Structure based High-Throughput Virtual Screening, Molecular Docking and Molecular Dynamics Study of anticancer natural compounds against Fimbriae (FimA) protein of Porphyromonas gingivalis in oral squamous cell carcinoma

Abstract

Oral cancer is the eighth most common cancer in the world. Tobacco, alcohol, and viruses have been regarded as a well- known risk factors of OCC however, 15% of OSCC cases occurred each year without these known risk factors. Recently a myriad of studies has shown that bacterial infection leads to cancer. Accumulated shreds of evidence demonstrate the role of P. gingivalis in OSCC. The virulence factor FimA of P. gingivalis activated the oncogenic pathways of OSCC by upregulating various cytokines. It also led to the inactivation of a tumor suppressor protein p53 and the activation of the Matrix-metalloproteinase protein 9 (MMP9). The present Insilico study uses High-Throughput Virtual Screening, molecular docking, and molecular dynamics techniques to find the potential compounds against the target protein FimA. The goal of this study is to identify the anti-cancer lead compounds retrieved from natural sources that can be used to develop potent drug molecules to treat P.gingivalis-related OSCC. The anticancer natural compounds library was screened to identify the potential lead compounds. Further, these lead compounds were subjected to precise docking, and based on the docking score potential lead compounds were identified. The top docked receptor-ligand complex was subjected to molecular dynamics simulation. A study of this Insilco finding provides potent lead molecules which help in the development of therapeutic drugs against the target protein FimA in OSCC.

Introduction

Oral cancer is among the most common cancer having a rank of eight all over the world[1]. About 90% of cases of oral cancer is the oral squamous cell carcinoma (OSCC). Oral cancer has a high incidence and recurrence rate[2]. The primary risk factors of OSCC are alcohol, tobacco, and nicotine[3]. However, around 15% of cases of OSCC develop without any of these known risk factors[4]. Recently the involvement of oral bacteria in the etiology of OSCC has been gradually attracted attention[4–6]. Among the oral bacteria, P. gingivalis a gram-negative periodontal pathogen implication in carcinogenesis has been extensively studied[5, 7–9]. The Overabundance of P. gingivalis has been seen in OSCC patients [10], and stipulated as a risk factor for OSCC [11]. Among the various cancer hallmarks[12] six major hallmarks have been promoted by P. gingivalis in OSCC[13]. This bacteria secretes varieties of virulence factors in order to survive within the host cell. The virulence factors are Fimbriae (FimA), gingipains, NDK, LPS etc. [14]. Several epidemiological and clinical studies revealed that these virulence factors promote various oncogenic signaling pathways[15–18]. FimA is essential for the adherence and colonization of P. gingivalis in host cell[19]. Based on the differences in the genotypes FimA are classified into six types (FimA I, IB, II, III, IV, V)[20, 21]. P. gingivalis invades the gingival epithelial cells via their FimA, which contains epithelial binding domain [22, 23]. Additionally, FimA also contains a binding site of salivary protein, proline-rich proteins (PRP1), cluster of differentiation 14 (CD14), Integrin αM (CD11B), cluster of differentiation 14 (CD18)[24]. FimA invasion helps in the production various cytokines like interleukin-8 (IL-8), interleukin-1(IL-1), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) which further activates a variety of oncogenic pathways [7, 25]. FimA promotes the epithelial cell proliferation by modifying the cyclin dependent kinase (CDK) and inactivating the tumor suppressor protein p53 [5, 10]. FimA also upregulates the Zinc finger E-box-binding homeobox 1 (Zeb1) via Phosphatidylinositol 3-kinases/protein kinase B (PI3K/Akt) and β-catenin pathway which increased the expression of Matrix-metalloproteinase (MMP9). MMP-9 helps in the enhancement and migration of cancer cells [17]. It has been proposed that FimA promotes the transition of the epithelial cell to cancer cell [5]. A high amount of FimA has been detected in the saliva of OSCC patients, suggesting the involvement of FimA in the carcinogenic process [26]. The aim of this study is to find the potent anti-cancer natural compounds against FimA of P. gingivalis using a bioinformatics approach.

