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Antimalarial Potential of Phytochemical Compounds from Garcinia atroviridis Griff ex. T. Anders Targeting Multiple Proteins of Plasmodium falciparum 3D7: An In Silico Approach

Nur Sofiatul Aini1, Arif Nur Muhammad Ansori2,3,4,5,*, Mochammad Aqilah Herdiansyah1,3,5, Viol Dhea Kharisma3,5,6, Muhammad Hermawan Widyananda3,5,7, Ahmad Affan Ali Murtadlo1,3,5, Dora Dayu Rahma Turista5,8, Teguh Hari Sucipto9, Sukma Sahadewa10, Fara Disa Durry11, Vikash Jakhmola4, Maksim Rebezov12,13, Valery P. Kartashev14, Kadek Rachmawati15, Putu Angga Wiradana16, T. Karpagam17 and Hery Purnobasuki1,*

1Department of Biology, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia

2Postgraduate School, Universitas Airlangga, Surabaya, Indonesia

3Division of Research and Development, Jalan Tengah, Surabaya, Indonesia

4Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, India

5Virtual Research Center for Bioinformatics and Biotechnology, Surabaya, Indonesia

6Doctoral Program in Mathematics and Natural Sciences, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia

7Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Malang, Indonesia

8Biology Education Department, Faculty of Teacher Training and Education, Mulawarman University, Samarinda, Indonesia

9Dengue Study Group, Institute of Tropical Diseases, Universitas Airlangga, Surabaya, Indonesia

10Faculty of Medicine, Universitas Wijaya Kusuma Surabaya, Surabaya, Indonesia

11Faculty of Medicine, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, Indonesia

12Department of Scientific Research, V. M. Gorbatov Federal Research Center for Food Systems, Moscow, Russian Federation

13Faculty of Biotechnology and Food Engineering, Ural State Agrarian University, Yekaterinburg, Russian Federation

14Department of Scientific Research, Russian State Social University (RSSU), Moscow, Russia

15Faculty of Veterinary Medicine, Universitas Airlangga, Surabaya, Indonesia

16Research Group of Biological Health, Study Program of Biology, Faculty of Health and Science, Universitas Dhyana Pura, Bali, Indonesia

17Department of Biochemistry, Shrimati Indira Gandhi College, Trichy, India

*Correspondence to: Arif Nur Muhammad Ansori, E-mail: ansori.anm@gmail.com; Hery Purnobasuki, E-mail: hery-p@fst.unair.ac.id

Received: September 8 2024; Revised: October 12 2024; Accepted: October 29 2024; Published Online: November 11 2024


Cite this paper:

Aini NS, Ansori ANM, Herdiansyah MA et al. Antimalarial Potential of Phytochemical Compounds from Garcinia atroviridis Griff ex. T. Anders Targeting Multiple Proteins of Plasmodium falciparum 3D7: An In Silico Approach. BIO Integration 2024; 5: 1–12.

DOI: 10.15212/bioi-2024-0075. Available at: https://bio-integration.org/

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© 2024 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See https://bio-integration.org/copyright-and-permissions/

Abstract

Background: Plasmodium falciparum is a malaria-causing unicellular parasite with strain 3D7 (Pf3D7) being the most lethal. Currently, antimalarial resistance has been reported which necessitates the development of novel antimalarial drugs to combat the spread of malaria. Garcinia atroviridis Griff. ex T. Anders contains phytochemical compounds that are useful for various activities, including targeting Pf3D7 proteins. This study explored novel antimalarial drugs from G. atroviridis against several target proteins of Pf3D7 in silico.

Methods: Phytocompounds from G. atroviridis were selected as ligands. After retrieval from the Protein Data Bank, the protein sequence was screened using BLASTp NCBI. Molecular docking analysis was performed on PyRx to compute binding affinity and identify the chemical interactions involved. The stability of the ligand-protein complex was evaluated using dynamic molecular approaches.

Results: Our findings showed that quercetin has a high binding affinity with apicoplast DNA polymerase (−8.3 kcal/mol), glutamyl-tRNA synthetase (−7.5 kcal/mol), and plasmepsin X (−7.8 kcal/mol). Kaempferol had a high binding affinity for the cytochrome c2 domain-swapped dimer (−8.4 kcal/mol).

