NRMJ_9_2_116-139

Introduction

Urinary tract infections (UTIs) are some of the most common infections that people experience. Uropathogenic bacteria make their way into the bladder from the perineum, overcoming the body’s natural defenses to lead to uncomplicated infections in vulnerable individuals (Sheerin, 2015). This infection raises important public health concerns, particularly in the United States, South America, Southeast Asia, and East Asia. The World Health Organization states that urinary tract infections are among the most prevalent bacterial infections, impacting 150 million individuals globally each year (Stamm and Norrby, 2001). E. coli bacteria are responsible for approximately 90% of urinary tract infections. While E. coli typically exists harmlessly in the human gastrointestinal (GI) tract, its occurrence in the urinary tract can lead to serious infections (Christiano, 2019). Pathogenic E. coli strains, like E. coli O69:H11 and E. coli O157:H7, can lead to human infections. Moreover, regarding public health, E. coli O157:H7 is the most prevalent pathogenic strain; however, other strains have often been associated with recurring cases and outbreaks (World Health Organization, 2018). Various kilobase RNA types are found in different E. coli strains. The RNA genomes of E. coli O69:H11 and E. coli O157:H7 measure 43 kb and 12 kb, respectively, and both include the significant curli protein (Durso et al., 2005; Lim et al., 2010). Intestinal bacteria produce extracellular protein filaments called curli. Curli is the primary subunit protein of curli, which are slender, coiled surface structures that play a role in biofilm development, colonization of inert surfaces, and the attachment of bacteria to different extracellular matrix and serum proteins. It is encoded by the csgA gene (Burgos-Morales et al., 2021). Van Gerven et al. (2018) state that they are essential for biofilm formation and surface adherence. Moreover, E. coli that develops biofilms has been linked to prolonged chronic inflammation, which can result in recurring UTIs. Biofilms create an environment that resists antibiotic penetration and facilitates the horizontal transfer of virulence genes, which contributes to the growth of multidrug-resistant organisms (MDRO) (Katongole et al., 2020). This setting is especially conducive to the growth of MDRO.

The many years of battling illnesses have contributed to an awareness of the use of medicinal plants, allowing humans to seek drugs from the barks, seeds, fruit bodies, and other parts of these plants (Petrovska, 2012). This clarifies why medicinal plants serve as the foundation for the majority of medications in use today. These biological entities consist of phytochemicals, which are substances that exhibit biological activity. Phytochemicals play an essential role in the human body, particularly as antioxidants. These substances serve as a protective barrier for cells, safeguarding them from damage caused by free radicals (Kocyigit et al., 2018; Park, 2023; Zhang, 2015).

In the Philippines, there are approximately 1,500 medicinal plants identified from over 13,500 plant species (Suba et al., 2019). 2-Pyridone is a kind of organic compound that can change how the main curli subunit CsgA makes amyloids, which might affect the formation of biofilms. Many biologically important natural compounds commonly contain the 2-Pyridone core structure. They influence various biological processes, including promoting neurite outgrowth and serving as antibacterial, antimalarial, anticancer, cardiotonic, and antifibrotic agents (Andersson et al., 2013; Sangwan et al., 2022). Coumarin has the ability to control the virulence traits of various bacterial pathogens. It stops biofilm formation at a level of 1.36 mM for the bacteria Pseudomonas aeruginosa (P. aeruginosa) PA14, E. coli MUH, Vibrio anguillarum (V. anguillarum), Edwardsiella tarda (E. tarda), and Staphylococcus aureus (S. aureus) NCDO949 (Mazur and Masłowiec, 2022). Methanol extracted from Enydra fluctuans, commonly known as Buffalo spinach, has demonstrated notable antibacterial effectiveness against the microorganisms that cause UTIs. The specific active compounds in the methanol extracts are very effective against the bacteria that cause UTIs (Acharjee et al., 2022). Cranberries contain two unique types of polyphenols, flavonones and phenolic acids, which have demonstrated their ability to combat UTIs. Polyphenols from cranberries have anti-adhesive properties that can prevent pathogens from adhering to uroepithelial cell receptors. This action is a crucial step in the development of these infections, particularly with E. coli (Maisto et al., 2023). Ladanein was identified as the primary polymethoxylated flavone indicated research showed that B-ring hydroxylation and methoxylation have a significant impact on antiadhesive activity and could potentially impair the function of the E. coli curli protein by decreasing bacterial adherence in a concentration-dependent way (Deipenbrock and Hensel, 2019). Ethanolic extracts from Quercus infectoria (Aleppo oak) have been shown to stop different types of E. coli, especially E. coli O157:H7. The extract from guava leaves (Psidium guajava L.) shows antibacterial effectiveness against various microorganisms (Kumar et al., 2021). The main compound in green tea, Epigallocatechin-3-gallate (EGCG), may help prevent biofilm formation and shows that there are complicated processes that control how biofilms form in E. coli. Overall, it was shown that the antibacterial properties of quercetin and kaempferol from Brassica rapa were effective against E. coli and other bacteria responsible for gastrointestinal disorders, increasing the permeability of both the outer and inner membranes (Alotaibi et al., 2021).

The Cefuroxime license was granted by the U.S. Food and Drug Administration (FDA) in December 1987. Due to its wide-ranging effectiveness against both gram-positive and gram-negative bacteria, it is suitable for treating various bacterial diseases (Scott et al., 2001). Cefuroxime helps bacteria move and spread while stopping the formation of a protective layer, which may explain how these compounds fight against biofilms. In silico research plays a significant role in the development of pharmaceuticals. Improving the way we test, design, and predict how well new drugs work speeds up finding possible drug candidates (Nimgampalle et al., 2021). Additionally, by simplifying toxicity forecasting, in silico methods assist research teams in conserving both time and resources by detecting potentially harmful effects early in the development process. Computer tools such as virtual ligand screening, docking-based virtual screening, and molecular modeling help predict how potential drugs will bind and interact with their targets, which supports the discovery of new and useful medications (Chang et al., 2022; Ekins et al., 2007). Molecular docking is a computational method employed in structure-based drug development to forecast how small molecules will interact with macromolecular targets. Studying bond conformations and binding free energy aids in understanding biomolecular interactions and contributes to the development of therapeutic drugs (Skariyachan and Garka, 2018). This study aims to find plant-based chemicals that can block curli proteins in E. coli, which could help create new antibiotics for treating UTIs, using molecular docking analysis. This study aims to find plant-based chemicals that can block curli proteins in E. coli, which could be used as antibiotics for treating UTIs, by using molecular docking analysis. Specifically, the study aims to achieve the following specific objectives: first, to identify phytochemical compounds in medicinal plants that inhibit E. coli curli protein, then analyze the binding affinity of phytochemicals and curli proteins through molecular docking analysis, and lastly, to assess the relationship between the structure-activity.

Materials and Methods

Curli protein structure and sequence retrieval

The structures of the E. coli curli proteins were obtained from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) at https://www.rcsb.org, specifically the CsgC and CsgFG proteins of E. coli O157:H7 (PDB ID: 2Y2T) and E. coli O69:H11 (PDB ID: 6L7A), respectively. Furthermore, the NCBI Basic Local Alignment Search Tool Protein (BLASTp) program, available at https://blast.ncbi.nlm.nih.gov/Blast.cgi?%20PROGRAM=blastp, was utilized to evaluate the alignment of the identified protein sequences with those in the existing database.

Phytochemical selection and preparation

A comprehensive literature review was carried out to pinpoint the phytochemicals that have been documented to show antibacterial properties. Phytochemicals from plants used in UTI treatments have mainly been employed because their structures have been investigated for drug discovery in medicinal research (Suresh and Abraham, 2020). The chemical structures of the chosen phytochemicals were subsequently acquired from the NCBI PubChem database at https://pubchem.ncbi.nlm.nih.gov/. The BIOVIA Discovery Studio software, which can be found at https://www.3ds.com/products/biovia, was subsequently utilized to prepare and optimize the 3D structures. Then, the created structures were changed into the Protein Data Bank (pdb) file format, using the molecular docking tool to avoid any mistakes from changes in structure.

