(9–18) 1.099 1.099 1.100
- - - - R(10–12) 1.390 1.390 1.392
- - - - R(11–13) 1.383 1.383 1.381
- - - - R(11–19) 1.081 1.081 1.082
- - - - R(12–14) 1.399 1.399 1.399
- - - - R(13–14) 1.392 1.392 1.391
- - - - R(15–22) 1.093 1.093 1.094
- - - - R(15–23) 1.100 1.100 1.101
- - - - R(15–24) 1.092 1.092 1.093
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Understanding the bond lengths during the optimization of a compound in various solvation methods is
a critical aspect of molecular characterization (Thamarai et al., 2020). The variations in bond lengths
provide valuable insights into the dynamic interplay between the compound's molecular structure and
the surrounding solvent environment. These changes reect the compound's adaptability to different
solvents, shedding light on its stability, reactivity, and potential interactions within diverse chemical
surroundings (Adindu et al., 2023).
The presented table (Table1) outlines bond lengths in Compound 1 and 2 across three solvents -
Dimethyl Sulfoxide (DMSO), Ethanol, and Methanol. Optimizing these compounds in Dimethyl Sulfoxide
(DMSO), Ethanol, and Methanol solvents is a crucial process aimed at comprehending and dening their
behavior in diverse environments. Both compound 1 & 2 bonds display slight variations in length, with
Dimethyl Sulfoxide (DMSO) and Ethanol yielding similar values, while methanol results in a slightly longer
bond. Certain bonds like R(7–9) and among other bonds in compound 1 and 2 have same bond length
with Dimethyl Sulfoxide (DMSO) and ethanol but slight variation in methanol. This observation implies
that the nature of the solvent can inuence the specic interatomic distances within the molecules, with
Dimethyl Sulfoxide (DMSO) and ethanol showing a similar impact on bond lengths, while methanol
induces a subtle but discernible alteration which further suggest that a notable insensitivity of both
compounds to variations in the solvent, particularly within the tested contexts of Dimethyl Sulfoxide
(DMSO), ethanol and methanol as methanol only shows a non-signicant variations. The molecule's
properties, encompassing factors like geometry and other molecular descriptors, demonstrate a
remarkable uniformity across these solvents. This coherence in results implies that both compounds
likely possesses a stable molecular structure, exhibiting comparable interactions and conformations in
both Dimethyl Sulfoxide (DMSO), ethanol and methanol.
3.2 Highest Occupied Molecular Orbital (HOMO) and
Lowest Unoccupied Molecular Orbital (LUMO) Energy
Levels
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Table 2
Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular
Orbital (LUMO) energy levels of the Compounds in Dimethyl Sulfoxide (DMSO),
Ethanol and Methanol Solvent. Highest Occupied Molecular Orbital and Lowest
Unoccupied Molecular Orbital (HOMO-LUMO) underscores the signicance of
solvation conditions in understanding the molecular properties and behavior of
these compounds, offering valuable insights for applications in diverse chemical
environments (Odey et al., 2023).
Molecule Solvation Method HOMO Energy(eV) LUMO Energy(eV)
Compound 1 DMSO -6.7882 -0.9072
Methanol -6.8883 -1.2694
Ethanol -6.8037 -0.9355
Compound 2 DMSO -6.3362 -0.4373
Methanol -6.4807 -0.5818
Ethanol -6.3362 -0.4373
Table2 outlines the HOMO and LUMO energy levels for Compound 1 and Compound 2 in different
solvation methods; Dimethyl Sulfoxide (DMSO), Methanol, and Ethanol, obtained through quantum
chemical calculations and measured in in electron volts (eV).
Comparing HOMO energy across solvents reveals a slight variation, with the most negative value in
Methanol (-6.8883 eV), followed by Ethanol (-6.8037 eV) and Dimethyl Sulfoxide (DMSO) (-6.7882 eV).
More negative HOMO energies indicate a greater tendency to donate electrons, suggesting that
compound 1 is most electron-donating in Methanol, followed by Ethanol and DMSO. Similarly, LUMO
energy follows a comparable trend, with the most negative value in Methanol (-1.2694 eV), followed by
Ethanol (-0.9355 eV), and DMSO (-0.9072 eV). Lower LUMO energies signify a higher electron-accepting
ability, indicating that compound 1 with methanol solvation is the most electron-accepting, followed by
Ethanol and Dimethyl Sulfoxide (DMSO). These variations across solvents highlight the impact of the
solvation environment on compound 1’s electronic structure and reactivity, crucial information for
understanding its behavior in different settings and guiding the optimization of processes involving the
compound.
In a similar vein, when considering Compound 2, an analysis of HOMO energy levels indicates minimal
uctuation across solvents. The HOMO energy remains consistently steady in both Dimethyl Sulfoxide
(DMSO) (-6.3362 eV) and Ethanol (-6.3362 eV), with a slightly more negative value in Methanol (-6.4807
eV). This suggests that the molecule's inclination to donate electrons is not signicantly altered by the
solvent choice. Similarly, the LUMO energy levels exhibit subtle variations, with the most negative value
observed in Methanol (-0.5818 eV), followed by Dimethyl Sulfoxide (DMSO) (-0.4373 eV) and Ethanol
(-0.4373 eV). These nuanced changes imply that the molecule's capacity to accept electrons is only
subtly affected by the solvent. The consistent HOMO and LUMO energy levels suggest that the electronic
structure and reactivity of compound 2 relatively stable across the solvents studied. While these solvent-
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induced alterations are modest compared to those in other molecules, they still bear signicance for the
compound's behavior in diverse chemical environments. A comprehensive understanding of the
electronic characteristics in different solvents is imperative for predicting and managing the molecule's
reactivity in various applications or reaction conditions.
In Figs.3 and Fig.4, the HOMO-LUMO diagrams of Compound 1 and Compound 2 are presented, offering
visual representations of their respective electronic structures.
3.3 Vibrational Frequencies
Table 3
Vibrational Frequencies of Compound 1
Vibrational Mode Frequency (cm)
DMSO Solvent Ethanol Solvent Methanol Solvent
C-N 1001.57 93.4343 39.5346
C = O 870.10 102.1166 94.7053
1145.16 108.5369 107.5694
1210.31 188.4963 -
C-H 434.58 311.543 421.3864
559.04 431.007 459.7723
6721.51 475.3764 468.6184
688.19 499.2952 497.0187
730.77 729.009 -
C-C 1475.99 553.7869 551.7419
C = C 553.04 559.8785 559.237
600.11 599.76 603.7468
1066.14 643.18 1066.903
3.3.1 The Vibrational Spectrum of Compound 1 In DMSO
Solvent
The vibrational spectrum of compound 1 (Fig.5), the frequencies and intensities provide a
comprehensive understanding of the compound's structural composition and its interaction with the
solvent, methanol. Each peak corresponds to specic functional groups, and the variations in intensity
highlight the compound's sensitivity to the solvent environment. In the vibrational spectrum (Table3) of
compound 1 in Dimethyl Sulfoxide (DMSO) solvent, several prominent peaks provide valuable insights
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into its molecular composition. The peak at 1001.57 cm¹ signies a strong C-N stretch vibration,
characteristic of the carbon-nitrogen bond, suggesting the presence of a guanidine group. Guanidine
compounds typically exhibit robust C-N stretching vibrations, making this peak indicative of the specic
nitrogen-containing functional group within the molecule. Moving on to carbon-oxygen vibrations, two
distinct peaks stand out. The rst, at 870.10 cm¹, corresponds to a potent C = O stretch vibration
commonly associated with carbonyl groups. This suggests the presence of a carbonyl group, potentially
in the moiety of compound 1. The second peak at 1145.16 cm¹ hints at another C = O stretch vibration,
possibly associated with a different carbonyl group within the compound. Additionally, the peak at
1210.31 cm¹ suggests a third C = O stretch vibration, highlighting the structural complexity of the
molecule. The carbon-hydrogen (C-H) vibrations in the range of 434.58 cm¹ to 730.77 cm¹ unveil the
intricate hydrocarbon framework of compound 1. Peaks at 434.58 cm¹, 559.04 cm¹, 672.51 cm¹,
688.19 cm¹, and 730.77 cm¹ indicate diverse C-H bend or stretch vibrations, reecting the presence of
various types of hydrogen atoms within the molecule. This multiplicity underscores the complexity of the
hydrocarbon moieties contributing to the overall vibrational spectrum. The C-C stretch vibration at
1475.99 cm¹ suggests the existence of carbon-carbon bonds, potentially within the carbon backbone or
cyclic structures of the molecule. Lastly, the C = C vibrations at 553.04 cm¹, 600.11 cm¹, and 1066.14
cm¹ indicate the presence of carbon-carbon double bonds, contributing to the overall molecular
geometry of compound 1. These double bonds play a crucial role in the compound's reactivity and
chemical behavior. The vibrational spectrum of compound 1 unveils a rich array of functional groups,
including the distinctive guanidine group, carbonyl moieties, diverse hydrocarbon structures, carbon-
carbon bonds, and carbon-carbon double bonds. Each peak in the spectrum provides valuable
information about the compound's structural composition and potential reactivity.