Conclusion: Collectively, quercetin and kaempferol are potential antimalarial candidates which warrant further investigation using in vitro and in vivo designs.

Keywords

Bioinformatics, Drug Discovery, Malaria, Medicine, Molecular Docking

Introduction

Malaria is one of the deadliest infectious diseases with global cases spanning different age groups [1]. According to data from the World Health Organization (WHO) in 2020, malaria is endemic in >50 countries, including Indonesia [2, 3]. Malaria is caused by the protozoan parasite, Plasmodium, which is transmitted to humans from the bites of female Anopheles mosquitoes [4, 5]. P. falciparum is a parasite responsible for major malaria cases worldwide, particularly in sub-Saharan Africa and Asia [6, 7]. P. falciparum is a unicellular protozoan belonging to the Plasmodiae family and Apicomplexa phylum [8]. Among thousands of identified strains, P. falciparum 3D7 (Pf3D7) is considered the most lethal [9]. Management of malaria is challenged by the presence of antimalarial drug resistance, such as chloroquine, artemisinin, and sulfadoxine-pyrimethamine, which has been reported in developed countries [10, 11].

To address this challenge, research has been conducted to explore phytocompounds that possess biological activities and drug-likeness [12]. Artemisinin and quinine are examples of such discoveries [13]. G. atroviridis Griff. ex. T. Anders has been specifically reported for its use in ethnomedicine in South and Southeast Asian countries [14, 15]. Previous studies have reported that the extract of G. atroviridis has antioxidant, antimicrobial, and anticancer activities [1618]. The leaf extract of G. atroviridis has been reported to act as an effective inhibitor of P. berghei parasitemia in a mouse model [19]. However, no further research has been conducted to investigate the potential of phytocompounds from G. atroviridis as antimalarial agents using in vivo, in vitro, or even in silico approaches [20].

In silico is considered the method of choice in the drug discovery process because of its efficiency in terms of economic cost and time consumption. Moreover, molecular docking provides insight in the predicted orientation and position of drug candidates as potential substrates for target molecules [8, 21, 22]. The target proteins include PfapPOL (PDB ID: 7SXQ), Pf pyruvate kinase complex (PDB ID: 7Z4M), Pf actin 1 filament (PDB ID: 6TU4), Pf aspartate transcabamoylase (PDB ID: 7ZCZ), PfERS (PDB ID: 7WAJ), Pfcyt c2 DSD (PDB ID: 7TXE), and PfPMX (PDB ID: 7RY7). All of the aforementioned proteins are Pf3D7 receptors that can debilitate the P. falciparum 3D7 body. These proteins are crucial receptors in the Pf3D7 strain that determine the survival and virulence of the organism and serve as a preliminary study to investigate the potential of G. atroviridis-based compounds as antimalarial drug candidates.

Methods

Ligand collection

The phytochemical compounds from G. atroviridis, which are based on previous studies conducted by Shahid et al. (2022), were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) [15]. The identification and selection of phytochemical compounds from G. atroviridis were performed using ChemDraw to obtain the 2D structure and Chem3D to visualize the 3D structure. The 3D structure was retrieved from. sdf format. The compound identifier (CID) and canonical SMILES of each compound were collected from the PubChem database. Ligand minimization was conducted using Open Babel in PyRx software (version 0.9;The Scripps Research Institute, La Jolla, CA, USA) [23].

Antimalarial probability prediction

Canonical SMILES of each compound was pasted on the PASS Online webserver to determine antimalarial potency. Potential activity (Pa) > Potential inactivity (Pi), and Pa >0.3 were set as the standard. Compounds with these values for one or two categories in antiprotozoal and antimalarial activities are thought to operate properly in the human body [24].

Drug absorption parameter analysis

Selective antimalarial potential phytochemical compounds from G. atroviridis were compared of the drug similarity using SwissADME (http://www.swissadme.ch/index.php). Lipinski rules were utilized to identify the drug absorption parameter in this analysis with categories comprising hydrogen bond donor (nOHNH) ≤5, hydrogen bond acceptor (nON) ≤10, coefficient partition in water-lipid (miLogP) ≤5, and relative molecular mass (BM) ≤500 [25, 26].

