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Research Article
Molecular docking-based virtual screening and computational investigations of biomolecules (curcumin analogs) as potential lead inhibitors for SARS-CoV-2 papain-like protease
expand article infoTaufik Muhammad Fakih‡§, Ritmaleni|, Rahadian Zainul, Muchtaridi Muchtaridi§#
‡ Universitas Islam Bandung, Bandung, Indonesia
§ Universitas Padjadjaran, Sumedang, Indonesia
| Universitas Gadjah Mada, Yogyakarta, Indonesia
¶ Universitas Negeri Padang, Padang, Indonesia
# Research Collaboration Centre for Theranostic Radio Pharmaceuticals, National Research and Innovation Agency (BRIN), Sumedang, Indonesia

Corresponding author: Taufik Muhammad Fakih ( taufikmuhammadf@unisba.ac.id )

Academic editor: Emilio Mateev
© 2024 Taufik Muhammad Fakih, Ritmaleni, Rahadian Zainul, Muchtaridi Muchtaridi.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Fakih TM, Ritmaleni, Zainul R, Muchtaridi M (2024) Molecular docking-based virtual screening and computational investigations of biomolecules (curcumin analogs) as potential lead inhibitors for SARS-CoV-2 papain-like protease. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e123948
Open Access

Abstract

In the effort to combat SARS-CoV-2 infection, researchers are currently exploring the repurposing of conventional antiviral drugs, despite their limited efficacy. The SARS-CoV-2 virus encodes a papain-like protease (PLpro), which not only plays a crucial role in viral replication but also cleaves ubiquitin and interferon-stimulated gene 15 protein (ISG15) from host proteins, making it a prime target for the development of new antiviral medications. In this study, we conducted a multi-step in silico screening to identify novel, noncovalent PLpro inhibitors. Curcumin, an antioxidant derived from turmeric rhizomes (Curcuma longa L.), has undergone extensive preclinical investigations and shown significant efficacy against viruses and other ailments in both laboratory and animal studies. However, the pharmacological limitations of curcumin have prompted the synthesis of numerous novel curcumin analogs, necessitating evaluation for their therapeutic potential. The selectivity of the top-scoring compounds was assessed through molecular docking studies and molecular dynamics simulations to determine their binding affinity to PLpro. As a result, we identified 20 potential, selective PLpro inhibitors, from which the top two compounds (THA111 and THHGV6) were selected based on their binding free energy values towards PLpro as estimated by MM-PBSA calculations. These selected candidates demonstrate promising activity against the protein, with binding free energy values ranging from approximately −105 to −108 kJ/mol, and largely adopt a similar binding mode to known noncovalent SARS-CoV-2 PLpro inhibitors (GRL0617 = −100.98 kJ/mol). We further propose these two most promising compounds for future in vitro evaluation. The findings for the top potential PLpro inhibitors have been deposited in a database (Curcumin Research Center) to aid research on anti-SARS-CoV-2 drugs.

Keywords

SARS-CoV-2 PLpro, Curcumin analogs, COVID-19 therapy, Virtual drug screening, Computational investigations

Introduction

Due to the rapid and widespread transmission rates, the World Health Organization (WHO) officially classified coronavirus disease 2019 (COVID-19) as a global health emergency and declared it a pandemic on March 11, 2020 (Kong et al. 2021). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as the causative agent of COVID-19 (Gil et al. 2020). Individuals infected with SARS-CoV-2 may exhibit mild symptoms such as fever, cough, and fatigue, but the virus can also lead to severe respiratory complications, organ failure, and death. Elderly individuals and those with underlying health conditions are particularly vulnerable to experiencing a severe course of the illness (Wurtzer et al. 2020; Fernández-Castañeda et al. 2022). As of March 2021, the global death toll from COVID-19 has surpassed 2.5 million, with more than 114 million confirmed cases reported worldwide (Abduljalil and Abduljalil 2020). Given its significant impact on public health and its profound socio-economic ramifications, the scientific community has dedicated substantial efforts to the development of novel treatments.

SARS-CoV-2 belongs to the Coronaviridae family and is an enveloped positive-sense RNA virus (Cheung et al. 2021). During the infection process, viral polypeptides (pp1a and pp1ab) are synthesized and require proteolytic cleavage by specific enzymes to become functional peptides. In SARS-CoV-2, papain-like protease (PLpro) has been identified as essential for this cleavage process (Li et al. 2021; Tan et al. 2022). Since this protease plays a crucial role in viral replication, it emerges as a promising target for drug development. Inhibiting viral proteases involved in polypeptide processing has proven to be an effective strategy for treating other viral infections, such as hepatitis C virus (HCV) and human immunodeficiency virus (HIV) (Bianchi and Pessi 2002; Hulce et al. 2022).

The SARS-CoV-2 PLpro enzyme plays a crucial role in viral replication and in dampening the host immune response, making it an attractive target for intervention (Zhao et al. 2022). Consequently, it has garnered significant attention from the scientific community, leading to investigations into its structure, functions, and comparisons with PLpro enzymes from related coronaviruses, particularly SARS-CoV (Yapasert et al. 2021). Efforts to identify PLpro inhibitors are already underway, with current studies primarily focusing on adapting existing noncovalent inhibitors developed for SARS-CoV or designing specific covalent inhibitors (van Vliet et al. 2022). However, the latter approach carries risks of toxicity due to high reactivity and potential off-target binding. In light of this, there is a growing interest in developing noncovalent PLpro inhibitors, leveraging existing data on SARS-CoV PLpro inhibitors to inform the design of analogous inhibitors for SARS-CoV-2 (Osipiuk et al. 2021; Sencanski et al. 2022). Despite recent efforts, the binding affinities of newly developed SARS-CoV-2 PLpro inhibitors remain moderate. Therefore, there is a pressing need for a more comprehensive approach to design novel PLpro inhibitors with enhanced binding affinities and reduced toxicity risks.