Molecular docking simulation

The interactions between the curli proteins and chosen phytochemicals were evaluated using molecular docking simulations with Python Prescription (PyRx), which can be accessed at https://pyrx.sourceforge.io/. This open-source program was specifically selected to perform docking-based virtual screening because it utilizes various open-source software, including Open Babel, Autodock Vina, and Python (Dallakyan and Olson, 2014). The prepared curli protein structure was loaded into PyRx and converted into a macromolecule using Autodock. In this step, the file format was converted from Protein Data Bank (pdb) to Protein Data Bank with partial charge Q and atom type T (pdbqt), which is better suited for docking analysis. After this, the ligand molecules were added one by one to the Open Babel software. To achieve an optimized geometry, the energies of the ligands were minimized using the default parameters to secure a low-energy conformation for each ligand. Autodock was used to convert the ligands into the pdbqt file format in preparation for molecular docking. The macromolecule and the prepared ligands were both loaded into the Vina Wizard software, and the docking grid box was positioned to cover the active sites on the curli protein. The dimensions of the grid box for the 2y2t protein were established at X: 7.3904, Y: 13.3810, and Z: -2.1490. At the same time, the dimensions for the 6I7a protein grid box were established as X:169.0203, Y:168.9720, and Z:174.7008. The exhaustiveness was set to 8 within the defined grid box settings to investigate various conformations of the ligands. Additionally, the thorough analysis conducted in Vina Wizard software was then released to start the docking process. After finishing the simulation, we predicted and organized the binding scores for each ligand, resulting in a total of 9 binding poses generated. Furthermore, the simulation led to the creation of protein-ligand complexes, with their pharmacophore models visualized through BIOVIA Discovery Studio. The molecular docking simulation was conducted individually for the E. coli O157:H7 and E. coli O69:H11 strains.

Post-docking analysis

After conducting molecular docking simulations, Agu et al. (2023) mentioned that the next step is to look at the binding affinity scores to find the best phytochemicals and how they interact with the binding site. The root mean square deviation (RMSD) values and the binding affinity (kcal/mol) from the docking results were taken from the DLG file created by the molecular docking simulation. This data offers quantitative predictions of binding energetics, rankings of docked compounds, and ligand-receptor binding affinities.

ADMET prediction

ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction tools, such as SwissADME, were used to assess how the best plant-based chemicals behave in the body. ADMET is crucial in the drug development and discovery process. The way ADMET qualities show how the phytochemical will act in the body is more important than how good and effective the drug is against the target (Guan et al., 2018). SwissADME, which can be found at http://www.swissadme.ch, was used to check important factors like how well the substance dissolves, how easily it can be absorbed in the body, its suitability as a drug, and any possible harmful effects, all of which are important for determining if phytochemicals could be good options for treating UTIs.

SwissADME: Structure and bioavailability radar

SwissADME features a bioavailability radar that offers a summary of drug-likeness. The set consists of six physicochemical properties: lipophilicity (LIPO), where the XLOGP3 value should range from -0.7 to +5.0; size (SIZE), which indicates that molecular weight should be between 150 and 500 g/mol; polarity (POLAR), requiring a Topological Polar Surface Area (TPSA) between 20 and 130 Å²; solubility (INSOL), where the logarithm of solubility (Log S) value should not exceed 6; saturation (INSATU), which necessitates a fraction of carbons in sp³ hybridization of at least 0.25; and flexibility (FLEX), which must have no more than 9 rotatable bonds. Ideally, the properties of a compound should be within the area highlighted in pink, as it represents the optimal values for each property (Kamble and Mitkar, 2023). After running the plant phytochemicals through SwissADME, I quickly checked the bioavailability radar before diving deeper into the parameters.

SwissADME: Physicochemical properties

This section provides an overview of the 2-dimensional chemical structure entered into the tool. The information given is as follows: The chemical formula, molecular weight, count of heavy atoms, count of aromatic heavy atoms, fraction of Csp3, number of rotatable bonds, H-bond acceptors, H-bond donors, Molar Refractivity (MR), and TPSA. These descriptors offer valuable insights into various models and guidelines employed in drug discovery. This section’s information was utilized to verify any violations that took place in the rule-based filters.

SwissADME: Lipophilicity

Lipophilicity is an important characteristic in drug action because it affects pharmacokinetics, pharmacodynamics, and the potential for drug toxicity (Tsopelas et al., 2017). SwissADME offers five predictive models that employ various methods and descriptors, enhancing the accuracy of predictions. The predictive models include iLOGP, XLOGP3, WLOGP, MLOGP, and SILICOS-IT. Furthermore, the Consensus Log Po/w shows the average of the values derived from these predictive models. The researchers utilized the Consensus Log Po/w model for tabulating results, as it offered the mean value.

SwissADME: Water solubility

Water solubility is an important factor in drug design because it influences drug delivery and gastrointestinal absorption into the systemic circulation. A good solubility helps achieve the right amount of drug needed to produce effects, which means lower doses are needed after taking it by mouth. SwissADME implements two topological methods: the ESOL method and the Ali method. Each model uses different important equations for solubility, and both show strong linear relationships between the predicted and actual values. Additionally, a third method created by SILICOS-IT employs molecular weight corrections in the linear correlation coefficient. These methods categorize solubility according to the following values: A value less than -10 is deemed insoluble; values under -6 are classified as poorly soluble; those below -4 are moderately soluble; values under -2 are soluble; values below 0 are very soluble, and values below 0 are highly soluble. The SILICOS-IT method, which incorporated corrections, was mainly the parameter utilized to evaluate water solubility.

SwissADME: Pharmacokinetics

The pharmacokinetic parameters offer valuable insights into the body’s interaction with compounds, optimizing their absorption, distribution, metabolism, and excretion properties. The GI absorption and the blood-brain barrier (BBB) permeation are illustrated in the BOILED-Egg model, where the yolk symbolizes the BBB and the egg white represents the GI tract. The predicted GI absorption, whether high or low, shows the efficiency of the intestines in absorbing the compound. This parameter is essential since many medications are formulated for oral use. Conversely, BBB permeation indicates whether a compound can cross the BBB and access the Central Nervous System (CNS). This parameter is crucial in the development of drugs aimed at the CNS. However, the BBB permeability of a compound intended for other body systems may also influence neurological functions. Also, knowing if a compound is a substrate or non-substrate for the permeability glycoprotein (P-gp) can help us understand how it moves out of cells, which affects how well the drug is absorbed and removed from the body. Stopping cytochrome P450 (CYP) enzymes is crucial to think about when creating treatment plans, as these processes are linked to drug interactions that might cause harmful side effects and higher toxicity. Finally, the skin permeation parameter, known as Log Kp, relates to how well a compound can penetrate the skin to reach its intended target. This parameter is typically taken into account when formulating transdermal and topical medications. Negative log Kp values suggest restricted skin penetration, with more negative values leading to a lower potential (Deodhar et al., 2020; Ololade et al., 2023; Daina et al., 2017). The results from SwissADME focused more on predicting how well the drug would be absorbed in the gastrointestinal tract, cross the blood-brain barrier, act as a substrate for P-glycoprotein, and inhibit CYP enzymes.

SwissADME: Drug-likeness

The drug-likeness parameter evaluates a compound’s potential as an oral drug candidate, specifically focusing on its bioavailability. This tool applies five rule-based filters Lipinski, Ghose, Veber, Egan, and Muegge to compare the consistency of a compound’s chemical structure with that of established drugs. The Lipinski Rule of 5 (Ro5) from Pfizer, Inc. stands out as the first significant effort to support drug design, and it is now widely utilized in the selection of molecules for development. The lipinski rule of five outlines five criteria that a molecule must satisfy to be regarded as a viable candidate for oral drug development. If a molecule violates any of the following conditions, it is likely to demonstrate poor permeation and absorption: A molecule with a molar mass greater than 500, more than 10 hydrogen bond acceptors, more than 5 hydrogen bond donors, and a LogP greater than 5 (or MlogP greater than 4.15) is likely to exhibit poor permeation and absorption. For the majority of orally active drugs, a potential compound should not have more than one violation. Also, the Abbott Bioavailability Score is a tool that predicts how likely it is for a compound to have more than 10% absorption in rats, based on its potential surface area (PSA) and the Lipinski Rule of Five. The bioavailability score of a compound should ideally reach the threshold of 0.5 (Ivanović et al., 2020; Abdillah et al., 2023). For these reasons, the Lipinski Ro5 and Abbott Bioavailability Score were the criteria evaluated for drug-likeness.

SwissADME: Medicinal chemistry

The Pan-Assay Interference Compounds (PAINS) and the Brenk Rules serve as techniques for pinpointing troublesome fragments within the compound. PAINS evaluates fragments that can disrupt various assays, potentially leading to false positives in high-throughput screening. At the same time, the Brenk criteria pinpoint drug-like characteristics in a compound. Alerts flagged from these criteria should be thoroughly assessed, as the issues may arise from different types of structural components (Rakshit and Jayaprakash, 2022; Ononamadu and Ibrahim, 2021). This study also examined both PAINS and the Brenk criteria.