3.3.2 The Vibrational Spectrum of Compound 1 In Ethanol
Solvent
The vibrational spectrum of compound (Table3, Fig.6) 1 optimized in ethanol, distinct shifts and
intensity variations in certain peaks suggest a notable inuence of the solvent on the molecular
vibrations. Starting with the C-N (carbon-nitrogen) vibrations, the peak at 93.4343 cm¹ indicates a C-N
stretch vibration, characteristic of the carbon-nitrogen bond. The heightened intensity at this frequency
suggests a pronounced interaction within the molecule, possibly inuenced by the solvent, ethanol. The
solvent-solute interactions in the C-N bond may play a signicant role in altering the observed vibrational
characteristics. Moving to the C = O (carbon-oxygen) vibrations, the peaks at 102.1166 cm¹, 108.5369
cm¹, and 188.4963 cm¹ represent C = O stretch vibrations associated with carbonyl groups. The shifts
and intensity variations in these peaks compared to the previous spectrum point to changes in the
molecular environment due to the presence of ethanol. The solvent appears to induce modications in
the carbonyl environments, possibly inuencing the number and nature of carbonyl groups within
compound 1. The C-H (carbon-hydrogen) vibrations, spanning from 311.543 cm¹ to 499.2952 cm¹,
exhibit consistent patterns, reecting the diverse hydrocarbon moieties in the compound. However,
variations in intensity, notably at 431.007 cm¹ and 475.3764 cm¹, indicate the sensitivity of C-H
vibrations to the solvent environment. Ethanol, as a polar solvent, may interact differently with various
Page 11/34
hydrocarbon groups, leading to changes in the observed C-H vibrational characteristics. Examining the C-
C (carbon-carbon) vibrations at 553.7869 cm¹, the shift in position and intensity changes compared to
the previous spectrum suggests alterations in the molecular structure inuenced by the solvent. Ethanol
interactions may impact the carbon-carbon bonds, inuencing the overall conformation of compound 1.
Lastly, the C = C (carbon-carbon double bond) vibrations at 559.8785 cm¹, 599.76 cm¹, and 643.18
cm¹ indicate the presence of double bonds, and their positions and intensities are sensitive to solvent
effects. The changes observed in these peaks suggest structural adjustments in the presence of ethanol,
highlighting the dynamic nature of the compound-solvent interaction. In summary, the vibrational
spectrum of compound 1 in ethanol provides valuable insights into the solvent-induced changes in
molecular vibrations. Ethanol alters the positions and intensities of specic vibrational modes, revealing
the compound's responsiveness to the solvent environment and offering crucial information about its
behavior in different solvents.
3.3.3 The Vibrational Spectrum of Compound 1 In Methanol
Solvent
In the vibrational spectrum (Table3, Fig.7) of compound 1 optimized in methanol as the solvent, we
observe distinctive changes in the molecular vibrations compared to the previous solvent-optimized
spectrum. Beginning with the C-N (carbon-nitrogen) vibrations at 39.5346 cm¹, the potential C-N stretch
vibration indicative of the carbon-nitrogen bond is still present. However, the low intensity suggests that
other vibrations may contribute to this frequency, emphasizing the intricate nature of nitrogen-containing
functional groups within the molecule. Moving to the C = O (carbon-oxygen) vibrations, the peaks at
94.7053 cm¹ and 107.5694 cm¹ signify C = O stretch vibrations associated with carbonyl groups. The
moderate intensity at 94.7053 cm¹ suggests the presence of this functional group, while the higher
intensity at 107.5694 cm¹ indicates a more signicant contribution, possibly from a different carbonyl
group within compound 1. The use of methanol as the solvent introduces a distinct molecular
environment, inuencing the observed vibrational modes of carbonyl groups. In the C-H (carbon-
hydrogen) vibrations, the peaks at 316.7589 cm¹, 421.3864 cm¹, 459.7723 cm¹, 468.6184 cm¹, and
497.0187 cm¹ indicate C-H bend or stretch vibrations. The higher intensity at 421.3864 cm¹
emphasizes a signicant contribution from C-H vibrations, highlighting the complex hydrocarbon
framework in compound 1. Changes in intensity and position, especially at 459.7723 cm¹ and 468.6184
cm¹, suggest a sensitivity of C-H vibrations to the methanol solvent, inuencing the molecular
interactions within the hydrocarbon moieties. Examining the C-C (carbon-carbon) vibrations at 551.7449
cm¹, there is a distinct shift in position and intensity compared to the previous solvent-optimized
spectrum, indicating alterations in the molecular structure inuenced by methanol. The solvent-induced
changes in the C-C vibrations underscore the impact of the choice of solvent on the compound's
conformation. Finally, focusing on the C = C (carbon-carbon double bond) vibrations at 559.237 cm¹ and
603.7468 cm¹, these peaks showcase the sensitivity of double bond vibrations to methanol effects. The
shifts in position and intensity suggest structural adjustments inuenced by the solvent, providing
insights into the dynamic behavior of compound 1 in a methanol environment. In summary, the
vibrational spectrum of compound 1 in methanol reveals notable shifts and intensity variations
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compared to the previous solvent-optimized spectrum. The choice of methanol as the solvent introduces
unique interactions that inuence the compound's molecular vibrations, providing valuable information
about its behavior in diverse solvent environments.
Table 4
Vibrational Frequencies of Compound 2 in DMSO, Ethanol and Methanol
Solvent.