At present, numerous researchers have indicated the potential of plant-derived chemical compounds in combating SARS-CoV-2 infection, which could potentially prevent the onset or severity of COVID-19. Among these compounds, curcumin, the primary polyphenolic component found in turmeric, has garnered considerable attention due to its diverse biological effects, including its anti-tumor, anti-inflammatory, immunomodulatory, antioxidant, and antimicrobial properties (Zorofchian Moghadamtousi et al. 2014; Urošević et al. 2022). Moreover, curcumin has demonstrated inhibitory effects against the replication of various viruses, including dengue virus, hepatitis B virus, zika virus, influenza A virus, and chikungunya virus (Adamczak et al. 2020). Its antiviral actions can target the viral particle directly or interfere with different stages of the viral replication cycle by interacting with viral proteins or modulating critical cellular processes and pathways necessary for viral replication (Sun et al. 2010; Kotha and Luthria 2019).

Interestingly, previous in vitro studies have shown that post-infection treatment with curcumin at a concentration of 10 µg/mL exhibits significant antiviral effects against SARS-CoV-2, with inhibition rates of 99% and 99.8% against the DG614 strain and Delta variant respectively (Marín-Palma et al. 2021). Another study also demonstrated that administration of curcumin at a concentration of 10 μM to the virus before inoculation into cell culture resulted in inhibition of SARS-CoV-2 replication, with a reduction rate of over 99% in Vero E6 cells (Zupin et al. 2022). Additionally, several recent molecular docking studies have suggested that curcumin may inhibit the viral protein translation process by interacting with the active sites of the 3CLPro and PLPro enzymes (Linda Laksmiani et al. 2020; Das et al. 2021). Thus, specifically concerning SARS-CoV-2, computational modeling studies have indicated that curcumin shows promising binding affinities with the PLPro.

In this study, we aimed to discover new, potent, noncovalent, and specific inhibitors of PLpro. These compounds are anticipated to exhibit greater binding affinity to SARS-CoV-2 PLpro compared to existing inhibitors. To accomplish this objective, we conducted thorough in silico screening, a modern and efficient approach in drug design. We placed a strong emphasis on the accuracy of our predictions by extensively validating the techniques utilized to ensure their applicability to our project. Consequently, we employed a variety of computational methods, merging both ligand-based and structure-based strategies. Initially, our focus was on curcumin analogs, comprising a total of 20 compounds synthesized successfully in our laboratory. Subsequently, we assessed the binding affinities of selected molecules to SARS-CoV-2 PLpro through molecular docking and molecular dynamics simulations. These findings have been archived in a publicly accessible database to facilitate future research endeavors aimed at combating the COVID-19 pandemic.

Materials and methods

Preparation and optimization of curcumin analogs

The structural configurations of the compounds intended for the study were derived from both two-dimensional and three-dimensional representations generated using ChemDraw Professional 16.0 and Chem3D 16.0 software (Brown 2014). Subsequently, these structures underwent optimization procedures using GaussView 6.0 and Gaussian16 software, employing various parameters such as Density Functional Theory (DFT) or Hartree-Fock calculation methods, 3-21G basis sets, and simulation conditions under vacuum (Frisch et al. 2009). The optimization process yielded data on the geometry of the compounds, including their energy stability, electron distribution, and reactive conformations. This structural optimization aimed to attain a stable conformation suitable for physiological conditions, accomplished by incorporating polar hydrogen atoms and calculating Gasteiger charges (Fakih 2023).

Preparation of SARS-CoV-2 papain-like protease (PLpro) macromolecules

The three-dimensional configuration of the SARS-CoV-2 papain-like protease (PLpro) receptor macromolecule was acquired from the Protein Data Bank (PDB) website via the URL https://www.rcsb.org/structure/3e9s (Ratia et al. 2008). Afterwards, the structure underwent preparation procedures using the Discovery Studio 2019 Client to eliminate undesirable components such as native ligands (GRL0617), water and solvent molecules, and irregular elements (BIOVIA 2017). This preparation aimed to generate a refined structure in accordance with specified conditions, achieved by adding polar hydrogen atoms and computing Kollman charges. These steps were taken to establish a stable structure suitable for molecular docking and molecular dynamics simulations. Following this, the identification of active binding sites was conducted to pinpoint locations where the compound could potentially bind to the receptor. Active binding sites were identified by assessing their potential and evaluating the interactions between these sites and the compounds (Nurisyah et al. 2024).

Molecular docking studies

Molecular docking investigations of curcumin derivative compounds against SARS-CoV-2 PLpro macromolecules were conducted using AutoDock 4.2 to explore potential interactions between these curcumin analog compounds and SARS-CoV-2 PLpro macromolecules (Forli et al. 2012). The molecular docking studies employed a grid box measuring 64 × 60 × 60 points with a spacing of 0.375 Å to encompass the binding site of the target. The Lamarckian Genetic Algorithm (LGA) was utilized with 100 conformations for each simulation, while other docking parameters were kept at default settings (Forli et al. 2012). LGA, an optimization technique employed in AutoDock 4.2, aimed to determine the optimal conformation of curcumin analog compounds binding to receptor proteins. This algorithm seeks to optimize the interaction energy between curcumin analog compounds and receptor proteins by identifying the most stable conformation through a combination of mutation and crossover.

