Comprehensive Approach to in silico Identification and in vitro Validation of Anti-Filarial Lead Molecules Targeting the Dimer Interface of Thioredoxin Peroxidase 1 in Wuchereria bancrofti: A Progress in Anti-Filariasis Drug Development

Comprehensive Approach to in silico Identification and in vitro Validation of Anti-Filarial Lead Molecules Targeting the Dimer Interface of Thioredoxin Peroxidase 1 in Wuchereria bancrofti: A Progress in Anti-Filariasis Drug Development | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive Approach to in silico Identification and in vitro Validation of Anti-Filarial Lead Molecules Targeting the Dimer Interface of Thioredoxin Peroxidase 1 in Wuchereria bancrofti: A Progress in Anti-Filariasis Drug Development Sureshan Muthusamy, Rajamanikandan Sundarraj, Saraboji Kadhirvel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3982867/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Lymphatic filariasis (LF) remains a significant health challenge for populations in developing countries, including India. LF is a parasitic disease transmitted by mosquitoes, caused by three types of nematodes: Wuchereria bancrofti, Brugia malayi , and Brugia timori , prevalent in tropical and subtropical regions. Filarial nematodes are transmitted to humans by infected mosquitoes carrying L3 larvae during a blood meal. The human immune system generates reactive oxygen species (ROS) in response to the invading nematodes. Nevertheless, the nematode's antioxidant enzymes effectively counteract the oxidative cytotoxicity induced by the host. The enzyme thioredoxin peroxidase 1 (TPx1) is a member of the peroxiredoxin family, which facilitates the conversion of hydrogen peroxide (H 2 O 2 ) into water (H 2 O), which is highly used by nematodes to maintain oxidative stress in the host throughout their life cycle. Therefore, developing appropriate therapeutic compounds by targeting TPx1 might aid in resolving difficulties with treatment efficacy and activity at various stages of filarial parasitic worms. To find the potent inhibitors, we modelled the three-dimensional structure of Wb TPx1, and its dynamic stability was assessed by molecular dynamic (MD) simulation studies. A structure-based virtual screening approach was used to find the most promising leads from the small molecule collection, focusing on the dimer interface region of Wb TPx1. The predicted in silico ADMET profiles for the top-ranked hits revealed that the non-toxic lead compounds had the greatest docking score, ranging from − 7.410 kcal/mol to -6.887 kcal/mol, and the identified leads showed higher affinity to Wb TPx1 than Hs TPx1 based on the cross-docking studies. Throughout the simulation of the Wb TPx1-lead complex, the lead molecules exhibited stability and remained intact within the binding site. Furthermore, the in vitro validation of the chosen leads based on computational results in the filarial worm Setaria digitata exhibited higher inhibition and better IC 50 than the standard drug ivermectin. Hence, the identified leads have the potential to inhibit enzyme activity, serving as possible drug candidates for the control of LF. Lymphatic filariasis W. bancrofti WbTPx1 MD simulation DFT PCA MTT Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Lymphatic filariasis (LF), is a disease transmitted to humans by infected mosquitoes. It is also known as elephantiasis, and this disfiguring disease is caused by three species of parasitic nematodes namely Brugia malayi, Brugia timori , and Wuchereria bancrofti [1, 2]. More than 90% of LF cases are due to W. bancrofti . Early report states that, the number of people affected due to LF stands at around 120 million across 76 countries throughout the tropical regions of Africa, Asia, and other sub-tropical regions [3, 4]. Another insight is that India and Sub-Saharan parts of Africa are the major regions affected by LF. As per the 2021 World Health Organization (WHO) statistics, LF continues to concern 882 million people in 44 countries worldwide. According to the global baseline estimate, over 15 million people were affected by lymphoedema and 25 million males from hydrocele. If we consider the filarial infection in Asia, it accounts for nearly 63% global burden, mostly in the region of Southeast Asia, India alone harbours 40% of the world filarial burden, despite the existence of the National Filaria Control Programme (NFCP) since 1955 [5]. This vector-borne disease is transmitted to humans, especially through the members of the Anopheles, Culex, Aedes, Mansonia , and Ochlerotatus genera of mosquitoes serve as carriers, transmitting them based on the distinct geographical locations and biological features of each species [6]. Eventually, they grow and affect the lymphatic system, causing permanent damage to humans and leading to disfiguration and disability of the limbs. Being a digenetic parasite, W. bancrofti has its lifecycle in two hosts. The humans support the adult form, and the mosquitoes support the larval form. During the blood ingestion, a mosquito transports infective filarial larvae and injects them into a healthy human. The larvae reach the lymphatic system and mature into adult worms (both male and female). Over time, these adult worms reproduce sexually, producing larval offspring known as microfilariae. Subsequently, these microfilariae migrate from the lymphatic system to the bloodstream and enter the mosquito vector during their blood meal, and the cycle continues [7, 8]. Under the Global Programme to Eliminate Lymphatic Filariasis (GPELF, 1997), formed by the WHO, Mass Drug Administration, and morbidity management activities were initiated to educate people on personal hygiene management and the health impact of preventing LF. Ivermectin, albendazole (ALB), and diethylcarbamazine (DEC) are generally used to treat filarial worms. However, their mechanism has yet to be deciphered clearly, and these drugs are effective against the larval stage of the worms and not against adult and larval parasites [9, 10]. Due to the prolonged use of these drugs, the nematode developed resistance due to the single nucleotide polymorphisms in its molecular targets. There is no conclusive evidence establishing a clear relationship between these genes and the development of resistance [11]. Upon W. bancrofti infection, the host's initial immune system reaction activates macrophages, neutrophils, and eosinophils. This response leads to the production of Reactive Nitrogen Intermediates (RNI) and Reactive Oxygen Intermediates (ROI), which include the superoxide anion radical (O 2 . − ), hydroxyl radicals (OH. − ), peroxyl (RO 2 .) and hydrogen peroxide (H 2 O 2 ). Collectively, these are referred to as reactive oxygen species (ROS) generated by the host to control the invading nematode [12, 13]. Nevertheless, the primary characteristics of a lymphatic filariasis infection involve the nematode's ability to survive within the host for an extended period (years). Their effective neutralisation of ROS facilitates this resilience through well-adapted antioxidant enzymes. The crucial survival tactic employed by filarial nematodes revolves around their antioxidant enzymes, which include superoxide dismutase, glutathione peroxidase, peroxiredoxins, catalase, and glutathione S transferases. These enzymes play a pivotal role in neutralizing the host's ROS activity, thereby securing the ongoing viability of the nematodes within the host [14, 15] So, these enzymes could be a possible effective target to strike the worms at all stages of their life cycle. Peroxiredoxins (Prxs) display remarkable efficiency as cysteine-dependent peroxidases, actively participating in antioxidant, regulatory, and signalling systems with remarkable efficiency. Prxs play a crucial role in defending against peroxynitrite (ONOO − ) and reactive oxygen species (ROS) that arise from both the host and endogenous sources [16]. In many eukaryotes, Prxs are a ubiquitous family of peroxidases that are vital in reducing hydrogen peroxide (H 2 O 2 ), alkyl hydroperoxides and peroxynitrite with varying affinity. Prxs have a uniform fold, active site, and catalytic cycle, utilizing a conserved Cysteine residue called the peroxidatic Cys (CP) to interact with the peroxide substrate [17, 18]. Based on sequence similarity, peroxiredoxins can be classified into five subfamilies: Prx1, Prx6, Prx5, Tpx, and BCP. As a thiol-specific antioxidant protein family member, Thioredoxin peroxidase-1 (TPx-1) is predominantly located in the cytoplasm. TPx-1 utilizes its redox-active cysteine to efficiently reduce and detoxify hydrogen peroxides. Three varieties of TPxs are distinguished by the number and location of cysteine (Cys) residues: 1-Cys, typical 2-Cys, and atypical 2-Cys type [19, 20]. They follow a uniform reaction mechanism in which the catalytic cysteine, referred to as the peroxidatic cysteine, undergoes oxidation by hydroperoxides, resulting in hyper oxidation and forming sulfenic (-SOH), sulfinic (-SO 2 H), or sulfonic acid (-SO 3 H) states of the cysteine. Subsequently, the cysteine is regenerated to its reduced form by various molecules, such as thioredoxin (TRX), glutaredoxin (GRX) or glutathione [21]. Because the filarial parasite is susceptible to disruption of its antioxidant system, it is an ideal potential target for developing new drug candidates [22]. In the current study, We have used computational methods, such as homology modelling, and molecular dynamics (MD) simulation to study the dynamic structural stability of Wb TPx1. The structure-based virtual screening, prime MMGBSA (molecular mechanics, the generalized born model and solvent accessibility), and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) studies were carried out to identify the potent leads that could effectively bind in the binding site of Wb TPx1. Moreover, the lead molecules undergo complex dynamics using MD simulations to evaluate their dynamic intactness with the enzymes. Density Functional Theory (DFT) examines the reactivity of identified lead molecules. The lead compounds identified in this present study were further substantiated through in vitro validation using the Setaria digitata model. Our study's findings and associated observations could pave the way for designing and developing innovative therapeutic drug molecules that effectively target all stages of the filarial worm life cycle, with the ultimate goal of eradicating lymphatic filariasis. 2. Materials and methods 2.1. Homology Modelling of thioredoxin peroxidase 1 (TPx1) of Wuchereria bancrofti ( Wb TPx1) The three-dimensional (3D) structure of the Wb TPx1 has not been determined by experimental methods. Therefore, homology modelling techniques have been employed to predict a reliable 3D structure for the target protein. The protein sequence of Wb TPx1 (B8YNX5) was retrieved from the UniProt sequence database, which comprises 228 amino acids. The protein BLAST was carried out against the Protein Databank (PDB) to identify the suitable template, and the best template was selected based on the E-value, query coverage, percentage identity, and resolution. The Prime module of the Schrödinger suite [23] was used to model the atomic coordinates of Wb TPx1 based on the selected template structure, and the structural deviation between the target and template was determined by calculating the Root Mean Square Deviation (RMSD) using PyMol. The PROCHECK (Structural Analysis and Verification Server; SAVES) server was used to assess the stereochemical quality of the modelled structure. 2.2. Molecular dynamics simulations of Wb TPx1 The molecular dynamic simulation was performed by using GROMACS 5.1.2 suite [24], to study the dynamic stability of the modeled Wb TPx1, and GROMOS96 53a6 force field [25] was used to create the protein topology. The Wb TPx1 system was parameterized in the centre of the cubic box with a dimension of 1.2 nm in all directions. The system was solvated with around 22958 water molecules using an SPC water model [26]. The total charge of the system was neutralized by adding appropriate sodium (Na + ) and chloride ions (Cl − ) with 0.1 M of ionic strength. To eliminate the steric conflicts and poor geometry in the solvated system, the energy minimization was carried out using the steepest descent method [27] for 50000 steps. Before running the production MD simulation, the energy-minimized system was brought to equilibrium for 500 ps under the NVT ensemble (isothermal-isochoric) at a constant number of particles, temperature, and volume using the Berendsen external bath [28]. Further, Parrinello-Rahman barostat [29] was used to equilibrate for 2000 ps at a constant number of particles, pressure and temperature under NPT ensemble (isothermal-isobaric) at 310 K. LINCS algorithms [30] were used to implement the constraints for the bonds and bond length involving the hydrogen bonds with the protein and the geometry of the solvent were constraints by implementing the SETTLE algorithm [31]. The long-range electrostatic interactions were calculated using the Particle Mesh Ewald (PME) technique [32], with a grid spacing of 1.6 nm and a cutoff order of 4 and 1.2 nm for the non-bonded Coulomb and van der Waals interactions, respectively. Finally, the well-equilibrated protein system was subjected to production MD simulation for the time scale of 200 ns, where the structural trajectories were stored for every 10 ps. The dynamic stability profile of the Wb TPx1was examined by analyzing the changes in the root mean square fluctuation (RMSF), RMSD, the radius of gyration (Rg), and solvent accessible surface area (SASA); and the GROMACS utilities and VMD [33] was used to analyze and visualize the trajectories. 2.3. Preparation of Wb TPx1 for structure-based virtual screening and molecular docking Potential inhibitors were screened using the Virtual Screening Workflow (VSW) feature of the Schrödinger suite. The VSW in the Schrödinger suite comprises three primary processes: Ligand Preparation, Filtering, and Docking. Initially, the small molecules dataset was prepared using the Ligprep module [34], which involves the conversion of 2D into 3D conforms using the OPLS-3e force field [35] that generates up to 32 conformations for each molecule. The ligand molecules prepared were filtered based on the Lipinski rule of five, ADME properties, and the elimination of compounds with special reactive groups, such as alkali metals and halogens. Next, we prepared the Wb TPx1 protein using the protein preparation wizard of Schrödinger, which refines and corrects the charges, bond order, bond angle, and side chains of the atoms in proteins. By utilizing the OPLS-3e force field, the intra-molecular H-bonds were optimized, and the modeled protein structure underwent energy minimization until it reached a maximum convergence of the RMSD threshold of 0.30 Å. Identifying and selecting suitable binding pockets have become an essential aspect of computational screening. By utilizing the SiteMap module [36] within Schrödinger, we predict the binding site of the Wb TPx1. It computes the residue properties such as acceptor, donor, and hydrophilic regions in Wb TPx1 and ranks the possible binding pocket based on the SiteScore function. Finally, the best-ranked site was selected, and the grid box was generated with 15 Å from all three sides that cover all the binding site residues using the receptor grid generation module. Glide module of Schrödinger [37], uses the three-stage docking method to identify the best binding small molecules that include High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP) depending on the level of accuracy. In each docking stage, only the top 10% of the molecules were allowed for the subsequent level of virtual screening. Further, Prime MM-GBSA modules which use the OPLS-AA force field are used to calculate the binding free energies (ΔG bind ) for the Wb TPx1-ligand bound complexes with default parameters. 2.4. Density Functional Theory (DFT) Calculation The Gaussian 09 software [38] was utilized to perform the DFT calculation for the top five identified lead molecules. The electronic structure is a fundamental component in understanding the thermodynamic properties of molecular interactions and the electronic, chemical, and molecular dynamics nature of small molecules. To determine the energy levels of the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) for the identified leads, the B3LYP-D3 hybrid function and the 6-311G** ++ (2d, 2p) basis set were utilized. Calculating the energy gap (HOMO-LUMO) enables the determination of various electronic properties, including chemical potential, electron affinity, chemical stability, and hardness [39]. This information is critical in comprehensively assessing the chemical potential of the leads and helps in understanding their electronic properties. 2.5. The stability analysis of protein-ligand complexes through MD simulations. The MD simulations assess the dynamic stability of the top five non-toxic leads complex with the Wb TPx1. The lead topologies were parameterized using the online PRODRG server [40]. The Wb TPx1-lead complexes system was constructed similarly implemented in native Wb TPx1, which includes replicating the protein topology, setting up the system using the periodic boundary condition, solvating the system followed by neutralization and equilibration. At a temperature of 310 K, the Wb TPx1-lead complexes were subjected to a 50 ns production MD simulation to study the dynamicity of the protein and ligand complexes. 2.6. Principal component analysis (PCA) for the native and leads bound Wb TPx1 To analyze the conformational changes and atomic movements in the native Wb TPx1 and lead-bound Wb TPx1 complexes, a Principal Component Analysis (PCA) was employed for their respective trajectory structure obtained from MD simulations [41]. PCA, a widely utilized statistical method, is employed to explore the intricate protein motions that are essential for revealing the biological functions of proteins [42]. It is an unsupervised multivariate statistical approach frequently used for the dimensionality reduction of large datasets and project data that explains the maximum variability among the datasets [43]. The initial step in PCA construction is to prepare the MD simulation trajectory in cartesian coordinates, which involves removing the water molecules and periodicity that establish the necessary initial parameters. A crucial parameter is the construction of a covariance matrix with dimensions of 3N × 3N, where N represents the number of atoms present in the trajectory structure obtained from the MD simulation for the Wb TPx1 and lead-bound systems. The eigenvectors and eigenvalues that describe the motion and magnitude of the protein are obtained through the diagonalization of the covariance matrix [44]. Most often, the highest proportion of variability in the data can be attributed to the first, second, and third eigenvalues, which correspond to the principal components PC1, PC2, and PC3. Using the bio3D package in R software, the PCA analysis was carried out on the Wb TPx1 protein in two distinct states: the unbound-native and lead-bound Wb TPx1 [45]. 