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BY 4.0 license Open Access Published by De Gruyter Open Access November 24, 2023

Utilization of computational methods for the identification of new natural inhibitors of human neutrophil elastase in inflammation therapy

  • Abdulrahim A. Alzain EMAIL logo , Fatima A. Elbadwi , Ahmed A. Al-Karmalawy , Rashid Elhag , Wadah Osman and Ramzi A. Mothana
From the journal Open Chemistry

Abstract

Human neutrophil elastase (HNE) plays a crucial role in causing tissue damage in various chronic and inflammatory disorders, making it a target for treating inflammatory diseases. While some inhibitors of HNE’s activity have been identified, only a few have made it to clinical trials. In this study, computational methods were employed to identify potential natural products (NPs) capable of targeting the active site of HNE. The protein–ligand complex has been used to generate a pharmacophore model. A library of 449,008 NPs from the SN3 database was screened against the generated model, resulting in 29,613 NPs that matched the pharmacophore hypothesis. These compounds were docked into the protein active site, resulting in the identification of six promising NPs with better docking scores than the bound ligand to HNE. The top two NPs (SN0338951 and SN0436937) were further evaluated for their interaction stability with HNE through molecular dynamics simulations. Further, the pharmacokinetics and toxicity properties of these compounds were predicted. The results indicated that these two compounds have stable interactions with HNE, as well as, acceptable pharmacokinetic properties. These findings pave the path for further in vitro and in vivo studies of SN0338951 and SN0436937 as promising agents against inflammatory diseases.

1 Introduction

Human Neutrophil Elastase (HNE) is classified as a serine protease and belongs to the chymotrypsin-like family. It is primarily found in neutrophils, which are important components of the immune system. HNE serves a crucial role in the pathogen’s destruction, both inside and outside the cells [1]. It is a relatively small, positively charged, soluble glycoprotein with a molecular weight of approximately 30 kDa. The proteolytic nature of HNE allows it to degrade various extracellular matrix proteins (EMPs), including elastin, collagen, fibronectin, laminin, and proteoglycans, [2]. This broad substrate specificity enables HNE to participate in numerous physiological processes, including blood coagulation, apoptosis, and inflammation. HNE can also modulate the expression of cytokines and growth factors [1]. In the context of an inflammatory response, HNE plays a role in the formation of neutrophil extracellular traps (NETs), which are released by neutrophils. NETs have several functions, including the degradation of virulence factors and EMPs, participation in immune responses, and regulation of inflammatory cytokines. HNE’s activity is normally regulated by endogenous inhibitors known as serpins. This family of inhibitors includes elafin, α1antitrypsin, and secretory leukocyte protease inhibitor [3,4]. These serpins help prevent excessive tissue damage caused by HNE and regulate inflammatory processes [3,4,5].

Imbalances in the activity of HNE and serpins can lead to various pathologies [6]. Excessive HNE activity can result in the hydrolysis of elastin and other extracellular matrix proteins, as well as inflammatory mediators, cell surface receptors, and lung surfactants [7]. Furthermore, HNE can activate other proteases and cytokines, leading to an amplified inflammatory response. Therefore, maintaining the balance between HNE and serpin activity is crucial for proper physiological function and the prevention of pathological conditions [4,6,8]. Several lung illnesses have been linked to disturbances in the balance between HNE functioning and its regulatory inhibitors, including chronic obstructive pulmonary disease, cystic fibrosis, acute respiratory distress syndrome (ARDS), and acute lung injury (ALI) [9,10,11,12,13], as well as neutrophil-driven inflammatory diseases like psoriasis, dermatitis, atherosclerosis, and rheumatoid arthritis, and inflammatory bowel disease [14,15,16]. HNE has also been implicated in the progression of cancer. Its proteolytic activity can contribute to the uncontrolled growth, invasive behavior, and spread of tumors by degrading extracellular matrix components and promoting angiogenesis [17]. Additionally, HNE plays a crucial role in both the immediate pathogenesis and the ongoing functional recovery following traumatic brain injury [18]. Its involvement in cardiovascular diseases, myocardial injury, and aortic calcification has also been observed. In the context of COVID-19, HNE and neutrophil degranulation have been linked to disease severity, with increased levels of HNE and NETs contributing to microvascular thrombosis, airway epithelial damage, inflammation, and respiratory failure. Targeting HNE has emerged as a potential therapeutic approach for COVID-19 [19].

Given the significant role of HNE in immune response, tissue remodeling, and inflammation, compounds capable of modulating its proteolytic activity hold promise as therapeutic agents for inflammatory diseases associated with excessive HNE activity [20,21]. During the previous decades, research efforts have influenced the development of various elastase inhibitors, in different generations [22,23,24]. The first generation focused on targeting the binding cleft of the enzyme but had limitations such as instability under oxidative stress and limited administration routes. The second generation introduced small molecules with reversible or irreversible covalent binding mechanisms, including Sivelestat, which is a non-peptide selective HNE inhibitor primarily used in Japan and Korea for ALI and ARDS and showed moderate potency [25,26]. Non-reactive and reversible inhibitors derived from lead compounds like pyridone and dihydropyridine (DHPI) were used in the third generations and fourth generations. AZD9668 and BAY-678 were two effective molecules that are orally bioavailable and were assessed for safety and tolerability. However, the compound AZD9668 demonstrated limited or negligible advantages, while BAY-678 demonstrated favorable pharmacokinetics [27]. DHPI’s fifth generation of inhibitors further improved the potency by incorporating additional substituents, such as SO2Me. This aroused the pressing need to search for an effective HNE inhibitor with suitable pharmacokinetics [23,28,29].