ADMET properties of curcumin analogs

The pharmacological and pharmacokinetic characteristics of all curcumin derivative compounds were assessed through the SwissADME (Daina et al. 2017) and pkCSM (Pires et al. 2015) online platforms. These tools analyze the input structure against a set of data and predict various properties based on specific parameters. The SwissADME and pkCSM webservers compare the input structure with the training set and calculate the probable properties based on different parameters, providing valuable insights for drug development and optimization.

PASS identification of curcumin analogs

The PASS website was employed for forecasting the pharmacological and biological traits of the substances (Lohidashan et al. 2018). It scrutinizes a compound’s biological potential by examining its structure-activity relationship. By comparing the desired structure with a pre-existing training set encompassing diverse biological functions, the server anticipates potential biological activities using the ratio of ‘probability of being active (Pa)’ to ‘probability of being passive (Pi)’. A higher Pa value suggests a greater likelihood of a chemical possessing the investigated biological characteristic.

Molecular dynamics simulations

Molecular dynamics simulations lasting 100 ns were conducted utilizing Gromacs 2016.3 with the AMBER99SB-ILDN force field, which enhances the accuracy of MD simulations by incorporating the Improved Lipophilic Efficiency Descriptors for Nucleic Acids (ILDN) parameter for nucleic acids (Aragones et al. 2013; Abraham et al. 2015; Smith et al. 2015). The parameterization of curcumin analog compounds was executed using the AnteChamber PYthon Parser interface (ACPYPE) to generate the necessary input files for molecular dynamics simulations (Aragones et al. 2013; Abraham et al. 2015; Smith et al. 2015). Electrostatic forces were calculated using the Particle Mesh Ewald (PME) method, which divides the electric potential formed by charged particles into two components: the potential from nearby charged particles and the potential from distant charged particles (Ramadhan et al. 2022). System neutralization was achieved by introducing sodium (Na+) and chloride (Cl-) ions around the receptor-ligand complex. Solvation was performed using the Transferable Intermolecular Potentials 3-Point (TIP3P) water model, which defines the hydrogen, oxygen, and hydrogen bond interactions between atoms in a water molecule.

Binding free energy MM-PBSA calculation

The Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) calculations were conducted using the g_mmpbsa package, which is integrated into the Gromacs 2016.3 software (Wang et al. 2018; Mishra et al. 2022). Energy computations involved averaging data from 100 snapshots, extracted at ten ps intervals, covering the molecular dynamics trajectory from 0 to 100 ns. To calculate polar desolvation energy, the Poisson–Boltzmann equation was applied with a grid size set to 0.5 Å. The dielectric constant for the solvent was set to 80, representing water. Nonpolar energy contributions were assessed by analyzing the solvent-accessible surface area with a solvent radius of 1.4 Å. A comprehensive evaluation of receptor-ligand free energy was conducted based on stable interactions observed within the complex during molecular dynamics simulations.

Results and discussion

Molecular docking studies

The initial stage involves conducting molecular docking simulations between curcumin analogs and SARS-CoV-2 papain-like protease (PLpro) macromolecules using AutoDock 4.2 equipped with the Lamarckian Genetic Algorithm (LGA). Molecular docking serves as a computational approach to elucidate the interactions between a specific chemical compound (ligand) and a protein (target) (Ariyanto et al. 2023). The objective of this docking simulation is to identify the most stable protein-ligand complex that may occur under physiological conditions. This process entails a systematic exploration of all feasible orientations and placements of the ligands within the active site of the protein, followed by an assessment of the energy associated with each resultant complex. Various methods are employed for energy calculations, including empirical, physicochemical, and hybrid methods (Kumar et al. 2023). These energy calculations quantify the interactions among the atoms involved in the complexes, encompassing hydrogen bonds, van der Waals forces, and electrostatic interactions.

The outcomes of molecular docking investigations reveal that all curcumin analog compounds exhibit favorable affinity towards SARS-CoV-2 PLpro macromolecules. However, only five derivative compounds, namely A108, A113, A146, THA113, and THA146, demonstrate superior affinity compared to native ligands (GRL0617) (Table 1). Simulation findings indicate that these five compounds exhibit relatively high affinity and effectively bind to the active site of SARS-CoV-2 PLpro macromolecules. Detailed structural analysis of the compound-receptor complexes reveals that the five curcumin analog compounds engage with the active site of SARS-CoV-2 PLpro through hydrogen bonding and van der Waals interactions. This observation suggests that these five compounds can establish stable and efficient binding with SARS-CoV-2 PLpro. Based on the docking study results, these compounds hold promise as candidates for the development of new COVID-19 drugs. However, further assessment using molecular dynamics simulations is imperative to confirm the stability of the interaction.

Table 1.
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The free energy of binding between curcumin analogs and SARS-CoV-2 PLpro macromolecules.