Target protein collection

Target proteins were collected from RCSB PDV (https://rcsb.org/) in the P. falciparum 3D7 (Pf3D7) category. Recent publications from 2022–2023 were collected. The protein was then identified by comparative sequence analysis using the BLASTp tool in NCBI [27]. A threshold value ≤35% was established for sequence identity [28]. A low significance of similarity demonstrates a low homology with human proteins [8, 29].

Molecular docking, validation protocol, and complex visualization

Molecular docking was performed to screen the interaction between the ligand and the target protein via specific docking [22]. The process was performed using PyRx software (version 0.9; The Scripps Research Institute, La Jolla, CA, USA) [23]. Moreover, the binding affinity score and position of the most negative phytochemical ligands were collected and compared [30]. To validate the stability of the molecular docking method, molecular dynamics were assessed with CABS-flex 2.0 (https://biocomp.chem.uw.edu.pl/CABSflex2/index) [31]. The simulation parameters included protein rigidity, restraints, C-alpha restraint weight, number of cycles, side-chain restraints, temperature range, trajectory, and RNG seed [32, 33]. Next, the selected ligands with predicted target proteins were displayed using Biovia Discovery Studio software (Vélizy-Villacoublay, France). The selected ligands were visualized in 2D and 3D to determine the details of the binding site interaction between the ligand and the target protein [8].

The crystallographic structure conformer of each target protein with the natural ligand was docked to the receptor using AutoDock Tools (PyRx software, version 0.9; The Scripps Research Institute, La Jolla, CA, USA) by setting a grid box scale. If the root mean square deviation (RMSD) value was >2 Å, the procedure was invalid. Therefore, the grid boxes (X, Y, and Z) and spacing values (center X, Y, and Z) were manually adjusted until a RMSD <2 Å was obtained. The validation molecular docking process needs to determine the grid box to understand the interaction of the ligand and protein as the active site of the protein. The center of the grid box was determined based on the center of mass of the naturally occurring ligand. The dimensions of the grid box were based on the size of the ligand and binding site (Table 1) [34, 35].

Table 1 Grid Box Scale of Molecular Docking

Target protein Ref Grid box scale
Center (Å) Dimension (Å)
X Y Z X Y Z
7SXQ [36] −50.263 3.428 15.813 117.116 91.309 118.546
7WAJ [37] −27.384 −9.320 20.247 71.059 85.187 101.557
7TXE [38] −13.662 −4.523 14.643 72.095 67.269 42.993
7RY7 [39] 68.445 5.069 39.239 87.576 62.283 77.647

Results

G. atroviridis contains 35 bioactive phytocompounds derived from different parts of the plant, as suggested by a previous comprehensive critical review. Some parts of G. atroviridis have been reported as a source of bioactive phytocompounds, including fruits, stem bark, and leaves [15]. The structure of each compound was retrieved from the PubChem database, where only 24 of the structures had a CID and canonical SMILES notation (Table 2).

Table 2 Phytochemical Compounds of G. atroviridis from Previous Research [15]

Compound Sources PubChem CID References
Citric acid Fruit 311 [73, 74]
Malic acid Fruit 525 [73, 75]
Succinic acid Fruit 1110 [73, 76]
Tartaric acid Fruit 875 [73]
Hydroxycitric acid Fruit 123908 [7779]
Pentadecanoic acid Fruit 13849 [73, 8083]
Nonadecanoic acid Fruit 12591 [73]
Dodecanoic acid Fruit 3893 [73]
14-cis-docosenoic acid Fruit Unknown [84]
1,1″-dibutyl methyl hydroxycitrate Fruit Unknown [85]
2-(butoxycarbonylmethyl)-3-butoxycarbonyl-2-hydroxy-3-propanolide Fruit Unknown [85]
Atroviridin Stem bark 11267348 [86]
Benzoquinone atroviridine Root Unknown [87, 88]
Atrovirisidone Root 10342405 [88]
Atrovirisidone B Root Unknown [88]
Garcineflovanol A Stem bark Unknown [89]
Garcineflovanone A Stem bark Unknown [89]
Naringenin Root Unknown [90]
3,8″-binaringenin Root Unknown [90]
Morelloflavone Root 5464454 [84]
Fukugiside Root 73157060 [84]
Kaempferol Stem bark 5280863 [91]
Quercetin Stem bark 5280343 [91]
Garcinol Fruit 5281560 [91]
Isogarcinol Fruit 11135781 [91]
α-humulene Fruit 5281520 [92]
(−)-β-caryophyllene Fruit 1742210 [92]
Cambroginol Fruit Unknown [73]
4-methylhydroatrovirinone Root 101249096 [84]
Garcinexanthone G Stem bark Unknown [91]
Gentisein Stem bark 5281635 [91]
Stigmasterol 5280794 [91]
Stigmasta-5,22-dien-3-O-β-glucopyranoside Stem bark Unknown [91]
3β-acetoxy-11α,12α-epoxyoleanan-28,13β-olide Stem bark Unknown [91]
2,6-dimethoxy-p-benzoquinone Stem bark 68262 [91]