The pharmaceutical industry has benefited from the use of natural ingredients in both conventional and alternative treatments. Natural products (NPs) derived from plants, animals, or their byproducts have been used as conventional or supplementary treatments for various illnesses, both treatable and incurable. These natural ingredients have gained global popularity for promoting healthcare and disease prevention [30]. SN 3.0 is a database that offers a comprehensive collection of NPs and their derivatives [31]. It contains a significant number of bioactive ingredients that are beneficial for human health [32,33,34] and is involved in the production of approximately 50% of current drugs [35,36]. NPs offer advantages such as better bioavailability and minimal adverse effects compared to synthetic drugs [37,38].

Computational-aided drug design (CADD) techniques have become increasingly important in early-stage drug discovery, offering benefits such as accelerated development, cost reduction, and minimizing late-stage failures [39,40,41,42]. In the context of HNE inhibition, several research groups have made significant contributions using CADD approaches. The study conducted by Nunes et al. focused on developing new inhibitors for HNE with a 4-oxo-β-lactam scaffold, specifically targeting HNE in the context of psoriasis. They employed molecular docking techniques and confirmed the potential of a specific compound, styphelioidin, to interact with HNE [43]. Al-Sayed et al. conducted a study highlighting a natural compound derived from Melaleuca styphelioides as a promising candidate with hepatoprotective and anti-inflammatory properties. Their work involved molecular docking to identify and evaluate the compound’s potential interactions with HNE [44]. Jakimiuk et al. concentrated on elastase inhibitors derived from natural sources, particularly flavonoids. They characterized the biochemical properties of these inhibitors, utilizing CADD methods to assess their binding affinity and selectivity towards HNE [45]. The study by Nayak and Sundararajan aimed to identify potent natural inhibitors targeting the active site of HNE using computational methods. Their approach involved virtual screening, molecular docking, absorption, distribution, metabolism, excretion and toxicity (ADMET) property predictions, and molecular dynamic simulations. They identified ten compounds with strong interactions with HNE [46]. These studies collectively demonstrate the utilization of CADD techniques in the design, evaluation, and identification of potential HNE inhibitors, showcasing the value of computational methods in drug discovery and development.

In this context, the aim of this study is to identify natural inhibitors using a targeting approach that combines pharmacophore modeling, docking, molecular dynamics (MD) simulations, and ADMET prediction. These computational techniques enable the identification and evaluation of potential natural compounds that can inhibit HNE activity.

2 Methods

All computational studies were carried out using Maestro v 12.8 by Schrödinger and academic Desmond v6.5 by D.E. Shaw Research for MD.

2.1 Preparation of protein

The HNE’s 3D structure, obtained from the protein data bank (PDB) with ID 5A8Y including its bound ligand VBM (methyl{(7r)-6-cyano-7-(4-cyanophenyl)-5-methyl-4-[3-(trifluoromethyl)phenyl]-4,7-dihydro [1,2,4] and triazolo[1,5-a]pyrimidin-2-yl}carbamate), was initially downloaded but required preparation and refinement before analysis [19]. The downloaded structure lacked hydrogen atoms, had missing bond connections and charge information, and omitted some amino acid residues. To address these issues, the protein preparation wizard of Schrödinger was utilized. This tool helped with tasks such as assigning bond orders, adding hydrogen atoms, filling in missing side chains and loops using Prime, removing solvent and crystalline solute molecules, optimizing the hydrogen bond (HB) network, and performing energy minimization with OPLS4 force field [47,48]. These steps aimed to refine the protein structure, ensuring it was in a favorable conformation for subsequent analysis [47,49].

2.2 Retrieval of the database and grid generation

A dataset containing 449,008 NPs in structures data file format was obtained from the SN 3.0 database. These NPs underwent a minimization process using the macroModel program. The goal of the minimization process is to optimize the molecular geometry and energetics of the NPs, ensuring that they are in a favorable conformation for subsequent studies [50]. In this case, the OPLS4 force field, known for its accuracy in describing protein–ligand interactions, was employed during the minimization process to ensure that the NPs’ conformations were compatible with the parameters of the force field.

The Receptor Grid Generation tool in Maestro is designed to generate a grid specific to the prepared protein for accurate docking studies [51]. In the case of the HNE protein, the active site was determined around the bound ligand (VBM) by utilizing its coordinates. This information is utilized to build a 3D grid with precise dimensions that represent the active area or binding pocket of the HNE protein. The generated grid allows for precise ligand alignment to the active site and enables the evaluation of potential binding interactions [52,53].