Compound Molecule Molecular Structure Binding Free Energy
Native (GRL0617) −10.05 kcal/mol
A102 −8.01 kcal/mol
A103 −7.27 kcal/mol
A104 −8.57 kcal/mol
A108 −9.22 kcal/mol
A111 −7.90 kcal/mol
A113 −9.85 kcal/mol
A129 −8.52 kcal/mol
A146 −9.96 kcal/mol
HGV5 −8.18 kcal/mol
HGV6 −8.65 kcal/mol
THA102 −7.83 kcal/mol
THA103 −7.61 kcal/mol
THA104 −8.83 kcal/mol
THA108 −8.89 kcal/mol
THA111 −7.15 kcal/mol
THA113 −9.29 kcal/mol
THA129 −8.14 kcal/mol
THA146 −9.56 kcal/mol
THHGV5 −7.47 kcal/mol
THHGV6 −8.93 kcal/mol

In Fig. 1, the molecular interactions between SARS-CoV-2 PLpro macromolecules and curcumin analog compounds are primarily characterized by multiple hydrogen bonds and hydrophobic interactions. The analog compounds derived from curcumin, particularly A108, A113, A146, THA113, and THA146, exhibit notable interactions with amino acid residues LYS159, GLU163, LEU164, ASP166, GLU169, TYR266, THR303, and ASP304, as depicted in the figure. Notably, compounds A113 and THA113 are surrounded by numerous amino acid residues, likely attributed to the presence of a bromide (Br) atom in their molecular structure. In molecular docking studies, compounds containing such atoms tend to attract amino acid residues due to the electronegativity of bromide, resulting in a partial negative charge around the bromide atom and a partial positive charge around the bonded atom (Misran et al. 2020; Mohamed Thamby et al. 2023). Consequently, amino acid residues with a positive charge may interact with the partial negative charge induced by the bromide atom.

Figure 1. 

A graphical representation in two dimensions of the outcomes obtained from molecular docking investigations involving curcumin analogs and SARS-CoV-2 PLpro macromolecules.

It’s intriguing that having two symmetrically linked 1,3-dicarbonyl or α, β unsaturated carbonyl units facilitates binding with DNA, protein sites, and metals through a well-established process known as keto-enol tautomerism (Nocito et al. 2021). Additionally, the conversion from the keto to the enol form of curcumin heavily relies on the polarity of the system, making it adept at overcoming diverse barriers during biochemical processes (Shah et al. 2022). Another critical structural aspect of curcumin is its hydrophobic aromatic unit with hydroxyl and methoxy substitutions, which also significantly contribute to enhancing curcumin’s efflux action (Deshmukh et al. 2020). With two hydroxyl groups present, curcumin can also serve as a potent antioxidant. While curcumin is utilized for treating various ailments (Fig. 2), exploring its antiviral potential against SARS-CoV-2 further adds to its intrigue.

Figure 2. 

The chemical structure of curcumin along with its main pharmacophores and potential positions (Tomeh et al. 2019; Kaur et al. 2021).

ADMET properties of curcumin analogs

Physiological and pharmacokinetic traits play a critical role in the selection and advancement of drug-like substances. Compounds that successfully undergo screening for physicochemical and ADMET properties stand a better chance of achieving clinical success. The pkCSM platform computes physicochemical and ADMET parameters for all compounds chosen through docking screening. For every curcumin derivative compound, a range of ADMET parameters is evaluated concurrently with PAINS screening. This process identifies twenty compounds possessing outstanding physicochemical characteristics and devoid of any PAINS patterns (Angamuthu et al. 2019). The ADMET characteristics derived from this selection are detailed in Table 2. These chosen compounds demonstrate favorable ADMET attributes, as indicated by the absence of AMES toxicity in the pkCSM forecast.

Table 2.
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The ADMET criteria of the curcumin analogs utilizing the pkCSM Web tool.

Compound Molecule Molecular Structure GI Absorption Water Solubility (log mol/L) BBB Permeability (log BB) CYP2D6 Inhibitor Renal OCT2 Substrate AMES Toxicity
A102 High −3.91 Yes Yes No No
A103 High −3.91 Yes Yes No No
A104 High −4.75 No No No No
A108 High −4.31 Yes Yes No No
A111 High −4.13 Yes Yes No No
A113 High −5.76 No No No No
A129 High −4.67 Yes Yes No No
A146 High −3.95 Yes No No No
HGV5 High −3.10 No No No No
HGV6 High −4.89 Yes No No No
THA102 High −3.91 Yes Yes No No
THA103 High −3.91 Yes Yes No No
THA104 High −4.75 No No No No
THA108 High −4.31 Yes Yes No No
THA111 High −4.13 Yes Yes No No
THA113 High −5.76 Yes No No No
THA129 High −4.76 Yes Yes No No
THA146 High −3.95 Yes No No No
THHGV5 High −3.10 No No No No
THHGV6 High −4.89 Yes No No No

PASS identification of curcumin analogs

The PASS server holds an extensive dataset for training, encompassing diverse bioactive compounds and their associations between structure and activity derived from a range of clinical and preclinical investigations. Using this dataset, the PASS server predicts the biological activity of chemical compounds (Islam et al. 2022). Examination of the biological activity of the refined compounds reveals that most derivatives of curcumin display inhibitory activity against 3C-like protease (Human coronavirus) and successfully clear the PASS biological activity screening (Table 3). The interpretation and application of PASS prediction outcomes are adaptable: only activities exceeding Pi are deemed plausible for a specific compound; if Pa surpasses 0.7, the likelihood of experimental activity discovery is considerable; for Pa values between 0.5 and <0.7, the prospect of experimental activity discovery is diminished, although the compound might diverge from established pharmaceutical agents; should Pa fall below 0.5, the probability of experimental activity discovery is minimal, yet the likelihood of encountering structurally novel chemical entities (NCEs) is heightened (Ramadhan et al. 2020).

Table 3.
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The primary 20 pertinent biological characteristics of the clarified curcumin analog compounds.