2.7. In vitro anti-filarial activity for the identified leads The lead molecules identified from in silico methods were validated through in vitro experiments using Setaria digitata , a filarial nematode that infects cattle and is closely related to the human-infecting filarial nematode, W. bancrofti [46, 47]. S. digitata is a well-known and extensively studied in vitro model organism popularly used for screening macrofilaricidal activity [48–50]. Motile adult worms were collected from the peritoneal cavity of a calf that had been newly slaughtered, and they were subsequently washed multiple times using normal physiological saline (0.95%). Without delay, the adult worms were expeditiously transferred to a container containing Dulbecco's modified eagle's medium (DMEM), which was supplemented with 10% heat-inactivated fetal bovine serum and 0.01% Pen-Strep, and then transported to the laboratory within one hour. The compound was purchased from ChemBridge Corporation, San Diego, CA. To evaluate the viability of S. digitata , the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) reduction assay was performed. The worms were exposed to different concentrations (0.625, 1.250, 2.500, 5.000, 10.000, 15.000, and 20.000 µM), for a duration of 24 hours [51]. The testing was conducted in DMEM with Pen-Strep (0.01%) and 10% heat-inactivated fetal bovine serum as a supplement [52, 53]. After 24 hours of exposure, each worm was incubated with MTT (0.25 mg mL-1) in phosphate-buffered saline (pH 7.4) for 30 minutes. Upon completion of the incubation, the worms were transferred to a microtiter plate containing 400 µL of spectroscopic-grade dimethyl sulphoxide (DMSO), and then gently shaken at room temperature for one hour. The absorbance of the formazan solution was measured at 492 nm using a multimode plate reader (Synergy H1, BioTek, USA). $$\varvec{P}\varvec{e}\varvec{r}\varvec{c}\varvec{e}\varvec{n}\varvec{t}\varvec{a}\varvec{g}\varvec{e} \varvec{o}\varvec{f} \varvec{i}\varvec{n}\varvec{h}\varvec{i}\varvec{b}\varvec{i}\varvec{t}\varvec{i}\varvec{o}\varvec{n}=100-\left[\frac{(\varvec{T}-\varvec{H})}{(\varvec{C}-\varvec{H})}\right]\times 100$$ The values for the absorbance of formazan produced by treated, control (untreated), and heat-killed worms are represented by T, C, and H, respectively. Adult females exposed to solvents but not with the test solution (leads) served as the positive control. The negative control (H), adult worms were subjected to heat-killing (at 56°C for 30 minutes) and subsequently exposed to MTT during incubation. The controls and test concentrations were carried out in triplicate and the plot was generated by plotting the percentage of inhibition against the concentration of the inhibitor [52–54]. The half-maximal inhibitory concentration (IC 50 ) values were calculated using GraphPad PRISM 8.0.1 through dose-response analysis, and different compounds were compared to the standard compound ivermectin. 3. Results and discussions 3.1. Homology modelling and validation of Wb TPx1 NCBI-BLASTP sequence similarity search carried out using RCSB-PDB showed the crystal structure of peroxiredoxin-1 (PDB ID: 4FH8) from Ancylostoma ceylanicum (AcePrx-1) as the best template with the sequence identity of 64.71%, query coverage of 82% and e-value of 1e- 89 . Thus, the three-dimensional structure of Wb TPx1 was modeled using the template (PDB ID: 4FH8) and the structural alignment showed an RMSD deviation of 0.68 Å. Examining the Ramachandran plot with respect to φ and ψ angles reveals that 75.3% of residues occupy the most favored regions, 22.2% of residues in the additional allowed region, 2.5% of residues in the generously allowed region, and none of the residues were present in the disallowed regions indicating the high quality of the modeled Wb TPx1 structure (Fig. 1 ). The Wb TPx1 possesses an amino acid of 228 residues that forms a compact and globular structure featuring the characteristic fold of the Prxs (peroxiredoxins) family that is organized around a twisted sheet composed of seven β-strands, encircled by five α-helices, particularly it forms thioredoxin fold-a unique structural motif consists of four-stranded β-sheet and three flanking α-helices [55]. The existence of conserved motif ‘‘GGLG’’ (Gly-Gly-Leu-Gly) and ‘‘YF’’ (Tyr-Phe) that are the invariant features of Prxs family, the sequence of Wb TPx1 also found to have conserved motif as like in other parasites [16]. The amino acid sequence of Wb TPx1 has two conserved cysteine residues (peroxidative cysteine) at positions 78 and 199 that are essential for the reduction and detoxification of hydrogen peroxides as well as aiding in head-to-tail dimer interface formation composed of residues 166 to 192 as remain conserved across Prxs family [17]. 3.2. Dynamic structure stability of Wb TPx1 during 200ns MD simulations The MD simulations were carried out for 200 ns at 310 K in the SPC water model to examine the dynamic stability of the modeled Wb TPx1. The processed trajectories (20000 structures) were used to study the dynamic structural changes in the Wb TPx1. After 20 ns, it was observed that the systems had achieved a stable equilibrium. The RMSD of Wb TPx1 was plotted, which showed a deviation between 0.38 nm to 0.49 nm with the average RMSD and standard deviation of 0.44 nm and 0.015 nm, respectively. A lower deviation in the RMSD value signifies dynamic stability and equilibrium in the modeled structure, making it suitable for further studies. The RMS fluctuation was calculated to assess and evaluate the flexibility of each amino acid residue in Wb TPx1, backbone residue-wise flexibility of the Wb TPx1 exhibits fluctuations between 0.04 nm to 0.51 nm with an average RMSF and SD of 0.15 nm and 0.09 nm, respectively. It was observed that the N-terminal loop region of Wb TPx1, starting from positions Met1 to Phe42, showed higher fluctuations. In contrast, the residue Ser23 exhibited the highest N-terminal fluctuation with the RMSF value of 0.46 nm. The loop-III that spans from the residues Met57 to Lys63, which connects the β-sheet III (β-III) with the α-helix I (α-I), exhibits a higher backbone fluctuation between 0.08 nm (Met57) to 0.12 nm (Lys61). The longer loop-V that starts from Thr116 to Pro129 showed fluctuation between 0.05 (Pro129) to 0.17 nm (Glu120). Similarly, the loop-VI (143–154 residues) that connect the α-III with β-V showed higher residual fluctuation that ranged from 0.10 nm to 0.23 nm; among them, the residue Glu148 had the maximum fluctuation. Furthermore, it was noted that the C-terminal region, ranging from residues 195 to 228 and encompassing loop-IX and α-V, showed higher fluctuations, mainly due to its inherent terminal flexibility. The RMSF results revealed that the elevated fluctuations observed in the residues align with the loop regions of both the N- and C-terminals of the Wb TPx1. The dynamic compactness of the Wb TPx1 was monitored based on changes in the radius of gyration (Rg), which was observed to fluctuate between 1.78 nm and 1.89 nm, averaging 1.82 nm. This observation indicates the structural compactness and stability of the intact architecture of Wb tpx1 throughout the simulation period. Further, the folding profile and the protein's exposure to solvent molecules were assessed over time by calculating the solvent-accessible surface area (SASA); the SASA value for Wb TPx1 was observed to be within 113–130 nm 2 with an average of 120 nm 2 (Fig. 2 ). These overall MD simulation results revealed that the modeled Wb TPx1 is stable with intact structural architecture during the timescale of 200 ns simulation. 3.3. Binding pocket prediction and structure-guided virtual screening of Wb TPx1 inhibitors Utilizing the SiteMap module from the Schrödinger suite, we predicted the binding site regions on the surface of Wb TPx1. The predicted site revealed a highest site score of 0.929 and a druggability score of 0.957 through diverse physical descriptors such as size, volume, cavity enclosure, solvent exposure degree, interaction tightness, and hydrogen bond acceptor and donor characteristics to predict the binding site of Wb TPx1. The predicted binding site is comprised of the following residues: Ser8, Leu27, Ala28, Ile30, Pro35, Ile165, Arg166, His167, Ser168, Leu169, Val170, Glu181, Arg184, Thr185, Ala188, Phe189, Val192, Glu193, Glu197, Val198, Cys199, Pro200, Ala201, Asn202 and Trp203 respectively. Interestingly, the predicted binding site lies in the reported region involved in the dimer interface (166 to 192) [56], and targeting the interface region was shown to be an effective strategy in the inhibition of the activity of the proteins and enzymes [57–59]. The grid for docking was created to cover all the binding site residues of Wb TPx1 with the grid center dimension of X = 53.05, Y = 47.62, and Z = 47.15 with the inner grid box of X = 10 Å, Y = 10 Å and Z = 10 Å and the outer grid dimensions of 32.41 X 32.41 X 32.41 Å 3 to perform molecular docking within the specified grid space boundaries. We utilized small molecule compounds from the In-house ChemBridge database to screen against Wb TPx1. Initially, the prefiltering process excludes molecules that violate Lipinski’s rule of five and contains reactive functional groups. After the filtering process, approximately 698,709 small molecules from the ChemBridge database were selected. The final dataset, comprising 2,086,614 structures, was meticulously prepared and subsequently screened against Wb TPx1 using a three-phase docking protocol (HTVS, SP, and XP). After screening through HTVS and SP docking, a total of 601 molecules were retrieved based on XP results, with docking scores ranging from − 7.84 to -5.46 kcal/mol. Based on the binding affinity towards Wb TPx1, the top 25 hit molecules were subjected to check toxicity profile prediction using the Mclue server[60]. The prediction revealed five non-toxic lead molecules (ChemBridge ID: 26319496, 58522350, 24425757, 38651207 & 29025530) and ADME calculations indicate that the molecules are in the acceptable range (Tables 1 & 2 ). The highest drug-likeliness attribute is represented by the #star descriptor value of zero across the top predicted non-toxic leads, signifying a higher probability of drug-like quality. All the top five leads exhibit favourable aqueous solubility, cell permeability, absorption, and zero violation in Lipinski’s rule of five, which clearly shows the identified leads have higher oral bio-availability. The extra Precision (XP) docking results (Fig. 3 ) for the five leads of Wb TPx1 are given in Table 3 where the glide score ranges from − 7.41 kcal/mol to -6.88 kcal/mol and the glide energy lies from − 48.73 kcal/mol to -36.22 kcal/mol respectively. The 2D interaction profiles of the leads with the Wb TPx1, were shown in Fig. 4 . The first lead, N-benzyl-N-(2-hydroxyethyl)-3-[5-(1H-indol-3-ylmethyl)-1,3,4-oxadiazol-2-yl] propenamide (ID-26319496) has established three hydrogen bonds with residues Ser168, Arg184 and Pro200 with the Glide score of -7.41 kcal/mol. Table 1 ADMET properties of the identified hits for Wb Tpx1 from ChemBridge collection S. No ChemBridge ID Molecular weight a QP logPo/w b QP logS c QP logHERG d Rule of five e #stars f Molecule Toxicity 1. 26319496 404.468 3.147 -4.944 -5.691 0 0 Non Toxic 2. 58522350 359.467 0.157 -1.181 -3.493 0 0 Non Toxic 3. 24425757 391.426 3.434 -5.635 -4.035 0 0 Non Toxic 4. 38651207 397.491 3.169 -4.567 -7.402 0 0 Non Toxic 5. 29025530 336.448 1.123 -2.091 -1.624 0 0 Non Toxic a Molecular weight of the molecule (130.0 to 725.0) b Predicted octanol/water partition coefficient (− 2.0 to 6.5) c Predicted aqueous solubility, logs. S in moldm-3 is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid (− 6.5 to 0.5) d Predicted IC50 value for blockage of HERG K + channels (concern below − 5) e Number of violation of Lipinski’s rule of five (0–4) f #stars Number of property or descriptor values that fall outside the 95% range of similar values for known drugs. (0–5) Table 2 ADMET properties of the identified hits for Wb Tpx1 from ChemBridge collection S. No ChemBridge ID CIQPlogS a QPPCaco b QPlogBB c QPlogKhsa d PHOA e QPlogKp f RuleOf Three g 1. 26319496 -5.086 203.136 -1.824 0.004 86.678 -2.128 0 2. 58522350 -0.841 13.776 -1.453 -0.756 48.250 -5.674 1 3. 24425757 -5.876 9.129 -2.326 0.395 64.239 -4.819 1 4. 38651207 -3.961 372.840 -0.212 0.178 91.524 -3.448 0 5. 29025530 -1.644 301.103 -0.826 -0.700 77.882 -2.740 0 CIQPlogS: Conformation-independent predicted aqueous solubility, log S. S in mol dm–3 is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid. (–6.5–0.5) QPPCaco: Predicted apparent Caco-2 cell permeability in nm/sec. (> 500 great ;<25 poor) QPlogBB: Predicted brain/blood partition coefficient. (–3.0–1.2). QPlogKhsa: Prediction of binding to human serum albumin. (–1.5–1.5) PHOA: PercentHumanOralAbsorption Predicted human oral absorption on 0 to 100% scale. (> 80% is high; <25% is poor) QPlogKp: Predicted skin permeability (–8.0 – − 1.0) RuleOfThree: Number of violations of Jorgensen’s rule of three. (0–3) The lead also formed three pi-pi stacking interactions with the His167 moiety. The second lead, 1-(3-{[(S*)-(cis-3-hydroxycyclo butyl) (phenyl)methyl]amino}-3-oxopropyl) piperidine-4-carboxamid (ID: 58522350) forms four hydrogen bonds with residues Ala28, Ser168, Arg184 and Trp203 with the Glide score of -7.37 kcal/mol whereas the thirds lead, 4-[(1-{[3-(4-hydroxyphenyl)-1H-pyrazol-5-yl]carbonyl}pyrrolidin-3-yl)methyl]benzoic acid (ID: 24425757) exhibit three hydrogen bonds and one salt bridge with Leu27, His167, Trp203 and Arg184 having the Glide score of -7.13 kcal/mol. Table 3 Extra precision (XP) docking results of best five hits for Wb Tpx1 obtained from virtual screening along with its binding free energies. S. No ChemBridge ID Glide score (kcal/ mol) Glide energy (kcal/ mol) ΔG Bind (kcal/mol) Wb Tpx1 Interaction Bond Distance (Å) 1 26319496 -7.410 -48.739 -47.15 Ser 168 Arg 184 Pro 200 His 167 1.90- HB 2.00- HB 1.80- HB pi-pi stacking 2 58522350 -7.372 -45.555 -63.06 Ala 28 Ser 168 Arg 184 Trp 203 1.90- HB 1.90- HB 1.80- HB 2.00 - HB 3 24425757 -7.134 -43.417 -50.15 Leu 27 His 167 Trp 203 Arg 184 1.90- HB 2.10- HB 2.40- HB 1.9-SB 4 38651207 -6.932 -47.132 -56.78 Leu 27 Arg 166 Ser 168 His 167 Phe 189 2.00- HB 2.10- HB 1.90-HB pi-pi stacking pi-pi stacking 5 29025530 -6.887 -36.292 -48.81 Cys 199 Trp 203 His 167 2.00- HB 2.60- HB pi-pi stacking The fourth lead, 1-{[5-(hydroxymethyl)-2-furyl]methyl}-N-[4-(1,3 -thiazol-4-yl)phenyl]-3-piperidine carboxamide (ID: 38651207) showed three hydrogen bonds and two pi-pi stacking with residues Leu27, Arg166, Ser168, His167, and Phe189 with the Glide score of -6.93 kcal/mol whereas the fifth lead, N-[(cis-3-hydroxycyclobutyl)(2-thienyl)methyl]-2-(2-oxoazepan-1-yl)acetamide (ID: 29025530) two hydrogen bonds and one pi-pi stacking with the residues Cys199, Trp203 and His167 having the Glide score of -6.88 kcal/mol, respectively. The distance of the hydrogen bond formed between the lead molecule and the protein is also one factor that decides the strength and stability of the protein-ligand complex formation. The H-bond is considered stronger if the distance is less than 2.5Å, and it is considered moderate if it is between 2.5 to 3.5Å [61, 62]. All five leads exhibited a strong H-bond with a distance lesser than 2.5 Å. Besides the bonded interactions, the stability and effective binding of potential leads are influenced by non-bonded contributions, including hydrophobic, charged, and polar groups from Wb TPx1. The Prime MMGBSA method uses the molecular mechanics and continuum solvation models to calculate the binding free energy analysis between the docked complex structure of the selected five lead molecules of Wb TPx1. It showed the ΔG bind values between − 47.15 kcal/mol to -63.06 kcal/mol, which lies in the order of lead ID: 58522350 > 38651207 > 24425757 > 26319496 > 29025530. Further, the cross-docking study was performed with human TPx1 (PDB ID: 5UCX) to evaluate the drug specificity. Hs and Wb TPx1 show an identity of 56.19% with a structural RMSD value of 0.8 Å. Both organism exhibits similar thioredoxin structural folds, with five α-helices and seven β-strands which are the invariant features of members peroxiredoxins family. We have carried out cross-docking with the top five identified potent leads at the same dimer interface area of Hs TPx1 to confirm the selectivity of the identified leads towards the Wb TPx1. The extra Precision (XP) docking results showed the Glide score of -3.14, -3.33, -4.51, -1.75 and − 3.14 kcal/mol for the leads IDs: 26319496, 58522350, 24425757, 38651207, and 29025530, respectively. Additionally, we observed differences in the volume of the binding site cavity and the lack of bonded interaction between the leads and Hs Tpx1, which may account for the lower docking scores and affinity, and these results revealed that the identified leads have a higher affinity towards Wb . Thus, studies were carried out for these selected lead molecules to analyze binding stability and complex integrity. 3.5. Density functional theory (DFT) studies on the potent leads of Wb TPx1 In quantum calculations, the HOMO and LUMO play a crucial role in determining the reactivity of the leads. The HOMO signifies an inclination to donate electrons, whereas the LUMO accepts electrons, and its energy aligns with the electron affinity. The energy gap (E gap is utilized to assess the chemical reactivity of the leads. The calculated energies of the LUMO orbital for five leads lie between − 0.02558 to -0.06691eV, whereas the HOMO orbital energies lie between − 0.20061 to -0.24194 eV. Additionally, the five leads energy gaps (E gap ) ranged from 0.12 to 0.29 eV (Table 4 ). The HOMO and LUMO energy of the five leads, along with the molecular orbitals diagram, are shown in Fig. 5 . The energy distribution of the lead molecules is shown by the colors green and red, which indicate the positive and negative charge groups. Based on E gap value, the chemically reactive of the leads lies in the order of ChemBridge ID: (29025530 (0.20966 eV) > 58522350 (0.20094 eV) > 26319496 (0.18668 eV) > 38651207 (0.16548 eV) > 24425757 (0.15936 eV), respectively. Table 4 Chemical reactive properties -HOMO, LUMO and energy gap for the top five non-toxic leads of Wb Tpx1. S.No ChemBridge ID HOMO (eV) LUMO (eV) Energy gap (eV) 1. 26319496 -0.21789 -0.03121 0.18668 2. 58522350 -0.22652 -0.02558 0.20094 3. 24425757 -0.22629 -0.06691 0.15936 4. 38651207 0.20061 -0.03513 0.16548 5. 