Data interpretation and analysis

The most effective phytochemicals were evaluated based on their capacity to inhibit curli proteins in E. coli. The analysis of docking and molecular dynamics simulations explained this. Combining these two approaches provided a more comprehensive understanding of molecular interactions, enhancing the synthesis of findings. Roy et al. (2015) identified binding affinity, binding mode, ligand efficiency, docking pose prediction, scoring, and ranking as essential concepts related to molecular docking. The study used these molecular concepts as metrics to interpret and identify the appropriate phytochemicals and the effectiveness of molecular simulations.

Additional analysis of the chosen phytochemicals was conducted, focusing on the mechanism underlying their disruptive action. These mechanisms can include how the chemicals attach to targets, their ability to fight off damage from free radicals, and how they affect the stability, energy use, or communication processes of the target (Takó et al., 2020). They evaluated their potential to prevent biofilm formation and decrease virulence in UTIs. Additionally, primary literature was used as a foundation and for comparison.

Results and Discussion

Target identification and protein sequence retrieval

When retrieving Curli protein sequences, it’s essential to check whether an identified protein sequence aligns with one from a protein sequence database. This comparison helps in understanding similarities and offers insights into their function and structure. In this study, we conducted a BLASTp analysis and retrieved crystalline structures of curli proteins from E. coli from the Protein Data Bank to identify the curli protein most suitable for molecular docking. Figures 1 and 2 show the BLASTp results for the protein sequences of 6I7a and 2y2t. These figures assist in pinpointing areas crucial for protein stability and possible ligand interactions.

According to Madden (2002) states that the bit score in BLASTp indicates how well the tested protein sequence aligns with those in the database, with a higher score reflecting a superior alignment. This score is typically calculated using a formula that assesses the alignment of identical or similar residues while also considering any gaps introduced to align the sequences. Furthermore, the E-value reflects the statistical significance of a specific pairwise alignment, taking into consideration the size of the database and the scoring method used. A lower E-value indicates a more significant hit. A sequence alignment with an E-value of 0.05 shows that there is a 5% chance that this similarity could occur by random chance (1 in 20). The finding could be important for a statistician, but it may not hold biological significance. Therefore, the alignments need to be analyzed further.

The results from the BLASTp analysis in the study showed that the query sequence has a strong match with the E. coli O69:H11 curli protein data sequence (Table 1). The total query coverage matched the database sequence, which was further confirmed by the high score of 571 and the complete query coverage of 100%. This indicates a strong similarity between the sequences, highlighting the importance of accurately identifying potential inhibitors that can target the curli protein. The BLASTp results for the E. coli O157:H7 curli protein showed that the query sequence aligns with the sequence found in the E. coli O157:H7 curli protein database. The results also showed a strong correlation between the curli protein and the identified inhibitors, indicating that while the alignment significance is a bit lower than E. coli O69:H11, the curli protein sequence can still probably bind to the inhibitors effectively.

Table 1: Blastp hit results of the Escherichia coli O69:H11 curli protein.

Max score	Total score	Query cover	E-value	Percent identity
571	571	100%	0.0	100.00%

The query coverage remains at 100%, indicating that the entire sequence matches exactly, despite the score of 223 being lower than that of E. coli O69:H11. This result is critical as it confirms that the phytochemical inhibitors being studied are likely to effectively target the specified curli protein sequence, paving the way for the development of antibiotic drugs with strong therapeutic properties against UTIs. Additionally, the E-value of 5e-73 suggests a strong match, which remains highly significant. The results indicate a complete 100% identity, signifying that the sequences are identical. Low E-values, such as the result, indicate a strong statistical significance. This enhances the reliability of the outcome by highlighting that the likelihood of the match happening by chance is impossible.

Table 2: Blastp hit results of the Escherichia coli O157:H7 curli protein.

Max

score

Total

score

Query

cover

E-value

Percent identity

223

100%

5e-73

100.00%

The comparison of the curli protein sequences from E. coli O69:H11 and E. coli O157:H7 shows a high level of confidence. Nonetheless, E. coli O69:H11 showed a higher score, indicating a stronger alignment or possibly a longer sequence compared to E. coli O157:H7. This study offered insight that E. coli O69:H11 could be linked to enhanced biofilm formation and adherence characteristics. The data parameters showed a general similarity, match strength, and significant alignment, though the E-values varied.

Preparation of phytochemical molecules

According to Rabizadeh et al. (2022) state that plants naturally generate metabolites known as phytochemicals, which are crucial for their survival and overall performance. The research highlights 12 specific phytochemical compounds obtained from different plants (Table 3). The phytochemicals obtained from NCBI PubChem were assessed for their known structures, which formed the foundation for selection. Additionally, literature reviews obtained from Google Scholar, PubMed, and ScienceDirect were utilized to gather information on whether the phytochemicals linked to the treatment of UTIs are solely derived from medicinal plants and if they are available in the Philippines. Cefuroxime is the top choice because it is well-researched and works well against both gram-positive and gram-negative bacteria, successfully stopping the production of curli matrix. The stable and effective antibacterial properties of cefuroxime make it a valuable comparative ligand. Brogden et al. (2022) state that Cefuroxime is effective against a range of infections, including E. coli, due to its stability against most β-lactamases produced by Gram-negative bacteria. Moreover, differences in inoculum size ranging from 10³ to 10⁵ viable cells per milliliter typically have minimal impact on the inhibitory activity of Cefuroxime, except for specific strains of Serratia sp. and Morganella morganii, in vitro.

Molecular docking of protein and ligand molecules

Understanding the physicochemical principles of protein–ligand interactions is crucial for understanding how a protein recognizes its ligand at the molecular level. Protein–ligand binding happens naturally only when the system reaches a stable state at a steady temperature and pressure, and the change in Gibbs free energy (ΔG) is negative. One could say that ΔG affects how stable a protein–ligand complex is, or how strongly a ligand binds to a specific acceptor, since a larger negative ΔG shows a stronger protein–ligand interaction (Du et al., 2016).

2-Pyridone is a chemical that changes how amyloid is made in the main curli part called CsgA, which might stop biofilm from forming (Andersson et al., 2013). Furthermore, researchers found that additional 2-Pyridone compounds improve the CsgA polymerization process (Evans and Chapman, 2014; Andersson et al., 2013). By guiding CsgA into “off-pathway” oligomers that can’t form or start making amyloid, 2-Pyridones mainly target the CsgA part by interacting with certain amino acids of the E. coli O157:H7 curli protein: Lys49, The49, and Gln47. It also creates π-Alkyl, Carbon Hydrogen Bond, and Unfavorable Bump as shown in Table 4. Furthermore, it creates π-Alkyl, Carbon Hydrogen Bond, and Unfavorable Bump, as illustrated in Table 4. In the E. coli O69:H11 curli protein (Table 5), it connects with these amino acids: Arg142, Gly189, Gly147, Ala148, Phe12, Asp149, Gln187, Asn11, Glu203, Ala188, and Arg8. It creates conventional hydrogen bonds, van der Waals interactions, π-sigma bonds, and π-alkyl interactions. Table 3 illustrates that this phytochemical compound is derived from Camptotheca acuminata.

Coumarin can regulate the virulence characteristics of various bacterial pathogens, indicating its potential use in treating infections. The bacteria P. aeruginosa PA14, E. coli MUH, V. anguillarum, E. tarda, and S. aureus NCDO949 showed reduced biofilm formation when exposed to a concentration of 1.36 mM, as reported by Mazur and Masłowiec (2022). Additionally, Coumarin can alter bacterial communication and decrease biofilm production. Table 4 indicates that it targets the CsgA domain, interacting with the amino acids Thr48, Lys49, and Gln47, and this interaction is considered an Unfavorable Bump.

Table 3: Molecular docking of plant phytochemical ligands against Escherichia coli O157:H7 and Escherichia coli O69:H11 curli proteins.

Plant phytochemical	Molecular formula	Plant	Reference
2-Pyridone	C5H5NO	Camptotheca acuminata	Andersson et al. (2013); Sangwan (2022)
Coumarin	C9H6O2	Dipteryx odorata	Mazur and Masłowiec (2022)
Epigallocatechin-3-gallate	C22H18O11	Camellia sinensis	Buchmann et al. (2023)
Ethanol	CH3CH2OH	Quercus infectoria	Voravuthikunchai et al. (2004)
Flavonoid	C15H10O4	Psidium guajava L.	Kumar et al. (2021)
Flavonones	C21H18O6	Vaccinium macrocarpon	Maisto (2023)
Kaempferol	C15H10O6	Brassica rapa	Alotaibi et al. (2021)
Ladanein	C17H14O6	Orthosiphon stamineus	Deipenbrock and Hensel (2019)
Methanol	CH₃OH	Enydra fluctuans	Acharjee et al. (2022)
Phenolic acids	C6H5OH	Vaccinium macrocarpon	Maisto (2023)
Polyphenol	C6H6O	Vaccinium macrocarpon	Maisto (2023)
Quercetin	C15H10O7	Brassica rapa	Alotaibi et al. (2021)

Table 4: Molecular docking interactions of plant phytochemical ligands with the Escherichia coli O157:H7 curli protein.