Selected phytochemical compounds with known CIDs were analyzed to identify the anti-protozoal and anti-Plasmodium probabilities using PASS online. From the probability screening using PASS online, there were five compounds that matched the criteria as antimalarials: fukugisisde (CID 73157060); kaempferol (CID 5280863); quercetin (CID 5280343); (−)-β-caryophyllene (CID 1742210); and gentisein (CID 5281635). All selected compounds are shown in Table 3. Kaempferol and quercetin had the highest scores for both anti-protozoal and anti-Plasmodium agents compared to fukugiside, (−)-β-caryophyllene, and gentisein, which only fulfilled the criteria for anti-protozoal probability. Based on the Lipinski rules, fukugiside received three violations and was discarded from the subsequent docking analysis (Table 3). Drug absorption analysis was performed to determine the possibility of drug absorption in the body (Table 4).

Table 3 Antiprotozoal and Anti-Plasmodium Probabilities of Phytochemical Compounds from G. atroviridis Collected from PASS Online

Compounds Antiprotozoal Anti-plasmodium
Pa Pi Pa Pi
Ascorbic acid
Citric acid
Malic acid
Succinic acid 0.175 0.084
Tartaric acid 0.025 0.007
Hydroxycitric acid
Pentadecanoic acid 0.019 0.007 0.181 0.076
Nonadecanoic acid 0.019 0.007 0.181 0.076
Dodecanoic acid 0.019 0.007 0.181 0.076
Atroviridin 0.211 0.096 0.192 0.065
Atrovirisidone 0.248 0.072 0.224 0.039
Morelloflavone 0.194 0.111 0.176 0.083
Fukugiside 0.342 0.033 0.298 0.014
Kaempferol 0.461 0.013 0.345 0.009
Quercetin 0.446 0.014 0.365 0.008
Garcinol 0.298 0.047
Isogarcinol
α-humulene
(−)-β-caryophyllene 0.578 0.006 0.231 0.034
4-methylhydroatrovirinone 0.205 0.100 0.193 0.064
Genistein 0.335 0.034 0.262 0.022

(-) No probability of antiprotozoal or anti-plasmodium candidate.

Table 4 Drug Absorption Analysis of Selected Phytochemical Compounds from G. atroviridis

Compounds nOHNH (≤5) nON (≤10) miLogP (≤5) BM (≤500 g/mol) Violation
Fukugiside 16 10 1.62 718.61 3
Kaempferol 6 4 2.17 286.24 0
Quercetin 7 5 1.68 302.24 0
(−)-β-caryophyllene 1 0 4.14 220.35 0
Genistein 5 3 2.27 244.20 0

In contrast, 514 target protein structures retrieved from Pf3D7 were collected from RCSB PDB. Seven selected proteins with native ligands were filtered from 2022–2023 publications and compared to human homology proteins using BLASTp NCBI (Table 5).

Table 5 Target Protein Screening

Protein target (PDB ID) Resolution (Å) Homology protein (accession number) Native ligand similarity (%)
Pf apicoplast DNA polymerase (7SXQ) 2.50 4XVL_A 23.67
Pf pyruvate kinase complex (7Z4M) 1.90 5SC8_A 47.77
Pf actin 1 filament (6TU4) 2.60 7P1H_B 83.06
Pf aspartate transcabamoylase (7ZCZ) 2.45 5G1N_A 36.31
Pf glutamyl-tRNA synthetase (PfERS) (7WAJ) 2.25 4YE8_A 31.11
Pf cyt c2 DSD (7TXE) 2.30 5EXO_A 32.14
Pf plasmepsin X (PMX) (7RY7) 2.10 3O9L_A 32.72

Four proteins were obtained with similarities <35%, including apicoplast DNA polymerase [apPOL] (PDB ID 7SXQ), glutamyl-tRNA synthetase [ERS] (PDB ID 7WAJ), cytochrome c2 domain-swapped dimer [cyt c2 DSD] (PDB ID 7TXE), and plasmepsin X [PMX] (PDB ID 7RY7). Filtered proteins were further utilized in docking analysis to identify ligand-target protein interactions (Table 5) [3639].