29025530 -0.24194 -0.03228 0.20966 3.6. MD Simulation: Dynamic Stability of Lead-Bound Wb Tpx1 complexes: Individual MD simulations were carried out for the five leads bound to Wb Tpx1 to study the dynamic stability. All the five Wb Tpx1 complex systems ( Wb Tpx1 + ChemBridge ID: 26319496, 58522350, 24425757, 38651207 & 29025530) attain equilibrium after 5 ns. The backbone RMSD deviation of complex 1 ( Wb Tpx1 + ChemBridge ID: 26319496) ranged from 0.30 nm to 0.44 nm, with an average RMSD and SD value of 0.37 nm and 0.02 nm, whereas the complex 2 ( Wb Tpx1 + ChemBridge ID: 58522350) and complex 3 ( Wb Tpx1 + ChemBridge ID: 24425757) exhibited the RMSD deviation between 0.31 nm to 0.47 nm and 0.30 to 0.46 nm with the average of 0.38 and 0.39 nm and the standard deviation of 0.02 and 0.01 nm. Complex 4 ( Wb Tpx1 + ChemBridge ID: 38651207) and 5 ( Wb Tpx1 + ChemBridge ID: 29025530) showed similar trends in the RMSD values, that fluctuated between 0.33 to 0.52 nm and 0.41 to 0.55 nm with the average of 0.42 and 0.45 nm with the standard deviation of 0.03 and 0.01 nm, respectively. To investigate the level fluctuations of residues upon lead binding, the backbone RMSF of Wb Tpx1 was examined. As observed in the native Wb TPx1 all the five lead bound complexes exhibited higher reside-wise RMSF fluctuations in both the terminal regions due to the terminal flexibility. The N-terminal loop that spans from Met1 to Phe42 showed a higher RMSF value and similarly, the C- C-terminal from His195 to Thr228 has the highest RMSF value due to terminal flexibility. The loop-V spanning from the residues Thr116 to Pro129 and loop-VI from Glu143 to Arg154 was observed to exhibit higher RMSF value in all the five complex systems of Wb TPx1. The RMSF of the complex1 fluctuation between 0.05 to 0.69 nm with an average of 0.17 nm and the residue Ser119 of loop-V had the maximum backbone fluctuation of 0.29 nm whereas in loop-VI the residue His147 exhibited the highest fluctuation of 0.20 nm. In complexes 2 and 3, the average RMSF fluctuation was found to be 0.16 and 0.15 nm, exhibiting a similar range between 0.05 to 0.71 nm. In both complexes, the loop-V residue Ser119 displays higher fluctuations of 0.22 and 0.15 nm and the residue Glu148 of loop-VI shows larger backbone fluctuations of 0.17 and 0.21 nm, respectively. The average RMSF of complex 4 and 5 are 0.16 and 0.17 nm with a range from 0.05 to 0.49 nm and 0.06 to 0.80 nm respectively, where the loop-V residue Glu120 and Ser119 account for higher fluctuation of 0.17 and 0.22 nm while the loop-VI the residue Glu148 was observed to have higher fluctuation of 0.22 and 0.17 nm among other counterparts residues of loop-VI. The radius of gyrations (Rg) measures the compactness of the lead-bound state of Wb TPx1, the five Wb Tpx1 complex systems exhibit intact Rg with the average value of 1.95, 1.93, 1.91, 1.93 and 1.92 nm with the SD of 0.02, 0.01, 0.01, 0.02 and 0.02 respectively. Further, SASA measured the surface area of the enzyme-leads complex that interacts with the surrounding water molecules was determined. The results showed that the average SASA for Wb TPx1-lead complexes was 134.65, 135.07, 137.54, 131.70 and 132.69 nm 2 with the SD of 2.74, 3.17, 2.51, 3.16 and 2.78 nm 2 (Fig. 6 ). The complex MD simulation based on the profiles of RMSD, RMSF, Rg and SASA revealed that throughout the simulation period, all the five leads (ChemBridge ID: 26319496, 58522350, 24425757, 38651207& 29025530) bind intact inside the binding site of Wb TPx1. The Coulomb and L-J energies from the simulated trajectories were computed to examine and quantify the non-bonded energy contribution between the Wb TPx1 and the lead interactions. The energy profiles in Table 5 depict that the L-J potential energy contribution was more dominant than the coulomb energy contribution that accounts for the stability of the five complexes. Based on the calculation of binding energy, it is evident that non-bonded energies were also crucial in maintaining the intact and stable binding of leads within the Wb TPx1 throughout complex dynamics. Supplementary Fig. 1 showcases ensembles of Wb TPx1 complexes with lead molecules, captured every 10 ns throughout the simulation. It's evident from these snapshots that the leads remained securely bound within the binding site of Wb TPx1 throughout the entire simulation period. Table 5 Energy contributions (accounting Coulomb and L-J energies) of the non-toxic leads with Wb Tpx1. Energy ChemBridge ID 26319496 58522350 24425757 38651207 29025530 Coulomb Energy (KJ/mol) -62.30 ± 5.90 -44.22 ± 8.50 -58.93 ± 4.40 -37.65 ± 6.30 -38.31 ± 3.50 L-J Energy (KJ/mol) -136.00 ± 4.10 -98.27 ± 8.10 -146.28 ± 3.0 -119.52 ± 7.30 -95.48 ± 4.70 3.7. Comparison of native and leads bound dynamics of Wb TPx1 - PCA and FEL analysis: PCA projects the molecular trajectories generated in the MD simulation onto a two-dimensional plane, with the first three principal components (PC1, PC2, and PC3) elucidating the maximum variability in the data (Table 6 ). A comparison between the leads bound and native Wb TPx1 revealed that the native Wb TPx1 PC1 magnitude was smaller, whereas the magnitude of PC2 of the native is found to be higher than the leads bound Wb TPx1. Table 6 The first three principal components (PCA) for the native Wb Tpx1 and the lead bound Wb Tpx1. Principal component Native Wb Tpx1 ChemBridge ID 26319496 58522350 24425757 38651207 29025530 PC1 25.31% 29.05% 34.87% 30.61% 50.58% 43.89% PC2 24.78% 20.59% 15.15% 17.86% 11.35% 16.96% PC3 19.80% 16.13% 11.01% 8.15% 7.88% 8.57% Although the initial two principle components were adequate to explain the fluctuations in conformational changes and motion throughout the MD simulation, we also considered the variability explained by the PC3, which is clearly shown in the supplementary Fig. 2–7. Further, the correlated motions at the residue level were also determined by analyzing the cross-correlation map for the backbone atoms of both native Wb TPx1 and Wb TPx1 bound ( Wb Tpx1 + ChemBridge ID: 26319496, 58522350, 24425757, 38651207 & 29025530). The cyan regions signified the enzyme peak positive correlated backbone motion, while the magenta region depicted the highest negative or anticorrelated backbone motion. The cross-correlation map revealed that both the native and leads bound Wb TPx1 exhibit an almost similar trend in the correlated motion among the backbone atoms of the residues. We observed a minute change in the motion of the residue’s backbone located in N- C- terminals and in the dimer interface region where the leads bind with the WbTPx1, as shown in Fig. 7 . The porcupine plotting was plotted for the first principal component (PC1), which accounts for the greatest variability of the native and lead-bound forms. Observations indicated that the Wb TPx1 bound to leads exhibits reduced backbone atom movement compared to the native Wb TPx1 (Fig. 8 ). To explore the conformational dynamics of native and leads-bound Wb TPx1, a free energy landscape (FEL) was calculated. This enabled the visualization of transition states, including local and global minima, sampled throughout the entire time scale period of the MD simulation. The Gibbs free energy varies from 0 to 11.50 KJ/mol for the native Wb TPx1 while for the complexes- Wb TPx1 + ChemBridge ID: 26319496, 58522350, 24425757, 38651207 & 29025530, it ranges from 0 to 11.50,11.50,11.60, 13.40, and 11.60 KJ/mol, respectively and FEL is represented as contour diagram is shown in Fig. 9 . The dark blue region signifies energetic minima and conformationally stable states, while the red and yellow regions indicate higher energy states linked to unfavorable conformational states. The native Wb TPx1 FEL is confined by a sharp and narrow valley single basin with a single global minimum energy conformation. The FEL analysis of complexes 1 and 2 exhibit a similar trend in energy state with a broad basin that converges into numerous global energy states from multiple local minimum states. Complexs 3, 4, and 5 adopted a similar energy spread to the native, containing multiple local energy states but confined into a single sharp global minima energy state. The findings from PCA, cross-correlation map, porcupine plot, and FEL provided clear insights into the conformational changes occurring during the simulation period upon the binding of leads in the dimer interface binding site of Wb TPx1. 3.8. Viability Evaluation of Filarial Worms through MTT-Formazan Colorimetric Assay The MTT assay was performed at concentrations spanning from 0.625 to 20 µM over a 24-hour duration. At the maximum dosage of 20 mM concentration, all three leads exhibited inhibition efficacy greater than 92% (94.50 ± 1.56, 93.83 ± 0.96 and 92.78 ± 0.91). Table 7 In vitro anti-filarial activity of identified leads, ChemBridge ID-58522350, 24425757, 26319496 and the standard drug Ivermectin against adult S. digitata filarial worm S. No Concentration of Inhibitor (µM) Percentage of Inhibition 58522350 Percentage of Inhibition 24425757 Percentage of Inhibition 26319496 Percentage of Inhibition Ivermectin 1. 20.000 94.50 ± 1.56 93.83 ± 0.96 92.78 ± 0.91 83.08 ± 68 2. 15.000 87.70 ± 0.95 83.72 ± 1.04 84.64 ± 0.74 77.82 ± 0.97 3. 10.000 77.73 ± 2.10 75.37 ± 0.80 77.20 ± 1.19 71.94 ± 1.07 4. 5.000 66.07 ± 2.29 70.75 ± 0.90 66.24 ± 0.74 62.52 ± 2.01 5. 2.500 59.76 ± 1.44 60.75 ± 1.20 54.33 ± 0.85 53.25 ± 2.18 6. 1.250 45.38 ± 1.26 47.32 ± 1.27 46.16 ± 1.46 42.96 ± 2.31 7. 0.625 32.70 ± 0.74 38.79 ± 0.85 34.60 ± 1.27 27.40 ± 3.13 In contrast, at lower concentrations (0.625 µM), it showed the inhibition percentages of 32.70 ± 0.74%, 38.79 ± 0.85% and 34.60 ± 1.27%, respectively, for the leads- ChemBridge ID: 58522350, 24425757 and 26319496. Interestingly, all three leads had greater inhibition percentages at concentrations between 0.625 and 20 µM, which is higher than the standard anti-filarial drug Ivermectin, as shown in Table 7 . The IC 50 values were calculated by the Michalis Menten equation and were compared with the standard drug ivermectin, IC 50 value of leads of Wb TPx1 58522350, 24425757 and 26319496 lies in the range of 1.069 ± 0.204 µM, 1.373 ± 0.259 µM and 1.429 ± 0.246 µM, respectively. Interestingly, we noted that the IC 50 value of two leads (24425757 & 26319496) was lesser than the IC50 value of Ivermectin with 1.374 ± 0.152 µM, shown in Fig. 10 . The MTT reduction assay results revealed better IC 50 and greater inhibition than Ivermectin, thus the leads identified based on the in silico method possess the potential to eradicate adult worms. 4. Conclusion Our current research is primarily on employing structure-based drug design to develop potent inhibitors targeting the dimer interface region of Wb TPx1. The Wb TPx1 exhibited heightened binding affinity with the top five predicted non-toxic leads obtained from the ChemBridge small molecule database (ChemBridge ID-26319496, 58522350, 24425757, 38651207 & 29025530). The DFT analysis identified the reactive regions within the lead molecules, providing valuable insights into the chemical moieties to be targeted for lead enrichment and improved activity. Additionally, MD simulations of these protein-lead complexes revealed the stable and intact binding of the five leads within the binding site of Wb TPx1. Interestingly, these five leads exhibited greater binding specificity towards Wb than Hs . The in vitro validation revealed that the two identified leads (ID:24425757 & 26319496) exhibited a higher percentage of inhibition and a superior IC 50 compared to the standard drug Ivermectin. The identified leads serve as a basis for the creation of novel chemical scaffolds and fragments with potent drug-like properties that may aid in treating LF infection. Declarations Conflict of interest The authors declare no competing interests. Author Contribution Muthusamy Sureshan: data curation, investigation, visualization, validation, writing - original draft.Sundarraj Rajamanikandan: data curation, writing - review and editing.Kadhirvel Saraboji: contributed to conceptualization, writing-review and editing, resources, supervision, and funding acquisition. The final version of the manuscript submitted was approved by all authors. Acknowledgement Authors are grateful to the management of SASTRA Deemed University for providing all necessary computational facilities. KS thankfully acknowledges DST-SERB for providing financial support in the form of research projects (No: EMR/2017/002841 and No: CVD/2020/000604) to conduct the study. Authors like to thank Dr. Venkatasubramanian U, Associate Professor and Mr. Adithyan J, Research Scholar, School of Chemical and Biotechnology, SASTRA Deemed University for their help and suggestion in vitro experiments. Authors also thank Dr. R. Velusamy, M.V.Sc., Ph.D., Assistant Professor and Head, Department of Veterinary Parasitology, Veterinary College and Research Institute Orathanadu, Thanjavur, Tamil Nadu, India for authenticating the filarial worm S. digitata . MS is thankful for the funding as Senior Research Fellow grant from ICMR-SRF (No. Fellowship/96/2022-ECD-II, IRIS ID No. 2021–11346/F96). References Simonsen PE, Mwakitalu ME (2013) Urban lymphatic filariasis. Parasitol Res 112:35–44. https://doi.org/10.1007/s00436-012-3226-x ROUTH HB, BHOWMIK KR (1993) History of Elephantiasis. Int J Dermatol 32:913–916. https://doi.org/10.1111/j.1365-4362.1993.tb01418.x Michael E, Bundy DAP, Grenfell BT (1996) Re-assessing the global prevalence and distribution of lymphatic filariasis. Parasitology 112:409–428. https://doi.org/10.1017/s0031182000066646 Ramaiah KD, Das PK, Michael E, Guyatt HL (2000) The economic burden of lymphatic filariasis in India. Parasitol. Today 16:251–253 Bizhani N, Hashemi Hafshejani S, Mohammadi N, et al Lymphatic filariasis in Asia: a systematic review and meta-analysis. https://doi.org/10.1007/s00436-020-06991-y Famakinde DO (2018) Mosquitoes and the Lymphatic Filarial Parasites: Research Trends and Budding Roadmaps to Future Disease Eradication. Trop Med Infect Dis 3:. https://doi.org/10.3390/tropicalmed3010004 Freedman DO (1998) Immune dynamics in the pathogenesis of human lymphatic filariasis. Parasitol Today 14:229–234. https://doi.org/10.1016/S0169-4758(98)01244-7 Paily KP, Hoti SL, Das PK (2009) A review of the complexity of biology of lymphatic filarial parasites. J Parasit Dis Off Organ Indian Soc Parasitol 33:3. https://doi.org/10.1007/S12639-009-0005-4 Rao UR, Chandrashekar R, Subrahmanyam D (1987) Effect of ivermectin on serum dependent cellular interactions to Dipetalonema viteae microfilariae. Trop Med Parasitol Off organ Dtsch Tropenmedizinische Gesellschaft Dtsch Gesellschaft fur Tech Zusammenarbeit 38:123–127 Critchley J, Addiss D, Gamble C, et al (2005) Albendazole for lymphatic filariasis. Cochrane database Syst Rev CD003753. https://doi.org/10.1002/14651858.CD003753.pub3 Cobo F (2016) Determinants of parasite drug resistance in human lymphatic filariasis. Rev Esp Quimioter Publ Of la Soc Esp Quimioter 29:288–295 Weiss SJ, Test ST, Eckmann CM, et al (1986) Brominating oxidants generated by human eosinophils. Science (80- ) 234:200–203. https://doi.org/10.1126/science.3018933 Cross AR, Jones OTG (1991) Enzymic mechanisms of superoxide production. BBA - Bioenerg. 1057:281–298 Callahan HL, Crouch RK, James ER (1988) Helminth anti-oxidant enzymes: a protective mechanism against host oxidants? Parasitol Today 4:218–225. https://doi.org/10.1016/0169-4758(88)90162-7 ME S, VP S, GR T, K G (1998) Resistance of filarial nematode parasites to oxidative stress. Int J Parasitol 28:1315–1332. https://doi.org/10.1016/S0020-7519(98)00107-6 Gretes MC, Poole LB, Karplus PA (2012) Peroxiredoxins in parasites. Antioxid Redox Signal 17:608–633. https://doi.org/10.1089/ars.2011.4404 Wood ZA, Schröder E, Robin Harris J, Poole LB (2003) Structure, mechanism and regulation of peroxiredoxins. Trends Biochem Sci 28:32–40. https://doi.org/10.1016/s0968-0004(02)00003-8 Flohé L (2010) Changing paradigms in thiology from antioxidant defense toward redox regulation. Methods Enzymol 473:1–39. https://doi.org/10.1016/S0076-6879(10)73001-9 Knoops B, Loumaye E, Van Der Eecken V (2007) Evolution of the Peroxiredoxins BT - Peroxiredoxin Systems: Structures and Functions. In: Flohé L, Harris JR (eds). Springer Netherlands, Dordrecht, pp 27–40 Hakimi H, Asada M, Angeles JMM, et al (2012) Cloning and characterization of Plasmodium vivax thioredoxin peroxidase-1. Parasitol Res 111:525–529. https://doi.org/10.1007/s00436-012-2864-3 Dayer R, Fischer BB, Eggen RIL, Lemaire SD (2008) The peroxiredoxin and glutathione peroxidase families in Chlamydomonas reinhardtii. Genetics 179:41–57. https://doi.org/10.1534/genetics.107.086041 Selkirk ME, Smith VP, Thomas GR, Gounaris K (1998) Resistance of filarial nematode parasites to oxidative stress. Int J Parasitol 28:1315–1332. https://doi.org/10.1016/S0020-7519(98)00107-6 Prime (2020) Schrödinger Release 2020-4, Schrödinger, LLC, New York, NY Abraham MJ, Murtola T, Schulz R, et al (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/J.SOFTX.2015.06.001 Oostenbrink C, Villa A, Mark AE, Gunsteren WF Van (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25:1656–1676. https://doi.org/10.1002/JCC.20090 Berendsen HJC, Postma JPM, Gunsteren WF van, Hermans J (1981) Interaction Models for Water in Relation to Protein Hydration. 331–342. https://doi.org/10.1007/978-94-015-7658-1_21 Vrahatis MN, Androulakis GS, Lambrinos JN, Magoulas GD (2000) A class of gradient unconstrained minimization algorithms with adaptive stepsize. J Comput Appl Math 114:367–386. https://doi.org/10.1016/S0377-0427(99)00276-9 Berendsen HJC, Postma JPM, Gunsteren WF van, et al (1998) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684. https://doi.org/10.1063/1.448118 Parrinello M, Rahman A (1998) Polymorphic transitions in single crystals: A new molecular dynamics method. J Appl Phys 52:7182. https://doi.org/10.1063/1.328693 Hess B (2008) P-LINCS: A parallel linear constraint solver for molecular simulation. J Chem Theory Comput 4:116–122. https://doi.org/10.1021/ct700200b Miyamoto S, Kollman PA (1992) Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. J Comput Chem 13:952–962. https://doi.org/10.1002/JCC.540130805 Darden T, York D, Pedersen L (1998) Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. J Chem Phys 98:10089. https://doi.org/10.1063/1.464397 W H, A D, K S (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38. https://doi.org/10.1016/0263-7855(96)00018-5 LigPrep (2020) Schrödinger Release 2020-4, Schrödinger, LLC, New York, NY Harder E, Damm W, Maple J, et al (2015) OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins. J Chem Theory Comput 12:281–296. https://doi.org/10.1021/ACS.JCTC.5B00864 Schrödinger Release 2020-4: (2020) SiteMap, Schrödinger, LLC, New York, NY Friesner RA, Banks JL, Murphy RB, et al (2004) Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J Med Chem 47:1739–1749. https://doi.org/10.1021/jm0306430 Frisch MJ, Trucks GW, Schlegel HB, et al (2016) G16_D09. Gaussian 16, Revision D.09, Gaussian, Inc., Wallin Chang-Guo Zhan, Jeffrey A. Nichols † and, Dixon* DA (2003) Ionization Potential, Electron Affinity, Electronegativity, Hardness, and Electron Excitation Energy: Molecular Properties from Density Functional Theory Orbital Energies. J Phys Chem A 107:4184–4195. https://doi.org/10.1021/JP0225774 Schüttelkopf AW, van Aalten DMF (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D Biol Crystallogr 60:1355–1363. https://doi.org/10.1107/S0907444904011679 David CC, Jacobs DJ (2014) Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins. Methods Mol Biol 1084:193. https://doi.org/10.1007/978-1-62703-658-0_11 S H, BL de G (2008) Normal modes and essential dynamics. Methods Mol Biol 443:89–106. https://doi.org/10.1007/978-1-59745-177-2_5 A W, KN K (2013) Principal component and clustering analysis on molecular dynamics data of the ribosomal L11·23S subdomain. J Mol Model 19:539–549. https://doi.org/10.1007/S00894-012-1563-4 M M, L K (2021) An atomistic investigation on the interaction of distamycin A and its derivative with the telomeric G-Quadruplex as anticancer agents by molecular dynamics simulation. Arch Biochem Biophys 701:. https://doi.org/10.1016/J.ABB.2021.108797 Grant BJ, Rodrigues APC, ElSawy KM, et al (2006) Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics 22:2695–2696. https://doi.org/10.1093/bioinformatics/btl461 Senanayake KS, Söderberg J, Põlajev A, et al (2020) The Genome of Setaria digitata: A Cattle Nematode Closely Related to Human Filarial Parasites. Genome Biol Evol 