Vibrational Mode Frequency (cm)
DMSO Solvent Ethanol Solvent Methanol Solvent
C-F 237.617 95.5276 58.054
C-O 247.9894 248.3084 122.358
1488.7221 1488.8298 191.6524
O-H 359.208 290.8983 637.8308
C-C 156.2992 156.4713 718.2607
C = C 126.453 198.6966 977.6243
N-H 3505.05 3505.2783 3495.9037
3.3.4 The Vibrational Spectrum of Compound 2 In DMSO
Solvent
The infrared spectrum of compound 2, recorded in Dimethyl Sulfoxide (DMSO) solvent and elucidates the
compound's molecular composition through distinct vibrational modes (Table4, Fig.8). The most
noteworthy peak at 237.617 cm¹ signies C-F stretching vibrations, underscoring the presence of
robust uorine-carbon bonds. With an intensity of 104.4944, this peak suggests a substantial
contribution from these bonds, offering insights into the compound's structural characteristics. The
second signicant peak at 247.9894 cm¹ corresponds to intense C-O stretching vibrations, pointing
towards the involvement of carbonyl or hydroxyl groups within the molecular framework. The high
intensity of 175.5993 emphasizes the prominence of oxygen-containing functional groups in the
compound. Together, these rst two peaks provide a strong foundation for understanding the
compound's chemical structure, hinting at the presence of both uorine and oxygen-related moieties.
Moving forward, the third major peak at 156.2992 cm¹ indicates signicant C-C stretching vibrations,
highlighting the presence of carbon-carbon bonds. With an intensity of 6.2459, this peak contributes
notably to the overall spectrum, showcasing the importance of carbon-carbon interactions in the
molecular architecture. Furthermore, additional peaks associated with C = C stretching, O-H stretching,
and N-H stretching vibrations provide a comprehensive overview of the compound's functional groups
and their relative abundances, deepening our understanding of its chemical nature. The infrared
spectrum of compound 2 in ethanol solvent reveals a rich tapestry of vibrational modes associated with
C-F, C-O, O-H, C-C, C = C, and N-H stretching vibrations. The intensity variations in these peaks offer
Page 13/34
valuable information about the prevalence of specic functional groups, providing a detailed molecular
ngerprint that enhances our comprehension of the compound's structure and potential reactivity.
3.3.5 The Vibrational Spectrum of Compound 2 In Ethanol
Solvent
Table4 and Fig.8 outlines the infrared (IR) spectrum of compound 2, analyzed in ethanol solvent, reveals
a diverse array of vibrational modes associated with specic functional groups. The most prominent
peak, with an intensity of 95.5276 cm¹, corresponds to C-F stretching vibrations, suggesting the
presence of strong uorine-carbon bonds in the compound. Additionally, the second highest intensity
peak at 248.3084 cm^-1 indicates signicant C-O stretching vibrations, indicative of carbonyl or hydroxyl
groups. The third major peak at 156.4713 cm¹ corresponds to C-C stretching vibrations, revealing the
presence of carbon-carbon bonds. Further exploration of the spectrum reveals several other key
vibrational modes. Peaks at 198.6966 cm¹ and 290.8983 cm¹ are associated with C = C stretching and
O-H stretching vibrations, respectively, suggesting the presence of double bonds and hydroxyl groups.
The spectrum also exhibits peaks corresponding to N-H stretching and bending vibrations, C-H
stretching and bending vibrations, and C-N stretching vibrations, pointing to the existence of amine
groups, alkyl groups, and nitrogen-carbon bonds in the molecular structure. The IR spectrum of
compound 2 in ethanol solvent provides valuable insights into its molecular composition. The intense
peaks at specic frequencies indicate the abundance of distinct functional groups, such as uorine-
carbon bonds, carbonyl or hydroxyl groups, and carbon-carbon bonds. The presence of amine groups,
double bonds, and alkyl groups further contributes to the complexity of the molecular structure,
enhancing our understanding of the compound's chemical composition and potential reactivity.
3.3.6 The Vibrational Spectrum of Compound 2 In Methanol
Solvent
Table4 and Fig.9 outlines the vibrational spectrum of compound 2, the frequencies and intensities
provide a comprehensive understanding of the compound's structural composition and its interaction
with the solvent, methanol. Each peak corresponds to specic functional groups, and the variations in
intensity highlight the compound's sensitivity to the solvent environment. The peaks in the C-F (Carbon-
Fluorine) vibrations, such as the one at 58.054 cm¹, suggest potential C-F stretch vibrations, indicating
the presence of carbon-uorine bonds. The low intensity may imply a moderate abundance of this
functional group, with the possibility of interactions inuenced by the methanol solvent. In the C-O
(Carbon-Oxygen) vibrations, peaks at 122.358 cm¹ and 191.6524 cm¹ represent C-O stretch vibrations
associated with hydroxyl and other oxygen-containing groups, respectively. The moderate and strong
intensities suggest signicant contributions from these functional groups, and variations in intensity may
be attributed to interactions with the methanol solvent. The O-H (Hydroxyl) vibration peak at 637.8308
cm¹ indicates O-H stretch vibrations characteristic of hydroxyl groups. The high intensity implies a
substantial presence of hydroxyl functional groups in the molecule, with potential variations inuenced
by the methanol solvent. The C-C (Carbon-Carbon) vibration peak at 718.2607 cm¹ suggests a C-C
Page 14/34
stretch vibration, reecting the presence of carbon-carbon bonds. The substantial intensity implies a
signicant contribution from C-C vibrations, and changes may be associated with the solvent
environment. The C = C (Carbon-Carbon Double Bond) vibration peak at 977.6243 cm¹ represents a C =
C stretch vibration indicative of carbon-carbon double bonds. The high intensity implies a signicant
contribution from double bond vibrations, with potential adjustments inuenced by the methanol solvent.
The vibrational frequencies of compound 2 in methanol reveal a complex interplay between the
compound's functional groups and the solvent. The intensity variations underscore the compound's
sensitivity to the methanol environment, providing valuable insights into its structural dynamics and
behavior in different solvent conditions.
3.3.7 Comparison of the Vibrational Spectrum of
Compound 1 & 2
While comparing the vibrational properties of both compounds, Compound 2 exhibits unique vibrational
modes not present in Compound 1. For example, in methanol solvent, Compound 2 shows a signicant
vibrational mode at 718.2607 cm¹ corresponding to C-C stretching, which is absent in Compound 1.
Conversely, Compound 1 displays unique vibrational modes not found in Compound 2. For example, in
DMSO solvent, Compound 1 shows a vibrational mode at 6721.51 cm¹, which is likely associated with
C-H stretching and is not observed in Compound 2.
In the DMSO solvent, Compound 2 exhibits higher frequencies for most vibrational modes compared to
Compound 1. For example, the C-F, C-O, and O-H frequencies are notably higher in Compound 2. In
contrast, in ethanol and methanol solvents, Compound 1 generally shows higher frequencies for most
vibrational modes compared to Compound 2. For instance, in ethanol solvent, the C-N and C = O
frequencies are higher in Compound 1.
Despite differences in solvent dependence, some vibrational modes are similar between the two
compounds. For instance, in ethanol solvent, both compounds exhibit similar frequencies for C = C
stretching vibrations, albeit with slight variations. Overall, the comparison highlights how the choice of
solvent can signicantly inuence the vibrational frequencies of compounds, and it underscores the
importance of considering solvent effects in vibrational spectroscopy studies.
3.4 Molecular Docking Studies
Molecular docking studies are generally utilized to explore binding energy and validate the molecular
mechanisms of ligands at a protein's active site (Guedes et al., 2014). In this study, two distinct
compounds (designated as compound 1 & 2) underwent molecular docking against a chosen protein,
namely 4z, employing AutoDock Vina command prompt to elucidate the binding modes and the
possible interactions.