Materials And Methods

Preparation of target and small molecules library

The 3D structure of FimA (PDB ID: 6JZK) was downloaded from Protein Data Bank (PDB) (www.rcsb.org). The structure was obtained from the X-Ray diffraction method with 2.10 resolution. The downloaded structure was prepared using Prepare Protein module of   BIOVIA Discovery Studio Client v20.1.0.19295.  In this module the alternate conformations were deleted, missing residues were fixed, terminal residues were adjusted and the bond orders were corrected. To generate a library of small molecules, anticancer natural compounds were retrieved from the Naturally occurring Plant based Anticancerous Compound-Activity-Target Database (NPACT) (https://webs.iiitd.edu.in/raghava/npact/index.html). The retrieved compounds were filtered based on Lipinski’s rule of five. Further, the filtered compounds were used to generate small molecules library using prepare ligand module from BIOVIA Discovery Studio Client v20.1.0.19295. Using this module tautomers and isomers were generated, bad valencies were fixed and finally, 3D coordinates of the compounds were generated.

High-Throughput Virtual Screening

To perform screening of the selected compounds, the epithelial binding domain of the FimA protein was selected as the docking site. Furthermore, the binding sphere was generated around the docking site. The Libdock module was used to perform the virtual screening.  Libdock uses the hotspots method in which a grid is placed at the binding site of the protein and the hotspots map was calculated using non-polar and polar groups of residues in the active site. Furthermore, these hot spots were then applied to align the ligands rigidly to form favorable interactions with binding site residues. Using fast conformation methods ten conformations were generated for each ligand. High-quality docking with a tolerance of 0.25 was selected to dock the ligands at the binding site. And the best-docked poses were selected based on the Libdock score.

Molecular Docking

The CDOCKER module was used for the precise docking of the topmost ligands of the virtual screening. COCKER uses a charmm36 force field to perform grid-based docking.  The docking was performed by keeping the receptor rigid and the ligand flexible. During the docking process, random conformations of the ligands were generated using molecular dynamics, and then the conformations were refined using the simulated annealing method. Ten random conformations followed by ten refined orientations were generated. The best docking poses were selected based on the best docking score i.e CDOCKER score (COCKER Energy, COCKER Interaction Energy). COCKER Energy scores include ligand strain energy and receptor-ligand interaction energy whereas CDOCKER Interaction Energy scores include only receptor ligand interaction energy. 

Molecular Dynamic Simulation

The molecular Dynamic Simulation method was used to evaluate the stability of the protein-ligand complex. WebGro web server (https: //simlab. uams.edu/) was used to perform the MD simulation of the best-docked complex. The ligand topology file was generated from the GlycoBioChem PRODRG2 Server (http://davapc1.bioch.dundee.ac.uk/cgi-bin/ prodrg). A total of 100 ns simulation was run using GROMOS9643a1 force field and a cubic box with an SPC water model was used to solvate the system. The whole system was neutralized using 0.15 M salt of NaCl. Steepest descent energy minimization with steps of 5,000 has been chosen to minimize the complex structure. The equilibration step was performed at a temperature of 300 K and pressure bar of 1, MD integrator of leap-frog. A total of 1,000 frames have been generated during the whole simulation process.

Results

High-Throughput Virtual Screening

The epithelial binding domain of FimA was an important site for bacteria and host cell interaction [23]. Amino acid residues of epithelial binding domain were selected as an active site. A total of 1548 anticancer natural compounds were retrieved from NPACT database. Prior to screening compounds were filtered based on their drug likeness property.1115 compounds passed the Lipinski’s rule of five. Furthermore, Libdock virtual screening analysis revealed that 300 compounds have favorable interactions with the FimA epithelial binding domain. Additionally, 20 compounds have >100 Libdock scores. From these 20 compounds top 10 compounds were selected to perform molecular docking shown in Fig.1. Table 1 displays the top 10 ranked compounds based on the Libdock scores.

Molecular Docking

CDOCKER module performed precise molecular docking using the CHARMm36 force field. The selected compounds were docked using the same binding pocket. This module gave docked ligand poses with CDOCKER scores. Ligand poses with the highest negative scores were selected as the best binding conformations. Higher negative CDOCKER energy indicates the favorable binding of the ligands with the lowest energy conformation.  The best poses of the top ten ligands having CDOCKER energy >20 are displayed in Fig.2. CDOCKER energy and CDOCKER interaction energy of top ten ligands listed in Table 2. The interaction of ligands with the target were analyzed by visualizing the non-bonded interaction and binding energy displayed in Table 3. Among the ten compounds, protocatechuic acid (NAPCT00881) shows the highest CDOCKER energy, CDOCKER interaction energy -24.55 kcal/mol, -25.12 kcal/mol, and binding energy -61.65 kcal/mol Fig.3. with five favorable and 0 unfavorable interactions indicating strong bonding between receptor and ligand. Favorable interactions contain three hydrogen bonds, two salt bridge interactions, and one pi-alkyl interaction. Therefore protocatechuic acid was used to perform MD simulation to check the stability and functionality of the docked complex. 