12:3971. https://doi.org/10.1093/GBE/EVAA017 Perumal ANI, Gunawardene YINS, Dassanayake RS (2016) Setaria digitata in advancing our knowledge of human lymphatic filariasis. J Helminthol 90:129–138. https://doi.org/10.1017/S0022149X15000309 Mathew N, Karunan T, Srinivasan L, Muthuswamy K (2010) Synthesis and screening of substituted 1,4-naphthoquinones (NPQs) as antifilarial agents. Drug Dev Res 71:188–196. https://doi.org/10.1002/DDR.20357 Nisha M, Kalyanasundaram M, Paily KP, et al In vitro screening of medicinal plant extracts for macrofilaricidal activity. https://doi.org/10.1007/s00436-006-0294-9 Senathilake KS, Karunanayake EH, Samarakoon SR, et al (2017) Oleanolic acid from antifilarial triterpene saponins of Dipterocarpus zeylanicus induces oxidative stress and apoptosis in filarial parasite Setaria digitata in vitro. Exp Parasitol 177:13–21. https://doi.org/10.1016/J.EXPPARA.2017.03.007 Mathew N, Misra-Bhattacharya S, Perumal V, Muthuswamy K (2008) Antifilarial lead molecules isolated from Trachyspermum ammi. Molecules 13:2156–2168. https://doi.org/10.3390/MOLECULES13092156 Sureshan M, Prabhu D, Rajamanikandan S, Saraboji K (2023) Discovery of potent inhibitors targeting Glutathione S-transferase of Wuchereria bancrofti: a step toward the development of effective anti-filariasis drugs. Mol Divers 1:1–21. https://doi.org/10.1007/S11030-023-10617-7/FIGURES/11 Tompa DR, Muthusamy S, Srikanth S, Kadhirvel S (2020) Molecular dynamics of far positioned surface mutations of Cu/Zn SOD1 promotes altered structural stability and metal-binding site: Structural clues to the pathogenesis of amyotrophic lateral sclerosis. J Mol Graph Model 100:. https://doi.org/10.1016/J.JMGM.2020.107678 AM L, CG G, SM L, AN P (2019) Investigation of Molecular Details of Keap1-Nrf2 Inhibitors Using Molecular Dynamics and Umbrella Sampling Techniques. Molecules 24:. https://doi.org/10.3390/MOLECULES24224085 Choi J, Choi S, Choi J, et al (2003) Crystal structure of Escherichia coli thiol peroxidase in the oxidized state: insights into intramolecular disulfide formation and substrate binding in atypical 2-Cys peroxiredoxins. J Biol Chem 278:49478–49486. https://doi.org/10.1074/jbc.M309015200 Nguyen JB, Pool CD, Wong CYB, et al (2013) Peroxiredoxin-1 from the human hookworm Ancylostoma ceylanicum forms a stable oxidized decamer and is covalently inhibited by conoidin A. Chem Biol 20:991–1001. https://doi.org/10.1016/j.chembiol.2013.06.011 Goyal B, Goyal D (2020) Targeting the Dimerization of the Main Protease of Coronaviruses: A Potential Broad-Spectrum Therapeutic Strategy. ACS Comb Sci 22:297–305. https://doi.org/10.1021/acscombsci.0c00058 Gunderwala AY, Nimbvikar AA, Cope NJ, et al (2019) Development of Allosteric BRAF Peptide Inhibitors Targeting the Dimer Interface of BRAF. ACS Chem Biol 14:1471–1480. https://doi.org/10.1021/acschembio.9b00191 Cardinale D, Guaitoli G, Tondi D, et al (2011) Protein-protein interface-binding peptides inhibit the cancer therapy target human thymidylate synthase. Proc Natl Acad Sci U S A 108:E542-9. https://doi.org/10.1073/pnas.1104829108 Kiss R, Sandor M, Szalai FA (2012) http://Mcule.com: a public web service for drug discovery. J Cheminform 4:P17. https://doi.org/10.1186/1758-2946-4-S1-P17 T S, T V, D P, et al (2018) Insecticide-resistance mechanism of Plutella xylostella (L.) associated with amino acid substitutions in acetylcholinesterase-1: A molecular docking and molecular dynamics investigation. Comput Biol Chem 77:240–250. https://doi.org/10.1016/J.COMPBIOLCHEM.2018.09.004 Sureshan M, Prabhu D, Kadhirvel S (2022) Computational identification and experimental validation of anti-filarial lead molecules targeting metal binding/substrate channel residues of Cu/Zn SOD1 from Wuchereria bancrofti. J Biomol Struct Dyn 1–14. https://doi.org/10.1080/07391102.2022.2136245 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Feb, 2024 Editor assigned by journal 26 Feb, 2024 Submission checks completed at journal 26 Feb, 2024 First submitted to journal 23 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3982867","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275078490,"identity":"7e8f786b-a811-407c-b2a4-a760634f2b4a","order_by":0,"name":"Sureshan Muthusamy","email":"","orcid":"","institution":"SASTRA Deemed University","correspondingAuthor":false,"prefix":"","firstName":"Sureshan","middleName":"","lastName":"Muthusamy","suffix":""},{"id":275078491,"identity":"5d0a4670-49cd-47fb-8b2b-130418c34a61","order_by":1,"name":"Rajamanikandan Sundarraj","email":"","orcid":"","institution":"Karpagam Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Rajamanikandan","middleName":"","lastName":"Sundarraj","suffix":""},{"id":275078492,"identity":"02274b55-acd0-4480-ad2e-1854239b0567","order_by":2,"name":"Saraboji Kadhirvel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYJACZgYGNh4J9gYg08CCFC08B0BaJIjWwsAgIZEApggrl28/+/BzQQWfjOTM51c3/CiQYOBv707Aq8XgTLqx9IwzbDzS0jllN3uADpM4c3YDfi0MaQzSvG1sPHLSOWk3eIBaDCRy8WuR73/G/BusRfJM2s0/xGhhuJHGBrZFWoL92G2ibDG48YzNGuQXyZ4cttsyBhI8BP0i35/GfLug4pi9xPHjz26++WMjx9/eS8BhEHAMiHkMQCweYpSDQA0Qsz8gVvUoGAWjYBSMMAAAN9U+rLVRsDMAAAAASUVORK5CYII=","orcid":"","institution":"Central University of Punjab","correspondingAuthor":true,"prefix":"","firstName":"Saraboji","middleName":"","lastName":"Kadhirvel","suffix":""}],"badges":[],"createdAt":"2024-02-23 19:08:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3982867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3982867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51742680,"identity":"463cf854-ce0e-42ba-90f1-2ce08b51368a","added_by":"auto","created_at":"2024-02-28 09:24:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":243269,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Structural alignment between the target (\u003cem\u003eWb\u003c/em\u003eTPx1) and the template (\u003cem\u003eAncylostoma ceylanicum, \u003c/em\u003ePeroxiredoxin-1 (B) Cartoon representation of modelled 3D structure of \u003cem\u003eWb\u003c/em\u003eTPx1 that characterized with thioredoxin fold consisting of five α-helices and seven β-strands and the catalytic important residues peroxidatic cysteine (C78) are shown as red sticks, and (C) Ramachandran Plot for the modelled \u003cem\u003eWb\u003c/em\u003eTPx1.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/798c18f798b635e31b7c1f17.png"},{"id":51742685,"identity":"6bdb83f5-0eee-487f-bad9-94501d163b9e","added_by":"auto","created_at":"2024-02-28 09:24:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101822,"visible":true,"origin":"","legend":"\u003cp\u003eStructural changes are monitored through (A) RMSD, (B) RMSF, (C) Radius of gyrations and (D) Solvent accessibility surface area calculated for 200 ns simulations where dynamic structures are written for every 10ps for \u003cem\u003eWb\u003c/em\u003eTPx1\u003cem\u003e \u003c/em\u003eat 310 K, respectively and these results showed that the protein is stable and main its structural architecture during the simulation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/58f981d8222cdd885ab6d299.png"},{"id":51742682,"identity":"a816fc84-f2a7-468b-89f4-045efed638e3","added_by":"auto","created_at":"2024-02-28 09:24:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":355688,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Superimposed structures of five best leads which showed higher affinity with better satisfied the ADMET profile that bind in the binding site of \u003cem\u003eWb\u003c/em\u003eTPx1are shown in black, red, blue, green, and purple (ID: 26319496, 58522350, 24425757, 38651207 \u0026amp; 29025530) sticks respectively. The \u003cem\u003eWbTPx1 is\u003c/em\u003e shown in surface representation with the binding cavity. (B) Transparent surface representation embedded with blue cartoon representation clearly shows the leads bound in the binding site. (C) Cartoon representation of the \u003cem\u003eWbTPx1\u003c/em\u003eand the binding site residues are labelled in single letter and represented in the sticks.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/25cea27055c6e3eb31155efd.png"},{"id":51742681,"identity":"ad681a9f-63ef-42de-aca9-e7ec5f42d372","added_by":"auto","created_at":"2024-02-28 09:24:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":181392,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-dimensional (2D) representation of ligand interaction map of the five lead molecules, (A) 26319496, (B) 58522350, (C) 24425757, (D) 38651207) and (E) 38651207)) at the binding of WbTPx1. The 2D interaction map shows that the H-bonds and salt bridge interactions stabilizes the leads with WbTPx1. Further, Pi-cation interaction also observed for the leads (26319496, 38651207 and 38651207). (F) Structural superposition showing binding of the top five potentially non-toxic leads in the binding site of WbTPx1. The five lead molecules ID: 26319496, 58522350, 24425757, 38651207 \u0026amp; 29025530 are shown in black, red, blue, green and purple respectively\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/68aa419ab1985b025004230e.png"},{"id":51742689,"identity":"6b6356a0-4024-4b9d-ac87-0143be845a3f","added_by":"auto","created_at":"2024-02-28 09:24:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":571876,"visible":true,"origin":"","legend":"\u003cp\u003eHighest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) for the five potential leads (A) 26319496, (B) 58522350, (C) 24425757, (D) 38651207) and (E) 38651207)) were plotted and represented inform of molecular orbital contour map that clearly showed the most active and least active regions. The noticeable difference in energy gap between the LUMO and HOMO signifies the stability and chemical reactivity of the leads, revealing their strong binding affinity with \u003cem\u003eWb\u003c/em\u003eTpx1.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/4b575054d99f8e36497ddb4f.png"},{"id":51742684,"identity":"d39530e6-de81-472d-ab32-8160015a788b","added_by":"auto","created_at":"2024-02-28 09:24:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":230349,"visible":true,"origin":"","legend":"\u003cp\u003e(A) RMSD, (B) RMSF, (C) Radius of gyrations and (D) Solvent accessibility surface area are calculated for 50 ns simulations at 310 K for the protein (\u003cem\u003eWb\u003c/em\u003eTpx1) - leads complexes: complex 1 (26319496), complex 2 (58522350), complex 3 (24425757), complex 4 (38651207) and complex 5 (38651207) are showed in (black), 76509972 (red), 90485021 (blue), 12049064 (green) and 19901456 (purple) and these analysis showed the intact binding of the leads with \u003cem\u003eWb\u003c/em\u003eTpx1 during the complex dynamics.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/2c075f4be117a505f31df643.png"},{"id":51742686,"identity":"9c5e3167-c5c3-44a7-98c9-82ad93471dee","added_by":"auto","created_at":"2024-02-28 09:24:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1713409,"visible":true,"origin":"","legend":"\u003cp\u003eCross-correlation map for the backbone atoms of the (A) native \u003cem\u003eWb\u003c/em\u003eTPx1and (B-F) lead bound \u003cem\u003eWb\u003c/em\u003eTPx1 (\u003cem\u003eWb\u003c/em\u003eTPx1- ChemBridge ID-26319496, 58522350, 24425757, 38651207 and 29025530 where the positive correlation, and negative or anticorrelation were shown in cyan and magenta colors respectively.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/1266a18cec65f6cf17cf0fa6.png"},{"id":51742687,"identity":"82235207-dd8f-4b8b-a9e7-311f4e1def14","added_by":"auto","created_at":"2024-02-28 09:24:23","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1004434,"visible":true,"origin":"","legend":"\u003cp\u003ePorcupine plot showing the movement of the backbone atom of native (A) native \u003cem\u003eWb\u003c/em\u003eTPx1 and (B-F) lead bound \u003cem\u003eWb\u003c/em\u003eTPx1 (\u003cem\u003eWb\u003c/em\u003eTPx1- ChemBridge ID-26319496, 58522350, 24425757, 38651207 and 29025530 from the MD simulation trajectories. The direction the projected arrows portray the movement of the backbone atom of the enzyme.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/e87efe65e8889f82f56d3162.png"},{"id":51742688,"identity":"295b5de3-f383-465c-be2d-7e40e1d3ff5e","added_by":"auto","created_at":"2024-02-28 09:24:23","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1262634,"visible":true,"origin":"","legend":"\u003cp\u003eFree energy landscape for the (A) native \u003cem\u003eWb\u003c/em\u003eTPx1 and with five complexes (B-F) ((\u003cem\u003eWb\u003c/em\u003eTPx1- ChemBridge ID-26319496, 58522350, 24425757, 38651207 and 29025530) from the MD simulations. The contour map was plotted based on the first two principal component PC1 \u0026amp; PC2 along with the Gibb’s free energy in the Z-axis\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/9ef5ccf59ca57a83167d7476.png"},{"id":51742690,"identity":"1a3e249d-0c2a-4f52-861d-0feb7d50715e","added_by":"auto","created_at":"2024-02-28 09:24:23","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":109405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn vitro \u003c/em\u003eanti-filarial validation for the identified top three leads of \u003cem\u003eWb\u003c/em\u003eTpx1 along with the standard drug Ivermectin (ChemBridge ID-58522350, 24425757and 26319496) using the adult \u003cem\u003eS. digitata \u003c/em\u003eworms at different concentrations 0.625 to 20 µM.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/21bd69baf3fc0c744cbb7503.png"},{"id":51743118,"identity":"94fd2954-ef17-4869-be9d-6cd634c6ec4e","added_by":"auto","created_at":"2024-02-28 09:32:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4906730,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/7e3dc3ea-6ad7-4f25-b191-31350dd06689.pdf"},{"id":51742683,"identity":"c3bb5e40-1108-432e-81cb-d5c625fb2ef6","added_by":"auto","created_at":"2024-02-28 09:24:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3442474,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3982867/v1/82ce4191b32e4f8c090cb4d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive Approach to in silico Identification and in vitro Validation of Anti-Filarial Lead Molecules Targeting the Dimer Interface of Thioredoxin Peroxidase 1 in Wuchereria bancrofti: A Progress in Anti-Filariasis Drug Development","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLymphatic filariasis (LF), is a disease transmitted to humans by infected mosquitoes. It is also known as elephantiasis, and this disfiguring disease is caused by three species of parasitic nematodes namely \u003cem\u003eBrugia malayi, Brugia timori\u003c/em\u003e, and \u003cem\u003eWuchereria bancrofti\u003c/em\u003e [1, 2]. More than 90% of LF cases are due to \u003cem\u003eW. bancrofti\u003c/em\u003e. Early report states that, the number of people affected due to LF stands at around 120\u0026nbsp;million across 76 countries throughout the tropical regions of Africa, Asia, and other sub-tropical regions [3, 4]. Another insight is that India and Sub-Saharan parts of Africa are the major regions affected by LF. As per the 2021 World Health Organization (WHO) statistics, LF continues to concern 882\u0026nbsp;million people in 44 countries worldwide. According to the global baseline estimate, over 15\u0026nbsp;million people were affected by lymphoedema and 25\u0026nbsp;million males from hydrocele. If we consider the filarial infection in Asia, it accounts for nearly 63% global burden, mostly in the region of Southeast Asia, India alone harbours 40% of the world filarial burden, despite the existence of the National Filaria Control Programme (NFCP) since 1955 [5]. This vector-borne disease is transmitted to humans, especially through the members of the \u003cem\u003eAnopheles, Culex, Aedes, Mansonia\u003c/em\u003e, and \u003cem\u003eOchlerotatus\u003c/em\u003e genera of mosquitoes serve as carriers, transmitting them based on the distinct geographical locations and biological features of each species [6]. Eventually, they grow and affect the lymphatic system, causing permanent damage to humans and leading to disfiguration and disability of the limbs. Being a digenetic parasite, \u003cem\u003eW. bancrofti\u003c/em\u003e has its lifecycle in two hosts. The humans support the adult form, and the mosquitoes support the larval form. During the blood ingestion, a mosquito transports infective filarial larvae and injects them into a healthy human. The larvae reach the lymphatic system and mature into adult worms (both male and female). Over time, these adult worms reproduce sexually, producing larval offspring known as microfilariae. Subsequently, these microfilariae migrate from the lymphatic system to the bloodstream and enter the mosquito vector during their blood meal, and the cycle continues [7, 8]. Under the Global Programme to Eliminate Lymphatic Filariasis (GPELF, 1997), formed by the WHO, Mass Drug Administration, and morbidity management activities were initiated to educate people on personal hygiene management and the health impact of preventing LF. Ivermectin, albendazole (ALB), and diethylcarbamazine (DEC) are generally used to treat filarial worms. However, their mechanism has yet to be deciphered clearly, and these drugs are effective against the larval stage of the worms and not against adult and larval parasites [9, 10]. Due to the prolonged use of these drugs, the nematode developed resistance due to the single nucleotide polymorphisms in its molecular targets. There is no conclusive evidence establishing a clear relationship between these genes and the development of resistance [11].\u003c/p\u003e \u003cp\u003eUpon \u003cem\u003eW. bancrofti\u003c/em\u003e infection, the host's initial immune system reaction activates macrophages, neutrophils, and eosinophils. This response leads to the production of Reactive Nitrogen Intermediates (RNI) and Reactive Oxygen Intermediates (ROI), which include the superoxide anion radical (O\u003csub\u003e2\u003c/sub\u003e.\u003csup\u003e\u0026minus;\u003c/sup\u003e), hydroxyl radicals (OH.\u003csup\u003e\u0026minus;\u003c/sup\u003e), peroxyl (RO\u003csub\u003e2\u003c/sub\u003e.) and hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e). Collectively, these are referred to as reactive oxygen species (ROS) generated by the host to control the invading nematode [12, 13]. Nevertheless, the primary characteristics of a lymphatic filariasis infection involve the nematode's ability to survive within the host for an extended period (years). Their effective neutralisation of ROS facilitates this resilience through well-adapted antioxidant enzymes. The crucial survival tactic employed by filarial nematodes revolves around their antioxidant enzymes, which include superoxide dismutase, glutathione peroxidase, peroxiredoxins, catalase, and glutathione S transferases. These enzymes play a pivotal role in neutralizing the host's ROS activity, thereby securing the ongoing viability of the nematodes within the host [14, 15] So, these enzymes could be a possible effective target to strike the worms at all stages of their life cycle. Peroxiredoxins (Prxs) display remarkable efficiency as cysteine-dependent peroxidases, actively participating in antioxidant, regulatory, and signalling systems with remarkable efficiency. Prxs play a crucial role in defending against peroxynitrite (ONOO\u003csup\u003e\u0026minus;\u003c/sup\u003e) and reactive oxygen species (ROS) that arise from both the host and endogenous sources [16]. In many eukaryotes, Prxs are a ubiquitous family of peroxidases that are vital in reducing hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e), alkyl hydroperoxides and peroxynitrite with varying affinity. Prxs have a uniform fold, active site, and catalytic cycle, utilizing a conserved Cysteine residue called the peroxidatic Cys (CP) to interact with the peroxide substrate [17, 18]. Based on sequence similarity, peroxiredoxins can be classified into five subfamilies: Prx1, Prx6, Prx5, Tpx, and BCP. As a thiol-specific antioxidant protein family member, Thioredoxin peroxidase-1 (TPx-1) is predominantly located in the cytoplasm. TPx-1 utilizes its redox-active cysteine to efficiently reduce and detoxify hydrogen peroxides. Three varieties of TPxs are distinguished by the number and location of cysteine (Cys) residues: 1-Cys, typical 2-Cys, and atypical 2-Cys type [19, 20]. They follow a uniform reaction mechanism in which the catalytic cysteine, referred to as the peroxidatic cysteine, undergoes oxidation by hydroperoxides, resulting in hyper oxidation and forming sulfenic (-SOH), sulfinic (-SO\u003csub\u003e2\u003c/sub\u003eH), or sulfonic acid (-SO\u003csub\u003e3\u003c/sub\u003eH) states of the cysteine. Subsequently, the cysteine is regenerated to its reduced form by various molecules, such as thioredoxin (TRX), glutaredoxin (GRX) or glutathione [21]. Because the filarial parasite is susceptible to disruption of its antioxidant system, it is an ideal potential target for developing new drug candidates [22].