Molecular docking analysis was used to determine the binding affinity and chemical interactions of amino acids between the ligand and target protein. The lowest binding affinity was quercetin. Quercetin had the lowest binding affinity against apPOL, ERS, and PMX (−8.3, −7.5, and −7.8 kcal/mol, respectively). Kaempferol had the lowest result compared to other selected compounds targeting cyt c2 DSD (−8.4 kcal/mol; Table 6).

Table 6 Chemical Interaction between Ligand and Target Protein Complex

Ligand-protein complex Binding affinity (kcal/mol) Interaction Amino acids
Quercetin−apPOL −8.3 HI Lys29(A), Ile76(A), Tyr105(A)
PHI Lys29(A), Lys74(A), Lys77(A), Tyr78(A), Glu103(A)
vdw Lys27(A), Leu28(A), Ile72(A), Cys79(A), Asn104(A)
UF Tyr105(A)
Quercetin−ERS −7.5 HI Leu576(A), Arg577(A)
PHI Asn371(A), Leu580(A), Lys785(A), Asp797(A), Asp797(A)
vdw Thr581(A), Lys582(A), Val782(A), Ser783(A), Ile795(A), Ile808(A)
Quercetin-Cyt c2 DSD −8.2 HI Pro60(A)
PHI Asn52(A), Arg68(A)
Vdw Leu97(A), Met101(A), Val65(A), His42(A), Ala70(A), Gly71(A), Thr51(A), Lys74(A), Trp58(B), Leu132(B)
Quercetin-PMX −7.8 HI Asp112(A), Ile358(A)
PHI Asp245(A), Gln247(A), Tyr462(A)
vdw Asn72(A), Asp73(A), His74(A), Thr110(A), Leu111(A), His242(A), Asp356(A), Tyr357(A), Ser359(A)
UF Pro71(A)
Kaempferol-apPOL −8.1 HI Tyr105(A)
PHI Lys29(A), Ile72(A)
vdw Tyr78(A), Glu103(A), Val102(A), Asn104(A), Asp75(A), Lys74(A), Lys27(A), Leu28(A)
UF Lys77(A)
Kaempferol-ERS −7.3 PHI Glu669(A), Ser628(A), Thr769(A)
vdw Asp625(A), Leu670(A), Glu671(A), Arg790(A), Asp672(A), His723(A)
Kaempferol-Cyt c2 DSD −8.4 HI Pro60(A), Pro60(A), Leu62(A), Trp92(A), Trp92(A), Tyr100(A)
PHI Arg68(A), Arg68(A)
UF Asn52(A)
Kaempferol-PMX −7.4 PHI Pro71(A), His242(A), Asp73(A), Tyr357(A)
vdw Ile358A), Asp112(A), Leu111(A), Lys76(A), Tyr462(A), His74(A), Gln247(A), Asn72(A), Arg244(A), Ser359(A), Thr110(A)
(−)-β-caryophyllene-apPOL −6.8 HI Lys244(A), Leu557(A), Ile241(A)
vdw Asn603(A), Leu557(A), Tyr607(A), Ile237(A)
(−)-β-caryophyllene-ERS −6.1 HI Arg509(A), Leu508(A), Asn334(A), Thr510(A), His327(A), Asp350(A), Pro317(A), Glu318(A)
vdw Tyr491(A), Pro316(A), Phe315(A), Phe537(A), Ala330(A)
(−)-β-caryophyllene-Cyt c2 DSD −6.5 HI Lys74(A), Phe79(A)
vdw Thr87(A), Ser77(A), Pro78(A), Ser73(A), Thr69(A), Phe55(A)
(−)-β-caryophyllene-PMX −6.6 HI Ile99(A), Ile316(A), His242(A), Val340(A), Phe360(A)
vdw Lys241(A), Trp273(A)
Gentisein-apPOL −7.9 PHI Thr85(A), Asn139(A), Gln138(A), Ile83(A), Asp143(A). Asn82(A)
vdw Gln195(A), Trp199(A). Thr86(A), Gln84(A), Phe142(A), Leu178(A)
Gentisein-ERS −6.7 vdw Ser628(A), Asp625(A), Thr769(A), Arg790(A). Asp672(A)
Gentisein-Cyt c2 DSD −7.8 HI Lys39(A)
PHI Ser77(B)
vdw Ser57(A), Gln40(A), Tyr149(B), Thr(56), Gly75(B), Lys74(B), Asn76(B)
Gentisein-PMX −7.5 HI Leu84(A)
PHI Gln527(A), Val89(A), Asn475(A)
vdw Ser467(A), Lys90(A), Tyr91(A), Met470(A), Leu474(A)