2.3 E-pharmacophore generation

Structure-based pharmacophore design leverages the structural information of the ligand and protein complex to create a pharmacophore model. This approach aids in virtual screening and the discovery of compounds with desired pharmacophoric features, which could be further optimized and developed as potential therapeutics or modulators of the target protein [54]. In this study, the researchers used the PHASE module of Schrödinger’s software to perform pharmacophore modeling [55]. The first step involves preparing the input files for the PHASE module. This typically includes the protein–ligand complex structure obtained from X-ray crystallography or other structural determination methods. The ligand is usually chosen based on its bioactive conformation and binding interactions. The PHASE module employs an algorithm to generate the pharmacophore model using the input structure. It utilizes an “auto” method, known as e-pharmacophore, which automatically determines the relevant features and their spatial arrangement based on the protein–ligand complex. The PHASE module selects specific chemical features to construct the pharmacophore model [56,57]. These features include positive hydrophobic (H), aromatic ring (R), negative ionizable (N), ionizable (P), HB donor (D), and HB acceptor (A). The selection is based on their relevance to the binding interactions observed in the complex. The module constructs pharmacophoric sites around the atoms that contribute to the overall energies within the complex. These sites represent spatial regions where specific chemical features are present. Each pharmacophoric site is associated with energy terms calculated from the atoms within the site. The energy terms are summed up, and the sites are ranked based on their energies. This ranking helps identify the most important and energetically favorable pharmacophoric features.

2.4 Pharmacophore-based ligand screening

We used the generated pharmacophore model, which included the specified feature, to screen the SN3 database. The objective was to identify potential inhibitors of HNE. During the screening process, every molecule in the SN3 database was thoroughly examined to determine if it matched all the sites of the pharmacophore model. Partial matches were not considered; instead, all sites of the pharmacophore had to be satisfied for a molecule to be considered a hit [58,59]. The molecules will exhibit structural characteristics and spatial arrangements that are consistent with those of the native ligand and will have the necessary features and interactions to potentially inhibit HNE activity. These molecules could be considered for further investigation and experimental validation as potential HNE inhibitors in subsequent stages of drug discovery.

2.5 Molecular docking

The compounds selected from the pharmacophore screening, which were predicted to have interactions similar to the native ligand, underwent docking simulations using the glide module [60]. High throughput virtual screening (HTVS) is a rapid virtual screening method employed in the initial stage of the docking simulations. It efficiently screens an enormous number of ligands against the HNE protein’s active site. HTVS prioritizes speed over accuracy, allowing for a quick assessment of vast chemical space and identifying potential ligands for further analysis. Following HTVS, the top-ranked compounds from the initial screening were subjected to extra precision (XP) docking. The XP method employs a more rigorous scoring function compared to HTVS, which helps filter out false positives and provides a more accurate assessment of ligand binding affinities. XP docking allows for a more precise examination of ligand-protein interactions and is particularly useful in lead optimization stages. In both HTVS and XP docking, the ligands were docked flexibly, meaning that their conformational flexibility was considered during the docking process. This allows for the generation of multiple ligand conformations internally, enabling a more comprehensive exploration of ligand binding modes and conformations within the HNE active site. The ligand-interaction diagram tool was utilized to analyze the bonding interactions between the selected compounds and the HNE protein [61,62]. This analysis helps assess the potential stability and affinity of the ligands within the binding site.

2.6 MD simulations

In the context of studying protein–ligand interactions, MD allows for the exploration of conformational changes, stability, and the nature of intermolecular interactions between the protein and ligand over time [63,64,65]. During the MDS performed using Desmond software, the system was carefully prepared by neutralizing the charges with Na+ and Cl ions. The solvation process using the water model TIP3P ensures that the system is immersed in a realistic physiological environment, resembling the conditions within the human body. The simulation system, including the protein–ligand complex, was placed in an orthorhombic box with dimensions of 10 Å × 10 Å × 10 Å. By maintaining a distance of 10 Å from the box edges, potential artifacts from periodic boundary conditions were minimized. Energy minimization using the LBFGS algorithm was performed to relax the system and eliminate any steric clashes or unfavorable interactions. The minimization process involved adjusting the system until the gradient, a measure of the energy change, reached a threshold of 25 kcal/mol/Å. This step ensures that the system is in a reasonable energy state before initiating the actual simulation. The treatment of electrostatic interactions is crucial in MD simulations. The smooth particle mesh Ewald method was employed to handle long-range electrostatic interactions accurately. Short-range electrostatic interactions were evaluated within a cutoff radius of 9 Å. The MD was made for a length of 100 ns in the isothermal–isobaric ensemble, where the system is subjected to a constant temperature of 300 K and an atmospheric pressure of 1 bar. Temperature control was achieved using the Nose–Hoover thermostat, which maintains the average temperature of the system. Pressure control was maintained using the Martyna–Tobias–Klein barostat, keeping the system at constant pressure. Throughout the simulations, the positions and velocities of atoms in the system were updated based on Newton’s laws of motion. The resulting trajectories captured the dynamic behavior of the protein–ligand complex, including conformational changes, flexibility, and intermolecular interactions [66,67].