\u003c/p\u003e \u003cp\u003eIn the current study, We have used computational methods, such as homology modelling, and molecular dynamics (MD) simulation to study the dynamic structural stability of \u003cem\u003eWb\u003c/em\u003eTPx1. The structure-based virtual screening, prime MMGBSA (molecular mechanics, the generalized born model and solvent accessibility), and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) studies were carried out to identify the potent leads that could effectively bind in the binding site of \u003cem\u003eWb\u003c/em\u003eTPx1. Moreover, the lead molecules undergo complex dynamics using MD simulations to evaluate their dynamic intactness with the enzymes. Density Functional Theory (DFT) examines the reactivity of identified lead molecules. The lead compounds identified in this present study were further substantiated through \u003cem\u003ein vitro\u003c/em\u003e validation using the \u003cem\u003eSetaria digitata\u003c/em\u003e model. Our study's findings and associated observations could pave the way for designing and developing innovative therapeutic drug molecules that effectively target all stages of the filarial worm life cycle, with the ultimate goal of eradicating lymphatic filariasis.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Homology Modelling of thioredoxin peroxidase 1 (TPx1) of \u003cem\u003eWuchereria bancrofti\u003c/em\u003e (\u003cem\u003eWb\u003c/em\u003eTPx1)\u003c/h2\u003e \u003cp\u003eThe three-dimensional (3D) structure of the \u003cem\u003eWb\u003c/em\u003eTPx1 has not been determined by experimental methods. Therefore, homology modelling techniques have been employed to predict a reliable 3D structure for the target protein. The protein sequence of \u003cem\u003eWb\u003c/em\u003eTPx1 (B8YNX5) was retrieved from the UniProt sequence database, which comprises 228 amino acids. The protein BLAST was carried out against the Protein Databank (PDB) to identify the suitable template, and the best template was selected based on the E-value, query coverage, percentage identity, and resolution. The Prime module of the Schr\u0026ouml;dinger suite [23] was used to model the atomic coordinates of \u003cem\u003eWb\u003c/em\u003eTPx1 based on the selected template structure, and the structural deviation between the target and template was determined by calculating the Root Mean Square Deviation (RMSD) using PyMol. The PROCHECK (Structural Analysis and Verification Server; SAVES) server was used to assess the stereochemical quality of the modelled structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Molecular dynamics simulations of \u003cem\u003eWb\u003c/em\u003eTPx1\u003c/h2\u003e \u003cp\u003eThe molecular dynamic simulation was performed by using GROMACS 5.1.2 suite [24], to study the dynamic stability of the modeled \u003cem\u003eWb\u003c/em\u003eTPx1, and GROMOS96 53a6 force field [25] was used to create the protein topology. The \u003cem\u003eWb\u003c/em\u003eTPx1 system was parameterized in the centre of the cubic box with a dimension of 1.2 nm in all directions. The system was solvated with around 22958 water molecules using an SPC water model [26]. The total charge of the system was neutralized by adding appropriate sodium (Na\u003csup\u003e+\u003c/sup\u003e) and chloride ions (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) with 0.1 M of ionic strength. To eliminate the steric conflicts and poor geometry in the solvated system, the energy minimization was carried out using the steepest descent method [27] for 50000 steps. Before running the production MD simulation, the energy-minimized system was brought to equilibrium for 500 ps under the NVT ensemble (isothermal-isochoric) at a constant number of particles, temperature, and volume using the Berendsen external bath [28]. Further, Parrinello-Rahman barostat [29] was used to equilibrate for 2000 ps at a constant number of particles, pressure and temperature under NPT ensemble (isothermal-isobaric) at 310 K. LINCS algorithms [30] were used to implement the constraints for the bonds and bond length involving the hydrogen bonds with the protein and the geometry of the solvent were constraints by implementing the SETTLE algorithm [31]. The long-range electrostatic interactions were calculated using the Particle Mesh Ewald (PME) technique [32], with a grid spacing of 1.6 nm and a cutoff order of 4 and 1.2 nm for the non-bonded Coulomb and van der Waals interactions, respectively. Finally, the well-equilibrated protein system was subjected to production MD simulation for the time scale of 200 ns, where the structural trajectories were stored for every 10 ps. The dynamic stability profile of the \u003cem\u003eWb\u003c/em\u003eTPx1was examined by analyzing the changes in the root mean square fluctuation (RMSF), RMSD, the radius of gyration (Rg), and solvent accessible surface area (SASA); and the GROMACS utilities and VMD [33] was used to analyze and visualize the trajectories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Preparation of \u003cem\u003eWb\u003c/em\u003eTPx1 for structure-based virtual screening and molecular docking\u003c/h2\u003e \u003cp\u003ePotential inhibitors were screened using the Virtual Screening Workflow (VSW) feature of the Schr\u0026ouml;dinger suite. The VSW in the Schr\u0026ouml;dinger suite comprises three primary processes: Ligand Preparation, Filtering, and Docking. Initially, the small molecules dataset was prepared using the Ligprep module [34], which involves the conversion of 2D into 3D conforms using the OPLS-3e force field [35] that generates up to 32 conformations for each molecule. The ligand molecules prepared were filtered based on the Lipinski rule of five, ADME properties, and the elimination of compounds with special reactive groups, such as alkali metals and halogens. Next, we prepared the \u003cem\u003eWb\u003c/em\u003eTPx1 protein using the protein preparation wizard of Schr\u0026ouml;dinger, which refines and corrects the charges, bond order, bond angle, and side chains of the atoms in proteins. By utilizing the OPLS-3e force field, the intra-molecular H-bonds were optimized, and the modeled protein structure underwent energy minimization until it reached a maximum convergence of the RMSD threshold of 0.30 \u0026Aring;. Identifying and selecting suitable binding pockets have become an essential aspect of computational screening. By utilizing the SiteMap module [36] within Schr\u0026ouml;dinger, we predict the binding site of the \u003cem\u003eWb\u003c/em\u003eTPx1. It computes the residue properties such as acceptor, donor, and hydrophilic regions in \u003cem\u003eWb\u003c/em\u003eTPx1 and ranks the possible binding pocket based on the SiteScore function. Finally, the best-ranked site was selected, and the grid box was generated with 15 \u0026Aring; from all three sides that cover all the binding site residues using the receptor grid generation module. Glide module of Schr\u0026ouml;dinger [37], uses the three-stage docking method to identify the best binding small molecules that include High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP) depending on the level of accuracy. In each docking stage, only the top 10% of the molecules were allowed for the subsequent level of virtual screening. Further, Prime MM-GBSA modules which use the OPLS-AA force field are used to calculate the binding free energies (ΔG\u003csub\u003ebind\u003c/sub\u003e) for the \u003cem\u003eWb\u003c/em\u003eTPx1-ligand bound complexes with default parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Density Functional Theory (DFT) Calculation\u003c/h2\u003e \u003cp\u003eThe Gaussian 09 software [38] was utilized to perform the DFT calculation for the top five identified lead molecules. The electronic structure is a fundamental component in understanding the thermodynamic properties of molecular interactions and the electronic, chemical, and molecular dynamics nature of small molecules. To determine the energy levels of the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) for the identified leads, the B3LYP-D3 hybrid function and the 6-311G** ++ (2d, 2p) basis set were utilized. Calculating the energy gap (HOMO-LUMO) enables the determination of various electronic properties, including chemical potential, electron affinity, chemical stability, and hardness [39]. This information is critical in comprehensively assessing the chemical potential of the leads and helps in understanding their electronic properties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. The stability analysis of protein-ligand complexes through MD simulations.\u003c/h2\u003e \u003cp\u003eThe MD simulations assess the dynamic stability of the top five non-toxic leads complex with the \u003cem\u003eWb\u003c/em\u003eTPx1. The lead topologies were parameterized using the online PRODRG server [40]. The \u003cem\u003eWb\u003c/em\u003eTPx1-lead complexes system was constructed similarly implemented in native \u003cem\u003eWb\u003c/em\u003eTPx1, which includes replicating the protein topology, setting up the system using the periodic boundary condition, solvating the system followed by neutralization and equilibration. At a temperature of 310 K, the \u003cem\u003eWb\u003c/em\u003eTPx1-lead complexes were subjected to a 50 ns production MD simulation to study the dynamicity of the protein and ligand complexes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Principal component analysis (PCA) for the native and leads bound \u003cem\u003eWb\u003c/em\u003eTPx1\u003c/h2\u003e \u003cp\u003eTo analyze the conformational changes and atomic movements in the native \u003cem\u003eWb\u003c/em\u003eTPx1 and lead-bound \u003cem\u003eWb\u003c/em\u003eTPx1 complexes, a Principal Component Analysis (PCA) was employed for their respective trajectory structure obtained from MD simulations [41]. PCA, a widely utilized statistical method, is employed to explore the intricate protein motions that are essential for revealing the biological functions of proteins [42]. It is an unsupervised multivariate statistical approach frequently used for the dimensionality reduction of large datasets and project data that explains the maximum variability among the datasets [43]. The initial step in PCA construction is to prepare the MD simulation trajectory in cartesian coordinates, which involves removing the water molecules and periodicity that establish the necessary initial parameters. A crucial parameter is the construction of a covariance matrix with dimensions of 3N \u0026times; 3N, where N represents the number of atoms present in the trajectory structure obtained from the MD simulation for the \u003cem\u003eWb\u003c/em\u003eTPx1 and lead-bound systems. The eigenvectors and eigenvalues that describe the motion and magnitude of the protein are obtained through the diagonalization of the covariance matrix [44]. Most often, the highest proportion of variability in the data can be attributed to the first, second, and third eigenvalues, which correspond to the principal components PC1, PC2, and PC3. Using the bio3D package in R software, the PCA analysis was carried out on the \u003cem\u003eWb\u003c/em\u003eTPx1 protein in two distinct states: the unbound-native and lead-bound \u003cem\u003eWb\u003c/em\u003eTPx1 [45].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. \u003cem\u003eIn vitro\u003c/em\u003e anti-filarial activity for the identified leads\u003c/h2\u003e \u003cp\u003eThe lead molecules identified from \u003cem\u003ein silico\u003c/em\u003e methods were validated through \u003cem\u003ein vitro\u003c/em\u003e experiments using \u003cem\u003eSetaria digitata\u003c/em\u003e, a filarial nematode that infects cattle and is closely related to the human-infecting filarial nematode, \u003cem\u003eW. bancrofti\u003c/em\u003e [46, 47]. \u003cem\u003eS. digitata\u003c/em\u003e is a well-known and extensively studied \u003cem\u003ein vitro\u003c/em\u003e model organism popularly used for screening macrofilaricidal activity [48\u0026ndash;50]. Motile adult worms were collected from the peritoneal cavity of a calf that had been newly slaughtered, and they were subsequently washed multiple times using normal physiological saline (0.95%). Without delay, the adult worms were expeditiously transferred to a container containing Dulbecco's modified eagle's medium (DMEM), which was supplemented with 10% heat-inactivated fetal bovine serum and 0.01% Pen-Strep, and then transported to the laboratory within one hour. The compound was purchased from ChemBridge Corporation, San Diego, CA.\u003c/p\u003e \u003cp\u003eTo evaluate the viability of \u003cem\u003eS. digitata\u003c/em\u003e, the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) reduction assay was performed. The worms were exposed to different concentrations (0.625, 1.250, 2.500, 5.000, 10.000, 15.000, and 20.000 \u0026micro;M), for a duration of 24 hours [51]. The testing was conducted in DMEM with Pen-Strep (0.01%) and 10% heat-inactivated fetal bovine serum as a supplement [52, 53]. After 24 hours of exposure, each worm was incubated with MTT (0.25 mg mL-1) in phosphate-buffered saline (pH 7.4) for 30 minutes. Upon completion of the incubation, the worms were transferred to a microtiter plate containing 400 \u0026micro;L of spectroscopic-grade dimethyl sulphoxide (DMSO), and then gently shaken at room temperature for one hour. The absorbance of the formazan solution was measured at 492 nm using a multimode plate reader (Synergy H1, BioTek, USA).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\varvec{P}\\varvec{e}\\varvec{r}\\varvec{c}\\varvec{e}\\varvec{n}\\varvec{t}\\varvec{a}\\varvec{g}\\varvec{e} \\varvec{o}\\varvec{f} \\varvec{i}\\varvec{n}\\varvec{h}\\varvec{i}\\varvec{b}\\varvec{i}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}=100-\\left[\\frac{(\\varvec{T}-\\varvec{H})}{(\\varvec{C}-\\varvec{H})}\\right]\\times 100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe values for the absorbance of formazan produced by treated, control (untreated), and heat-killed worms are represented by T, C, and H, respectively.\u003c/p\u003e \u003cp\u003eAdult females exposed to solvents but not with the test solution (leads) served as the positive control. The negative control (H), adult worms were subjected to heat-killing (at 56\u0026deg;C for 30 minutes) and subsequently exposed to MTT during incubation. The controls and test concentrations were carried out in triplicate and the plot was generated by plotting the percentage of inhibition against the concentration of the inhibitor [52\u0026ndash;54]. The half-maximal inhibitory concentration (IC\u003csub\u003e50\u003c/sub\u003e) values were calculated using GraphPad PRISM 8.0.1 through dose-response analysis, and different compounds were compared to the standard compound ivermectin.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussions","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Homology modelling and validation of \u003cem\u003eWb\u003c/em\u003eTPx1\u003c/h2\u003e \u003cp\u003eNCBI-BLASTP sequence similarity search carried out using RCSB-PDB showed the crystal structure of peroxiredoxin-1 (PDB ID: 4FH8) from \u003cem\u003eAncylostoma ceylanicum\u003c/em\u003e (AcePrx-1) as the best template with the sequence identity of 64.71%, query coverage of 82% and e-value of 1e-\u003csup\u003e89\u003c/sup\u003e. Thus, the three-dimensional structure of \u003cem\u003eWb\u003c/em\u003eTPx1 was modeled using the template (PDB ID: 4FH8) and the structural alignment showed an RMSD deviation of 0.68 \u0026Aring;. Examining the Ramachandran plot with respect to φ and ψ angles reveals that 75.3% of residues occupy the most favored regions, 22.2% of residues in the additional allowed region, 2.5% of residues in the generously allowed region, and none of the residues were present in the disallowed regions indicating the high quality of the modeled \u003cem\u003eWb\u003c/em\u003eTPx1 structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003eWb\u003c/em\u003eTPx1 possesses an amino acid of 228 residues that forms a compact and globular structure featuring the characteristic fold of the Prxs (peroxiredoxins) family that is organized around a twisted sheet composed of seven β-strands, encircled by five α-helices, particularly it forms thioredoxin fold-a unique structural motif consists of four-stranded β-sheet and three flanking α-helices [55]. The existence of conserved motif \u0026lsquo;\u0026lsquo;GGLG\u0026rsquo;\u0026rsquo; (Gly-Gly-Leu-Gly) and \u0026lsquo;\u0026lsquo;YF\u0026rsquo;\u0026rsquo; (Tyr-Phe) that are the invariant features of Prxs family, the sequence of \u003cem\u003eWb\u003c/em\u003eTPx1 also found to have conserved motif as like in other parasites [16]. The amino acid sequence of \u003cem\u003eWb\u003c/em\u003eTPx1 has two conserved cysteine residues (peroxidative cysteine) at positions 78 and 199 that are essential for the reduction and detoxification of hydrogen peroxides as well as aiding in head-to-tail dimer interface formation composed of residues 166 to 192 as remain conserved across Prxs family [17].