Page 15/34
Table 5
Molecular Docking Results of Compound 1, & 2 against Mouse Double Minute 2 (PDB ID: 4ZFI)
Compounds Protein
Code Binding
Anity
(kcal/mol)
H bonds Residue Interactions
Hydrophobic and
Electrostatic
Interactions
Van der Waal’s
Interactions
Compound
14ZFI -5.9 Conventional-
Hydrogen-A:LEU85
(2.36 Å), and
Conventional-
Hydrogen-
A:ASN106 (2.14 Å)
Unfavorable
positive-positive
Pi-Alkyl-A:LYS31
(5.12 Å), Pi-Alkyl-
A:LEU33 (5.22 Å),
Pi-Alkyl-B:LEU85
(5.38 Å)
A:ASP 84,
B:ARG 105,
B:ASN 106,
B:LEU 33,
B:ASP 84,
A:ARG 105.
Compound
24ZFI -6.6 Conventional-
Hydrogen-
D:THR101 (2.79 Å),
Carbon-Hydrogen-
D:LYS98 (3.34 Å),
and Pi-Donor-
Hydrogen-
D:THR101 (3.92Å)
Hydrophobic-Pi-
Sigma-D:THR101
(3.88 Å),
Hydrophobic-Pi-
Alkyl-B:LYS31
(5.18 Å), and
Hydrophobic-Pi-
Alkyl-B:PRO32
(4.38 Å)
-
Keys
leusine (LEU), asparagine (ASN), lysine (LYS), aspartic acid (ASP), arginine (ARG), Threonine (THR),
Proline (PRO).
Table. 5 reveals the results obtained from the molecular docking study of 1-guanidinosuccinimide (1-
GSI) with the protein 4ZFI. The ndings indicate that 1-GSI binds to 4ZFI with a binding anity of -5.9
kcal/mol. This binding anity is attributed to a combination of interactions, including hydrogen bonds,
pi-alkyl interactions, hydrophobic interactions, and van der Waal's interactions.
Compound 1 (1-Guanidinosuccinimide) forms two conventional hydrogen bonds with residues LEU85
(2.36 Å) and ASN106 (2.14 Å) within the binding site of 4ZFI. These hydrogen bonds are anticipated to
play a pivotal role in enhancing the stability of the complex, imparting specic and directional
interactions crucial for ligand-protein binding (Morozov & Kortemme, 2005). This assertion gains support
from the observation that 1-Guanidinosuccinimide is involved in pi-alkyl interactions with residues LYS31
(5.12 Å), albeit characterized as unfavorable positive-positive Pi-Alkyl interactions. Despite their
unfavorable nature, these interactions may introduce distinctive nuances to the overall binding dynamics.
Additionally, the ligand engages in pi-alkyl interactions with LEU33 (5.22 Å) and LEU85 (5.38 Å), where
the pi-electrons of the ligand's aromatic ring interact with the alkyl side chains of the amino acid
residues. These interactions signicantly contribute to the overall binding anity, providing non-polar
interactions that effectively stabilize the ligand-protein complex.
Page 16/34
In addition to the aforementioned interactions, 1-Guanidinosuccinimide also participates in van der
Waal's interactions with residues ASP84, ARG105, ASN106, and LEU33. These interactions are weak but
additive, meaning that the sum of all van der Waal's interactions contributes to the overall binding
anity. These interactions provide close-range attractions between the ligand and the protein, further
stabilizing the complex.
The 2D and 3D ligand-protein interactions of compound 1 with 4ZFI are illustrated in Fig.11. The 3D ball
and stack models delineate the binding pocket structure of 4ZFI in association with compound 1.
Hydrogen bonds formed between the compound and amino acids are represented by green dashed lines,
an unfavoarable positive-positive interactions depicted with a red dashed lines, while pi-alkly interactions
are depicted by pink dashed lines purple and van der Waals intereactions are deepicted as light green.
Compound 2 (Benzene-ethanamine, 2,5-diuoro-, 3,4-trihydroxy-n-methyl) exhibits a binding anity of
-6.6 kcal/mol towards protein 4ZFI as depicted in Table5, implying a tight and stable interaction between
the compound and the protein. This interaction is further reinforced by the presence of three hydrogen
bonds: a conventional hydrogen bond with THR101 (2.79 Å), a carbon-hydrogen bond with LYS98 (3.34
Å), and a pi-donor hydrogen bond with THR101 (3.92 Å). These hydrogen bonds play a crucial role in
stabilizing the complex by providing directional and specic interactions between the ligand and the
protein.
In addition to hydrogen bonds, compound 2 also engages in hydrophobic interactions with three
residues: THR101 (3.88 Å), LYS31 (5.18 Å), and PRO32 (4.38 Å). These interactions arise from the
attraction between the non-polar regions of the ligand and the hydrophobic regions of the amino acid
residues. They contribute to the overall binding anity by providing non-polar contacts that minimize
solvent exposure. Moreover, van der Waal's interactions, albeit weak, are also present between
compound 1 and the protein. These interactions arise from the close proximity of the ligand and the
protein, resulting in weak but additive attractions that further stabilize the complex.
Compound 2 forms diverse interactions with protein 4ZFI, encompassing hydrogen bonds, hydrophobic
interactions, and van der Waal's interactions. These interactions collectively contribute to the high
binding anity, making compound 1 a potential therapeutic candidate for targeting 4ZFI.
Figure 12 illustrates the binding interactions between compound 2 and 4ZFI, revealing the molecular
mechanisms governing their association. The 2D representation provides a planar view of the
interactions, highlighting the amino acids involved in hydrogen bonding, hydrophobic interactions, and
van der Waal's interactions. Conventional and carbon hydrogen bonds, depicted by green/sea green
dashed lines, represent direct interactions between the ligand's functional groups and the protein's
backbone or side chains. Pi-alkyl interactions, represented by pink dashed lines, signify non-polar
interactions between the ligand's hydrophobic moieties and the protein's hydrophobic regions. Van der
Waal's interactions, represented by light green lines, are weak but additive attractions between the ligand
and the protein, arising from the close proximity of their electron clouds. These interactions collectively
contribute to the overall binding anity. The 3D ball and stick models provide a (3D) perspective of the
Page 17/34
binding interactions, allowing for a clearer understanding of the ligand's orientation within the protein's
binding pocket. The ball model showcases the ligand and the protein residues in their respective shapes
and sizes, while the stack model emphasizes the layering of the ligand and the protein residues within
the binding pocket.
3.5
In-Silico
Drug-likeness Predictions using SwissADME
Drug-likeness prediction involves assessing whether a given pharmacological agent possesses
attributes consistent with effective oral drug administration (Agoni et al., 2023). Lipinski's rule outlines
specic criteria for potential drug-like molecules, emphasizing attributes such as having fewer than ve
hydrogen-bond donors (HBDs), fewer than ten hydrogen-bond acceptors (HBAs), a molecular mass
below 500 Da, a log P not exceeding ve, and a total polar surface area (TPSA) not exceeding 140 Å
(Baell et al., 2013).
Table 6
In-silico
Pharmacokinetics Predictions of the Compounds
(SwissADME)
Parameters Compound 1 Compound 2
Molecular Formula C5H7N3O2C9H17F2NO3
Molecular Weight (g/mol) 141.13 219.19
NHA 3 6
NHD 2 4
NRB 1 3
BS 0.55 0.55
SA 1.49 2.41
MLOGP -1.04 0.90
TPSA (A°2) 87.25 72.72
Log P (iLOGP) 0.58 1.54
Log S (ESOL) -0.01 -1.38
Lipinski’s rule of ve 0 0
Keys: NBA: Number of H-bond acceptors; NHD: Number of H-bond donors; NRB: Number of Rotatable
Bond; BA: Bioavailability Score; SA: Synthetic Accessibility, TPSA: Total Polar Surface Area, MLOGP:
Molecular Logarithm of the Octanol-Water Partition Coecient.