Molecular Dynamic Simulation

MD simulation has been performed to discern the stability of the complex via the atomic motion of the protein-ligand complex. The stability of the FimA- protocatechuic complex was analyzed by comparing RMSD, RMSF, and RG with respect to the apo protein. Combined RMSD plots of both the FimA-protocatechuic complex and apo protein during 100 ns simulation displayed in Fig.4. Plot show that the apo protein showed several deviations at 10 ns, 18 ns, and in between 60-70 ns whereas the FimA-protocatechuic complex showed only one deviation at 15ns. The FimA-ascorbic complex formed a plateau and reached its equilibrium after 15ns.   After 90 ns the RMSD curve of the apo protein increases however the RMSD curve of the FimA-protocatechuic complex has maintained a similar trend indicating a stable complex structure. The average RMSD of apo and FimA-ascorbic complex were 0.45 nm and 0.36 nm indicating that the apo protein has gone through more conformational changes than that of the complex during 100ns simulations. In addition, the RMSF plot displayed in Fig.4. showed lesser fluctuations in receptor-interacting residues i.e MET103, GLU104, LYS108, and LEU116 THR117 indicating strong protein-ligand interactions. The average RMSF of apo and FimA-ascorbic complex was 0.194 nm and 0.183 nm. Rg plot of the FimA-protocatechuic complex displayed in Fig.4. shows initial fluctuations till 30 ns indicating small changes in the conformation of protein after the binding of ascorbic acid in the active site. After 30 ns the Rg curves remain stable throughout the simulation.

Discussion

OSCC treatment and prevention is a major cause of cancer concern because it is often detected at late stages[27]. This happens because the pain developed in OSCC patients at later stages only[28]. As OSCC is detected at a late stage, local invasion and metastasis risks are very high. Accompanied by this fact, limited treatment and prevention options are available due to metastasis. Hence, early detection and deciphering of the molecular mechanism causing OSCC are crucial. Although well-known carcinogen roles have been revealed, still 15% of oral cancer occurrences are not explained by the well-known carcinogen. The involvement of P. gingivalis in the carcinogenesis of OSCC has been extensively studied. The virulence factors of this bacteria promote the development of cancer. FimA has been seen to activate the oncogenic pathways in OSCC. This protein was detected in the saliva of OSCC patients indicating its role in oral cancer. P. gingivalis gets entry into the host cell with the help of their FimA[29]. The epithelial binding domain of FimA has been recognized as a the first entrance site of this bacteria into the host cell[30]. Blocking the epithelial binding domain might prevent the entry of this bacteria to the gingival epithelial cell of the host and also helps in preventing the activation of oncogenic signaling pathways. This computational study provides therapeutic compounds that could contribute to developing drugs against OSCC facilitated by FimA of P. gingivalis. This study reinforces the belief that the chemical constituents of the plants are a source of developing natural pharmaceutical adjuvants with lesser harmful effects. The anticancer properties of chemical constituents used in this study have already been reported, therefore these chemicals act as traditional therapeutic agents in oral cancer. Our Insilico study utilizes the High-Throughput Virtual Screening followed by precise molecular docking which further provides the top-ranked dock poses and scores of the ligands. Further MD simulation analysis of the topmost ligands was performed to check the overall stability of the protein-ligand complex. This computational study suggests that protocatechuic acid can be used in the treatment of P. gingivalis-related OSCC. Further invitro and invivo studies are required to validate our Insilico work.

Conclusion

Virulence factor FimA has been involved in the molecular mechanism of OSCC. This Insilico analysis provides a therapeutic intervention against this target. Our structure-based drug design approach provides natural anticancer lead compounds that can be used to develop potent drugs in the treatment of OSCC related to P. gingivalis. Further invitro and invivo validation are required.

Declarations

Conflict of Interest: The Author(s) declares there is no conflict of interest among authors to publish this article. 

Acknowledgment: The Author(s) acknowledge the Department of Bioinformatics, Central University of South Bihar, and Gaya to provide a basic computational facility for this work. 

Funding: None.

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Tables

Table 1 Top 20 ranked compounds with highest Libdock score.

Table 2 Top 10 ranked compounds with highest -CDOCKER Energy and -CDOCKER Interaction Energy.

Table 3 Top 10 ranked compounds with binding energy and Non-bonded interactions.