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Dynamic structure stability of \u003cem\u003eWb\u003c/em\u003eTPx1 during 200ns MD simulations\u003c/h2\u003e \u003cp\u003eThe MD simulations were carried out for 200 ns at 310 K in the SPC water model to examine the dynamic stability of the modeled \u003cem\u003eWb\u003c/em\u003eTPx1. The processed trajectories (20000 structures) were used to study the dynamic structural changes in the \u003cem\u003eWb\u003c/em\u003eTPx1. After 20 ns, it was observed that the systems had achieved a stable equilibrium. The RMSD of \u003cem\u003eWb\u003c/em\u003eTPx1 was plotted, which showed a deviation between 0.38 nm to 0.49 nm with the average RMSD and standard deviation of 0.44 nm and 0.015 nm, respectively. A lower deviation in the RMSD value signifies dynamic stability and equilibrium in the modeled structure, making it suitable for further studies. The RMS fluctuation was calculated to assess and evaluate the flexibility of each amino acid residue in \u003cem\u003eWb\u003c/em\u003eTPx1, backbone residue-wise flexibility of the \u003cem\u003eWb\u003c/em\u003eTPx1 exhibits fluctuations between 0.04 nm to 0.51 nm with an average RMSF and SD of 0.15 nm and 0.09 nm, respectively. It was observed that the N-terminal loop region of \u003cem\u003eWb\u003c/em\u003eTPx1, starting from positions Met1 to Phe42, showed higher fluctuations. In contrast, the residue Ser23 exhibited the highest N-terminal fluctuation with the RMSF value of 0.46 nm. The loop-III that spans from the residues Met57 to Lys63, which connects the β-sheet III (β-III) with the α-helix I (α-I), exhibits a higher backbone fluctuation between 0.08 nm (Met57) to 0.12 nm (Lys61). The longer loop-V that starts from Thr116 to Pro129 showed fluctuation between 0.05 (Pro129) to 0.17 nm (Glu120). Similarly, the loop-VI (143\u0026ndash;154 residues) that connect the α-III with β-V showed higher residual fluctuation that ranged from 0.10 nm to 0.23 nm; among them, the residue Glu148 had the maximum fluctuation. Furthermore, it was noted that the C-terminal region, ranging from residues 195 to 228 and encompassing loop-IX and α-V, showed higher fluctuations, mainly due to its inherent terminal flexibility. The RMSF results revealed that the elevated fluctuations observed in the residues align with the loop regions of both the N- and C-terminals of the \u003cem\u003eWb\u003c/em\u003eTPx1. The dynamic compactness of the \u003cem\u003eWb\u003c/em\u003eTPx1 was monitored based on changes in the radius of gyration (Rg), which was observed to fluctuate between 1.78 nm and 1.89 nm, averaging 1.82 nm. This observation indicates the structural compactness and stability of the intact architecture of \u003cem\u003eWb\u003c/em\u003etpx1 throughout the simulation period. Further, the folding profile and the protein's exposure to solvent molecules were assessed over time by calculating the solvent-accessible surface area (SASA); the SASA value for \u003cem\u003eWb\u003c/em\u003eTPx1 was observed to be within 113\u0026ndash;130 nm\u003csup\u003e2\u003c/sup\u003e with an average of 120 nm\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These overall MD simulation results revealed that the modeled \u003cem\u003eWb\u003c/em\u003eTPx1 is stable with intact structural architecture during the timescale of 200 ns simulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Binding pocket prediction and structure-guided virtual screening of \u003cem\u003eWb\u003c/em\u003eTPx1 inhibitors\u003c/h2\u003e \u003cp\u003eUtilizing the SiteMap module from the Schr\u0026ouml;dinger suite, we predicted the binding site regions on the surface of \u003cem\u003eWb\u003c/em\u003eTPx1. The predicted site revealed a highest site score of 0.929 and a druggability score of 0.957 through diverse physical descriptors such as size, volume, cavity enclosure, solvent exposure degree, interaction tightness, and hydrogen bond acceptor and donor characteristics to predict the binding site of \u003cem\u003eWb\u003c/em\u003eTPx1. The predicted binding site is comprised of the following residues: Ser8, Leu27, Ala28, Ile30, Pro35, Ile165, Arg166, His167, Ser168, Leu169, Val170, Glu181, Arg184, Thr185, Ala188, Phe189, Val192, Glu193, Glu197, Val198, Cys199, Pro200, Ala201, Asn202 and Trp203 respectively. Interestingly, the predicted binding site lies in the reported region involved in the dimer interface (166 to 192) [56], and targeting the interface region was shown to be an effective strategy in the inhibition of the activity of the proteins and enzymes [57\u0026ndash;59]. The grid for docking was created to cover all the binding site residues of \u003cem\u003eWb\u003c/em\u003eTPx1 with the grid center dimension of X\u0026thinsp;=\u0026thinsp;53.05, Y\u0026thinsp;=\u0026thinsp;47.62, and Z\u0026thinsp;=\u0026thinsp;47.15 with the inner grid box of X\u0026thinsp;=\u0026thinsp;10 \u0026Aring;, Y\u0026thinsp;=\u0026thinsp;10 \u0026Aring; and Z\u0026thinsp;=\u0026thinsp;10 \u0026Aring; and the outer grid dimensions of 32.41 X 32.41 X 32.41 \u0026Aring;\u003csup\u003e3\u003c/sup\u003e to perform molecular docking within the specified grid space boundaries. We utilized small molecule compounds from the In-house ChemBridge database to screen against \u003cem\u003eWb\u003c/em\u003eTPx1. Initially, the prefiltering process excludes molecules that violate Lipinski\u0026rsquo;s rule of five and contains reactive functional groups. After the filtering process, approximately 698,709 small molecules from the ChemBridge database were selected. The final dataset, comprising 2,086,614 structures, was meticulously prepared and subsequently screened against \u003cem\u003eWb\u003c/em\u003eTPx1 using a three-phase docking protocol (HTVS, SP, and XP). After screening through HTVS and SP docking, a total of 601 molecules were retrieved based on XP results, with docking scores ranging from \u0026minus;\u0026thinsp;7.84 to -5.46 kcal/mol. Based on the binding affinity towards \u003cem\u003eWb\u003c/em\u003eTPx1, the top 25 hit molecules were subjected to check toxicity profile prediction using the Mclue server[60].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe prediction revealed five non-toxic lead molecules (ChemBridge ID: 26319496, 58522350, 24425757, 38651207 \u0026amp; 29025530) and ADME calculations indicate that the molecules are in the acceptable range (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest drug-likeliness attribute is represented by the #star descriptor value of zero across the top predicted non-toxic leads, signifying a higher probability of drug-like quality. All the top five leads exhibit favourable aqueous solubility, cell permeability, absorption, and zero violation in Lipinski\u0026rsquo;s rule of five, which clearly shows the identified leads have higher oral bio-availability. The extra Precision (XP) docking results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) for the five leads of \u003cem\u003eWb\u003c/em\u003eTPx1 are given in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e where the glide score ranges from \u0026minus;\u0026thinsp;7.41 kcal/mol to -6.88 kcal/mol and the glide energy lies from \u0026minus;\u0026thinsp;48.73 kcal/mol to -36.22 kcal/mol respectively. The 2D interaction profiles of the leads with the \u003cem\u003eWb\u003c/em\u003eTPx1, were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The first lead, N-benzyl-N-(2-hydroxyethyl)-3-[5-(1H-indol-3-ylmethyl)-1,3,4-oxadiazol-2-yl] propenamide (ID-26319496) has established three hydrogen bonds with residues Ser168, Arg184 and Pro200 with the Glide score of -7.41 kcal/mol.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eADMET properties of the identified hits for \u003cem\u003eWb\u003c/em\u003eTpx1 from ChemBridge collection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemBridge ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMolecular weight\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQP logPo/w\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQP logS\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQP logHERG\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRule of five\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e#stars\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMolecule Toxicity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26319496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e404.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNon Toxic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58522350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e359.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNon Toxic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24425757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e391.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNon Toxic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38651207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e397.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNon Toxic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29025530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e336.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNon Toxic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e Molecular weight of the molecule (130.0 to 725.0)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e Predicted octanol/water partition coefficient (\u0026minus;\u0026thinsp;2.0 to 6.5)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ec\u003c/sup\u003e Predicted aqueous solubility, logs. S in moldm-3 is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid (\u0026minus;\u0026thinsp;6.5 to 0.5)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ed\u003c/sup\u003e Predicted IC50 value for blockage of HERG K\u0026thinsp;+\u0026thinsp;channels (concern below \u0026minus;\u0026thinsp;5)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ee\u003c/sup\u003e Number of violation of Lipinski\u0026rsquo;s rule of five (0\u0026ndash;4)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ef\u003c/sup\u003e #stars Number of property or descriptor values that fall outside the 95% range of similar values for known drugs. (0\u0026ndash;5)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eADMET properties of the identified hits for \u003cem\u003eWb\u003c/em\u003eTpx1 from ChemBridge collection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemBridge ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCIQPlogS\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQPPCaco\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQPlogBB\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQPlogKhsa\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePHOA\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQPlogKp\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRuleOf\u003c/p\u003e \u003cp\u003eThree\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26319496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e203.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e86.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58522350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-5.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24425757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-4.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38651207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e372.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-3.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29025530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e301.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCIQPlogS: Conformation-independent predicted aqueous solubility, log S. S in mol dm\u0026ndash;3 is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid. (\u0026ndash;6.5\u0026ndash;0.5)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eQPPCaco: Predicted apparent Caco-2 cell permeability in nm/sec. (\u0026gt;\u0026thinsp;500 great ;\u0026lt;25 poor)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eQPlogBB: Predicted brain/blood partition coefficient. (\u0026ndash;3.0\u0026ndash;1.2).\u003c/p\u003e \u003cp\u003eQPlogKhsa: Prediction of binding to human serum albumin. (\u0026ndash;1.5\u0026ndash;1.5)\u003c/p\u003e \u003cp\u003ePHOA: PercentHumanOralAbsorption Predicted human oral absorption on 0 to 100% scale. (\u0026gt;\u0026thinsp;80% is high; \u0026lt;25% is poor)\u003c/p\u003e \u003cp\u003eQPlogKp: Predicted skin permeability (\u0026ndash;8.0 \u0026ndash; \u0026minus;\u0026thinsp;1.0)\u003c/p\u003e \u003cp\u003eRuleOfThree: Number of violations of Jorgensen\u0026rsquo;s rule of three. (0\u0026ndash;3)\u003c/p\u003e \u003cp\u003eThe lead also formed three pi-pi stacking interactions with the His167 moiety. The second lead, 1-(3-{[(S*)-(cis-3-hydroxycyclo butyl) (phenyl)methyl]amino}-3-oxopropyl) piperidine-4-carboxamid (ID: 58522350) forms four hydrogen bonds with residues Ala28, Ser168, Arg184 and Trp203 with the Glide score of -7.37 kcal/mol whereas the thirds lead, 4-[(1-{[3-(4-hydroxyphenyl)-1H-pyrazol-5-yl]carbonyl}pyrrolidin-3-yl)methyl]benzoic acid (ID: 24425757) exhibit three hydrogen bonds and one salt bridge with Leu27, His167, Trp203 and Arg184 having the Glide score of -7.13 kcal/mol.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExtra precision (XP) docking results of best five hits for \u003cem\u003eWb\u003c/em\u003eTpx1 obtained from virtual screening along with its binding free energies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChemBridge ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGlide score (kcal/ mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGlide energy (kcal/ mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΔG Bind (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eWb\u003c/em\u003eTpx1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInteraction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBond\u003c/p\u003e \u003cp\u003eDistance (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26319496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-48.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-47.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSer 168\u003c/p\u003e \u003cp\u003eArg 184\u003c/p\u003e \u003cp\u003ePro 200\u003c/p\u003e \u003cp\u003eHis 167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.90- HB\u003c/p\u003e \u003cp\u003e2.00- HB\u003c/p\u003e \u003cp\u003e1.80- HB\u003c/p\u003e \u003cp\u003epi-pi stacking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58522350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-45.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-63.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAla 28\u003c/p\u003e \u003cp\u003eSer 168\u003c/p\u003e \u003cp\u003eArg 184\u003c/p\u003e \u003cp\u003eTrp 203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.90- HB\u003c/p\u003e \u003cp\u003e1.90- HB\u003c/p\u003e \u003cp\u003e1.80- HB\u003c/p\u003e \u003cp\u003e2.00 - HB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24425757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-43.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-50.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeu 27\u003c/p\u003e \u003cp\u003eHis 167\u003c/p\u003e \u003cp\u003eTrp 203\u003c/p\u003e \u003cp\u003eArg 184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.90- HB\u003c/p\u003e \u003cp\u003e2.10- HB\u003c/p\u003e \u003cp\u003e2.40- HB\u003c/p\u003e \u003cp\u003e1.9-SB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38651207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-47.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-56.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeu 27\u003c/p\u003e \u003cp\u003eArg 166\u003c/p\u003e \u003cp\u003eSer 168\u003c/p\u003e \u003cp\u003eHis 167\u003c/p\u003e \u003cp\u003ePhe 189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00- HB\u003c/p\u003e \u003cp\u003e2.10- HB\u003c/p\u003e \u003cp\u003e1.90-HB\u003c/p\u003e \u003cp\u003epi-pi stacking\u003c/p\u003e \u003cp\u003epi-pi stacking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29025530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-36.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-48.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCys 199\u003c/p\u003e \u003cp\u003eTrp 203\u003c/p\u003e \u003cp\u003eHis 167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00- HB\u003c/p\u003e \u003cp\u003e2.60- HB\u003c/p\u003e \u003cp\u003epi-pi stacking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe fourth lead, 1-{[5-(hydroxymethyl)-2-furyl]methyl}-N-[4-(1,3 -thiazol-4-yl)phenyl]-3-piperidine carboxamide (ID: 38651207) showed three hydrogen bonds and two pi-pi stacking with residues Leu27, Arg166, Ser168, His167, and Phe189 with the Glide score of -6.93 kcal/mol whereas the fifth lead, N-[(cis-3-hydroxycyclobutyl)(2-thienyl)methyl]-2-(2-oxoazepan-1-yl)acetamide (ID: 29025530) two hydrogen bonds and one pi-pi stacking with the residues Cys199, Trp203 and His167 having the Glide score of -6.88 kcal/mol, respectively. The distance of the hydrogen bond formed between the lead molecule and the protein is also one factor that decides the strength and stability of the protein-ligand complex formation. The H-bond is considered stronger if the distance is less than 2.5\u0026Aring;, and it is considered moderate if it is between 2.5 to 3.5\u0026Aring; [61, 62]. All five leads exhibited a strong H-bond with a distance lesser than 2.5 \u0026Aring;. Besides the bonded interactions, the stability and effective binding of potential leads are influenced by non-bonded contributions, including hydrophobic, charged, and polar groups from \u003cem\u003eWb\u003c/em\u003eTPx1. The Prime MMGBSA method uses the molecular mechanics and continuum solvation models to calculate the binding free energy analysis between the docked complex structure of the selected five lead molecules of \u003cem\u003eWb\u003c/em\u003eTPx1. It showed the ΔG\u003csub\u003ebind\u003c/sub\u003e values between \u0026minus;\u0026thinsp;47.15 kcal/mol to -63.06 kcal/mol, which lies in the order of lead ID: 58522350\u0026thinsp;\u0026gt;\u0026thinsp;38651207\u0026thinsp;\u0026gt;\u0026thinsp;24425757\u0026thinsp;\u0026gt;\u0026thinsp;26319496\u0026thinsp;\u0026gt;\u0026thinsp;29025530. Further, the cross-docking study was performed with human TPx1 (PDB ID: 5UCX) to evaluate the drug specificity. Hs and \u003cem\u003eWb\u003c/em\u003eTPx1 show an identity of 56.19% with a structural RMSD value of 0.8 \u0026Aring;. Both organism exhibits similar thioredoxin structural folds, with five α-helices and seven β-strands which are the invariant features of members peroxiredoxins family. We have carried out cross-docking with the top five identified potent leads at the same dimer interface area of \u003cem\u003eHs\u003c/em\u003eTPx1 to confirm the selectivity of the identified leads towards the \u003cem\u003eWb\u003c/em\u003eTPx1. The extra Precision (XP) docking results showed the Glide score of -3.14, -3.33, -4.51, -1.75 and \u0026minus;\u0026thinsp;3.14 kcal/mol for the leads IDs: 26319496, 58522350, 24425757, 38651207, and 29025530, respectively. Additionally, we observed differences in the volume of the binding site cavity and the lack of bonded interaction between the leads and \u003cem\u003eHs\u003c/em\u003eTpx1, which may account for the lower docking scores and affinity, and these results revealed that the identified leads have a higher affinity towards \u003cem\u003eWb\u003c/em\u003e. Thus, studies were carried out for these selected lead molecules to analyze binding stability and complex integrity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Density functional theory (DFT) studies on the potent leads of \u003cem\u003eWb\u003c/em\u003eTPx1\u003c/h2\u003e \u003cp\u003eIn quantum calculations, the HOMO and LUMO play a crucial role in determining the reactivity of the leads. The HOMO signifies an inclination to donate electrons, whereas the LUMO accepts electrons, and its energy aligns with the electron affinity. The energy gap (E\u003csub\u003egap\u003c/sub\u003eis utilized to assess the chemical reactivity of the leads. The calculated energies of the LUMO orbital for five leads lie between \u0026minus;\u0026thinsp;0.02558 to -0.06691eV, whereas the HOMO orbital energies lie between \u0026minus;\u0026thinsp;0.20061 to -0.24194 eV.