2.7 ADMET prediction

In this study, we aimed to address the issue of poor properties leading to the failure of drug candidates during clinical trials. To identify compounds with unsatisfactory ADMET properties, early screening techniques were implemented. The QikProp module was utilized to predict the ADME characteristics of the hit molecules obtained in the study [68]. A total of eight ADME properties were analyzed, with a focus on human oral absorption (HOA) and Lipinski’s Rule of Five. Given that oral routes are considered the most convenient form of targeted therapy administration, these properties were given more priority. Additionally, other properties such as central nervous system (CNS) activity, Star, predicted aqueous solubility (QPlogS), blood-brain barrier partition coefficient (QPlogBB), predicated IC50 value for blockage of HERG potassium channels (QPlogHERG), and predicted binding of human serum albumin (QPlogKHSA) were calculated for the hit molecules using the QikProp module [69,70]. This comprehensive analysis aimed to provide a broader understanding of the compounds’ ADME profiles. To estimate the side effects and toxicity of the compounds, the web server ProTox-II (https://tox-new.charite.de/protox_II/index) was employed. This web server predicted toxicity class, organ toxicity (specifically hepatotoxicity), and toxicological endpoints such as cytotoxicity and immuno-toxicity for the query molecules. The combined use of QikProp for ADME prediction and the ProTox-II web server for toxicity estimation allowed us to assess the ADMET properties and potential side effects of the hit molecules, providing valuable information for further drug development and optimization [71,72].

3 Results and discussion

HNE is a serine proteinase that has been extensively studied to develop effective inhibitors [73]. It plays multiple roles in pathogen killing, inflammation regulation, and neutrophil migration [74,75]. However, excessive HNE activity can lead to imbalances and contribute to inflammatory diseases [76,77]. Therefore, targeting HNE offers an encouraging approach to treating various inflammatory conditions [28,29,78]. In this study, a combination of computational methods was employed to calculate the binding affinities and stability of natural ligands for HNE.

3.1 E-pharmacophore model and pharmacophore-based ligand screening

We utilized the PHASE module to construct an e-pharmacophore model for designing inhibitors of HNE. This model aimed to identify favorable regions within the active site of bound ligand (VBM) and incorporated both structural and energetic aspects. It consisted of four pharmacophoric sites: one HB acceptor (A) site and three aromatic rings (R) sites (ARRR), with a tolerance of 2 Å for each feature as depicted in Figure 1. Each feature in the e-pharmacophore model was assigned an XP score of −0.09, −1.18, −1.41, and −0.12 kcal/mol for R11, R12, R13, and A1 features, respectively. Using the e-pharmacophore model, we performed a screening of the SN3 database to prioritize the most promising candidates for HNE inhabitation. The ligands in the database were examined in different conformations, and their sites were predefined to match the four constructed e-pharmacophore. The screening process resulted in the retrieval of 17,415 hit molecules from the SN3 database, which initially contained 449,008 NPs. These hit molecules demonstrated alignment with the e-pharmacophore model and exhibited the necessary characteristics for binding to HNE. As a result, these molecules were considered potential candidates for further investigation.

Figure 1 
                  The generated pharmacophore hypothesis.
Figure 1

The generated pharmacophore hypothesis.

The 17,415 hit molecules were subsequently subjected to additional in silico screening processes to identify the most promising candidates for experimental investigation.

3.2 Docking studies

Docking of NPs with target enzymes/proteins has become highly beneficial in the identification of novel inhibitors for various deadly diseases. It is a predominant tool in CAAD, enabling the prediction of the binding mode of a ligand with a known target protein [79]. In silico methodologies, including molecular docking, have significantly contributed to drug discovery and clinical trial research in unexplored research areas. Interestingly, the number of articles published on molecular docking has shown an increasing trend for more than two decades. In this particular study, docking screening was conducted to identify NPs with better glide scores and glide energies compared to a reference ligand (≤−9.06 kcal/mol). The initial pool of 17,415 hit molecules obtained from the phase screening was utilized for the subsequent screening process, which consisted of two stages. In the first stage, the 17,415 hit molecules underwent HTVS docking. As a result, 464 molecules were selected with Glide scores lower than −6.00 kcal/mol. These 464 molecules demonstrated favorable binding affinities and were chosen for further analysis. In the second stage, the top 100 molecules from HTVS docking were subjected to XP docking. From the XP docking results, 43 hit molecules exhibited glide scores lower than −7.00 kcal/mol, indicating improved binding affinities compared to the reference ligand. Additionally, six molecules were identified with higher glide scores than the reference ligand, suggesting a potential for further optimization or modification. These results, including the glide scores and glide energies of the hit molecules, are summarized in Table 1.

Table 1

2D structures, docking score, and the interaction of the top six compounds and the bound ligand with the HNE protein (PDB ID: 5A8Y)

Title 2D structures docking score H-bond Hydrophobic Pi–pi
SN0338951 −10.278 SER214, CYS191, TYR94 TYR94, PRO90, LEU99, PHE215, VAL216, CYS191, VAL190, PHE192, ALA213, CYS220
SN0436937 −9.839 SER214, TYR94 TYR94, PRO90, LEU99, LEU100, VAL216, CYS191, VAL190, PHE192, ALA213
SN0313210 −9.67 SER195, GLY193, VAL216 TYR94, PRO90, LEU99, PHE215, VAL216, CYS191, VAL190, PHE192, ALA213, ALA60
SN0015734 −9.6 TYR94, HIS57 TYR94, PRO90, LEU99, PHE215, VAL216, CYS191, VAL190, PHE192, ALA213, CYS220 HIS57
SN0013792 −9.351 SER214, HIS57, GLY193, PRO96, VAL216 TYR94, PRO90, LEU99, PHE215, VAL216, CYS191, VAL190, PHE192, ALA213
SN0030933 −9.296 VAL216, SER214, HIS57 LEU99, PHE215, VAL216, CYS191, VAL190, PHE192, CYS220
5A8Y bound ligand (VBM) −9.055 HIS57, VAL216 TYR94, PRO90, LEU99, LEU100, PHE215, VAL216, CYS191, VAL190, PHE215, PHE41, ALA213 HIS57