Plant phytochemical	Inhibited site	Amino acid involved	Interactions
2-Pyridone	CsgA	Lys49, The49, Gln47	π-Alkyl, Carbon Hydrogen Bond, Unfavorable Bump
Coumarin	CsgA	Thr48, Lys49, Gln47	Unfavorable Bump
Epigallocatechin-3-gallate	CsgA	Ile35, Leu36, Gln50, Leu67, Ser37, Ser46, Lys49, Thr48, Gln47	Conventional Hydrogen Bond, van der Waals, Unfavorable Bump
Ethanol	CsgA	Lys49, Thr48, Gln47	Alkyl, van der Waals, Unfavorable Bump
Flavonoid	CsgA	Lys49, Thr48, Gln47	van der Waals, Unfavorable Bump
Flavonones	CsgA	Ile71, Val77, Gln12, Asp15, Ser72, Asp75, Tyr17, Trp95, Pro96, Pro73, Arg76	Conventional Hydrogen Bond, van der Waals, π-Donor Hydrogen Bond, Alkyl, π-Alkyl
Kaempferol	CsgA	Ser37, Leu36, Lys49, Thr48, Gln47	van der Waals, Unfavorable Bump
Ladanein	CsgA	Lys49, Thr48, Gln47	π-Alkyl, Unfavorable Bump
Methanol	CsgA	Gln47, Thr48, Lys49	Conventional Hydrogen Bond, van der Waals
Phenolic acids	CsgA	Thr48, Lys49, Gln47	Unfavorable Bump
Polyphenol	CsgA	Gln50, Leu36, Leu67, Leu69, Ile35, Ser46, Lys49. Gln47, Ser37, Thr48	van der Waals, Carbon Hydrogen Bond, π-Donor Hydrogen Bond, Unfavorable Bump
Quercetin	CsgA	Ser37, Leu36, Thr48, Lys49, Gln47	van der Waals, Unfavorable Bump
Cefuroxime	CsgA	Leu67, Ser37, Thr48, Lys49, Gln47	van der Waals, Carbon Hydrogen Bond, Unfavorable Bump

Table 5: Molecular docking interactions of plant phytochemical ligands with the Escherichia coli O69:H11 curli protein.

Plant phytochemical	Inhibited site	Amino acid involved	Interactions
2-Pyridone	CsgA	Arg142, Gly189, Gly147, Ala148, Phe12, Asp149, Gln187, Asn11, Glu203, Ala188, Arg8	Conventional Hydrogen Bond, Van der Waals, π-Sigma, π-Alkyl
Coumarin	CsgA	Thr4, Phe48, Thr2, Lys49, Pro50, Ser132, Asn133, Ala53, Pro52	van der Waals, Carbon Hydrogen Bond, Amide-π Stacked, π-Alkyl
Epigallocatechin-3-gallate	CsgA	Thr4, Thr2, Gln151, Glu185, Gln5, Gly14, Gly13, Gln6, Met3, Phe 5, Arg8, Pro16, Asn15, Asn18	Conventional hydrogen bond, Van der waals, Carbon hydrogen bond, π-Alkyl, Unfavorable donor-donor
Ethanol	CsgA	Val190, Glu201, Asn9, Gly202, Arg142, Glu203, Gly189, Phe191, Phe22	Conventional Hydrogen Bond, Van der Waals, π-Alkyl, Alkyl
Flavonoid	CsgA	Thr4, Thr2, Gln47, Ser132, Asn133, Tyr51, Gln153, Thr2, Gly1, Ala53, Pro50, Pro52, Lys49	Conventional Hydrogen Bond, Van der Waals, π-Sigma, π-Alkyl, Unfavorable Donor-Donor
Flavonones	CsgA	Gln6, Arg8, Thr4, Pro52, Thr2, Tyr51, Gly1, Asn133, Gln153, Gln6, Thr4, Gly4,Gly13, Pro50, Pro52	Conventional Hydrogen Bond, Van der Waals, π-Alkyl
Kaempferol	CsgA	Gln151, Asn15, Phe7, Gly14, Glu13, Arg8	Conventional Hydrogen Bond, van der Waals, Amide-π Stacked, π-carbon
Ladanein	CsgA	Ser132, Lys49, Asn133, Thr4, Gln47, Gly1, Tyr51, Ser54, Phe48, Ser57, Thr2, Asn133, Gln153, Ala53, Pro52, Pro50	Conventional Hydrogen Bond, van der Waals, Carbon Hydrogen Bond, π-Sigma, Amide-π Stacked, π-carbon
Methanol	CsgA	Val190, Gly202, Gly189, Glu203, Arg142, Phe191, Glu201	Conventional Hydrogen Bond, van der Waals
Phenolic acids	CsgA	Arg142, Gly189, Ala188, Arg8	Conventional Hydrogen Bond, van der Waals, Amide-π Stacked, π-Alkyl
Polyphenol	CsgA	Arg162, Gly85, Asn88, Asn40, Glu124, Gln84, Ile41, Leu86, Asp43	Conventional Hydrogen Bond, Carbon Hydrogen Bond, Halogen (Fluorine), π-Alkyl, Unfavorable Donor-Donor
Quercetin	CsgA	Gln151, Phe7, Gly14, Gly13, Arg8, Asn15	Conventional Hydrogen Bond, van der Waals, Amide-π Stacked, π-carbon, Unfavorable Donor-Donor
Cefuroxime	CsgA	Arg8, Gln151, Gln153, Thr2, Asn15, Gln6, Thr4, Gly1, Met3, Gly14, Adn15, Gly13, Pro16	Conventional Hydrogen Bond, van der Waals, Carbon Hydrogen Bond, π-Alkyl

The illustration of the amino acids Thr4, Phe48, Thr2, Lys49, Pro50, Ser132, Asn133, Ala53, and Pro52 can be found in Table 5. Van der Waals interactions, carbon-hydrogen bonds, amide-π stacking, and π-alkyl interactions are all recognized outcomes of those interactions. This phytochemical was derived from Dipteryx odorata.

EGCG shows possible ways to block biofilms and points out the complicated rules that control how biofilms form in E. coli (Buchmann et al., 2023). Additionally, Arita-Morioka (2018) discovered that EGCG inhibited the production of curli by lowering the levels of proteins associated with curli formation. The CsgA domain interacts with specific amino acids in the E. coli O157:H7 curli protein. Ile35, Leu36, Gln50, Leu67, Ser37, Ser46, Lys49, Thr48, and Gln47 are the ones listed here. Moreover, it establishes a Conventional Hydrogen Bond, van der Waals interaction, and an Unfavorable Bump as illustrated in Table 4. Table 5 shows that the curli protein from E. coli O69:H11 connects with the amino acids Thr4, Thr2, Gln151, Glu185, Gln5, Gly14, Gly13, Gln6, Met3, Phe5, Arg8, Pro16, Asn15, and Asn18. It establishes a benchmark for hydrogen bonds, van der Waals forces, carbon-hydrogen bonds, π-alkyl interactions, and an unfavorable donor-donor interaction. Additionally, as indicated in Table 3, this phytochemical compound is derived from Camellia sinensis.

Ethanolic extracts have shown the capability to inhibit various strains of E. coli, especially E. coli O157:H7 (Voravuthikunchai et al., 2004). Table 3 shows that it focuses on the CsgA and highlighted the CsgA domain and its connections with the amino acids Lys49, Thr48, and Gln47, describing their interactions Favorable Bump. Additionally, the amino acids present in the compound, according to Table 5, include Val190, Glu201, Asn9, Gly202, Arg142, Glu203, Gly189, Phe191, and Phe22. This process establishes conventional hydrogen bonds, van der Waals forces, π-alkyl interactions, and alkyl connections. Additionally, Table 3 shows that it comes from Quercus infectoria.

Flavonoids show antibacterial effectiveness against various microbes (Kumar et al., 2021). Inhibition happens at the CsgA domain, where it interacts with different amino acids like Lys49, Thr48, and Gln47, helped by van der Waals forces and an Unfavorable Bump, as shown in Table 4. Table 5 shows that the amino acids involved are Thr4, Thr2, Gln47, Ser132, Asn133, Tyr51, Gln153, Gly1, Ala53, Pro50, Pro52, and Lys49. Their interactions include conventional hydrogen bonds, van der Waals interactions, π-sigma bonds, π-alkyl bonds, and unfavorable donor-donor bonds. Also, Table 3 shows the flavonoid content from the extract of guava leaves (P. guajava L.).