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the five leads energy gaps (E\u003csub\u003egap\u003c/sub\u003e) ranged from 0.12 to 0.29 eV (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The HOMO and LUMO energy of the five leads, along with the molecular orbitals diagram, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The energy distribution of the lead molecules is shown by the colors green and red, which indicate the positive and negative charge groups. Based on E\u003csub\u003egap\u003c/sub\u003e value, the chemically reactive of the leads lies in the order of ChemBridge ID: (29025530 (0.20966 eV)\u0026thinsp;\u0026gt;\u0026thinsp;58522350 (0.20094 eV)\u0026thinsp;\u0026gt;\u0026thinsp;26319496 (0.18668 eV)\u0026thinsp;\u0026gt;\u0026thinsp;38651207 (0.16548 eV)\u0026thinsp;\u0026gt;\u0026thinsp;24425757 (0.15936 eV), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical reactive properties -HOMO, LUMO and energy gap for the top five non-toxic leads of \u003cem\u003eWb\u003c/em\u003eTpx1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemBridge ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHOMO (eV)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLUMO (eV)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnergy gap (eV)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26319496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.21789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58522350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.22652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.02558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24425757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.22629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.06691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38651207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29025530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.24194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6. MD Simulation: Dynamic Stability of Lead-Bound \u003cem\u003eWb\u003c/em\u003eTpx1 complexes:\u003c/h2\u003e \u003cp\u003eIndividual MD simulations were carried out for the five leads bound to \u003cem\u003eWb\u003c/em\u003eTpx1 to study the dynamic stability. All the five \u003cem\u003eWb\u003c/em\u003eTpx1 complex systems (\u003cem\u003eWb\u003c/em\u003eTpx1\u0026thinsp;+\u0026thinsp;ChemBridge ID: 26319496, 58522350, 24425757, 38651207 \u0026amp; 29025530) attain equilibrium after 5 ns. The backbone RMSD deviation of complex 1 (\u003cem\u003eWb\u003c/em\u003eTpx1\u0026thinsp;+\u0026thinsp;ChemBridge ID: 26319496) ranged from 0.30 nm to 0.44 nm, with an average RMSD and SD value of 0.37 nm and 0.02 nm, whereas the complex 2 (\u003cem\u003eWb\u003c/em\u003eTpx1\u0026thinsp;+\u0026thinsp;ChemBridge ID: 58522350) and complex 3 (\u003cem\u003eWb\u003c/em\u003eTpx1\u0026thinsp;+\u0026thinsp;ChemBridge ID: 24425757) exhibited the RMSD deviation between 0.31 nm to 0.47 nm and 0.30 to 0.46 nm with the average of 0.38 and 0.39 nm and the standard deviation of 0.02 and 0.01 nm. Complex 4 (\u003cem\u003eWb\u003c/em\u003eTpx1\u0026thinsp;+\u0026thinsp;ChemBridge ID: 38651207) and 5 (\u003cem\u003eWb\u003c/em\u003eTpx1\u0026thinsp;+\u0026thinsp;ChemBridge ID: 29025530) showed similar trends in the RMSD values, that fluctuated between 0.33 to 0.52 nm and 0.41 to 0.55 nm with the average of 0.42 and 0.45 nm with the standard deviation of 0.03 and 0.01 nm, respectively. To investigate the level fluctuations of residues upon lead binding, the backbone RMSF of \u003cem\u003eWb\u003c/em\u003eTpx1 was examined. As observed in the native \u003cem\u003eWb\u003c/em\u003eTPx1 all the five lead bound complexes exhibited higher reside-wise RMSF fluctuations in both the terminal regions due to the terminal flexibility. The N-terminal loop that spans from Met1 to Phe42 showed a higher RMSF value and similarly, the C- C-terminal from His195 to Thr228 has the highest RMSF value due to terminal flexibility. The loop-V spanning from the residues Thr116 to Pro129 and loop-VI from Glu143 to Arg154 was observed to exhibit higher RMSF value in all the five complex systems of \u003cem\u003eWb\u003c/em\u003eTPx1. The RMSF of the complex1 fluctuation between 0.05 to 0.69 nm with an average of 0.17 nm and the residue Ser119 of loop-V had the maximum backbone fluctuation of 0.29 nm whereas in loop-VI the residue His147 exhibited the highest fluctuation of 0.20 nm. In complexes 2 and 3, the average RMSF fluctuation was found to be 0.16 and 0.15 nm, exhibiting a similar range between 0.05 to 0.71 nm. In both complexes, the loop-V residue Ser119 displays higher fluctuations of 0.22 and 0.15 nm and the residue Glu148 of loop-VI shows larger backbone fluctuations of 0.17 and 0.21 nm, respectively. The average RMSF of complex 4 and 5 are 0.16 and 0.17 nm with a range from 0.05 to 0.49 nm and 0.06 to 0.80 nm respectively, where the loop-V residue Glu120 and Ser119 account for higher fluctuation of 0.17 and 0.22 nm while the loop-VI the residue Glu148 was observed to have higher fluctuation of 0.22 and 0.17 nm among other counterparts residues of loop-VI. The radius of gyrations (Rg) measures the compactness of the lead-bound state of \u003cem\u003eWb\u003c/em\u003eTPx1, the five \u003cem\u003eWb\u003c/em\u003eTpx1 complex systems exhibit intact Rg with the average value of 1.95, 1.93, 1.91, 1.93 and 1.92 nm with the SD of 0.02, 0.01, 0.01, 0.02 and 0.02 respectively. Further, SASA measured the surface area of the enzyme-leads complex that interacts with the surrounding water molecules was determined. The results showed that the average SASA for \u003cem\u003eWb\u003c/em\u003eTPx1-lead complexes was 134.65, 135.07, 137.54, 131.70 and 132.69 nm\u003csup\u003e2\u003c/sup\u003e with the SD of 2.74, 3.17, 2.51, 3.16 and 2.78 nm\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe complex MD simulation based on the profiles of RMSD, RMSF, Rg and SASA revealed that throughout the simulation period, all the five leads (ChemBridge ID: 26319496, 58522350, 24425757, 38651207\u0026amp; 29025530) bind intact inside the binding site of \u003cem\u003eWb\u003c/em\u003eTPx1. The Coulomb and L-J energies from the simulated trajectories were computed to examine and quantify the non-bonded energy contribution between the \u003cem\u003eWb\u003c/em\u003eTPx1 and the lead interactions. The energy profiles in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depict that the L-J potential energy contribution was more dominant than the coulomb energy contribution that accounts for the stability of the five complexes. Based on the calculation of binding energy, it is evident that non-bonded energies were also crucial in maintaining the intact and stable binding of leads within the \u003cem\u003eWb\u003c/em\u003eTPx1 throughout complex dynamics. Supplementary Fig.\u0026nbsp;1 showcases ensembles of \u003cem\u003eWb\u003c/em\u003eTPx1 complexes with lead molecules, captured every 10 ns throughout the simulation. It's evident from these snapshots that the leads remained securely bound within the binding site of \u003cem\u003eWb\u003c/em\u003eTPx1 throughout the entire simulation period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnergy contributions (accounting Coulomb and L-J energies) of the non-toxic leads with \u003cem\u003eWb\u003c/em\u003eTpx1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eChemBridge ID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26319496\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58522350\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24425757\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38651207\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29025530\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoulomb Energy (KJ/mol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-62.30\u0026thinsp;\u0026plusmn;\u0026thinsp;5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-44.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-58.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e-37.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-38.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-J Energy\u003c/p\u003e \u003cp\u003e(KJ/mol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-136.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-98.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-146.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e-119.52\u0026thinsp;\u0026plusmn;\u0026thinsp;7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-95.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Comparison of native and leads bound dynamics of \u003cem\u003eWb\u003c/em\u003eTPx1 - PCA and FEL analysis:\u003c/h2\u003e \u003cp\u003ePCA projects the molecular trajectories generated in the MD simulation onto a two-dimensional plane, with the first three principal components (PC1, PC2, and PC3) elucidating the maximum variability in the data (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A comparison between the leads bound and native \u003cem\u003eWb\u003c/em\u003eTPx1 revealed that the native \u003cem\u003eWb\u003c/em\u003eTPx1 PC1 magnitude was smaller, whereas the magnitude of PC2 of the native is found to be higher than the leads bound \u003cem\u003eWb\u003c/em\u003eTPx1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe first three principal components (PCA) for the native \u003cem\u003eWb\u003c/em\u003eTpx1 and the lead bound \u003cem\u003eWb\u003c/em\u003eTpx1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrincipal component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNative \u003cem\u003eWb\u003c/em\u003eTpx1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eChemBridge ID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26319496\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58522350\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24425757\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38651207\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29025530\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.96%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlthough the initial two principle components were adequate to explain the fluctuations in conformational changes and motion throughout the MD simulation, we also considered the variability explained by the PC3, which is clearly shown in the supplementary Fig.\u0026nbsp;2\u0026ndash;7.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther, the correlated motions at the residue level were also determined by analyzing the cross-correlation map for the backbone atoms of both native \u003cem\u003eWb\u003c/em\u003eTPx1 and \u003cem\u003eWb\u003c/em\u003eTPx1 bound (\u003cem\u003eWb\u003c/em\u003eTpx1\u0026thinsp;+\u0026thinsp;ChemBridge ID: 26319496, 58522350, 24425757, 38651207 \u0026amp; 29025530). The cyan regions signified the enzyme peak positive correlated backbone motion, while the magenta region depicted the highest negative or anticorrelated backbone motion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe cross-correlation map revealed that both the native and leads bound \u003cem\u003eWb\u003c/em\u003eTPx1 exhibit an almost similar trend in the correlated motion among the backbone atoms of the residues. We observed a minute change in the motion of the residue\u0026rsquo;s backbone located in N- C- terminals and in the dimer interface region where the leads bind with the WbTPx1, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The porcupine plotting was plotted for the first principal component (PC1), which accounts for the greatest variability of the native and lead-bound forms. Observations indicated that the \u003cem\u003eWb\u003c/em\u003eTPx1 bound to leads exhibits reduced backbone atom movement compared to the native \u003cem\u003eWb\u003c/em\u003eTPx1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). To explore the conformational dynamics of native and leads-bound \u003cem\u003eWb\u003c/em\u003eTPx1, a free energy landscape (FEL) was calculated. This enabled the visualization of transition states, including local and global minima, sampled throughout the entire time scale period of the MD simulation. The Gibbs free energy varies from 0 to 11.50 KJ/mol for the native \u003cem\u003eWb\u003c/em\u003eTPx1 while for the complexes-\u003cem\u003eWb\u003c/em\u003eTPx1\u0026thinsp;+\u0026thinsp;ChemBridge ID: 26319496, 58522350, 24425757, 38651207 \u0026amp; 29025530, it ranges from 0 to 11.50,11.50,11.60, 13.40, and 11.60 KJ/mol, respectively and FEL is represented as contour diagram is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The dark blue region signifies energetic minima and conformationally stable states, while the red and yellow regions indicate higher energy states linked to unfavorable conformational states. The native \u003cem\u003eWb\u003c/em\u003eTPx1 FEL is confined by a sharp and narrow valley single basin with a single global minimum energy conformation. The FEL analysis of complexes 1 and 2 exhibit a similar trend in energy state with a broad basin that converges into numerous global energy states from multiple local minimum states. Complexs 3, 4, and 5 adopted a similar energy spread to the native, containing multiple local energy states but confined into a single sharp global minima energy state. The findings from PCA, cross-correlation map, porcupine plot, and FEL provided clear insights into the conformational changes occurring during the simulation period upon the binding of leads in the dimer interface binding site of \u003cem\u003eWb\u003c/em\u003eTPx1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Viability Evaluation of Filarial Worms through MTT-Formazan Colorimetric Assay\u003c/h2\u003e \u003cp\u003eThe MTT assay was performed at concentrations spanning from 0.625 to 20 \u0026micro;M over a 24-hour duration. At the maximum dosage of 20 mM concentration, all three leads exhibited inhibition efficacy greater than 92% (94.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56, 93.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96 and 92.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e anti-filarial activity of identified leads, ChemBridge ID-58522350, 24425757, 26319496 and the standard drug Ivermectin against adult \u003cem\u003eS. digitata\u003c/em\u003e filarial worm\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcentration of Inhibitor (\u0026micro;M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of Inhibition\u003c/p\u003e \u003cp\u003e58522350\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of Inhibition\u003c/p\u003e \u003cp\u003e24425757\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of Inhibition\u003c/p\u003e \u003cp\u003e26319496\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage of Inhibition\u003c/p\u003e \u003cp\u003eIvermectin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e94.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e93.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e92.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e83.08\u0026thinsp;\u0026plusmn;\u0026thinsp;68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e87.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e83.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e84.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e77.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e77.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e75.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e77.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e71.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e66.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e70.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e66.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e62.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e59.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e60.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e54.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e53.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e45.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e47.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e46.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e42.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e32.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e38.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e34.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e27.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, at lower concentrations (0.625 \u0026micro;M), it showed the inhibition percentages of 32.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74%, 38.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85% and 34.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27%, respectively, for the leads- ChemBridge ID: 58522350, 24425757 and 26319496. Interestingly, all three leads had greater inhibition percentages at concentrations between 0.625 and 20 \u0026micro;M, which is higher than the standard anti-filarial drug Ivermectin, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The IC\u003csub\u003e50\u003c/sub\u003e values were calculated by the Michalis Menten equation and were compared with the standard drug ivermectin, IC\u003csub\u003e50\u003c/sub\u003e value of leads of \u003cem\u003eWb\u003c/em\u003eTPx1 58522350, 24425757 and 26319496 lies in the range of 1.069\u0026thinsp;\u0026plusmn;\u0026thinsp;0.204 \u0026micro;M, 1.373\u0026thinsp;\u0026plusmn;\u0026thinsp;0.259 \u0026micro;M and 1.429\u0026thinsp;\u0026plusmn;\u0026thinsp;0.246 \u0026micro;M, respectively. Interestingly, we noted that the IC\u003csub\u003e50\u003c/sub\u003e value of two leads (24425757 \u0026amp; 26319496) was lesser than the IC50 value of Ivermectin with 1.374\u0026thinsp;\u0026plusmn;\u0026thinsp;0.152 \u0026micro;M, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The MTT reduction assay results revealed better IC\u003csub\u003e50\u003c/sub\u003e and greater inhibition than Ivermectin, thus the leads identified based on the \u003cem\u003ein silico\u003c/em\u003e method possess the potential to eradicate adult worms.