The interactions between small molecules and proteins have a significant impact on protein inhibition, influencing the design of effective therapeutic interventions. These interactions include HBs, hydrophobic interactions, electrostatic interactions, and pi-stacking/pi-cation interactions. HBs stabilize the inhibitor-protein complex, while hydrophobic interactions prevent protein access to substrates. Electrostatic interactions modulate protein conformation, and pi interactions contribute to complex stability. Designing molecules that interact strategically with specific protein residues allows for the modulation of protein function. The strength, specificity, and selectivity of these interactions, along with the protein’s structural context, determine the impact on protein inhibition. Understanding these interactions is essential for the development of potent protein inhibitors and targeted therapies. Here we utilized Maestro’s ligand-interaction diagram tool to validate the binding mechanisms of the top six hit molecules. This tool enabled us to predict how these molecules would bind and interact with the crucial active site residues of HNE, as shown in Figure 2. We chose the native ligand from the PDB’s crystal structure of HNE as a reference for the docking study.

Figure 2 
                  The top two compounds bound to HNE. (a) 3D interaction diagrams. (b) Position of compounds in the HNE protein cavities. (c) 2D interaction diagrams.
Figure 2

The top two compounds bound to HNE. (a) 3D interaction diagrams. (b) Position of compounds in the HNE protein cavities. (c) 2D interaction diagrams.

HNE is a glycoprotein composed of a single peptide chain consisting of 218 amino acid residues. It contains four disulfide bridges that contribute to its structural stability. The catalytic activity of HNE is facilitatead by a catalytic triad, which is a conserved feature among serine proteases, compromising His57, Asp102, and Ser195 residues. In inhibitor design, interactions with these residues are important for inhibiting the catalytic function of the enzyme. Disrupting the charge relay system formed by these residues can prevent the proper functioning of the catalytic machinery, thereby inhibiting the enzymatic activity of HNE. Interactions that hinder the positioning or proton transfer ability of the catalytic triad residues can effectively inhibit the proteolytic activity of the enzyme, especially HBs. The S1 pocket, also known as the primary enzyme specificity pocket, plays a crucial role in determining the substrate specificity of HNE. It primarily includes Phe41, His57, residues 42–58, Val190, Phe192, Ala213, Val216, Phe228, and other residues within the range of 191–220. The S1 pocket exhibits a hemispheric shape and is predominantly hydrophobic. The hydrophobic nature of the S1 pocket allows it to accommodate medium-sized aliphatic side chains. This characteristic is important for substrate recognition and binding. The hydrophobic environment of the S1 pocket enhances the affinity for substrates that possess hydrophobic regions or side chains. Inhibitors that can form favorable hydrophobic contacts with specific residues within the S1 pocket contribute to enhance the binding affinity and inhibition potency [2]. The analysis of docking interactions revealed that the hit molecules can bind to both the catalytic triad and the S1 pocket of HNE, as indicated in Table 1. These interactions involved the establishment of HB with key residues including SER214, CYS191, TYR94, GLY193, VAL216, HIS57, and PRO96. Upon comparing the hit molecules with the reference ligand, it was observed that they shared HB interactions with HIS57 and VAL216. The presence of HB with these important residues is critical for determining the binding strength of ligands to proteins, making it a crucial aspect in the design of new inhibitors. A detailed analysis of the HBs, including their number and distance, can be found in Table 2. Furthermore, the hit molecules exhibited hydrophobic interactions with essential residues such as TYR94, PRO90, LEU99, PHE215, VAL216, CYS191, VAL190, PHE192, ALA213, ALA60, and CYS220. Comparing the hit molecules with the reference inhibitor bound to HNE, sharing hydrophobic interactions were observed with residues TYR94, PRO90, LEU99, PHE215, VAL216, CYS191, VAL190, PHE192, ALA213, and ALA60. Additionally, one of the hit ligands displayed a pi–pi interaction with HIS57, similar to the native ligand mentioned in Table 1.

Table 2

H-bond analysis of the best two compounds

Compound HB
No. of H-bonds Receptor residue Ligand group Distance (Å)
SN0021307 3 TYR94: OH− −O═C− 2.08
CYS191: C═O− −OH 2.02
SER214: C═O− −OH 1.89
SN0449787 3 TYR94: N═ −OH 2.19
SER214: NH− −O═C− 2.67
SER214: NH− −O═C− 1.96

To confirm our results and compare the binding affinity, we searched for previous computational studies on HNE in the literature. These studies can offer valuable insights into similar interactions and provide a reference for evaluating the docking scores observed in this research and assess the consistency and reliability of the results. Feng et al. conducted a computational study on HNE, focusing on ONO-5046’s binding properties using molecular docking [80]. Their results showed that ONO-5046 formed HBs with Ser195, Gly193, and Ser214, while also interacting hydrophobically with residues such as Phe41, His57, Val62, Leu99B, Cys191, Phe192, Phe215, and Val216. This outcome supports the results of our study. Vergelli et al. also employed docking to design new scaffold inhibitors for HNE [81]. Their ligands exhibited hydrophobic interactions with residues Cys191, Phe192, Phe215, Val216, and Cys220, which is consistent with our study. Giovannoni et al. [7], Crocetti et al. [82], and Mohan et al. [83] performed in silico studies using docking to evaluate the affinity of phytochemical ligands for HNE . Their identified ligands formed HBs with Gly193, His57, and Ser195, corresponding to our results. Steinbrecher et al. conducted a comprehensive computational study on HNE using a docking study [84]. The ligands they discovered formed HBs with Gly193, Val216, and Ser195. By comparing our results with these literature studies, we find consistent findings regarding hydrogen bonding and hydrophobic interactions with specific residues, while also noting the improved docking scores.