The ndings from the current study indicate that all compounds adhere to Lipinski's rule of ve (Table6),
suggesting their potential as candidates for anti-cancer investigations. The number of hydrogen bond
Page 18/34
acceptors (NHA), hydrogen bond donors (NHD), and rotatable bonds (NRB) are important factors in
determining the drug-likeness of a compound. Lipinski's rule of ve serves as a heuristic for anticipating
a compound's drug-likeness. According to this rule, a compound is deemed drug-like if it possesses no
more than ve hydrogen bond donors (comprising nitrogen–hydrogen and oxygen–hydrogen bonds), no
more than ten hydrogen bond acceptors (all derived from nitrogen or oxygen atoms), a molecular mass
below 500 daltons, and a calculated octanol-water partition coecient (Clog P) not surpassing 5 (Baell
et al., 2013).
Compound 1 and Compound 2 exhibit promising drug-like properties based on their molecular
characteristics and predicted values. Both compounds possess molecular weights within the desirable
range for oral bioavailability, suggesting potential for effective absorption into the bloodstream. Their
molecular formulas indicate relatively small and exible structures, potentially facilitating distribution
within the body. Moreover, both compounds exhibit the presence of rotatable bonds, allowing for
conformational exibility and potential interactions with biological targets.
Compound 1 demonstrates a moderate Bioavailability Score (BA) of 0.55, indicating a reasonable
likelihood of absorption into the bloodstream. Its Synthetic Accessibility (SA) value of 1.49 suggests that
the compound can be synthesized with relative ease, an important factor in drug development feasibility.
The Molecular Logarithm of the Octanol-Water Partition Coecient (MLOGP) value of -1.04 and
Topological Polar Surface Area (TPSA) of 87.25 Ų suggest moderate lipophilicity and polar surface area,
respectively, potentially inuencing solubility and membrane permeability. While compound 2 also
exhibits a moderate Bioavailability Score (BS) of 0.55, similar to Compound 1. Its Molecular Polar
Surface Area (SA) of 2.41 suggests a limited polar region compared to Compound 1, potentially affecting
solubility and absorption. The Molecular Logarithm of the Octanol-Water Partition Coecient (MLOGP)
value of 0.90 and Topological Polar Surface Area (TPSA) of 72.72 Ų suggest moderate lipophilicity and
polarity, respectively. The Log P (iLOGP) value of 1.54 indicates a higher degree of hydrophobicity
compared to Compound 1, potentially inuencing membrane penetration. However, the Log S (ESOL)
value of -1.38 suggests limited solubility in water compared to Compound 1.
Page 19/34
Table 7
ADMET Proles of the Compound 1 & 2
Property Parameter Predicted value
Compound
1Compound
2
Absorption (% Absorbed) Human Intestinal Absorption 71.335 82.383
Distribution BBB Permeability -0.536 -0.872
CSN Permeability -3.517 -2.806
Metabolism (Cytochrome
P450, CYP) CYP2D6 Substrate No No
CYP3A4 Substrate No No
CYP1A2 Inhibitor No No
CYP2C19 Inhibitor No No
CYP2C9 Inhibitor No No
CYP2D6 Inhibitor No No
CYP3A4 Inhibitor No No
Excretion Total clearance 0.728 0.698
Toxicity AMES Toxicity Yes Yes
Human Max. tolerated dose (log
mg/kg/day) 0.728 0.269
Table7, the predicted values for absorption, distribution, metabolism, excretion (ADME), and toxicity
properties for Compound 1 and Compound 2 provide insights into their pharmacokinetic and safety
proles. In terms of absorption, both compounds show high human intestinal absorption, with
Compound 2 having a slightly higher percentage absorbed (82.383%) compared to Compound 1
(71.335%). Regarding distribution, the blood-brain barrier (BBB) permeability values for both compounds
are negative, indicating limited penetration into the central nervous system (CNS). The cytochrome P450
(CYP) metabolism analysis reveals that neither compound is a substrate or inhibitor for major CYP
enzymes (2D6, 3A4, 1A2, 2C19, 2C9), suggesting a lower likelihood of drug-drug interactions. In terms of
excretion, both compounds exhibit comparable total clearance values, with Compound 1 at 0.728 and
Compound 2 at 0.698. This suggests that both compounds have relatively ecient clearance rates from
the body. However, in terms of toxicity, both compounds show AMES toxicity, indicating a potential to
cause genetic mutations. The predicted human maximum tolerated dose, represented by the log
mg/kg/day, is higher for Compound 1 (0.728) compared to Compound 2 (0.269), suggesting a potentially
higher tolerance for Compound 1.
4. CONCLUSION
Page 20/34
In conclusion, this comprehensive study aimed at elucidating the anticancer potential of 1-
Guanidinosuccinimide and Benzene-ethanamine, 2,5-diuoro-β, 3,4-trihydroxy-n-methyl for possible
interactions with Mouse double minute 2, a pivotal protein in cancer pathways. Gaussian 09 was
employed for quantum chemical calculations, utilizing the Becke-3-Lee-Yang-Par (B3LYP) level of theory
and the standard 6-311(d,p) basis set to investigate the molecular structures of the compounds in
various solvation environments, including DMSO, ethanol, and methanol. Optimizing both compounds in
Dimethyl Sulfoxide (DMSO), ethanol, and methanol is crucial for understanding their behavior in diverse
environments. Both compounds exhibit slight variations in bond length, with methanol resulting in
slightly longer bonds. The Highest Occupied Molecular Orbitals (HOMO) and Lowest Unoccupied
Molecular Orbitals (LUMO) energy levels reveal Compound 1's sensitivity to its environment, especially in
methanol, while compound 2 shows minimal changes. Vibrational spectra in different solvents indicate
notable though insignicant shifts, emphasizing the solvent's inuence on molecular vibrations.
Molecular docking analysis highlights Compound 1's stabilization of interaction with 4ZFI through
various forces, while compound 2 shows strong binding anity. Both compounds have therapeutic
potential, with compound 2 standing out for its diverse interactions. Despite favorable drug-like
properties, including oral bioavailability and absorption, toxicity concerns, especially Ames Mutagenicity
Evaluation System (AMES) toxicity and lower predicted maximum tolerated dose for Compound 2,
suggest the need for further experimental validation to guide their pharmaceutical development.
Declarations
Data Availability Statement
All unique contributions made in this research are incorporated within the article. For additional
information, inquiries may be directed to the corresponding authors.
Author Contributions
BB: conceptualization, methodology, and investigation; contributed to the draft, writing, and conducted
computational, optimization, and molecular docking studies; responsible for data visualization and
analysis. E.E.E: conceptualization, methodology, and investigation; involved in the draft, investigation,
supervision and validation J.P.S: conceptualization, methodology, and investigation; conducted
computational, optimization, and molecular docking studies; contributed to data visualization, analysis,
and validation. H.S.S: conceptualization and methodology; conducted a thorough review and editing of
the manuscript. L.J.M: conceptualization and methodology; participated in a comprehensive review and
editing of the manuscript.
Funding
No nancial support was received for this research.
Conict of Interest
Page 21/34
The authors declare that they have no known competing nancial interests or personal relationships that
could have appeared to inuence the work reported in this paper.