HI: hydropobic interaction; PHI: polar H interaction; vdw: van der Walls interaction; UF: unfavorable acceptor.

Visualization of ligand-target protein interaction is shown with red stain for the target protein and zoomed in to display the ligand interaction. Chemical interactions between ligands binding to the A domain of each protein are shown. Hydrogen bonds, hydrophobic, and van der Waals (vdw) interactions formed between compounds and target proteins. Conventional hydrogen bonds, pi-donor hydrogen bonds, pi-sigma, pi-pi T-shaped, pi-alkyl, pi-pi stacked, and amide-pi stacked consist of various hydrogen bonds and hydrophobic interactions were involved in the four selected target proteins (Figures 1 and Figures 2). However, only the quercetin and ERS complex interaction did not result in an unfavorable acceptor-acceptor bump (Table 6).

Figure 1 Chemical interaction between receptor and compounds (A: quercetin−apPOL; B: quercetin−ERS; C: quercetin-cyt c2 DSD; D: quercetin-PMX; E: kaempferol-apPOL; F: kaempferol-ERS; G: kaempferol-cyt c2 DSD; H: kaempferol-PMX.

Next follows the figure caption

Figure 2 Chemical interaction between receptor and compounds [I: (−)-β-caryophyllene-apPOL; J: (−)-β-caryophyllene-ERS; K: (−)-β-caryophyllene-cyt c2 DSD; L: (−)-β-caryophyllene-PMX; M: gentisein-apPOL; N: gentisein-ERS; O: gentisein-cyt c2 DSD; P: gentisein-PMX].

Next follows the figure caption

Validation docking was also available in this study to determine the stability of the receptor when applied with the compound candidate. Molecular dynamic analysis results indicated that the interaction hotspot total root mean square fluctuation (RMSF) value was different for each of the receptors. The most stable receptor was 7TXE, with stable fluctuations between the ligand and protein atoms. Receptor 7TXE had an RMSF value <3 Å (Figure 3C). The receptor with the code, 7RY7, was the most unstable receptor against the compound because the fluctuations formed from the ligand and protein atoms had an RMSF value >3 Å (Figure 3D). Thus, all molecular dynamic validations are shown in Figure 3.

Figure 3 Molecular docking validation of the receptor using the root mean square fluctuation (RMSF) value through CABS-flex online tools (A: 7SXQ; B: 7WAJ; C: 7TXE; D: 7RY7).

Next follows the figure caption

Another docking simulation validation revealed the radius of gyration (Rg) for each receptor (Figure 4). As shown in Figure 4, the Rg of the complex receptor fluctuated between 0.100 and 1.000 Å. Rg showed little conformational change throughout the docking simulation [40]. A lower Rg value indicates that the system has higher compactness and vice versa [41]. The data in Figure 4 show that that 7TXE complex was the most stable, with a low Rg value. The 7SXQ complex had the most unstable interaction with the highest Rg value.

Figure 4 Molecular docking validation of the receptor using the radius of gyration (Rg) value through CABS-flex online tools (A: 7SXQ; B: 7WAJ; C: 7TXE; D: 7RY7).