Flavonones are one of the two unique polyphenols derived from Vaccinium macrocarpon or cranberries, as shown in Table 3, that have been shown to help fight UTIs. Because of their anti-adhesive properties, polyphenols derived from cranberries can prevent pathogens from adhering to uroepithelial cell receptors. This action is a crucial step in the development of these infections, particularly E. coli (Maisto et al., 2023). Flavonones are a type of flavonoid, which is a class of polyphenolic compounds found in plants. Flavonones inhibit bacteria in different ways, such as blocking DNA and proteins and breaking apart cell membranes (Shamsudin et al., 2022). Inhibition happens at the CsgA domain, where it interacts with several amino acids like Ile71, Val77, Gln12, Asp15, Ser72, Asp75, Tyr17, Trp95, Pro96, Pro73, and Arg76, using different types of bonds such as Conventional Hydrogen Bond, van der Waals, π-Donor Hydrogen Bond, Alkyl, and π-Alkyl, as shown in Table 4. Table 5 shows that the amino acids involved are Gln6, Arg8, Thr4, Pro52, Thr2, Tyr51, Gly1, Asn133, Gln153, Gln6, Thr4, Gly4, Gly13, Pro50, and Pro52. Their interactions include conventional hydrogen bonds, van der Waals forces, and π-Alkyl interactions.

Kaempferol fights E. coli and other bacteria that cause stomach problems by breaking down their outer and inner membranes (Alotaibi et al., 2021). Kaempferol, when used with ceftiofur, showed stronger effects against E. coli by changing how β-lactamase works and affecting biofilm formation. It mainly focuses on the CsgA part by connecting with the amino acids Ser37, Leu36, Lys49, Thr48, and Gln47 in the E. coli O157:H7 curli protein. Furthermore, it creates van der Waals interactions and an unfavorable bump, as illustrated in Table 4. In Table 5, the amino acids that connect with the curli protein of E. coli O69:H11 are Gln151, Asn15, Phe7, Gly14, Glu13, and Arg8. It creates standard hydrogen bonds, van der Waals forces, amide-π stacking, and π-carbon interactions. Table 3 illustrates that this phytochemical compound is derived from Brassica rapa.

Ladanein has changes in its B-ring that add hydroxyl and methoxy groups, which greatly influence how well it prevents bacteria from sticking and might reduce the effectiveness of the E. coli curli protein by lowering bacterial adhesion, depending on the amount used (Deipenbrock and Hensel, 2019). Ladanein is identified as the primary polymethoxylated flavone from Orthosiphon stamineus, as shown in Table 3. The study showed that adding hydroxyl and methoxy groups to the B-ring greatly affects how well it prevents bacteria from sticking, which can reduce the function of the E. coli curli protein based on the amount used (Deipenbrock and Hensel, 2019). Inhibition occurs at the CsgA domain. Table 4 clearly shows that Lys49 forms a π-Alkyl link, whereas Thr48 and Gln47 result in an unfavorable bump. Table 5 lists the amino acids involved: Ser132, Lys49, Asn133, and Thr4, which have van der Waals interactions; Gln47, Gly1, Tyr51, and Ser54, which form regular hydrogen bonds; Phe48, Ser57, Thr2, Asn133, and Gln153, which create carbon-hydrogen bonds; Ala53, which connects through π-sigma bonds; Pro52, which is part of amide-π stacking; and Pro50, which is involved in π-carbon interactions.

Methanol obtained from Enydra fluctuans (Buffalo spinach), as indicated in Table 3, has demonstrated notable antibacterial effectiveness against the microorganisms that cause UTIs. The specific active compounds in the methanol extracts are very effective against the bacteria that cause UTIs (Acharjee et al., 2022). The main focus is on the CsgA part, which has the amino acids Gln47, Thr48, and Lys49, and how they interact through Conventional Hydrogen Bonds and van der Waals, as shown in Table 4. Table 5 shows the amino acids involved, which are Val190, Gly202, Gly189, Glu203, Arg142, Phe191, and Glu201, and describes how they interact through Conventional Hydrogen Bonds and van der Waals forces. According to Table 5, it outlines the amino acids involved: Val190, Gly202, Gly189, Glu203, Arg142, Phe191, and Glu201, along with their interactions, which include Conventional Hydrogen Bonds and van der Waals forces.

Phenolic acid is one of the two unique polyphenols shown to be beneficial in fighting UTIs. According to Maisto et al. (2023), polyphenols prevent pathogens from adhering to uroepithelial cell receptors, which is a crucial factor in the transmission of infections, particularly those caused by E. coli. Phenolic chemicals prevent bacterial biofilm formation by blocking specific regulatory mechanisms while still allowing for their growth. They can disrupt bacterial communication and limit their movement, altering their functionality (Borges, 2012). Table 4 indicates that it follows the CsgA domain and engages with the amino acids Thr48, Lys49, and Gln47. It forms Unfavorable Bump and van der Waals. Additionally, Table 5 lists four amino acids: Arg142, Gly189, Ala188, and Arg8. It creates Conventional Hydrogen Bonds, van der Waals forces, Amide-π Stacking, and π-Alkyl Interactions. This phytochemical comes from Vaccinium macrocarpon.

Polyphenols derived from Vaccinium macrocarpon found in cranberries, as shown in Table 3, can prevent pathogens from attaching to uroepithelial cell receptors, which is a crucial step in the development of several infections, especially E. coli (Maisto et al., 2023). The main focus is on the CsgA domain, which specifically interacts with certain amino acids in the E. coli O157:H7 curli protein: Gln50, Leu36, Leu67, Leu69, Ile35, Ser46, Lys49, Gln47, Ser37, and Thr48. Additionally, it outlines van der Waals interactions, Carbon-Hydrogen Bonds, π-Donor Hydrogen Bonds, and Unfavorable Bumps, as shown in Table 4. Table 5 shows that the amino acids that connect with the curli protein from E. coli O69:H11 are Arg162, Gly85, Asn88, Asn40, Glu124, Gln84, Ile41, Leu86, and Asp43. It forms regular hydrogen bonds, carbon-hydrogen bonds, connections with halogens like fluorine, interactions between alkyl groups, and weak connections between similar donors.

Quercetin is effective against E. coli and other bacteria that lead to gastrointestinal disorders, increasing the permeability of both outer and inner membranes (Alotaibi et al., 2021). Furthermore, Quercetin blocked the AI-2 signaling in E. coli and the AGR system in S. aureus, leading to a decrease in the expression of genes associated with adhesion, virulence, biofilm formation, and key regulatory proteases (Li et al., 2024). Table 4 shows that it focuses on the CsgA domain, interacting with the amino acids Ser37, Leu36, Thr48, Lys49, and Gln47, and its interactions are categorized as Unfavorable Bump and van der Waals. Table 5 lists the amino acids Gln151, Phe7, Gly14, Gly13, Arg8, and Asn15. It creates Conventional Hydrogen Bonds, van der Waals Interactions, Amide-π Stacking, π-Carbon Bonds, and Unfavorable Donor-Donor interactions. According to Table 3, it is obtained from Brassica rapa.

Cefuroxime, which is used for comparison, is important because it blocks a specific site in the CsgA domain. Table 4 shows how E. coli O157:H7 interacts with amino acids: Leu67 connects through van der Waals forces, Ser37 makes a Carbon Hydrogen Bond, and Thr48, Lys49, and Gln47 have an Unfavorable bump interaction. Cefuroxime has a binding strength that ranks just below Quercetin, Kaempferol, Ladanein, EGCG, Flavanones, and Polyphenol when compared to different plant chemicals for E. coli O157:H7. Table 5 details the amino acids of E. coli O69:H11 and their interactions, which include Arg8, Gln151, Gln153, and Thr2 forming standard hydrogen bonds; Asn15, Gln6, Thr4, Gly1, Met3, Gly14, and Adn15 engaging in van der Waals interactions; Gly13 making a Carbon-Hydrogen Bond; and Pro16 connecting through a π-Alkyl Bond. E. coli O69:H11 has a strong binding affinity for Cefuroxime, ranking just below Quercetin, EGCG, and Polyphenol among various plant compounds.

Structural modeling and its binding affinities

The generated pharmacophore structures demonstrate the chemical characteristics and functionalities of certain compounds, as well as their spatial arrangements, which collectively contribute to their biological activity towards biological targets (Giordano et al., 2022). Figures 3 and 4 illustrate the 3D pharmacophore structures of the protein-ligand complexes of plant phytochemicals and the E. coli O157:H7 and O69:H11 curli proteins, respectively. Figure 3 illustrates the protein-ligand complexes formed after molecular docking. Structures A to L present the E. coli O157:H7 curli protein in yellow, while the respective phytochemical ligands are in red. As illustrated in Structure M, the protein-ligand complex of Cefuroxime is presented in a darker yellow shade to distinguish it as the reference ligand. Similarly, Figure 4 presents the protein-ligand complexes formed after molecular docking for the E. coli O69:H11 curli protein structure.