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eOur current research is primarily on employing structure-based drug design to develop potent inhibitors targeting the dimer interface region of \u003cem\u003eWb\u003c/em\u003eTPx1. The \u003cem\u003eWb\u003c/em\u003eTPx1 exhibited heightened binding affinity with the top five predicted non-toxic leads obtained from the ChemBridge small molecule database (ChemBridge ID-26319496, 58522350, 24425757, 38651207 \u0026amp; 29025530). The DFT analysis identified the reactive regions within the lead molecules, providing valuable insights into the chemical moieties to be targeted for lead enrichment and improved activity. Additionally, MD simulations of these protein-lead complexes revealed the stable and intact binding of the five leads within the binding site of \u003cem\u003eWb\u003c/em\u003eTPx1. Interestingly, these five leads exhibited greater binding specificity towards \u003cem\u003eWb\u003c/em\u003e than \u003cem\u003eHs\u003c/em\u003e. The \u003cem\u003ein vitro\u003c/em\u003e validation revealed that the two identified leads (ID:24425757 \u0026amp; 26319496) exhibited a higher percentage of inhibition and a superior IC\u003csub\u003e50\u003c/sub\u003e compared to the standard drug Ivermectin. The identified leads serve as a basis for the creation of novel chemical scaffolds and fragments with potent drug-like properties that may aid in treating LF infection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMuthusamy Sureshan: data curation, investigation, visualization, validation, writing - original draft.Sundarraj Rajamanikandan: data curation, writing - review and editing.Kadhirvel Saraboji: contributed to conceptualization, writing-review and editing, resources, supervision, and funding acquisition. The final version of the manuscript submitted was approved by all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eAuthors are grateful to the management of SASTRA Deemed University for providing all necessary computational facilities. KS thankfully acknowledges DST-SERB for providing financial support in the form of research projects (No: EMR/2017/002841 and No: CVD/2020/000604) to conduct the study. Authors like to thank Dr. Venkatasubramanian U, Associate Professor and Mr. Adithyan J, Research Scholar, School of Chemical and Biotechnology, SASTRA Deemed University for their help and suggestion \u003cem\u003ein vitro\u003c/em\u003e experiments. Authors also thank Dr. R. Velusamy, M.V.Sc., Ph.D., Assistant Professor and Head, Department of Veterinary Parasitology, Veterinary College and Research Institute Orathanadu, Thanjavur, Tamil Nadu, India for authenticating the filarial worm \u003cem\u003eS. digitata\u003c/em\u003e. MS is thankful for the funding as Senior Research Fellow grant from ICMR-SRF (No. Fellowship/96/2022-ECD-II, IRIS ID No. 2021\u0026ndash;11346/F96).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSimonsen PE, Mwakitalu ME (2013) Urban lymphatic filariasis. Parasitol Res 112:35\u0026ndash;44. https://doi.org/10.1007/s00436-012-3226-x\u003c/li\u003e\n\u003cli\u003eROUTH HB, BHOWMIK KR (1993) History of Elephantiasis. Int J Dermatol 32:913\u0026ndash;916. https://doi.org/10.1111/j.1365-4362.1993.tb01418.x\u003c/li\u003e\n\u003cli\u003eMichael E, Bundy DAP, Grenfell BT (1996) Re-assessing the global prevalence and distribution of lymphatic filariasis. Parasitology 112:409\u0026ndash;428. https://doi.org/10.1017/s0031182000066646\u003c/li\u003e\n\u003cli\u003eRamaiah KD, Das PK, Michael E, Guyatt HL (2000) The economic burden of lymphatic filariasis in India. Parasitol. Today 16:251\u0026ndash;253\u003c/li\u003e\n\u003cli\u003eBizhani N, Hashemi Hafshejani S, Mohammadi N, et al Lymphatic filariasis in Asia: a systematic review and meta-analysis. https://doi.org/10.1007/s00436-020-06991-y\u003c/li\u003e\n\u003cli\u003eFamakinde DO (2018) Mosquitoes and the Lymphatic Filarial Parasites: Research Trends and Budding Roadmaps to Future Disease Eradication. Trop Med Infect Dis 3:. https://doi.org/10.3390/tropicalmed3010004\u003c/li\u003e\n\u003cli\u003eFreedman DO (1998) Immune dynamics in the pathogenesis of human lymphatic filariasis. Parasitol Today 14:229\u0026ndash;234. https://doi.org/10.1016/S0169-4758(98)01244-7\u003c/li\u003e\n\u003cli\u003ePaily KP, Hoti SL, Das PK (2009) A review of the complexity of biology of lymphatic filarial parasites. J Parasit Dis Off Organ Indian Soc Parasitol 33:3. https://doi.org/10.1007/S12639-009-0005-4\u003c/li\u003e\n\u003cli\u003eRao UR, Chandrashekar R, Subrahmanyam D (1987) Effect of ivermectin on serum dependent cellular interactions to Dipetalonema viteae microfilariae. Trop Med Parasitol Off organ Dtsch Tropenmedizinische Gesellschaft Dtsch Gesellschaft fur Tech Zusammenarbeit 38:123\u0026ndash;127\u003c/li\u003e\n\u003cli\u003eCritchley J, Addiss D, Gamble C, et al (2005) Albendazole for lymphatic filariasis. Cochrane database Syst Rev CD003753. https://doi.org/10.1002/14651858.CD003753.pub3\u003c/li\u003e\n\u003cli\u003eCobo F (2016) Determinants of parasite drug resistance in human lymphatic filariasis. Rev Esp Quimioter Publ Of la Soc Esp Quimioter 29:288\u0026ndash;295\u003c/li\u003e\n\u003cli\u003eWeiss SJ, Test ST, Eckmann CM, et al (1986) Brominating oxidants generated by human eosinophils. Science (80- ) 234:200\u0026ndash;203. https://doi.org/10.1126/science.3018933\u003c/li\u003e\n\u003cli\u003eCross AR, Jones OTG (1991) Enzymic mechanisms of superoxide production. BBA - Bioenerg. 1057:281\u0026ndash;298\u003c/li\u003e\n\u003cli\u003eCallahan HL, Crouch RK, James ER (1988) Helminth anti-oxidant enzymes: a protective mechanism against host oxidants? Parasitol Today 4:218\u0026ndash;225. https://doi.org/10.1016/0169-4758(88)90162-7\u003c/li\u003e\n\u003cli\u003eME S, VP S, GR T, K G (1998) Resistance of filarial nematode parasites to oxidative stress. Int J Parasitol 28:1315\u0026ndash;1332. https://doi.org/10.1016/S0020-7519(98)00107-6\u003c/li\u003e\n\u003cli\u003eGretes MC, Poole LB, Karplus PA (2012) Peroxiredoxins in parasites. Antioxid Redox Signal 17:608\u0026ndash;633. https://doi.org/10.1089/ars.2011.4404\u003c/li\u003e\n\u003cli\u003eWood ZA, Schr\u0026ouml;der E, Robin Harris J, Poole LB (2003) Structure, mechanism and regulation of peroxiredoxins. Trends Biochem Sci 28:32\u0026ndash;40. https://doi.org/10.1016/s0968-0004(02)00003-8\u003c/li\u003e\n\u003cli\u003eFloh\u0026eacute; L (2010) Changing paradigms in thiology from antioxidant defense toward redox regulation. Methods Enzymol 473:1\u0026ndash;39. https://doi.org/10.1016/S0076-6879(10)73001-9\u003c/li\u003e\n\u003cli\u003eKnoops B, Loumaye E, Van Der Eecken V (2007) Evolution of the Peroxiredoxins BT - Peroxiredoxin Systems: Structures and Functions. In: Floh\u0026eacute; L, Harris JR (eds). Springer Netherlands, Dordrecht, pp 27\u0026ndash;40\u003c/li\u003e\n\u003cli\u003eHakimi H, Asada M, Angeles JMM, et al (2012) Cloning and characterization of Plasmodium vivax thioredoxin peroxidase-1. Parasitol Res 111:525\u0026ndash;529. https://doi.org/10.1007/s00436-012-2864-3\u003c/li\u003e\n\u003cli\u003eDayer R, Fischer BB, Eggen RIL, Lemaire SD (2008) The peroxiredoxin and glutathione peroxidase families in Chlamydomonas reinhardtii. Genetics 179:41\u0026ndash;57. https://doi.org/10.1534/genetics.107.086041\u003c/li\u003e\n\u003cli\u003eSelkirk ME, Smith VP, Thomas GR, Gounaris K (1998) Resistance of filarial nematode parasites to oxidative stress. Int J Parasitol 28:1315\u0026ndash;1332. https://doi.org/10.1016/S0020-7519(98)00107-6\u003c/li\u003e\n\u003cli\u003ePrime (2020) Schr\u0026ouml;dinger Release 2020-4, Schr\u0026ouml;dinger, LLC, New York, NY\u003c/li\u003e\n\u003cli\u003eAbraham MJ, Murtola T, Schulz R, et al (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1\u0026ndash;2:19\u0026ndash;25. https://doi.org/10.1016/J.SOFTX.2015.06.001\u003c/li\u003e\n\u003cli\u003eOostenbrink C, Villa A, Mark AE, Gunsteren WF Van (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25:1656\u0026ndash;1676. https://doi.org/10.1002/JCC.20090\u003c/li\u003e\n\u003cli\u003eBerendsen HJC, Postma JPM, Gunsteren WF van, Hermans J (1981) Interaction Models for Water in Relation to Protein Hydration. 331\u0026ndash;342. https://doi.org/10.1007/978-94-015-7658-1_21\u003c/li\u003e\n\u003cli\u003eVrahatis MN, Androulakis GS, Lambrinos JN, Magoulas GD (2000) A class of gradient unconstrained minimization algorithms with adaptive stepsize. J Comput Appl Math 114:367\u0026ndash;386. https://doi.org/10.1016/S0377-0427(99)00276-9\u003c/li\u003e\n\u003cli\u003eBerendsen HJC, Postma JPM, Gunsteren WF van, et al (1998) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684. https://doi.org/10.1063/1.448118\u003c/li\u003e\n\u003cli\u003eParrinello M, Rahman A (1998) Polymorphic transitions in single crystals: A new molecular dynamics method. J Appl Phys 52:7182. https://doi.org/10.1063/1.328693\u003c/li\u003e\n\u003cli\u003eHess B (2008) P-LINCS: A parallel linear constraint solver for molecular simulation. J Chem Theory Comput 4:116\u0026ndash;122. https://doi.org/10.1021/ct700200b\u003c/li\u003e\n\u003cli\u003eMiyamoto S, Kollman PA (1992) Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. J Comput Chem 13:952\u0026ndash;962. https://doi.org/10.1002/JCC.540130805\u003c/li\u003e\n\u003cli\u003eDarden T, York D, Pedersen L (1998) Particle mesh Ewald: An N\u0026sdot;log(N) method for Ewald sums in large systems. J Chem Phys 98:10089. https://doi.org/10.1063/1.464397\u003c/li\u003e\n\u003cli\u003eW H, A D, K S (1996) VMD: visual molecular dynamics. J Mol Graph 14:33\u0026ndash;38. https://doi.org/10.1016/0263-7855(96)00018-5\u003c/li\u003e\n\u003cli\u003eLigPrep (2020) Schr\u0026ouml;dinger Release 2020-4, Schr\u0026ouml;dinger, LLC, New York, NY\u003c/li\u003e\n\u003cli\u003eHarder E, Damm W, Maple J, et al (2015) OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins. J Chem Theory Comput 12:281\u0026ndash;296. https://doi.org/10.1021/ACS.JCTC.5B00864\u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;dinger Release 2020-4: (2020) SiteMap, Schr\u0026ouml;dinger, LLC, New York, NY\u003c/li\u003e\n\u003cli\u003eFriesner RA, Banks JL, Murphy RB, et al (2004) Glide:\u0026thinsp; A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J Med Chem 47:1739\u0026ndash;1749. https://doi.org/10.1021/jm0306430\u003c/li\u003e\n\u003cli\u003eFrisch MJ, Trucks GW, Schlegel HB, et al (2016) G16_D09. Gaussian 16, Revision D.09, Gaussian, Inc., Wallin\u003c/li\u003e\n\u003cli\u003eChang-Guo Zhan, Jeffrey A. Nichols \u0026dagger; and, Dixon* DA (2003) Ionization Potential, Electron Affinity, Electronegativity, Hardness, and Electron Excitation Energy:\u0026thinsp; Molecular Properties from Density Functional Theory Orbital Energies. J Phys Chem A 107:4184\u0026ndash;4195. https://doi.org/10.1021/JP0225774\u003c/li\u003e\n\u003cli\u003eSch\u0026uuml;ttelkopf AW, van Aalten DMF (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D Biol Crystallogr 60:1355\u0026ndash;1363. https://doi.org/10.1107/S0907444904011679\u003c/li\u003e\n\u003cli\u003eDavid CC, Jacobs DJ (2014) Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins. Methods Mol Biol 1084:193. https://doi.org/10.1007/978-1-62703-658-0_11\u003c/li\u003e\n\u003cli\u003eS H, BL de G (2008) Normal modes and essential dynamics. Methods Mol Biol 443:89\u0026ndash;106. https://doi.org/10.1007/978-1-59745-177-2_5\u003c/li\u003e\n\u003cli\u003eA W, KN K (2013) Principal component and clustering analysis on molecular dynamics data of the ribosomal L11\u0026middot;23S subdomain. J Mol Model 19:539\u0026ndash;549. https://doi.org/10.1007/S00894-012-1563-4\u003c/li\u003e\n\u003cli\u003eM M, L K (2021) An atomistic investigation on the interaction of distamycin A and its derivative with the telomeric G-Quadruplex as anticancer agents by molecular dynamics simulation. Arch Biochem Biophys 701:. https://doi.org/10.1016/J.ABB.2021.108797\u003c/li\u003e\n\u003cli\u003eGrant BJ, Rodrigues APC, ElSawy KM, et al (2006) Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics 22:2695\u0026ndash;2696. https://doi.org/10.1093/bioinformatics/btl461\u003c/li\u003e\n\u003cli\u003eSenanayake KS, S\u0026ouml;derberg J, P\u0026otilde;lajev A, et al (2020) The Genome of Setaria digitata: A Cattle Nematode Closely Related to Human Filarial Parasites. Genome Biol Evol 12:3971. https://doi.org/10.1093/GBE/EVAA017\u003c/li\u003e\n\u003cli\u003ePerumal ANI, Gunawardene YINS, Dassanayake RS (2016) Setaria digitata in advancing our knowledge of human lymphatic filariasis. J Helminthol 90:129\u0026ndash;138. https://doi.org/10.1017/S0022149X15000309\u003c/li\u003e\n\u003cli\u003eMathew N, Karunan T, Srinivasan L, Muthuswamy K (2010) Synthesis and screening of substituted 1,4-naphthoquinones (NPQs) as antifilarial agents. Drug Dev Res 71:188\u0026ndash;196. https://doi.org/10.1002/DDR.20357\u003c/li\u003e\n\u003cli\u003eNisha M, Kalyanasundaram M, Paily KP, et al In vitro screening of medicinal plant extracts for macrofilaricidal activity. https://doi.org/10.1007/s00436-006-0294-9\u003c/li\u003e\n\u003cli\u003eSenathilake KS, Karunanayake EH, Samarakoon SR, et al (2017) Oleanolic acid from antifilarial triterpene saponins of Dipterocarpus zeylanicus induces oxidative stress and apoptosis in filarial parasite Setaria digitata in vitro. Exp Parasitol 177:13\u0026ndash;21. https://doi.org/10.1016/J.EXPPARA.2017.03.007\u003c/li\u003e\n\u003cli\u003eMathew N, Misra-Bhattacharya S, Perumal V, Muthuswamy K (2008) Antifilarial lead molecules isolated from Trachyspermum ammi. Molecules 13:2156\u0026ndash;2168. https://doi.org/10.3390/MOLECULES13092156\u003c/li\u003e\n\u003cli\u003eSureshan M, Prabhu D, Rajamanikandan S, Saraboji K (2023) Discovery of potent inhibitors targeting Glutathione S-transferase of Wuchereria bancrofti: a step toward the development of effective anti-filariasis drugs. Mol Divers 1:1\u0026ndash;21. https://doi.org/10.1007/S11030-023-10617-7/FIGURES/11\u003c/li\u003e\n\u003cli\u003eTompa DR, Muthusamy S, Srikanth S, Kadhirvel S (2020) Molecular dynamics of far positioned surface mutations of Cu/Zn SOD1 promotes altered structural stability and metal-binding site: Structural clues to the pathogenesis of amyotrophic lateral sclerosis. J Mol Graph Model 100:. https://doi.org/10.1016/J.JMGM.2020.107678\u003c/li\u003e\n\u003cli\u003eAM L, CG G, SM L, AN P (2019) Investigation of Molecular Details of Keap1-Nrf2 Inhibitors Using Molecular Dynamics and Umbrella Sampling Techniques. Molecules 24:. https://doi.org/10.3390/MOLECULES24224085\u003c/li\u003e\n\u003cli\u003eChoi J, Choi S, Choi J, et al (2003) Crystal structure of Escherichia coli thiol peroxidase in the oxidized state: insights into intramolecular disulfide formation and substrate binding in atypical 2-Cys peroxiredoxins. J Biol Chem 278:49478\u0026ndash;49486. https://doi.org/10.1074/jbc.M309015200\u003c/li\u003e\n\u003cli\u003eNguyen JB, Pool CD, Wong CYB, et al (2013) Peroxiredoxin-1 from the human hookworm Ancylostoma ceylanicum forms a stable oxidized decamer and is covalently inhibited by conoidin A. Chem Biol 20:991\u0026ndash;1001. https://doi.org/10.1016/j.chembiol.2013.06.011\u003c/li\u003e\n\u003cli\u003eGoyal B, Goyal D (2020) Targeting the Dimerization of the Main Protease of Coronaviruses: A Potential Broad-Spectrum Therapeutic Strategy. ACS Comb Sci 22:297\u0026ndash;305. https://doi.org/10.1021/acscombsci.0c00058\u003c/li\u003e\n\u003cli\u003eGunderwala AY, Nimbvikar AA, Cope NJ, et al (2019) Development of Allosteric BRAF Peptide Inhibitors Targeting the Dimer Interface of BRAF. ACS Chem Biol 14:1471\u0026ndash;1480. https://doi.org/10.1021/acschembio.9b00191\u003c/li\u003e\n\u003cli\u003eCardinale D, Guaitoli G, Tondi D, et al (2011) Protein-protein interface-binding peptides inhibit the cancer therapy target human thymidylate synthase. Proc Natl Acad Sci U S A 108:E542-9. https://doi.org/10.1073/pnas.1104829108\u003c/li\u003e\n\u003cli\u003eKiss R, Sandor M, Szalai FA (2012) http://Mcule.com: a public web service for drug discovery. J Cheminform 4:P17. https://doi.org/10.1186/1758-2946-4-S1-P17\u003c/li\u003e\n\u003cli\u003eT S, T V, D P, et al (2018) Insecticide-resistance mechanism of Plutella xylostella (L.) associated with amino acid substitutions in acetylcholinesterase-1: A molecular docking and molecular dynamics investigation. Comput Biol Chem 77:240\u0026ndash;250. https://doi.org/10.1016/J.COMPBIOLCHEM.2018.09.004\u003c/li\u003e\n\u003cli\u003eSureshan M, Prabhu D, Kadhirvel S (2022) Computational identification and experimental validation of anti-filarial lead molecules targeting metal binding/substrate channel residues of Cu/Zn SOD1 from Wuchereria bancrofti. J Biomol Struct Dyn 1\u0026ndash;14. https://doi.org/10.1080/07391102.2022.2136245\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lymphatic filariasis, W. bancrofti, WbTPx1, MD simulation, DFT, PCA, MTT","lastPublishedDoi":"10.21203/rs.3.rs-3982867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3982867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLymphatic filariasis (LF) remains a significant health challenge for populations in developing countries, including India. LF is a parasitic disease transmitted by mosquitoes, caused by three types of nematodes: \u003cem\u003eWuchereria bancrofti, Brugia malayi\u003c/em\u003e, and \u003cem\u003eBrugia timori\u003c/em\u003e, prevalent in tropical and subtropical regions. Filarial nematodes are transmitted to humans by infected mosquitoes carrying L3 larvae during a blood meal. The human immune system generates reactive oxygen species (ROS) in response to the invading nematodes. Nevertheless, the nematode's antioxidant enzymes effectively counteract the oxidative cytotoxicity induced by the host. The enzyme thioredoxin peroxidase 1 (TPx1) is a member of the peroxiredoxin family, which facilitates the conversion of hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) into water (H\u003csub\u003e2\u003c/sub\u003eO), which is highly used by nematodes to maintain oxidative stress in the host throughout their life cycle. Therefore, developing appropriate therapeutic compounds by targeting TPx1 might aid in resolving difficulties with treatment efficacy and activity at various stages of filarial parasitic worms. To find the potent inhibitors, we modelled the three-dimensional structure of \u003cem\u003eWb\u003c/em\u003eTPx1, and its dynamic stability was assessed by molecular dynamic (MD) simulation studies. A structure-based virtual screening approach was used to find the most promising leads from the small molecule collection, focusing on the dimer interface region of \u003cem\u003eWb\u003c/em\u003eTPx1. The predicted \u003cem\u003ein silico\u003c/em\u003e ADMET profiles for the top-ranked hits revealed that the non-toxic lead compounds had the greatest docking score, ranging from \u0026minus;\u0026thinsp;7.410 kcal/mol to -6.887 kcal/mol, and the identified leads showed higher affinity to \u003cem\u003eWb\u003c/em\u003eTPx1 than \u003cem\u003eHs\u003c/em\u003eTPx1 based on the cross-docking studies. Throughout the simulation of the \u003cem\u003eWb\u003c/em\u003eTPx1-lead complex, the lead molecules exhibited stability and remained intact within the binding site. Furthermore, the \u003cem\u003ein vitro\u003c/em\u003e validation of the chosen leads based on computational results in the filarial worm \u003cem\u003eSetaria digitata\u003c/em\u003e exhibited higher inhibition and better IC\u003csub\u003e50\u003c/sub\u003e than the standard drug ivermectin. Hence, the identified leads have the potential to inhibit enzyme activity, serving as possible drug candidates for the control of LF.\u003c/p\u003e","manuscriptTitle":"Comprehensive Approach to in silico Identification and in vitro Validation of Anti-Filarial Lead Molecules Targeting the Dimer Interface of Thioredoxin Peroxidase 1 in Wuchereria bancrofti: A Progress in Anti-Filariasis Drug Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 09:24:18","doi":"10.21203/rs.3.rs-3982867/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-27T01:48:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-27T00:19:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-26T15:21:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Diversity","date":"2024-02-23T19:06:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"701685e7-e7cd-4a5e-8879-42929fba206b","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-23T16:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-28 09:24:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3982867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3982867","identity":"rs-3982867","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}