Benzoxazole Scaffolds as Novel Toll-Like Receptor-2 Inhibitors: A New Avenue for Pneumocystis Pneumonia Therapy

Pneumocystis pneumonia (PCP) is a critical life-threatening infection in immunocompromised individuals particularly those with HIV/AIDS, malignancies or on immunosuppressive therapy. The disease is caused by Pneumocystis jirovecii, an ascomycetous fungus and is transmitted through airborne routes. Despite advances in prophylaxis and treatment, PCP remains a significant cause of morbidity and mortality. Current treatments have limitations, including adverse effects and resistance, highlighting the need for novel therapeutic strategies^[1][2].

TLR2 is a key pattern recognition receptor involved in the innate immune response to fungal pathogens such as Pneumocystis jirovecii. It recognizes Pneumocystis cell wall components triggering a proinflammatory cascade essential for pathogen clearance. Targeting TLR2 offers a promising strategy to balance the immune response and minimize tissue damage in PCP^[3][4].

Benzoxazole, a versatile pharmacophore, is known for its broad spectrum of biological activities, making it a promising scaffold for designing novel therapeutics^[5].

The present work aims to design and perform molecular docking for Benzoxazole derivatives with the objective of overcoming the Pneumocystis pneumonia.

MATERIALS AND METHODS

Selection of Target

Toll-like receptor-2 (TLR2) is chosen as the target for Pneumocystis pneumonia (PCP) due to its ability to recognize fungal cell wall components, initiating a crucial proinflammatory signaling cascade essential for pathogen clearance. By modulating TLR2 signaling it may be possible to balance the immune response supporting pathogen clearance while minimizing excessive lung inflammation thereby addressing a critical unmet need in the management of PCP. To verify and fix any missing hydrogen atoms, residues, or heavy atoms, the selected protein is input into the Protein Repair and Analysis Server (PRAS; https://www.protein-scen ce.com/)^[6]. The description of target for the treatment of Pneumocystis pneumonia was listed in table 1. The 3D crystal structure of Toll-like receptor-2  was depicted in figure 2. Recent researchers suggested Toll-like receptor-2 as a therapeutic potential target to treat Pneumocystis pneumonia which made us select the Toll-like receptor-2 as the protein target in this study.

Table. No. 1 : Description about target

Figure. No. 2. The 3D crystal structure of Toll-like receptor-2

Active Site Prediction

Using the CB-DOCK2 online tool which clusters the solvent-accessible surface and cavities, the target active sites have been predicted. This method of prediction is based on structure and predicts cavities. Grid boxes can be tailored to fit the cavity’s centroid, dimensions, and volume predictions. This modification enhances the accuracy of predictions about ligand-receptor interactions by enhancing molecular docking simulations.^[7].

Pharmacophore Identification

Pharmacophore modeling was carried out using the Pharmit web server (http://pharmit.csb.pitt.edu/) which enables the interactive generation and screening of pharmacophores against a wide range of chemical compound libraries including the ZINC database, CHEMBL and PubChem. The 3D structure of the target protein TLR-2, was retrieved from the RCSB Protein Data Bank using the PDB ID: 6NIG that was entered into the website to identify the pharmacophore^[8]. Based on this structure, the key pharmacophoric features were identified and used as a framework for the design of novel ligands.

Designing of Ligands and its Novelty Check

The concepts of molecular hybridisation and pharmacophore modelling are used to create a virtual library of 180 newly created compounds. Using the Chemsketch program (ACD LABS, Version 2023.1.0), the ligands are sketched and stored in the .mol format for further computational tasks^[9]. The uniqueness of the created ligands is confirmed by an analysis of chemical databases such as PubChem and the Zinc 20 database ^{[10] [11]}.

ADMET Prediction

During the drug discovery stages of drug design, the qualitative idea of “drug-likeness” is employed to explain how “druglike” an element is in relation to factors like bioavailability. Verifying that Lipinski’s rule of five is adhered to is a recognised technique for evaluating drug similarity ^[12]. SwissADME makes it possible to compute physiochemical descriptors and forecast ADME parameters. There are numerous methods for determining log p such as p-glycoprotein substrate, topological approaches, fragment approaches, CYP450 inhibitors for pharmacokinetic predictions and Lipinski’s rule computation^[13]. A molecule’s toxicity profile may be evaluated with an online tool called Osiris Property Explorer. It looks for harmful consequences such as mutagenicity, tumorigenicity, irritability, and reproductive toxicity^[14].