Table 6: Ranking of plant phytochemical ligands of the Escherichia coli O157:H7 protein based on significant computed affinity efficacy.

Rank	Plant phytochemical	Lowest upper bound RMSD (Å)	Lowest lower bound RMSD (Å)	Binding affinity (kcal/mol)
1	Polyphenol	0	0	-7.3
2	Flavonones	0	0	-6.7
3	Epigallocatechin-3-gallate	0	0	-6.6
4	Ladanein	0	0	-6.2
5	Kaempferol	0	0	-6.1
6	Quercetin	0	0	-5.9
7	Flavonoid	0	0	-5.8
8	Coumarin	0	0	-5.1
9	Phenolic acids	0	0	-3.9
10	2-Pyridone	0	0	-3.7
11	Ethanol	0	0	-2.5
12	Methanol	0	0	-2
—	Cefuroxime	0	0	-5.8

Tables 6 and 7 present binding affinity results for both the E. coli O157:H7 and O69:H11 phytochemical-protein complexes. These were compared to an antibiotic, namely Cefuroxime, to identify any congruences in their properties and binding potential. The root-mean-square deviation (RMSD) value was used to assess the similarities of the protein and ligand structures, offering an understanding of relevant macromolecular interactions (Sargsyan et al., 2017). In a similar study conducted by Amrulloh et al. (2023), an RMSD value is ideal if the number is < 2 Å as it indicates smaller calculation errors. This means that smaller RMSD values depict closer ligand positions to that of the natural conformation. In the tabulated results, as the RMSD values for both the lowest upper bound and lowest lower bound were 0, it indicates that the structures being compared are both identical. Furthermore, it also implies that the ligands have stable interactions with the binding site. Xu et al. (2017) stated that the lowest energy score appears to be the most promising in terms of binding affinity strength. This denotes that more negative scores are indicative of a ligand tightly binding to its target protein, stabilizing the complex and potentially having better binding site interactions, which can provide insights relating to efficacy.

Following molecular docking, the phytochemicals, along with Cefuroxime, were ranked based on the computed significant efficacy. Table 6 presents the rankings for the E. coli O157:H7 curli protein, while Table 7 shows the rankings for O69:H11 curli protein. Among the sourced plant phytochemicals, Polyphenol yielded the highest binding affinity for the O157:H7 curli protein (-7.3), followed by Flavonones (-6.7) and EGCG (-6.6), while 2-Pyridone (-3.7), Ethanol (-2.5), and Methanol (-2) exhibited the lowest binding affinities. Similarly, Polyphenol ranked highest for O69:H11 curli protein (-8.8), followed by EGCG (-8) and Quercetin (-7.9), while Phenolic Acids (-4.6), Ethanol (-3.1), and Methanol (2.4) were among the lowest. That being said, Polyphenol exhibits the highest potential based on docking score results.

Table 7: Ranking of plant phytochemical ligands of the Escherichia coli O69:H11 protein based on significant computed affinity efficacy.

Rank	Plant phytochemical	Lowest upper bound RMSD (Å)	Lowest lower bound RMSD (Å)	Binding affinity (kcal/mol)
1	Polyphenol	0	0	-8.8
2	Epigallocatechin-3-gallate	0	0	-8
3	Quercetin	0	0	-7.9
4	Kaempferol	0	0	-7.5
5	Ladanein	0	0	-7.1
6	Flavonones	0	0	-7
7	Flavonoid	0	0	-6.9
8	Coumarin	0	0	-5.2
9	2-Pyridone	0	0	-4.7
10	Phenolic acids	0	0	-4.6
11	Ethanol	0	0	-3.1
12	Methanol	0	0	-2.4
—	Cefuroxime	0	0	-6.9

The antimicrobial activity of Polyphenols can be observed across multiple studies performed on a wide array of bacteria. Because of their structure consisting of an aromatic ring and hydroxyl groups, Polyphenols are able to form glycoconjugates and produce other functional derivatives, which add up to their range of functionalities. According to Manso et al. (2022), tested alcoholic extracts containing Polyphenols from Amaranthus retroflexus leaves against bacteria from the Enterobacteriaceae family. In this setup, Klebsiella pneumoniae and Bacillus subtilis produced the best results. The study demonstrated that using Polyphenol-extracts alongside antibiotics increased their antibacterial activity due to synergistic reactions. Particularly for E. coli, polyphenol compounds increased the bactericidal effects of both trimethoprim-sulphamethoxazole and piperacillin-tazobactam antibiotics. However, clinical studies, with emphasis on in vivo methods, must be continuously conducted to further assess mechanisms behind polyphenolic-based bactericides.

Flavonoids, specifically Quercetin and Flavonones, closely follow Polyphenols as beneficial components found in plants. Over the centuries, flavonoids have been utilized as a treatment for diseases because they boast several valuable properties, particularly antimicrobial, anti-inflammatory, anticarcinogenic, and antimutagenic. Quercetin is often studied as it is relatively abundant in nature, and readily available in food such as apples, peas, and onions. In a cited study discussed by Chagas et al. (2022), quercetin demonstrated antibacterial activity by means of inhibiting quorum sensing a communication mechanism used by bacteria in the Pseudomonas aeruginosa PAO1 strain. On the other hand, flavonone, which can be found in propolin D, also demonstrates the capacity to minimize fimbriae production and subdue curli protein gene expression of the E. coli O157:H7 strain. Moreover, it also inhibited its ability to form biofilms, along with other bacteria, particularly the uropathogenic E. coli O6:H1 and A. baumannii. Despite this, there were some strains that it remained ineffective with, such as the E. coli K-12 strain (Lee et al., 2019).

Another high-scoring phytochemical for both E. coli strains is EGCG. Similar to the previously mentioned natural compounds, it exhibits antibacterial activity and a potential to synergize with antibiotics. In a study by Lee et al. (2017), EGCG was used alongside β-lactam antibiotics targeting multidrug-resistant A. baumannii. Interestingly, it was responsible for the inhibition of efflux pumps. This shows that using this compound in combination with antibiotics can aid in treating bacterial infections, especially those known to present with drug resistance.

In contrast, Ethanol and Methanol resulted in the lowest binding affinity scores for both E. coli strains. In a study by Nigussie et al. (2021), Methanol was extracted from the medicinal plants Lawsonia inermis (L. inermis), Azadirachta indica (A. indica), and Achyranthes aspera (A. aspera). Upon testing, extracts from the L. inermis plant yielded the best results in terms of antimicrobial activity against all tested bacteria. However, it had the lowest minimum inhibitory concentration (MIC) when tested against E. coli and S. aureus. Comparably, the study by Valle et al. (2015) obtained Ethanol extracts from Philippine medicinal plants and tested them against a wide range of multi-drug resistant bacteria, such as methicillin-resistant S. aureus and carbapenem-resistant Enterobacteriaceae. The extracts from the P. guajava leaf extract presented no activity against Gram-negative bacteria, while the Piper betle extracts resulted in significant activities against both Gram-positive and Gram-negative bacteria. Both studies concluded that utilizing plants in treating microbial infections could better be explored, emphasizing the need to conduct additional studies using appropriate methods, such as phytochemical screening, in order to further evaluate the potential of natural plants in vivo.

Validation with known inhibitors

Upon conducting several literature reviews, the identified phytochemical ligands were filtered into validated or known inhibitors against E. coli and phytochemical ligands to be subjected to further testing. For instance, EGCG, as studied by Kiddee et al. (2024), can be used alongside ampicillin to bypass antibiotic resistance and enhance the efficacy of antibiotics to overcome E. coli infections. As such, the effectiveness of EGCG coincides with the docking results since it has the 3rd highest binding affinity in E. coli O157:H7 protein and the 2nd highest in E. coli O69:H11 protein. Moreover, the structural components of polyphenols, including the polyphenolic compound quercetin, were attributed to have inhibitory effects to E. coli enzyme inhibition, as mentioned by Ahmad et al. (2012). Results expound that the extent of E. coli inhibition relies on the nature of modulation or modification of polyphenols. This coincides with the docking results as polyphenol was ranked with the highest binding affinity with both E. coli strains, while quercetin ranked 3rd on the E. coli O69:H11 protein. The rest of the phytochemical ligands exhibited fewer evidence against their specific activity against E. coli, thus further testing is required.