Supplementary Materials
Supplementary materials will be made available on request
ACKNOWLEDGEMENT
The authors extend their sincere appreciation to everyone who has played a crucial role in making this
work possible. The authors also extend their appreciation to the Computational, Astrochemistry, and Bio-
simulation Research Group at the Federal University Wukari, Wukari, Nigeria, for their valuable
contributions and the provision of research facilities, enhancing the overall quality of this study.
ORCID
Bulus Bakohttps://orcid.org/0009-0001-3946-0712
Emmanuel E. Etimhttps://orcid.org/0000-0001-8304-9771
John Paul Shingguhttps://orcid.org/0009-0005-2216-3155
Humphrey Samuelhttps://orcid.org/0009-0001-7480-4234
Liberty Joshua Moses https://orcid.org/0009-0009-9432-6389
References
1. Abdullahi Bello Umar, Sagiru Hamza Abdullahi, Adamu Uzairu, Gideon Adamu Shallangwa, Sani Uba,
Molecular Docking Studies of some Coumarin Derivatives as Anti-Breast Cancer agents:
ComputerAided Design and Pharmacokinetics Studies. Prog. Chem. Biochem. Res, 6(3) (2023) 229–
243. DOI: 10.48309/pcbr.2023.408519.1267.
2. Abdullahi SH, Uzairu A, Shallangwa GA, Uba S, Umar AB. Computational modeling, ligand-based drug
design, drug-likeness and ADMET properties studies of series of chromen-2-ones analogues as anti-
cancer agents, Bulletin of the National Research Centre; 2022 Dec; 46(1):1–25.
3. Adindu, E. A., Godfrey, O. C., Agwupuye, E. I., Ekpong, B. O., Agurokpon, D. C., Ogbodo, S. E., ... &
Louis, H. (2023). Structural analysis, reactivity descriptors (HOMO-LUMO, ELF, NBO), effect of polar
(DMSO, EtOH, H2O) solvation, and libido-enhancing potential of resveratrol by molecular docking.
Chemical Physics Impact, 7, 100296.
4. Agarwal, L. P. (2007). MDM2 inhibitors: from bench to bedside. Cancer Cell, 12(3), 235–239.
5. Agoni, C., Olotu, F. A., Ramharack, P., & Soliman, M. E. (2020). Druggability and drug-likeness
concepts in drug design: are biomodelling and predictive tools having their say?. Journal of
molecular modeling, 26, 1–11.
Page 22/34
. Andrew, C., Etim, E. E., Ushie, O. A., & Khanal, G. P. (2018). Vibrational-Rotational Spectra of Normal
Acetylene and Doubly Deuterated Acetylene: Experimental and Computational Studies.
Chemical
Science
,
7
(1), 77–82.
7. Baell, J., Congreve, M., Leeson, P., & Abad-Zapatero, C. (2013). Ask the experts: past, present and
future of the rule of ve. Future medicinal chemistry, 5(7), 745–752.
. Bako, B. (2023). Applications of Justicia secunda Extracts in Functional Foods and Natural
Products: A Review. Advanced Journal of Chemistry-Section B, 6, 1–10.
https://doi.org/10.48309/ajcb.2024.412568.1192
9. Bako, B., Danladi, A. H., Bulus, G. G., & Ushie, O. A. (2023). Antimicrobial ecacy of Justicia secunda
stem crude extracts.
Journal of Chemical Research Advances.
01–04.
10. Bako, B., Danladi, A., Bulus, G., & Shinggu, J. (2023). A Comprehensive Review of Solvent-Induced
Variability in Antioxidant Proling of Plants Extract: Justicia secunda.
Progress in Chemical and
Biochemical Research
, (), 1–21. doi: 10.48309/pcbr.2024.417487.1292
11. Bako, B., Ushie, O. A., & Malu, S. P. (2023). Lupeol and Lauric Acid Isolated from Ethyl Acetate Stem
Extract of Justicia Secunda and their Antimicrobial Activity.
Journal of Chemical Society of Nigeria
,
48
(1).
12. Baldi, A. (2010). Computational approaches for drug design and discovery: An overview.
Systematic
reviews in Pharmacy
,
1
(1), 99.
13. Brogi, S., Ramalho, T. C., Kuca, K., Medina-Franco, J. L., & Valko, M. (2020). In silico methods for drug
design and discovery.
Frontiers in chemistry
,
8
, 612.
14. Cheville, A., Smith, S., Barksdale, T., & Asher, A. (2021). Cancer rehabilitation.
Braddom's Physical
Medicine and Rehabilitation
, 568–593.
15. Cragg, G. M., & Newman, D. J. (1999). Discovery and development of antineoplastic agents from
natural sources.
Cancer investigation
,
17
(2), 153–163.
1. Cragg, G. M., & Newman, D. J. (2009). Nature: a vital source of leads for anticancer drug
development.
Phytochemistry reviews
,
8
, 313–331.
17. Cragg, G. M., Grothaus, P. G., & Newman, D. J. (2009). Impact of natural products on developing new
anti-cancer agents.
Chemical reviews
,
109
(7), 3012–3043.
1. de Rodríguez, D. J., Carrillo-Lomelí, D. A., Rocha-Guzmán, N. E., Moreno-Jiménez, M. R., Rodríguez-
García, R., Díaz-Jiménez, M. L. V., ... & Villarreal-Quintanilla, J. A. (2017). Antioxidant, anti-
inammatory and apoptotic effects of Flourensia microphylla on HT-29 colon cancer cells.
Industrial
Crops and Products
,
107
, 472–481.
19. de Rodríguez, D. J., Salas-Méndez, E. D. J., Rodríguez-García, R., Hernández-Castillo, F. D., Díaz-
Jiménez, M. L. V., Sáenz-Galindo, A., ... & Carrillo-Lomelí, D. A. (2017). Antifungal activity in vitro of
ethanol and aqueous extracts of leaves and branches of Flourensia spp. against postharvest fungi.
Industrial Crops and Products
,
107
, 499–508.
20. Dzobo, K. (2022). The role of natural products as sources of therapeutic agents for innovative drug
discovery.
Comprehensive Pharmacology
, 408.
Page 23/34
21. Ejaz, S. A., Aziz, M., Ahmed, A., Alotaibi, S. S., Albogami, S. M., Siddique, F., & Batiha, G. E. S. (2023).
New Insight into the pharmacological importance of atropine as the potential inhibitor of AKR1B1
via detailed computational investigations: DFTs, ADMET, molecular docking, and molecular
dynamics studies.
Applied Biochemistry and Biotechnology
, 1–22.
22. Emelike, C. U., Anyaehie, U. S., Iyare, E. E., Obike, C. A., Aloke, C., Chukwu, D. F., ... & Chukwu, J. A.
(2021). Chemical composition and evaluation of methanol leaf extract of combretum
dolichopetalum on body weights and haematological indices of phenylhydrazine induced-anaemic
rats.
Toxicology International
,
28
(2), 135–144.
23. Etim, E. E., Abah, B. S., Mbakara, I. E., Inyang, E. J., & Ukaa, O. P. (2017b). Quantum Chemical
Calculations on Silicon Monoxide (SiO) and its Protonated Analogues.
Tropical Journal of Applied
Natural Sciences
,
2
(1), 61–68.
24. Etim, E. E., Asuquo, J. E., Ngana, O. C., & Ogofotha, G. O. (2022). Investigation on the
thermochemistry, molecular spectroscopy and structural parameters of pyrrole and its isomers: a
quantum chemistry approach.
Journal of Chemical Society of Nigeria
,
47
(1).
25. Etim, E. E., Khan, M. E., Godwin, O. E., & Ogofotha, G. O. (2021a). Quantum Chemical Studies on
C4H4N2 Isomeric Molecular Species.