Compound Molecule Molecular Structure Possible Activity (Pa) Possible Inactivity (Pi) Biological Activity
A102 0.416 0,074 Antiviral (Rhinovirus)
0.337 0.062 Antiviral (Adenovirus)
0.364 0.143 Antiviral (Picornavirus)
0.286 0.138 Simian immunodeficiency virus proteinase inhibitor
0.212 0.147 3C-like protease (Human coronavirus) inhibitor
A103 0.425 0.067 Antiviral (Rhinovirus)
0.371 0.042 Antiviral (Adenovirus)
0.330 0.183 Antiviral (Picornavirus)
0.238 0.096 3C-like protease (Human coronavirus) inhibitor
0.277 0.148 Simian immunodeficiency virus proteinase inhibitor
A104 0.552 0.031 Antiviral (Picornavirus)
0.488 0.023 Simian immunodeficiency virus proteinase inhibitor
0.459 0.012 Antiviral (Adenovirus)
0.396 0.004 3C-like protease (Human coronavirus) inhibitor
0.195 0.166 Antiviral (Poxvirus)
A108 0.285 0.035 3C-like protease (Human coronavirus) inhibitor
0.315 0.077 Antiviral (Adenovirus)
0.326 0.099 Simian immunodeficiency virus proteinase inhibitor
0.312 0.207 Antiviral (Picornavirus)
A111 0.328 0.097 Simian immunodeficiency virus proteinase inhibitor
0.272 0.047 3C-like protease (Human coronavirus) inhibitor
0.285 0.102 Antiviral (Adenovirus)
0.342 0.167 Antiviral (Picornavirus)
A113 0.546 0.004 Antiviral (Adenovirus)
0.284 0.037 3C-like protease (Human coronavirus) inhibitor
0.285 0.138 Simian immunodeficiency virus proteinase inhibitor
0.210 0.144 Antiviral (Poxvirus)
0.279 0.263 Antiviral (Picornavirus)
A129 0.376 0.130 Antiviral (Picornavirus)
0.376 0.130 Antiviral (Picornavirus)
0.301 0.088 Antiviral (Adenovirus)
0.316 0.107 Simian immunodeficiency virus proteinase inhibitor
A146 0.403 0.028 Antiviral (Adenovirus)
0.320 0.015 3C-like protease (Human coronavirus) inhibitor
0.349 0.081 Simian immunodeficiency virus proteinase inhibitor
0.381 0.126 Antiviral (Picornavirus)
0.194 0.168 Antiviral (Poxvirus)
HGV5 0.371 0.043 Antiviral (Adenovirus)
0.289 0.032 3C-like protease (Human coronavirus) inhibitor
0.322 0.193 Antiviral (Picornavirus)
0.266 0.162 Simian immunodeficiency virus proteinase inhibitor
HGV6 0.417 0.097 Antiviral (Picornavirus)
0.344 0.057 Antiviral (Adenovirus)
0.289 0.032 3C-like protease (Human coronavirus) inhibitor
0.285 0.138 Simian immunodeficiency virus proteinase inhibitor
0.187 0.180 Antiviral (Poxvirus)
THA102 0.523 0.041 Antiviral (Picornavirus)
0.431 0.062 Antiviral (Rhinovirus)
0.274 0.113 Antiviral (Adenovirus)
0.276 0.150 Simian immunodeficiency virus proteinase inhibitor
0.229 0.111 3C-like protease (Human coronavirus) inhibitor
0.214 0.138 Antiviral (Poxvirus)
THA103 0.531 0.038 Antiviral (Picornavirus)
0.439 0.056 Antiviral (Rhinovirus)
0.247 0.081 3C-like protease (Human coronavirus) inhibitor
0.261 0.127 Antiviral (Adenovirus)
0.267 0.161 Simian immunodeficiency virus proteinase inhibitor
0.201 0.157 Antiviral (Poxvirus)
THA104 0.713 0.005 Antiviral (Picornavirus)
0.476 0.026 Simian immunodeficiency virus proteinase inhibitor
0.410 0.004 3C-like protease (Human coronavirus) inhibitor
0.359 0.049 Antiviral (Adenovirus)
0.227 0.120 Antiviral (Poxvirus)
0.026 0.007 Protease (Human cytomegalovirus) inhibitor
THA108 0.510 0.046 Antiviral (Picornavirus)
0.294 0.028 3C-like protease (Human coronavirus) inhibitor
0.315 0.108 Simian immunodeficiency virus proteinase inhibitor
0.207 0.198 Antiviral (Adenovirus)
THA111 0.499 0.051 Antiviral (Picornavirus)
0.289 0.032 3C-like protease (Human coronavirus) inhibitor
0.317 0.106 Simian immunodeficiency virus proteinase inhibitor
0.315 0.222 Antiviral (Rhinovirus)
0.225 0.171 Antiviral (Adenovirus)
0.188 0.179 Antiviral (Poxvirus)
THA113 0.453 0.013 Antiviral (Adenovirus)
0.467 0.066 Antiviral (Picornavirus)
0.293 0.029 3C-like protease (Human coronavirus) inhibitor
0.242 0.103 Antiviral (Poxvirus)
0.275 0.150 Simian immunodeficiency virus proteinase inhibitor
THA129 0.536 0.037 Antiviral (Picornavirus)
0.298 0.026 3C-like protease (Human coronavirus) inhibitor
0.306 0.116 Simian immunodeficiency virus proteinase inhibitor
0.239 0.152 Antiviral (Adenovirus)
0.203 0.154 Antiviral (Poxvirus)
0.285 0.285 Antiviral (Rhinovirus)
THA146 0.585 0.023 Antiviral (Picornavirus)
0.330 0.012 3C-like protease (Human coronavirus) inhibitor
0.338 0.088 Simian immunodeficiency virus proteinase inhibitor
0.295 0.093 Antiviral (Adenovirus)
0.226 0.122 Antiviral (Poxvirus)
THHGV5 0.523 0.041 Antiviral (Picornavirus)
0.298 0.026 3C-like protease (Human coronavirus) inhibitor
0.260 0.128 Antiviral (Adenovirus)
0.257 0.175 Simian immunodeficiency virus proteinase inhibitor
0.203 0.154 Antiviral (Poxvirus)
0.292 0.270 Antiviral (Rhinovirus)
THHGV6 0.616 0.016 Antiviral (Picornavirus)
0.298 0.026 3C-like protease (Human coronavirus) inhibitor
0.275 0.150 Simian immunodeficiency virus proteinase inhibitor
0.219 0.132 Antiviral (Poxvirus)
0.233 0.160 Antiviral (Adenovirus)