We also examined the literature to determine the source and biological activity of the top two molecules (SN0338951 and SN0436937) with the best docking scores that were selected for further MD analysis. SN0338951 corresponds to trididemnic acids B, which are quinoline alkaloids derived from a species of Trididemnum found in British Columbia [85]. The potential of this compound as an anti-inflammatory drug is unquestionable, as Trididemnum is known to produce compounds with confirmed immunosuppressive activities that are even 100 times more potent than the immunosuppressive drug cyclosporin [86]. On the other hand, SN0436937 refers to Lepidine F, which has been discovered in brassicas and garden cresses (Lepidium sativum). This suggests that Lepidine F could serve as a potential biomarker for the consumption of these foods [87,88]. Lepidium sativum, a plant that has been used for culinary and medicinal purposes for many years, possesses a range of beneficial properties [89]. Numerous parts of the herb have been found to exhibit antioxidants, hepatoprotective, immunomodulatory, anti-inflammatory, antiasthmatic, antihypertensives, and hypoglycemic properties. Roasted Lepidium sativum is specifically used in the Unani system of medicine for its anti-inflammatory effects [90]. The plant has been associated with the treatment and management of several diseases, including asthma, pain, and inflammation, highlighting its therapeutic potential in addressing inflammatory conditions [91,92].

3.3 MD simulation

MD is an important computational tool used in conjunction with docking to investigate the behavior of protein–ligand complexes over time. It helps refine and validate docking results by assessing the reliability of predicted binding modes and filtering out compounds that cannot maintain stable interactions. By considering factors such as temperature and solvent effects, MD simulations provide insights into stability, flexibility, and conformational changes [93,94,95].

Root mean square deviation (RMSD) is a critical measure used to assess the stability and integrity of a protein’s structure. When a protein binds to compounds at its active site, it can undergo structural changes that affect its conformational stability. The plot presented in Figure 3 reveals the RMSD for the protein–ligand complexes. In the case of the complexes involving SN0338951, SN0436937, and the reference, the protein demonstrated an average RMSD of 1.56 Å with a standard deviation of 0.16 Å. These findings indicate that the protein maintains a stable conformation when bound to these ligands. The average RMSD value of 1.56 Å suggests that the protein’s structure remains relatively consistent throughout the simulation. Furthermore, the small standard deviation of 0.16 Å implies that any conformational changes that occur are not highly fluctuating. On the other hand, the ligands exhibited different average RMSD values. Specifically, SN0338951, SN0436937, and the reference had average RMSD values of 0.49, 1.44, and 0 Å, respectively. In the HNE-SN0338951 complex, the protein initially showed slight deviations ranging from 1.25 to 2 Å within the first 20 ns of the simulation. However, it then reached a steady state with an RMSD between 1.5 and 1.75 Å for the remainder of the simulation. The ligand, on the other hand, initially exhibited a steady state with an RMSD ranging from 0.5 to 0.75 Å. However, it later underwent larger deviations ranging from 0.75 to 1.75 Å. Despite these fluctuations, the ligand eventually stabilized and maintained a strong interaction with the protein for the remaining simulation time, with an RMSD between 1.25 and 1.70 Å. In the HNE-SN0436937 complex, the receptor initially showed fluctuations with an RMSD range of 1.25 to 1.50 Å in the first 20 ns. It then experienced a larger deviation of 2 Å at around 21 ns, after which it settled into a steady state with fluctuations between 1.5 and 1.75 Å. The ligand, on the other hand, exhibited small deviations in the first 20 ns but then experienced a larger deviation of 1.75 Å. Nevertheless, it maintained a strong interaction with the protein and remained stable with an RMSD between 1.5 and 1.75 Å for the remaining simulation time. In contrast, in the HNE-reference complex, the receptor remained in a steady state with slight deviations in the first 20 ns and remained in equilibrium for the remaining simulation time, with an RMSD ranging from 1.5 to 1.75 Å. Similarly, the ligand also stayed in equilibrium with slight deviations in the first 20 ns and maintained a stable interaction with the protein throughout the entire simulation.

Figure 3 
                  RMSD of HNE-complex. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).
Figure 3

RMSD of HNE-complex. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).

Indeed, Root mean square fluctuation (RMSF) is a valuable property for assessing the flexibility of proteins during MDS. It provides insights into the fluctuations of specific amino acid residues and helps evaluate the impact of ligand binding on protein stability. The analysis presented in Figures 4 and 5 illustrates the RMSF values for the selected complexes, which ranged from 0.42 to 3 Å. These findings suggest that the fluctuations of amino acids decrease after the ligand binds to the protein, indicating enhanced stability and reduced susceptibility to structural changes. The average RMSF values were determined as 0.35 Å for SN0338951, 0.8 Å for SN0436937, and 2.44 Å for the reference ligand. These values reflect both the stability and flexibility of the complexes during the MD simulations. Lower RMSF values indicate reduced fluctuations and increased stability of the protein–ligand complexes.