Journal of the Nigerian Society of Physical Sciences
, 429–
445.
2. Etim, E. E., Oko Emmanuel, G., Fridy, I., & Olagboye, A. (2020b). Quantum Chemical Studies on Furan
and Its Isomers.
International Journal of Modern Chemistry
,
12
, 77–98.
27. Etim, E. E., Oko Emmanuel, G., Sambo, I. F., & Adeoye, S. (2020d). Suspected Error in Some
Experimentally Reported Proton Anity Values: Insight from Quantum Chemical Calculations.
Int. J.
Modern Anal. Sep. Sci
,
8
(1), 28–44.
2. Etim, E. E., Oko Emmanuel, Godwin, Iok F. Sambo, Sulaiman Adeoye Olagboye. (2020a). Quantum
Chemical Studies on Furan and Its Isomers. International Journal of Modern Chemistry, 12(1): 77–
98.
29. Etim, E. E., Oko, G. E., Onen, A. I., Ushie, O. A., Andrew, C., Lawal., U, Khanal, G. P, (2018).
Computational Studies of Sulphur Trioxide (SO3) and its Protonated Analogues. J. Chem Soc.
Nigeria, 43 (2): 10–17.
30. Etim, E. E., Oko, G. E., Onen, A. I., Ushie, O. A., Lawal, U., & Khanal, G. P. (2018c). Computational
Studies of Sulphur trioxide (SO3) and its protonated analogues.
Journal of Chemical Society of
Nigeria
,
43
(2).
31. Etim, E. E., Olagboye, S. A., Godwin, O. E., & Atiatah, I. M. (2020c). Quantum Chemical studies on
Silicon tetrauoride and its protonated analogues.
Int. J. Modern Chem
,
12
(1), 26–45.
32. Etim, E. E., Onen, A. I., Andrew, C., Lawal, U., Udegbunam, I. S., & Ushie, O. A. (2018b). Computational
Studies of C5H5N Isomers.
Journal of Chemical Society of Nigeria
,
43
(2).
33. Etim, E.E., Abah, B.S., Mbakara, I.E, Inyang, E.J., Ukaa, O.P. (2017). Quantum Chemical Calculations
on Silicon Monoxide (SiO) and its Protonated Analogues. Trop. J. Appl. Nat. Sci., 2(1): 61–68. Doi:
https://doi.org/10.25240/TJANS.2017.2.1.10.
Page 24/34
34. Guedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2014). Receptor–ligand molecular docking.
Biophysical reviews
,
6
, 75–87
35. Hanumantharaju, K. N., Thangavel, K., Amirtham, D., & Rani, C. I. (2020). Physical and antimicrobial
properties of Cissus quadrangularis stem extract incorporated edible lms.
Indian Journal of
Ecology
,
47
(2), 497–501.
3. Hong, B., van den Heuvel, P. J., V Prabhu, V., Zhang, S., & S El-Deiry, W. (2014). Targeting tumor
suppressor p53 for cancer therapy: strategies, challenges and opportunities.
Current drug targets
,
15
(1), 80–89.
37. Izuagbe G. Osigbemhe, Emmanuella E.Oyoitab, Hitler Louis, Emmanuel M.Khan, Emmanuel E.Etim,
Henry O.Edet, Onyinye J.Ikenyirimba, Amoawe P.Oviawe, Faith Obuye, (2022b). Antibacterial
potential of N-(2-furylmethylidene)-1, 3, 4-thiadiazole-2-amine: Experimental and theoretical
investigations. Journal of the Indian Chemical Society,99(9):100597.
https://www.sciencedirect.com/science/article/abs/pii/S001945222200259X
3. Izuagbe G. Osigbemhe, Hitler Louis, Emmanuel M. Khan, Emmanuel E. Etim, Emmanuella E. Oyo-ita,
Amoawe P. Oviawe, Henry O. Edet, Faith Obuye. Antibacterial Potential of 2-(-(2-Hydroxyphenyl)-
methylidene)-amino)nicotinic Acid: Experimental, DFT Studies, and Molecular Docking Approach.
Appl Biochem Biotechnol (2022a). https://doi.org/10.1007/s12010-022-04054-9
39. Kaspar, H. (2019). Searching for therapies, seeking for hope: transnational cancer care in Asia.
Mobilities
,
14
(1), 120–136.
40. Katsila, T., Spyroulias, G. A., Patrinos, G. P., & Matsoukas, M. T. (2016). Computational approaches in
target identication and drug discovery.
Computational and structural biotechnology journal
,
14
,
177–184.
41. Kavitha, E., Sundaraganesan, N., Sebastian, S., & Kurt, M. (2010). Molecular structure, anharmonic
vibrational frequencies and NBO analysis of naphthalene acetic acid by density functional theory
calculations.
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
,
77
(3), 612–
619.
42. Lauria, A., Tutone, M., Ippolito, M., Pantano, L., & Almerico, A. M. (2010). Molecular modeling
approaches in the discovery of new drugs for anti-cancer therapy: the investigation of p53-MDM2
interaction and its inhibition by small molecules.
Current medicinal chemistry
,
17
(28), 3142–3154.
43. M. E. Khan, C. E. Elum, A. O. Ijeomah, P. J. Ameji, I. G. Osigbemhe, E. E. Etim, J. V. Anyam, A. Abel, C.
T. Agber. (2023). Isolation, Characterization, Antimicrobial and Theoretical Investigation of Some
Bioactive Compounds Obtained from the Bulbs of Calotropisprocera.
Journal of the Nigerian Society
of Physical Sciences
,
5
(3), 1576. https://doi.org/10.46481/jnsps.2023.1576
44. M. E. Khan, E. E. Etim, V. J. Anyam, A. Abel, I. G. Osigbemhe, C. T Agber (2021).Computational
studies on Emodin (C15H10O5) from Methanol extract of Pteridium acquilinum leaves. J. Nig. Soc.
Phys. Sci. 3 (2021) 360–384. https://doi.org/10.46481/jnsps.2021.301
45. Maharao, N., Antontsev, V., Wright, M., & Varshney, J. (2020). Entering the era of computationally
driven drug development.
Drug Metabolism Reviews
,
52
(2), 283–298.
Page 25/34
4. Majolo, F., Delwing, L. K. D. O. B., Marmitt, D. J., Bustamante-Filho, I. C., & Goettert, M. I. (2019).
Medicinal plants and bioactive natural compounds for cancer treatment: Important advances for
drug discovery.
Phytochemistry Letters
,
31
, 196–207.
47. Morozov, A. V., & Kortemme, T. (2005). Potential functions for hydrogen bonds in protein structure
prediction and design.
Advances in protein chemistry
,
72
, 1–38.
4. Munisamy, M., Mukherjee, N., Thomas, L., Pham, A. T., Shakeri, A., Zhao, Y., ... & Rao, M. (2021).
Therapeutic opportunities in cancer therapy: Targeting the p53-MDM2/MDMX interactions.
American Journal of Cancer Research
,
11
(12), 5762.
49. Nag, S., Qin, J., Srivenugopal, K. S., Wang, M., & Zhang, R. (2013). The MDM2-p53 pathway revisited.
Journal of biomedical research
,
27
(4), 254.
50. Odey, M. O., Antai, E. E., Adindu, E. A., Godfrey, O. C., Bassey, I. U., Nwaokolrie, F. O., ... & Louis, H.
(2023). Unraveling the impact of polar solvation on the molecular geometry, spectroscopy (FT-IR, UV,
NMR), reactivity (ELF, NBO, HOMO-LUMO) and antiviral inhibitory potential of Cissampeline by
molecular docking approach.