Binding free energy MM-PBSA calculation

The subsequent step involves conducting molecular dynamics simulations between all curcumin-analog compounds and the SARS-CoV-2 PLpro macromolecules. Molecular dynamics simulations represent a computational technique employed to assess molecular behavior within biological systems (Muchtaridi et al. 2023). During molecular dynamics simulations, the positions and velocities of molecules are tracked over time using Newton’s equations of motion and potential molecular interactions. Molecular dynamics simulations facilitate the examination of molecular dynamics under diverse conditions, including temperature, pressure, and concentration (Pitaloka et al. 2021). They enable the assessment of processes such as compound-receptor interactions, chemical reactions, and alterations in molecular structure. Furthermore, molecular dynamics simulations allow for the investigation of changes in molecular structure and dynamics across varied environments, such as intracellular or extracellular environments.

The Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method is employed to assess the interaction energy between molecules and their surroundings within molecular dynamics simulations. This approach integrates principles from molecular mechanics and Poisson-Boltzmann theory to compute the interaction energy of molecules within their environment. MM-PBSA evaluates the interaction energy between molecules by employing molecular mechanics calculations to determine the potential energy within a molecule, which represents the energy required for forming its molecular structure (Kotni Meena 2015). Meanwhile, the Poisson-Boltzmann Surface Area (PBSA) component calculates the interaction energy between molecules and their environment, representing the energy needed to alter a molecule’s shape from an ideal state to an observable form. MM-PBSA offers reliable estimations of molecular interaction energies within the systems under investigation, such as compound-receptor complexes.

Based on the results obtained from the MM-PBSA method for binding-free energy calculations, only five curcumin analog compounds demonstrated superior affinity and stability compared to the native ligands (GRL0617) during the 100 ns simulation in molecular dynamics interactions. Among these, A102, THA102, THA104, THA111, and THHGV6 exhibited the highest affinity stability, with binding free energy values of −108.975 kJ/mol, −108.931 kJ/mol, −108.975 kJ/mol, −106.188 kJ/mol, and −105.023 kJ/mol, respectively (Table 4). Generally, the primary contributors to energy in molecular dynamics simulations are kinetic and potential energies. Kinetic energy pertains to the energy associated with the motion of particles, while potential energy relates to their position within the system. Proper control of both kinetic and potential energies is essential in molecular dynamics simulations to maintain system stability in a thermodynamic state (Aulifa et al. 2024).

Table 4.
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The binding free energy MM-PBSA calculation of curcumin analogs with SARS-CoV-2 PLpro macromolecules.

Compound Molecule ∆Evdw (kJ/mol) ∆Eele (kJ/mol) ∆GPB (kJ/mol) ∆GNP (kJ/mol) ∆GBind (kJ/mol)
Native (GRL0617) −149.05 −43.41 107.66 −16.17 −100.98
A102 −163.03 −32.83 103.36 −16.47 −108.98
A103 −104.99 −13.20 60.00 −12.46 −70.65
A104 −124.74 −21.12 100.84 −12.90 −57.91
A108 −115.35 −13.82 96.43 −12.88 −45.61
A111 −206.94 −30.14 171.81 −20.06 −85.33
A113 −146.69 −69.31 156.90 −13.61 −72.70
A129 −126.14 −4.07 75.85 −13.87 −68.24
A146 −81.52 −17.58 77.34 −9.68 −31.44
HGV5 −155.07 −40.02 125.77 −17.70 −87.03
HGV6 −137.38 −32.72 119.76 −16.32 −66.65
THA102 −180.07 −24.07 113.11 −17.89 −108.93
THA103 −107.48 −22.78 77.19 −12.62 −65.69
THA104 −163.03 −32.83 103.36 −16.47 −108.98
THA108 −116.88 −14.94 79.21 −13.56 −66.17
THA111 −170.04 −28.31 110.86 −18.70 −106.19
THA113 −107.50 −12.34 58.94 −11.15 −72.06
THA129 −112.04 −15.79 66.62 −12.70 −73.91
THA146 −129.58 −31.90 100.00 −14.88 −76.37
THHGV5 −151.65 −36.58 104.74 −15.67 −99.16
THHGV6 −155.60 −29.61 97.08 −16.89 −105.02

Information: ∆Evdw = van der Waals contribution, ∆Eele = electrostatic contribution, ∆GPB = polar desolvation contribution, ∆GNP = non-polar desolvation contribution.