Energy Minimization

Iterations are performed until the minimised conformation is reached using the Chem3D super extreme software module (Perkin Elmer Informatics) to apply the MM2 Force field. It involves reducing the energy of the ligand^[15].

Docking Studies and Visualization of Interactions

The orientation and interaction between proteins and ligands are determined via molecular docking. In the present study, the binding energy of ligands with Toll-like Receptor-2 was predicted using the Autodock Tools 1.5.6 software. The receptor and ligand interactions are visualised using the Biovia Discovery Studio (Dassault Systems, V21.1.0.20298) ^{[16] [17] [18]}.

RESULT AND DISCUSSION

Active Site Prediction

The active site prediction details of the protein are available in the Table 2

Table. No. 2 : Active site dimensions of the target protein

Pharmacophoric features

The pharmacophoric features for the target include the hydrophobic group, hydrogen bond acceptor, hydrogen bond donor. Pharmacophore model generated through the Pharmit server consists of the features depicted as follows; Hydrogen Bond Donor (HBD) in white colour; Hydrogen bond Acceptor (HBA) in orange colour Hydrophobic feature (HY) in green colour which was represented in the figure 3

Figure. No. 3 : Pharmacophoric features of the target

Ramachandran plot

The Ramachandran plot analysis results indicate that the selected protein, predominantly (more than 90%) have their amino-acid residues situated within the most favored region as represented in the figure 4.

Figure. No. 4 : Ramachandran plot of selected protein

Construction of virtual library

A virtual library of 180 ligands is constructed by employing the concepts of pharmacophoric features and among them the best ligands that shows no toxicity and favorable ADME profile  are highlighted in the Table 3 with ligand code GYP 1 – GYP 180 . Most of the ligands were novel and non-toxic.

Table. No. 3 : Best ligands from the virtual library of ligands

GYP 05	GYP 06	GYP 07
GYP 09	GYP 12	GYP 29
GYP 30	GYP 31	GYP 37
GYP 38	GYP 45	GYP 47
GYP 52	GYP 58	GYP 60
GYP 61	GYP 80	GYP 83
GYP 84	GYP 87	GYP 93
GYP 101	GYP 109	GYP 126
GYP 134	GYP 135	GYP 138
GYP 139	GYP 140	GYP 141
GYP 142	GYP 144	GYP 145
GYP 146	GYP 147	GYP 148
GYP 152	GYP 153	GYP 157
GYP 158	GYP 159	GYP 160
GYP 162	GYP 175	GYP 176
GYP 180

Molecular docking

Ligands that show druglikeness property with no toxicity were chosen for molecular docking studies against the protein like TLR-2 (PDB ID: 6NIG). The binding energy of the ligands against the protein is presented in the table 4. Among the virtual library, ligands GYP-12, GYP-80, GYP-135, GYP-142 and GYP-158 showed superior docking scores (ranging from -7.75 to -8.52 kcal/mol) outperforming the standard drug TMP-SMX (-6.62 kcal/mol). All five selected ligands complied with Lipinski’s Rule of Five and exhibited green (non-toxic) profiles in the Osiris toxicity check which was given in the table 5 and 6 . These compounds shows significant potential anti-PCP activity.

Table. No. 4 : Docking score of optimized ligands against TLR-2

Table. No. 5 : Drug - likeness property of Lead compounds that showed superior docking results

CMPD – ID	DRUG LIKENESSS PROPERTY
GYP – 12
GYP – 80
GYP – 135
GYP – 142
GYP – 158

Table. No. 6 : Toxicity profile of Lead compounds that showed superior docking results

Cmpd – ID	TOXICITY EVALUATION	Cmpd – ID	TOXICITY EVALUATION
GYP – 12		GYP – 80
GYP – 135		GYP – 142
GYP – 158

Table. No. 7 : Binding scores and interaction with its 3D docking view of Lead compounds that showed superior docking results

LIGAND CODE	BINDING SCORE (kcals/mol)	BINDING INTERACTION	3D DOCKING VIEW
GYP – 12	-7.80
GYP – 80	-7.51