ADMET prediction

Although compounds presenting with high binding affinities to target proteins have an advantage in the discovery process, it is still crucial to assess their behavior in response to the body’s mechanisms to determine real inhibitory effects. For a compound to be effective, it should exhibit strong interactions with target proteins, all while staying biologically active long enough to induce its effects. For these reasons, a key aspect of drug development is the assessment of a compound’s absorption, distribution, metabolism, and excretion (ADME) properties (Tibbitts et al., 2016; Daina et al., 2017). The SwissADME tool used in this study helps assess various parameters including physicochemical properties, lipophilicity, water solubility, pharmacokinetics, drug-likeness, and medicinal chemistry for the purpose of early screening of drug candidates to filter out poorly performing compounds prior to subjecting them to further in vivo and in vitro studies.

Polyphenols derived from Vaccinium macrocarpon or commonly named cranberries are shown to be advantageous in UTIs as they inhibit pathogens from attaching to uroepithelial cell receptors through their anti-adhesive properties (Maisto et al., 2023). Displayed in Table 6, the pharmacokinetic results exhibited by Polyphenols show that it has a low GI absorption, is not BBB permeant, and violates 2 criteria in Lipinski’s Ro5. Since it is not BBB permeant, concerns of it crossing the BBB is eliminated as it consequently avoids CNS effects. However, its low GI absorption property suggests limited effectiveness when taken orally. It also violates Lipinski’s Ro5, suggestive of poor absorption and permeation which might limit its effectiveness as a drug. In correlation with Polyphenol’s binding affinity scores for both E. coli strains, the high score possibly indicates a false positive result as the ADMET results are relatively poor. Thus, despite having known antibacterial properties, Polyphenols may not be a good candidate for oral antibiotic drug development targeting UTIs. However, the applications of Polyphenols’ properties may further be explored in other therapeutic applications, particularly in synergy with existing drugs. Based on the study of Moro et al. (2024), it was suggested that intake of cranberry juice can be considered for managing UTIs, as they can help in its prevention.

EGCG is a primary catechin in Camillia sinensis or green tea. According to Alam et al. (2024), it facilitates a wide range of therapeutic biological effects having antioxidant, antibacterial, and anti-inflammatory effects, among others. It has antimicrobial properties and works synergistically with certain antibiotics. Table 6 demonstrates that EGCG has low GI absorption, is not BBB permeant, and has 2 criteria violated in the Lipinski’s Ro5. This suggests that EGCG has a low efficiency in absorption when taken orally. Similar to Polyphenols, EGCG may have also resulted in a false positive score as its ADMET results were not ideal. Hence, it is also a poor oral drug candidate it does, however, have other clinical applications. The bioavailability of EGCG, through rigorous studies, can be improved using nano-complexes. Moreover, a common application is through green tea consumption, which aids in managing the risk for type 2 diabetes (Chakrawarti et al., 2016).

Quercetin, a hydroxylated phenolic compound from Brassica rapa is also a phytochemical shown to have inhibitory properties to E. coli and other GI disorder causing bacteria. According to Chittoor and Sarawathi (2025), quercetin is associated with biological effects with anti-inflammatory, antioxidant, antiviral, and anticancer properties. It also exhibited capacity to damage E. coli cytoderm, evidenced by the leakage of alkaline phosphatase. Its ADMET results in Table 6 shows that quercetin has good solubility, high GI absorption, and meets most of Lipinski’s Ro5. This signifies that it is effective when taken orally and it has a good drug-likeness. It is also shown not to be BBB permanent. However, the presence of PAINS alerts also show potential issues concerning drug interaction and metabolism.

Similarly, kaempferol also derived from Brassica rapa found in various herbs and plants were shown to have antibacterial, antifungal, and antiprotozoal activities. Periferakis et al. (2022), relays that kaempferol showed the greatest effectiveness in damaging E. coli cell membrane, where findings were demonstrated by the bacterial protein leakage into the extracellular environment. This statement coincides with its ADMET results in Table 6, highlighting a high GI solubility, non BBB permeability, and non P-gp substrate status. Its bioavailability, which is 0.55 indicates that a significant portion of the compound can be readily absorbed and utilized by the body. It also meets most of the criteria in Lipinski’s Ro5 indicating that it has a good drug-likeness and has favorable properties for absorption and permeation.

Ladenein is a polymethoxylated flavone that is derived from Orthosiphon stamineus, commonly associated with Java tea or cat’s whiskers. This plant is used as traditional medicine in Southeast Asian countries as it achieves anti-adhesive effects without cytotoxicity in inhibiting strains of E. coli (Deipenbrock and Hensel, 2019). From Table 6, the ADME results of ladenein show a moderately high solubility and a high GI solubility, indicating that oral forms can be taken and moderately absorbed by the bloodstream since it has a 0.55 bioavailability score. It is not BBB permeant thus CNS effects can be ruled out. At the same time, it does not have a P-gp substrate, increasing its effectiveness. It also has good drug-likeness as it does not have violations in Lipinski’s Ro5.

Flavonones from Vaccinium macrocarpon or cranberries are associated with the inhibition of UTIs as they possess antimicrobial, anti-adhesive, and anti-inflammatory qualities similar to Polyphenols that reduce the likelihood of infection and prevent reccurent UTIs (Gonzalez de Llano, 2021). ADMET results in Table 6 demonstrate that flavanones have moderate solubility and high GI absorption, suggesting that they exhibit effectiveness when taken orally. They are non BBB permeable and do not have P-gp substrate status, also contributing to their effectiveness. Additionally, the bioavailability score of 0.55 signify that they have moderate bloodstream absorption and have good drug-likeness ess as they meet the Lipiski’s Ro5. However, the inhibition of CYP1A2 and CYP3A4 enzymes may lead to drug interactions.

Flavonoid is a naturally occurring phytochemical found in the leaves of guava (Psidium guajava L) extract. Flavonoids inhibit gram-negative bacteria like E. coli from making biofilm by stopping the production of amyloid (Pruteanu et al., 2020). Table 6 shows the characteristics provided when run on SwissADME, exhibiting high GI absorption, no BBB permeation, and has cytochrome inhibitors (CYP1A2, CYP2D6, CYP3A4). Moreover, it has no Lipinski violations, so it may have a good drug-likeness profile. It is moderately soluble in water (soluble) and has a bioavailability score of 0.55, indicating its moderate ability to be absorbed by the body.

Coumarin is another natural phytochemicals found in Dipteryx odorata. According to El-Sawy et al. (2024), this enables bacteria to be more susceptible to antibiotics and contributes to the reduction of antibiotic resistance. Table 6 shows the characteristics provided when run on SwissADME, exhibiting high GI absorption and has 1 cytochrome inhibitor (CYP1A2). However, it does permeate the blood-brain barrier. While BBB permeability is advantageous for medications that target the CNS, it may also signal the possibility of neurotoxicity or unintended CNS side effects if the molecule is not meant for use in brain-related applications (Geldenhuys et al., 2015). Additionally, it demonstrates that Coumarin complies with Lipinski’s third rule. Furthermore, it may have a favorable drug-likeness profile because it has no Lipinski violations. Its bioavailability score of 0.55 indicates that it has the ability to be absorbed by the body and is soluble in water.

2-Pyridone is a phytochemical found in Camptotheca acuminata. It has a high GI absorption, which means that when ingested or consumed, it is readily absorbed into the body, according to the SwissADME result on Table 6. It also has no direct effect on the brain since it cannot cross the blood-brain barrier (BBB). It might have a favorable drug-likeness profile because it has cytochrome inhibitors and no Lipinski violations. With a bioavailability score of 0.55, which indicates a moderate capacity for absorption by the body, it is readily soluble in water.

Phenolic acids, according to Table 6, also have high GI absorption, so they can be easily absorbed either via eating or drinking. It does, however, pass through the blood-brain barrier. Although BBB permeability is beneficial for drugs that act on the CNS, if the molecule is not intended for use in brain-related applications, it may also indicate the potential for neurotoxicity or unexpected CNS adverse effects (Geldenhuys et al., 2015). Being a CYP1A2 inhibitor, it can impact how other medications that pass through this enzyme are metabolized (Brøsen, 1995). Additionally, it contains no Lipinski violations, which suggests that it might have medicinal potential. Its bioavailability score of 0.55 indicates that it has a moderate capacity to be absorbed by the body and that it is water soluble.

Naturally occurring Quercus infectoria contains ethanol, which is a phytochemical. According to Lou et al. (2013), ethanol elution may result in the loss of E. coli’s multi-layered development morphology biofilm, which can significantly impact its structural integrity. Table 6 displays the characteristics obtained while running on SwissADME, which reveal low GI absorption, no BBB penetration, and no cytochrome inhibitors. Furthermore, it displays no Lipinski violations, implying a favorable drug-likeness profile. It is soluble in water (soluble) and has a bioavailability score of 0.55, indicating that it is moderately absorbable by the body.