Chemical Physics Impact
,
7
, 100346.
51. Oko E G, Peter A E, Emmanuel E E. Phytochemical and Antimicrobial Properties of African Peach
(Sarcocephalus Latifolius). Organic & Medicinal Chem IJ. 2020; 9(5): 555775. DOI:
10.19080/OMCIJ.2020.09.555775.
52. Onen, A. I., Joseph, J., Etim, E., & Eddy, N. O. (2017). Quantum Chemical Studies on the Inhibition
Mechanism of Ficus carica, FC and Vitellaria paradoxa, VP Leaf Extracts. Journal of Advanced
Chemical Sciences, 3(3), 496–498.
53. Osigbemhe, I. G., Louis, H., Khan, E. M., Etim, E. E., Odey, D. O., Oviawe, A. P., ... & Obuye, F. (2022c).
Synthesis, characterization, DFT studies, and molecular modeling of 2-(-(2-hydroxy-5-
methoxyphenyl)-methylidene)-amino) nicotinic acid against some selected bacterial receptors.
Journal of the Iranian Chemical Society
,
19
(8), 3561–3576.
54. Osigbemhe, I. G., Louis, H., Khan, E. M., Etim, E. E., Oyo-Ita, E. E., Oviawe, A. P., ... & Obuye, F. (2022b).
Antibacterial potential of 2-(-(2-Hydroxyphenyl)-methylidene)-amino) nicotinic Acid: Experimental,
DFT Studies, and molecular docking approach.
Applied Biochemistry and Biotechnology
,
194
(12),
5680–5701.
55. Osigbemhe, I. G., Oyoita, E. E., Louis, H., Khan, E. M., Etim, E. E., Edet, H. O., Ikenyirimba, O. J., Oviawe,
A. P., & Obuye, F. (2023). Antibacterial potential of N-(2-furylmethylidene)-1, 3, 4-thiadiazole-2-amine:
Experimental and theoretical investigations.
Journal of Molecular Structure
, 1253, 131408.
https://doi.org/10.1016/j.molstruc.2022.131408
5. Sarkar, S., Horn, G., Moulton, K., Oza, A., Byler, S., Kokolus, S., & Longacre, M. (2013). Cancer
development, progression, and therapy: an epigenetic overview.
International journal of molecular
sciences
,
14
(10), 21087–21113.
57. Shangary, S., & Wang, S. (2008). Targeting the MDM2-p53 interaction for cancer therapy.
Clinical
Cancer Research
,
14
(17), 5318–5324.
Page 26/34
5. Shinggu, J. P., Etim, E. E., & Onen, A. I. (2023). Quantum Chemical Studies on C2H2O Isomeric
Species: Astrophysical Implications, and Comparison of Methods.
Communication in Physical
Sciences
,
9
(2).
59. Shinggu, J. P., Etim, E. E., & Onen, A. I. (2023b). Protonation-Induced Structural and Spectroscopic
Variations in Molecular Species: A Computational Study on N2, H2, CO, CS, and PH3.
Communication in Physical Sciences
,
9
(4).
0. Thamarai, A., Vadamalar, R., Raja, M., Muthu, S., Narayana, B., Ramesh, P., ... & Aayisha, S. (2020).
Molecular structure interpretation, spectroscopic (FT-IR, FT-Raman), electronic solvation (UV–Vis,
HOMO-LUMO and NLO) properties and biological evaluation of (2E)-3-(biphenyl-4-yl)-1-(4-
bromophenyl) prop-2-en-1-one: Experimental and computational modeling approach.
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 226, 117609.
1. Thomsen R, Christensen MH. MolDock: a new technique for high-accuracy molecular docking,
Journal of medicinal chemistry; 2006 Jun 1; 49(11):3315-21. https://doi.org/10.1021/jm051197e
2. Tovar, C., Sellers, J. R., & Vogelstein, R. A. (2002). MDM2 antagonists: a new therapeutic approach to
overcome p53-mediated drug resistance. Current Cancer Drug Targets, 2(3), 213–226.
3. Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a
new scoring function, ecient optimization, and multithreading.
Journal of computational
chemistry
,
31
(2), 455–461
4. Ushie, O. A. ., Kendeson, A. C., Longbab, B. D., Ogofotha, P. U., Nongu, S. T. ., & Bako, B.. (2022).
Proling Phytochemicals And Antimicrobial Activities Of Hexane And Acetone Crude Extracts Of
Sterculia setigera LEAF. Journal of Chemical Society of Nigeria, 47(6).
https://doi.org/10.46602/jcsn.v47i6.827
5. Ushie, O. A., Etim, E. E., Onen, A. I., Andrew, C., Lawal, U., & Khanal, G. P. (2019). Computational
Studies of β-amyrin acetate (C32H52O2) Detected in Methanol Leaf Extract of Chrysophyllum
albidium. Journal of Chemical Society of Nigeria, 44(3), 561–581.
. Ushie, O., Neji, P., Etim, E., & Nsor, G. (2013). Phytochemical screening and antimicrobial activities of
Phyllanthus amarus stem bark extracts.
International Journal of Modern Biology and Medicines
,
3
(3), 101–112.
7. Venkatesh, S., & Lipper, R. A. (2000). Role of the development scientist in compound lead selection
and optimization.
Journal of pharmaceutical sciences
,
89
(2), 145–154.
. Wang, L., Lankhorst, L., & Bernards, R. (2022). Exploiting senescence for the treatment of cancer.
Nature Reviews Cancer
,
22
(6), 340–355.
9. Wang, S., Shi, X., & Yang, F. (2006). Structural basis of stabilization of p53 by mdm2 at the p53-DNA
interface. Cell, 124(5), 981–992.
70. Yabroff, K. R., Gansler, T., Wender, R. C., Cullen, K. J., & Brawley, O. W. (2019). Minimizing the burden
of cancer in the United States: Goals for a high-performing health care system.
CA: a cancer journal
for clinicians
,
69
(3), 166–183.
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Figures
Figure 1
The Structure of Mouse Double Minute 2 (PDB ID: 4ZFI)
Figure 2
Optimized Structures of Compound 1 & 2
Page 28/34
Figure 3
HOMO-LUMO of Compound 1 (1-Guanidinosuccinimide) in Dimethyl Sulfoxide (DMSO), Ethanol and
Methanol Solvent
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Figure 4
HOMO-LUMO of Compound 2 (Benzene-ethanamine, 2,5-diuoro-β, 3,4-trihydroxy-n-methyl) in Dimethyl
Sulfoxide (DMSO), Ethanol and Methanol Solvent.
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Figure 5
Vibrational Spectrum of Compound 1 in Dimethyl Sulfoxide (DMSO)
Figure 6
Vibrational Spectrum of Compound 1 in Ethanol
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Figure 7
Vibrational Spectrum of Compound 1 in Methanol
Figure 8
Vibrational Spectrum of Compound 1 in Dimethyl Sulfoxide (DMSO)
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Figure 9
Vibrational Spectrum of Compound 1 in Ethanol
Figure 10
Vibrational Spectrum of Compound 1 in Methanol
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Figure 11
The 2D and 3D Ligand-Protein Interactions Between Compound 1 (1-Guanidinosuccinimide) and Mouse
Double Minute 2 (PDB ID: 4ZFI)
Figure 12
The 2D and 3D Ligand-Protein Interactions Between Compound 2 (Benzenediamine) and Mouse Double
Minute 2 (PDB ID: 4ZFI)