Next follows the figure caption

Discussion

G. atroviridis has numerous benefits for various diseases [15]. The methanol extract from this plant showed better antioxidant activity than the aqueous extract. Among the various parts, methanol extracts from the stem had the highest total phenolic and flavonoid content as well as the strongest antioxidant extract based on 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2-azinobis 3-ethylbenzothiazoline 6-sulfonate (ABTS) scavenging assays, respectively. This finding demonstrated a significant correlation between phytochemical constituents that are responsible for radical scavenging and antioxidant effects [16]. In contrast, the antimicrobial activities of G. atroviridis phenol extract had a 6.67%–42.86% inhibition for various fungi that resembled the antioxidant properties with moderate inhibition to selective fungi and cell viability targeting human skin fibroblast (HSF) cells [17]. Two new ester derivatives of garcinia acid showed anti-tumor promoting activity against Epstein-Barr virus early antigen and non-cytotoxic characteristics towards several tested cell cultures [18]. In the current study the PubChem webserver collected restricted databases that facilitated detection of antimalarial probability for 23 compounds and showed good criteria for 5 compounds. Furthermore, the drug absorption analysis revealed that only four phytochemical compounds fulfilled these criteria.

In contrast, one study targeting Plasmodium parasitemia utilized G. atroviridis. However, the research was conducted to treat P. berghei parasitemia in mice via in vivo research and no records of specific phytochemical compounds suppressing parasitemia have been published [19]. There are 514 Pf3D7 protein structures deposited in the RCSB PDB. Several proteins have been identified, including plasmepsins, proteases, peptidases, and purine nucleosides [10]. A recently published protein was required to determine the similarity with homologous proteins from humans to select appropriate targets for antimalarial drugs. Following publication and homology selection, selected proteins were docked to select phytochemical compounds from G. atroviridis as ligands [8].

Apicoplast is non-photosynthetic plastid that has evolved from chloroplasts. This organelle arose through a secondary endosymbiotic event in with red algae [36]. The apicoplast participates in metabolic processes, such as fatty acid, heme, and isoprenoid biosynthesis, as well as Fe-S maturation [42, 43]. Furthermore, DNA polymerase targeting apPOL has a role in genome replication and repair. BLASTp analysis revealed the lowest similarity to orthologs in mammals. Therefore, this protein is a promising drug target for malaria prevention and treatment [36]. Based on molecular docking analysis, domain A of this protein is critical for replication inhibition. Quercetin interacts against apPOL in various ways, including hydrogen bonding and hydrophobic and vdw interactions (Table 6). Hydrogen bonds in this complex involved seven interactions, including conventional and pi-donor hydrogen bonds. The hydrogen bond exhibits the strongest chemical interaction [44]. Quercetin was predicted to be the most effective inhibitor of apPOL activity in this study.

Aminoacyl-tRNA synthetases (aaRSs) are vital enzymes in protein translation that charge tRNA for protein synthesis [45]. Most aaRSs do not require tRNA to produce amino acid and adenosine monophosphate (aa-AMP) complexes in the translation process. However, there are some exceptions at this stage, such as glutamyl- (GluRS), glutaminyl- (GlnRS), and arginyl-tRNA (ArgRS) synthetases [46, 47]. ATP is triggered by tRNA binding in these enzymes and forms an adenylate intermediate complex [48, 49]. There are several functional structures (pocket regions, cavities, and tunnels) that connect protein surfaces with buried active or binding sites in protein conformers. This structure is essential for the biological activity of most proteins [50]. Cytoplasmic PfERS has significant evolutionary divergence through the process of L-Glu production and has been studied as an antimalarial agent for decades [51]. Molecular docking results showed different interactions, such as hydrogen bonds and hydrophobic and vdw interactions. However, quercetin did not exhibit unfavorable bonds that provided a less satisfactory binding affinity throughout the molecular docking [52]. Compared to other phytochemical compounds, quercetin had the lowest binding affinity for inhibiting Pf3D7 cytoplasmic L-Glu biosynthesis.

P. falciparum attacks erythrocytes and degrades hemoglobin into crystalline hemozoin within the acidic parasite digestive vacuoles [53]. Heme from hemoglobin is an essential metabolic cofactor for P. falciparum. Parasites preserve a mitochondrial electron transport chain (ETC), including cyt c, as a mobile electron carrier between complexes III and I [31]. Complex III binds the reduced ubiquinol and transports electrons via the Q cycle reaction. The ETC function is critical for parasite viability, particularly ATP synthesis [54]. According to the docking analysis, hydrophobic interactions predominate in the kaempferol chemical interaction with cyt c2 DSD. Pi-sigma, pi-pi stacked, pi-pi T-shaped, and pi-alkyl interactions are hydrophobic interactions. Hydrophobic interactions are known to dominate protein stability in proteins with 36–534 residues, accounting for approximately 60±4% of all interaction [55]. Thus, the interaction between kaempferol and cyt c2 DSD is regarded as the most potent inhibitor of hemoglobin degradation and ATP synthesis compared to other phytochemical compounds from G. atroviridis.