Methanol is a phytochemical derived from Enydra fluctuans (Buffalo spinach). In addition, the methanol extract diminished the biofilm activity of both E. coli and S. aureus to values beneath the sub-minimum inhibitory concentration. The extract demonstrated antibacterial and antibiofilm effectiveness against the tested bacterial pathogens. As per Table 6, it has almost the same result with the ethanol. It shows that it also has a low GI absorption, no BBB penetration, and no cytochrome inhibitors. Furthermore, it displays no Lipinski violations, implying a favorable drug-likeness profile. It is soluble in water (soluble) and has a bioavailability score of 0.55, indicating that it is moderately absorbable by the body.

Analysis on the top-performing phytochemicals

Upon careful consideration of both binding affinity scores and ADMET predictions, the researchers have determined that the phytochemicals namely Kaempferol, Flavonoids, and Ladanein exhibit the best potential as candidates for drug development. Kaempferol, with a binding affinity of -6.5 and -7.5 for the respective E. coli strains, is depicted to have the best results among the selected phytochemicals. This can be attributed to its ADMET predictions, as it meets numerous parameters required for drug candidates. Flavonoids are ranked slightly above Ladanein, despite the latter having a higher binding affinity score, because flavonoids did not set off any alerts in both PAINS and the Brenk criteria.

Kaempferol is a well-studied compound mainly due to its numerous therapeutic qualities. Recently, Kaempferol has been a compound of growing interest in the field of microbiology. E. coli is one of the bacteria tested in this aspect. In a review by Periferakis et al. (2022), Kaempferol extracted from natural products, such as Bupleurum chinense and propolis, proved to be effective against this pathogen due to antibacterial properties. To further strengthen this, Kaempferol has been tested in in vivo and in vitro studies to determine inhibitory effects on biofilm formation. In a study conducted by Xu et al. (2023), the anti-virulence mechanisms of Kaempferol were found to interfere with the pore formation of pneumolysin (PLY) as well as sortase A (SrtA) peptidase activity through interactions with their active sites. It was also found that incubation with Kaempferol prevented biofilm formation and bacterial adhesion to the host cells. An in vivo infection model using Streptococcus pneumoniae further revealed that oral administration of Kaempferol decreased bacterial burden, implying that this compound has notable therapeutic benefits. In a separate study, Zeng et al. (2019) performed an in vitro study to determine the activity of Kaempferol against biofilms formed by Streptococcus mutans. The in vitro biofilm model revealed that Kaempferol exhibited anti-biofilm properties in comparison to the negative control, reducing attributes such as dry weight, viable cells per colony forming unit (CFU), and total protein. This highlights its potential to be explored in drug designs targeting UTIs, as it proves that Kaempferol has real inhibitory effects against biofilms.

Another phytochemical that can be considered are flavonoids. Mainly found in numerous fruits and vegetables, flavonoids also possess antimicrobial properties against numerous bacteria, one of which is E. coli. Lee et al. (2011) tested a natural Flavonoid, namely phloretin, against the E. coli O157:H7 strain. The study used the rat model of colitis induced by trinitrobenzene acid (TNBS) to assess the anti-inflammatory effect of this Flavonoid in vivo. Results indicate that flavonoids, specifically phloretin, possessed strong anti-biofilm properties, inhibiting biofilm formation by 89-93%. It is also worth noting that phloretin resulted in more prominent effects in the rat model in contrast to a known inflammatory bowel disease drug, mesalamine, in terms of body weight, colon weight, and MPO activity indicating its potential to be explored as a therapeutic agent. These findings can be explored and applied to target biofilm formation in UTIs, as some flavonoids prove to have inhibitory activities without harming commensal E. coli strains.

In a similar light, Ladanein exhibits antiadhesive properties to UPEC. It works synergistically with other active compounds to effectively downregulate type 1 fimbriae and curli genes, which are notorious for their role in bacterial virulence (Marouf et al., 2022). Although there are limited studies that detail the effects of Ladanein against bacteria such as UPEC, the docking results in this study, together with its ADMET predictions, may reveal the possibilities of studying Ladanein as a potential drug candidate for UTIs.

Evidently, phytochemicals from various plants broaden the options for drug discovery. Performing molecular docking further assesses their therapeutic and synergistic potential as it provides a deeper understanding of their interactions with the binding sites of target molecules; evaluating their mechanism of action enables scientists and researchers to optimize these phytochemicals to fully maximize their predicted efficacy. Furthermore, one of the most notable advantages of performing molecular docking is its capacity to screen thousands of phytochemicals virtually. This significantly reduces the time and cost spent in conducting experiments by refining the options of phytochemicals to be used in drug discovery and development. Although molecular docking setups still have room for improvement, decades of advancements in this field have collectively contributed in increasing its accuracy. As developments in computational strength and hardware efficiency take place over time, the numerous possibilities and benefits of molecular docking methods will further be realized (Torres et al., 2019). Furthermore, assessing the ADMET properties of each compound should not be neglected in interpreting molecular docking scores. As observed in the contrast between the docking score and ADMET predictions of both Polyphenols and EGCG, cross-checking these results with their pharmacokinetic applications is crucial to detect occurrences of false positive values. In processes as rigorous as drug discovery and development, eliminating potential compounds that theoretically will perform poorly in drug design and clinical trials can significantly reduce costs, especially in time and resources.

Conclusions and Recommendations

A method for identifying an antibiotic to treat the UTI caused by E. coli is the in silico drug discovery approach. The computational approach could significantly simplify the search for effective medication inhibitors targeting E. coli curli proteins. This research has found natural phytochemical inhibitors that target the E. coli curli protein CsgA, which plays a crucial role in biofilm development. The study utilized software such as PyRx, BIOVIA, and SwissADME to identify 12 potent phytochemical inhibitors. It was found that the highest binding affinities for both the E. coli O157:H7 and E. coli O69:H11 strains were associated with Polyphenol, followed by Flavonones, EGCG, and Quercetin. At the same time, the phytochmicals that showed the lowest binding affinities were 2-Pyridone, Ethanol, and Methanol. The researchers found that Kaempferol, Flavonoids, and Ladanein showed the most promise for drug development after carefully looking at their binding affinity scores and ADMET predictions. The pharmacokinetic properties noted were that the chemical does not easily cross the blood-brain barrier, dissolves well in the gastrointestinal tract, is not affected by P-glycoprotein, and can inhibit CYP enzymes. The chemical has a bioavailability of 0.55, allowing for rapid absorption and significant utilization by the body. Moreover, it meets most of Lipinski’s Rule of Five criteria, indicating that it possesses favorable absorption and penetration properties, aelong with a strong drug likeness. The findings of the study indicate that Kaempferol, Flavonoids, and Ladanein may serve as promising antibiotic drug candidates for treating UTIs. However, more experimental validation is necessary to prove their therapeutic potential. To validate our findings and assess the potential of Kaempferol, Flavonoids, and Ladanein as antibiotic drug compounds for treating UTIs, the next step is to conduct doing in vitro tests to determine their capacity to inhibit biofilms, then in vivo investigations to appraise their pharmacodynamic and pharmacokinetic characteristics. Determining their effectiveness and safety for therapeutic use will also need toxicity profiles, clinical trials, and mechanistic investigations of their molecular interactions with curli proteins.

Acknowledgements

The authors would like to express their gratitude to Far Eastern University, Department of Medical Technology, for their support throughout the whole process of the study. Furthermore, they would like to extend their heartfelt appreciation for their colleagues and group members whose support, cooperation, and shared enthusiasm made this research possible. In addition, they wish to express our sincerest appreciation for their family and friends whose relentless encouragement and support have given them the motivation to reach our academic endeavors.

Novelty Statement

This study is the first to employ in silico molecular docking approaches to identify potential phytochemical inhibitors of Escherichia coli curli proteins, highlighting their role as promising antibiotic drug candidates for urinary tract infection (UTI) management and offering a novel perspective on targeting bacterial adhesion mechanisms through plant-derived compounds.

Authors Contribution

Den Marc Exala: Conceptualization, investigation, methodology, formal analysis, reviewing and editing, writing original draft.

Earl Adriane Cano: Supervision, methodology formulation, formal analysis, validation, writing review.

Angela Nole Shayenne Coderos: Conceptualization, investigation, methodology, formal analysis, reviewing and editing, writing original draft.

Denise Alexandra Cruz: Conceptualization, investigation, methodology, formal analysis, reviewing and editing, writing original draft.

Samson Decasa, Juliana Carlidy Elauria, Jalen Rose Esguerra and Abigail Anne Ferrer: Conceptualization, reviewing, writing original draft.

Funding information

The authors received no financial support for the research, authorship, and/or publication of this article.

Conflict of interests

The authors have declared no conflicts of interest.

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