Figure 4 
                  RMSF profile of HNE protein with two complexes. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).
Figure 4

RMSF profile of HNE protein with two complexes. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).

Figure 5 
                  RMSF profile of the ligands. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).
Figure 5

RMSF profile of the ligands. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).

The analysis of the MD simulation results and the interactions between the designed molecules and key residues in the HNE binding site provide valuable insights into the potential of these molecules as inhibitors of HNE. Here a detailed analysis on the impact of these MD interaction results on the inhibition of HNE was made. In SN0338951, the extensive HB interactions observed between SN0338951 and key residues such as SER214, GLU193, TYR94, and HIS57 are indicative of strong binding affinity. These interactions play a crucial role in stabilizing the protein–ligand complex and inhibiting HNE activity. The high formation percentages of these HB interactions suggest the frequent occurrence and stability of the complex throughout the simulation. In addition to direct HB interactions, SN0338951 also formed indirect HB interactions with residues like HIS57, LEU99, PHE192, PHE215, and VAL216, further contributing to the stability of the complex. The hydrophobic interactions observed primarily with TYR94 residue further enhanced the binding affinity of SN0338951. In SN0436937, it also exhibited significant HB interactions with key residues in the HNE binding site. The formation percentages of HB interactions with ASN61 indicate its involvement in stabilizing the protein–ligand complex. Indirect HB interactions with residues like HIS57, LEU99, PHE192, PHE215, and VAL216 further contribute to the stability of the complex. Hydrophobic interactions primarily with TYR94 residue also play a role in enhancing the binding affinity of SN0436937. Although the formation percentages of the interactions with SN0436937 are slightly lower compared to SN0338951, they still indicate a stable and favorable binding mode. The analysis of the reference ligand’s interactions reveals a weak HB with ASN61 and a relatively strong indirect HB with PHE215. The strong hydrophobic interactions with LEU99 and ASP102 residues further contribute to its binding affinity. In summary, the MD interaction results demonstrate that both SN0338951 and SN0436937 have the potential to inhibit HNE activity. Their strong HB interactions with key residues, as well as hydrophobic interactions, contribute to the stability and binding affinity of the protein–ligand complexes. Only interactions with a formation percentage above 20% were reported, while others were notated in Figure 6.

Figure 6 
                  HNE-complex’s interaction diagram. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).
Figure 6

HNE-complex’s interaction diagram. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).

Ligand RMSD values for SN0338951, SN0436937, and the reference ligand ranged from 0.4 to 1.2, 1.2 to 1.8, and 0.6 to 0.9 Å, respectively (Figure 7). The average values were 0.49, 1.44, and 0.58 Å, respectively. The rGyr values, which indicate protein folding status, fluctuated between 4 and 4.4 Å for SN0338951, 3.7 and 4.5 Å for SN0436937, and 5.5 and 5.1 Å for the reference. Various surface area measurements, including molecular surface area (MolSA), solvent-accessible surface area (SASA), and Van der Waals surface area of polar nitrogen and oxygen (PSA), were assessed to evaluate the exposure of the protein’s complexes to solvent molecules and their structural stability. MolSA, SASA, and PSA for SN0338951 were in the range of 294–305 Å2, 146–310 Å2, and 311–300 Å2, respectively. The average values of MSA, SASA, and PSA were estimated to be 300.1 Å2, 243.1 Å2, and 321.9 Å2, respectively. For SN0436937, MolSA, SASA, and PSA were in the range of 316–352 Å2, 411–589 Å2, and 172–223 Å2, respectively. The average values of MSA, SASA, and PSA were estimated to be 339.2 Å2, 512.5 Å2, and 198.7 Å2, respectively. Also in the reference, MSA, SASA, and PSA were estimated to be 449–462 Å2, 380–605 Å2, and 231–209 Å2, respectively. It showed average values for MSA, SASA, and PSA of 334.4 Å, 441.7 Å2, and 117.1 Å2, respectively. The values for each ligand were within specific ranges, indicating stability and complex formation.

Figure 7 
                  PL-contacts diagram. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).
Figure 7

PL-contacts diagram. (a) SN0338951, (b) SN0436937, and (c) 5A8Y bound ligand (VBM).

Based on the analyses provided, it is evident that both SN0338951 and SN0436937 demonstrate conformational stability and exhibit strong interactions with the HNE protein throughout the MD simulations. Also, it supports the notion that these designed molecules may serve as effective inhibitors of HNE, thereby potentially offering therapeutic benefits in conditions where HNE activity needs to be regulated.