Pepsin-like aspartic proteases influence nutrient uptake, immune evasion, invasion, and egress, which are important processes for successful infection of the host cell [39]. P. falciparum, the most lethal Plasmodium species, expresses 10 pepsin-like aspartic proteases in plasmepsin (PfPM) [9]. Among the pepsin-like aspartic proteases, PfPMIX and PfPMX are involved in parasite invasion and egress [56, 57]. PMX is located in exonemes or secretory vesicles and is expressed in the schizonts, merozoites, gametocytes, and liver infections [39, 58]. Like previous results of selected ligands targeting essential target proteins of Pf3D7, quercetin chemical interactions include hydrogen bonds, hydrophobic interactions, vdw interactions, and an unfavorable bump. Vdw interactions are abundant, although the consecutive functions of vdw are weak. However, the close atomic and molecular distances encourage vdw interactions in the compactness and folding of secondary structured proteins [59]. Moreover, quercetin may have potential as a PMX preventive agent at various stages of parasitemia.

Previous studies have revealed numerous agents with selective targets against several proteins in P. falciparum. Various antibiotics, such as clindamycin and tetracycline, inhibit parasite differentiation into merozoites [60, 61]. The antibiotics may impede aminoacyl-tRNA binding to mRNA ribosomes and inhibit the protein synthetase pathway. Quinoline derivatives act via heme detoxification and the cytochrome BCI complex. However, several antimalarial drugs, such as artimisinin and primaquine, have unclear mechanisms [11, 13, 62]. In addition, resistance to synthetic drugs has been reported since 1957 in Southeast Asia and sub-Saharan Africa [63, 64].

Furthermore, the phytochemical compounds of G. atroviridis have been computationally approved for the antimalarial potential. Quercetin has a wide range of targets against proteins in various organelles, such as apicoplast (apPOS), cytoplasm (ERS), and vacuole (PMX). Kaempferol has been shown to prevent cyt c2 DSD activities of Pf3D7. Both quercetin and kaempferol are plant-derived aglycones (flavonol) from flavonoid glycosides that are synthesized by different enzymes. Quercetin and kaempferol also demonstrated broad spectrum functions, such as antioxidant, antimicrobial, antitumor, cardiovascular protection, and anti-inflammatory activities [6567]. The pharmacokinetics and toxicology assessments of quercetic and kaempferol have been categorized as safe in several dosage amounts [65, 68]. Further exploration should be conducted regarding the potential of these compounds to enrich the database of phytochemical and bioinformatic medicinal function [31].

The limitation of this in silico research was that the data generated are predictive of the properties of the drug substance against the test receptor. Further in vivo and in vitro studies by animal study and cell cultures are warranted to validate the in silico data obtained in this study [6972]. In this way, the completeness of the data for the development of antimalarial candidates can be achieved.

Conclusion

G. atroviridis has antimalarial potential based on a molecular docking experiment of phytochemical compounds against several Pf3D7 proteins (apPOL, ERS, PMX, and cyt c2 DSD). Quercetin had the strongest binding affinity for apPOL, ERS, and PMX. Kaempferol was the most effective inhibitor of cyt c2 DSD. Binding affinity indicates the ability of the drug to bind to the receptor. The smaller the binding affinity the higher the affinity of the complex. Further in vivo and in vitro analyses are required to demonstrate the efficacy of G. atroviridis as an antimalarial agent.

Acknowledgments

We thank Jalan Tengah, Indonesia (www.jalantengah.site) for editing the manuscript.

Conflict of interest

The authors declare that there are no conflicts of interest.

Authors’ contribution

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved the final manuscript for publication.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Ethics statement

This study involved the use of secondary data retrieved from open databases, so ethical approval was not required.

Funding

This study was funded by the Virtual Research Center for Bioinformatics and Biotechnology, Indonesia.

Disclosure

Underlying data are available upon requested from the corresponding author. All authors listed stated that there were no conflicts of interest within the publication or funding.

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