Interaction stability in molecular dynamics simulations

The purpose of trajectory visualization in molecular dynamics simulation is to enhance comprehension of particle behavior within the molecular system under investigation. In molecular dynamics simulations, particles, whether atoms or molecules, are discrete entities influenced by forces from other particles within the system. By visualizing particle trajectories, we can observe how particles interact and how alterations in simulation conditions, such as temperature or density, impact particle behavior. These visualizations aid in recognizing patterns and trends in particle motion, illustrating the correlation between particle movement and evolving system conditions, and portraying outcomes across various simulation scenarios.

Several factors can lead to the displacement of a compound from its binding site during molecular dynamics simulations. Primarily, the instability of the interaction potential energy between curcumin analog compounds and the binding sites of SARS-CoV-2 PLpro macromolecules plays a significant role. This potential energy, which governs the stability of the compound at the binding site, is determined by the molecular interaction potential employed in the simulation. Certain potential interactions promote the compound’s stability at the binding site, while others may facilitate its movement away from it. Fig. 3 illustrates that the five curcumin analog compounds exhibiting the most favorable MM-PBSA binding-free energy demonstrate robust stability against the binding sites of SARS-CoV-2 PLpro macromolecules. These visual representations offer valuable insights into the atomic-level molecular interactions and provide guidance on potential modifications to optimize binding affinity and therapeutic effectiveness.

Figure 3. 

The three-dimensional depiction of molecular dynamics simulation outcomes involving curcumin analogs and SARS-CoV-2 PLpro macromolecules.

Furthermore, molecular dynamics simulations also consider the thermal transfer of atoms participating in interactions. If atoms involved in the interaction are sufficiently large during the heat transfer simulation, they may induce the compound to shift away from the binding site. To delve deeper into the molecular interactions during the 100 ns molecular dynamics simulations, Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) analyses were conducted. RMSD quantifies the average disparity between the protein’s true conformation (derived from the crystal structure) and the conformation acquired from molecular dynamics simulations. It is computed by measuring the mean distance between atoms in the two conformations. A low RMSD value suggests a close resemblance between the conformation from molecular dynamics simulations and the actual conformation. Conversely, RMSF assesses the average fluctuation of atomic positions in dynamic conformations obtained from molecular dynamics simulations. RMSF is determined by computing the average distance of each atom from its mean position in the dynamic conformation. A high RMSF value indicates substantial atomic movement in the dynamic conformation obtained from molecular dynamics simulations (Hidayat and Fakih 2021).

The RMSD chart displayed in Fig. 4 illustrates notable fluctuations observed in the THA102 compound compared to the other four curcumin derivative compounds, with an average RMSD value of 2.74 Å. Conversely, the RMSF plot reveals that the THA111 compound induces fluctuations in various amino acid residues across different regions of the SARS-CoV-2 PLpro macromolecules, such as LYS192, HIE193, CYS194, VAL227, CYS228, GLY229, ARG230, ASP231, LYS281, and GLU282, averaging a RMSF value of 1.35 Å. RMSD and RMSF serve as metrics to assess alterations in protein structure during molecular dynamics simulations. Significant variations observed in the RMSD and RMSF graphs may stem from factors such as conformational shifts, protein-ligand interactions, thermal dynamics, and model precision.

Figure 4. 

Visual representation of RMSD and RMSF data from molecular dynamics simulations depicting the behavior of curcumin analogs against SARS-CoV-2 PLpro macromolecules.

Distribution of molecule movement during molecular dynamics simulations

Graphical examination of the radius of gyration (Rg) and solvent accessible surface area (SASA) is essential to validate the fluctuations observed in the results obtained from molecular dynamics simulations for each curcumin analog compound. Rg serves as a parameter to gauge the size and distribution of mass within molecules or molecular assemblies (Hikmawati et al. 2022). In the context of molecular dynamics simulations, Rg is utilized to assess the dimensions of the simulated protein, calculated as the average distance of each atom from the molecular geometry’s center of mass. On the other hand, SASA quantifies the solvent-accessible surface area within a protein structure. Computed using the rolling ball algorithm, SASA determines the surface area accessible to atoms based on a sphere with a radius equivalent to that of an oxygen atom (Smith et al. 2015). During molecular dynamics simulations, SASA is instrumental in monitoring variations in the surface area accessible to solvent molecules. Additionally, SASA can delineate the nature of interactions within proteins, identifying hydrophobic interactions through decreased SASA and hydrophilic interactions through increased SASA.

Fig. 5 demonstrates that there are no substantial distinctions observed in the Rg and SASA graph visualizations for the five curcumin analog compounds. Rg and SASA serve as pivotal parameters in molecular dynamics simulations, facilitating the examination of molecule movement in solution. The Rg and SASA values for the five curcumin analogs exhibited negligible variance throughout the molecular dynamics simulation, indicating their akin physicochemical characteristics in solution. This uniformity may relate to the compound’s solubility, stability, and potential impact on pharmacological efficacy. Nonetheless, it’s essential to recognize that Rg and SASA values are merely a subset of parameters when assessing molecule properties in solution, necessitating a comprehensive analysis to discern disparities among the five curcumin analog compounds.

Figure 5. 

Visual representation of the molecular dynamics simulation outcomes for Rg and SASA is pivotal to illustrate the behavior of curcumin analogs in interaction with SARS-CoV-2 PLpro macromolecules.