3.4 ADMET analysis

To evaluate the ADMET properties of the hit molecules, we employed the QikProp module and ProTox-II algorithms. The analysis focused on several descriptors, including the Lipinski rule, to assess the compounds’ oral bioavailability. All the tested compounds, including the hit molecules and the reference ligand, adhered to Lipinski’s rule of five, indicating a likelihood of high bioavailability (as indicated in Table 3). The molecules were also evaluated for their ability to cross the blood-brain barrier (BBB) using CNS descriptors. Both the hit molecules and the reference showed CNS values of −2, suggesting that they were unable to penetrate the BBB. Additionally, the hit molecules’ HOA properties were examined. SN0338951 demonstrated moderate HOA, while SN0436937 exhibited high HOA. The solubility of the hit molecules was calculated using QPlogS properties. SN0338951 had a solubility value of −2.7, and SN0436937 had a solubility value of −4.6, indicating efficient solubility. However, the reference molecule fell outside this solubility range. Other properties such as QPlogBB, star, QPlogHERG, and QPlogKHSA were also assessed for the hit molecules. The results showed that all these properties fell within an acceptable range for the two hit molecules (Table 4). Furthermore, the hit molecules and the reference ligand were analyzed for toxicity endpoints and hepatotoxicity values (as shown in Table 5). The hit molecules exhibited oral toxicity in classes four and five, indicating favorable safety profiles. However, the reference showed hepatotoxicity and mutagenic effects, suggesting potential toxicity concerns.

Table 3

Lipinski’s Rule of the best two compounds and the bound ligand with the HNE protein (PDB ID: 5A8Y)

Lipinski’s rule
Name Molecular weight (g/mol) Lipophilicity (MLog P) HB donors HB acceptors No. of rule violations Drug-likeness
Less than 500 Dalton Less than 5 Less than 5 Less than 10 Less than 2 violations Lipinski’s rule follows
SN0338951 341.276 0.059 4 8 0 Yes
SN0436937 346.388 3.027 4 4 0 Yes
5A8Y bound ligand (VBM) 479.42 3.419 1 8 0 Yes
Table 4

ADME parameters of the top compounds and the bound ligand with the HNE protein (PDB ID: 5A8Y)

Title # star CNS QPlogS/mol/L QPlogHERG QPlogKHSA QPlogBB HOA %
(0–5) −2 (in active) to +2 (active) (−6.5 to 0.5) Concern: below −5 (−1.5 to 1.5) (−3 to 1.2) <25 is poor
>80 is high
SN0338951 2 −2 −2.697 −3.361 −0.722 −2.890 31.119
SN0436937 0 −2 −4.614 −4.509 0.206 −1.619 86.664
5A8Y bound ligand (VBM) 1 −2 −8.101 −6.316 0.514 −1.976 77.829
Table 5

Toxicity predication of the top two compounds and the bound ligand with the HNE protein (PDB ID: 5A8Y)

Title Cytotoxicity Carcinogenicity Hepatotoxicity Immunogenicity Mutagenicity
Activity Probability Activity Probability Activity Probability Activity Probability Activity Probability
SN0338951 Inactive 0.75 Inactive 0.73 Inactive 0.67 Inactive 0.79 Inactive 0.61
SN0436937 Inactive 0.70 Inactive 0.52 Inactive 0.58 Inactive 0.87 Inactive 0.70
5A8Y bound ligand (VBM) Inactive 0.56 Inactive 0.57 Active 0.57 Inactive 0.96 Active 0.52

4 Conclusion

This study successfully identified two promising natural compounds, SN0338951 and SN0436937, as potential inhibitors of HNE, a therapeutic target for inflammatory diseases. Through a comprehensive computational approach, these compounds were found to selectively bind to key catalytic residues at the active site of HNE, suggesting their potential efficacy as inhibitors. Additionally, they exhibited favorable energetic properties, good ADME profiles, and minimal side effects. These findings provide a foundation for further exploration and development of SN0338951 and SN0436937 as HNE inhibitors for the treatment of inflammatory diseases.

List of abbreviations

ADME

absorption, distribution, metabolism, excretion and toxicity

ALI

acute lung injury

ARDS

acute respiratory distress syndrome

BBB

blood-brain barrier

CADD

computational-aided drug design

COPD

chronic obstructive pulmonary disease

DHPI

dihydropyridine

EMP

extracellular matrix proteins

HB

hydrogen bond

HNE

human neutrophil elastase

HOA%

percentage of human oral absorption

MD

molecular dynamics

MolSA

molecular surface area

NETs

neutrophil extracellular traps

NP

natural product

PSA

Van der Waals surface area of polar nitrogen and oxygen

QPlogS

predicted aqueous solubility

QPlogHERG

predicated IC50 value for blockage of HERG potassium channels.

QPlogKHSA

predicted binding of human serum albumin.

QPlogBB

blood-brain barrier partition coefficient

RMSD

root mean square deviation

RMSF

root mean square fluctuation

SASA

solvent-accessible surface area

SN 3.0

supernatural 3.0

XP

extra precision



Acknowledgements

The authors extend their appreciation to Researchers Supporting Project number (RSP2023R119), King Saud University, Riyadh, Saudi Arabia for funding this work.

  1. Funding information: This project was funded by the King Saud University, Riyadh, Saudi Arabia with Researchers Supporting Project number (RSP2023R119).

  2. Author contributions: Conceptualization: A.A.A.; methodology: F.A.E. and A.A.A.; software: A.A.A.; writing – original draft preparation: F.A.E., A.A.L., and A.A.A; writing – review and editing: R.E., W.O., and R.A.M. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: The authors declare that there are no conflicts of interest.

  4. Ethical approval: The conducted research is not related to either human or animal use.

  5. Data availability statement The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-10-02
Revised: 2023-11-04
Accepted: 2023-11-06
Published Online: 2023-11-24

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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