Atomic distribution in molecular dynamics simulations systems

The radial distribution function (RDF) serves as a metric utilized to assess the dispersion pattern of atoms or molecules within a system. In molecular dynamics simulations, RDF is employed to gauge the spatial arrangement of atoms within the simulated protein. This calculation involves dividing the mean distance between two distinct types of atoms by the average distance of identical atoms (Vijayakumar et al. 2022). The RDF analysis reveals that the THA102 compound exhibits dissimilar atomic distribution dynamics compared to other curcumin analog compounds throughout the 100 ns MD simulation. Variations in RDF can signify alterations in protein structure, potentially instigated by ligand interactions, environmental fluctuations, or thermal fluctuations. Additionally, RDF can discern the nature of protein interactions, indicating hydrophobic interactions when RDF decreases or hydrophilic interactions when RDF increases.

Compound A102 exhibits a consistently stable and adaptable atomic dispersion throughout the simulations (Fig. 6). This suggests that these compounds foster robust atomic interactions, evenly spread around the SARS-CoV-2 PLpro macromolecules. This phenomenon may arise from the presence of stable and robust chemical bonds within compound A102, enhancing atomic interactions and facilitating flexible molecular movement. With its stable and adaptable atomic distribution, compound A102 is anticipated to exert a more pronounced biological impact, as it can engage more effectively with the target. Hence, RDF analysis aids in forecasting the therapeutic potential of compounds in pharmaceutical development.

Figure 6. 

The graphical representation of RDF obtained from molecular dynamics simulations illustrates the outcome of the interactions between curcumin analogs and SARS-CoV-2 PLpro macromolecules.

Strength and geometry of hydrogen bonds in molecular dynamics simulations

Moreover, an assessment of the stability of the ligand-protein hydrogen bonds established during the molecular dynamics simulations was conducted. This entails scrutinizing the hydrogen bond dynamics derived from the simulations, encompassing the quantification of their quantity, strength, and configuration. This analysis involves parsing the atomic coordinate datasets generated from the simulations and aligning them with predefined geometric criteria for hydrogen bonding. The tally of hydrogen bonds formed serves as a metric to gauge system stability, with fluctuations indicating alterations in molecular conformation. Additionally, the vigor of the hydrogen bond is appraised through an examination of its potential energy, while its geometry is assessed by scrutinizing the distance and angle between the hydrogen and bonded atoms (Wibowo et al. 2022).

Based on the hydrogen bond occupancy data presented in Table 5, it is evident that compounds THA111 and THHGV6 maintain stable hydrogen bonds throughout the entire molecular dynamics simulations, as indicated by their total hydrogen bond occupancy percentages of 21.76% and 23.36%, respectively. In contrast, compounds A102, THA102, and THA104 exhibit lower hydrogen bond occupancy percentages, namely 11.28%, 6.29%, and 14.47%, respectively. The key amino acid residues in the SARS-CoV-2 PLpro macromolecules that predominantly form hydrogen bonds include LYS159, ASP166, GLU169, TYR266, TYR270, and GLN271. The high percentage of hydrogen bond occupancy observed in the molecular dynamics simulations signifies the stability of the hydrogen bonds formed, which is crucial as it can influence the physical and chemical properties of the simulated system.

Table 5.
Download as
CSV
XLSX

Percentage of hydrogen bonds formed by curcumin analogs with SARS-CoV-2 PLpro macromolecules.

Compound Molecule Donor Acceptor Occupancy Total Occupancy
A102 TYR266 A102 0.60% 11.28%
LYS159 A102 0.10%
GLN271 A102 0.30%
GLN271 A102 0.90%
GLN271 A102 9.18%
A102 ASP166 0.20%
THA102 THA102 ASP166 0.10% 6.29%
GLN271 THA102 0.50%
TYR270 THA102 5.39%
GLN271 THA102 0.30%
THA104 GLN271 THA104 14.37% 14.47%
GLN271 THA104 0.10%
THA111 GLN271 THA111 10.38% 21.76%
GLN271 THA111 1.50%
GLN271 THA111 5.69%
TYR266 THA111 4.19%
THHGV6 THHGV6 GLU169 1.20% 23.36%
GLN271 THHGV6 22.16%

Fig. 7 illustrates the consistent presence of stable hydrogen bonds between compounds THA111 and THHGV6 with SARS-CoV-2 PLpro macromolecules throughout the simulation. Compound THA111 displayed peak hydrogen bond occurrences at 38.7 ns, 77.5 ns, 91.1 ns, and 94.4 ns, while THHGV6 exhibited its highest hydrogen bond occurrences at 74.3 ns, 74.4 ns, 74.9 ns, and 75.3 ns. A significant number of hydrogen bonds observed during molecular dynamics simulations suggests that both THA111 and THHGV6 possess robust and enduring molecular structures. This observation implies that the curcumin analog compounds potentially engage in numerous hydrogen bonding interactions, forming a dense or interconnected network.

Figure 7. 

Visual representations of the outcomes of molecular dynamics simulations regarding hydrogen bonds formed between THA111 (depicted in blue) and THHGV6 (shown in purple) with SARS-CoV-2 PLpro macromolecules.

Conclusions

The simulations indicate that overall, curcumin analog compounds exhibit favorable binding to SARS-CoV-2 papain-like protease (PLpro) macromolecules. However, THA111 and THHGV6 compounds demonstrated superior stability and affinity for SARS-CoV-2 PLpro macromolecules in both molecular dynamics simulations and MM-PBSA binding-free energy calculations. Consequently, these two compounds emerge as promising therapeutic candidates for combating COVID-19.

Acknowledgments

Author thanks the Curcumin Research Centre, Faculty of Pharmacy, Universitas Gadjah Mada, for providing the database of curcumin analog compounds used in this study.

This research received no external funding.

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