Molecular docking of alpha-enolase to elucidate the promising candidates against Streptococcus pneumoniae infection

Abstract

Purpose

To predict potential inhibitors of alpha-enolase to reduce plasminogen binding of Streptococcus pneumoniae (S. pneumoniae) that may lead as an orally active drug. S. pneumoniae remains dominant in causing invasive diseases. Fibrinolytic pathway is a critical factor of S. pneumoniae to invade and progression of disease in the host body. Besides the low mass on the cell surface, alpha-enolase possesses significant plasminogen binding among all exposed proteins.

Methods

In-silico based drug designing approach was implemented for evaluating potential inhibitors against alpha-enolase based on their binding affinities, energy score and pharmacokinetics. Lipinski’s rule of five (LRo5) and Egan’s (Brain Or IntestinaL EstimateD) BOILED-Egg methods were executed to predict the best ligand for biological systems.

Results

Molecular docking analysis revealed, Sodium (1,5-dihydroxy-2-oxopyrrolidin-3-yl)-hydroxy-dioxidophosphanium (SF-2312) as a promising inhibitor that fabricates finest attractive charges and conventional hydrogen bonds with S. pneumoniae alpha-enolase. Moreover, the pharmacokinetics of SF-2312 predict it as a therapeutic inhibitor for clinical trials. Like SF-2312, phosphono-acetohydroxamate (PhAH) also constructed adequate interactions at the active site of alpha-enolase, but it predicted less favourable than SF-2312 based on binding affinity.

Conclusion

Briefly, SF-2312 and PhAH ligands could inhibit the role of alpha-enolase to restrain plasminogen binding, invasion and progression of S. pneumoniae. As per our investigation and analysis, SF-2312 is the most potent naturally existing inhibitor of S. pneumoniae alpha-enolase in current time.

Graphical abstract

Introduction

Streptococcus pneumoniae (S. pneumoniae) remains the dominant causative agent of meningitis, community-acquired pneumonia, bacteremia, and otitis media, globally [1]. In 2008, 1.6 million deaths of children (less than the age of 5 years) were caused by invasive pneumococcal disease (IPD) [2]. IPD burden is highest in younger children (less than two years), adults (older than 65 years), and those underlying immunocompromised and chronic conditions [3]. The world health organization (WHO) ranked S. pneumoniae at 12th place among critical pathogens because of its expanding ability of resistance against present antibiotics [4]. S. pneumoniae exhibits several factors that assist in disseminating and invading the host body system. However, binding of exposed cell surface proteins of S. pneumoniae with plasminogen (PLG) as a pro-fibrinolytic component, initiate proteolytic activity, is a significant step in pathogenesis to invade human. Once PLG is converted into active plasmin, it degrades the extracellular matrix and assists pathogen to transmigrate across tissue barriers into deeper tissue sites [5]. A study supervised by Pancholi and Fischetti revealed that alpha-enolase is involved in the pathogenic effect of S. pneumoniae because of potent cell-surface PLG binding protein. Over and above, alpha-enolase possesses a significant PLG binding as compare to other cell-surface proteins [6, 7].

Alpha-enolase, alternative name as 2-phospho-d-glycerate hydro-lyase, is a metalloenzyme that involves in catalyzing the reversible conversion of 2-phosphoglycerate into phosphoenolpyruvate in the glycolytic pathway [8]. Alpha-enolase comes from the cytosol and spread over the cell surface, therefore, it is abundantly present in the cytosolic compartment as compared to the cell surface. Because of highly conservative nature across millions of years, the glycolytic enzyme is considered “dull”. Immensely conserved enolase belonging to a member of moon lightening proteins. These proteins, processed by mysterious mechanisms, exhibit as cell-surface protein and evoke several adhere properties with unveiled localization [7, 9]. Glycolytic enzymes tagged as “void of sophisticated regulatory functions” because the minute quantity of enzyme is changed in the response of external stimuli. However, alpha-enolase is not a housekeeping gene, and its expression changes with the rate of pathological changes and cell growth. Former studies revealed that except for glycolytic activities, these enzymes perform a vital role in various pathophysiological and biological processes [7].

In the innate immune response, S. pneumoniae promotes ample neutrophil infiltration [10]. Alpha-enolase binds over neutrophil, myoblast antigen 24.1D5, to extrude mitochondrial DNA strands studded with antimicrobial proteins called Neutrophil Extracellular Traps (NETs). These NETs increase the motility and assist to eradicate S. pneumoniae extracellularly from the host body [11]. As a result, S. pneumoniae adopts an evolve scheme, because it persists in lungs besides active NETs, to escape from NETs by denaturing the traps through endonuclease, a product of the end A gene. This evolve scheme persuades the progression of S. pneumonia from the upper respiratory tract to lungs and from lungs into the bloodstream [12]. Surface-associated alpha-enolase is a multifunctional glycolytic enzyme persuaded in cellular stress, parasitic infections, autoantigen activates, and the reproduction, growth, and development of organisms [7]. Alpha-enolase is present in low concentration but possesses remarkable binding ability over other bulk cell-surface proteins. Therefore, alpha-enolase plays a vital role in the progression of the disease through the fibrinolytic pathway and it has the potential to be considered for the development of protein-based vaccines [13]. Several studies have done on the virulence factor of S. pneumoniae alpha-enolase that demonstrates its potential in invasion and evocation of immunogenic response over the host system. Nevertheless, an adequate study is required to manifest therapeutic ligand against the treatment of IPD by inhibiting S. pneumoniae alpha-enolase for the development of protein-based vaccine.

In this study, Computer-Aided Drug Designing (CADD) is used to identify the antagonistic binding interactions between ligand (inhibitor) and receptor (protein). Further, pharmacokinetics assisted to predict potential therapeutic ligands and orally active drug for the clinical trials against the treatment of IPD. In short, the present study provides a complete insight into potential inhibitors’ interactions with S. pneumoniae alpha-enolase and their pharmacokinetics that may lead to oral active drugs.

Methods

Physiochemical properties and secondary structure prediction

ProtParam tool [14] was used to predict the physicochemical properties of S. pneumoniae alpha-enolase. Edelhoch method [15] is used as a working principle of ProtParam to compute molecular weight, grand average of hydropathicity (GRAVY), theoretical pI, estimated half-life, extinct coefficient, instability index, amino acid, and atomic composition [16]. Retrieved alpha-enolase sequence from National Centre for Biotechnology Information (NCBI), was processed under the ProtParam tool to identify physicochemical properties. PSIPRED [17] was adopted to evaluate several coils, helixes, and strands in enolase. Two feed-forward neural networks were implemented in PSIPRED that obtain results from Position-Specific Iterated –BLAST (PSI-BLAST) and perform analysis for secondary structure prediction [16].

Protein preparation

The X-ray diffraction-based crystal structure of S. pneumoniae alpha-enolase (PDB ID: 1W6T) in complex with MG and 2PE inhibitors, was retrieved from Protein Data Bank (PDB) with 2.0 A° resolution [18]. The complex bonds and water molecules were removed by operating PyMol software (Fig. 1).

Fig. 1.

Visualization of alpha-enolase (1W6T) after removing inhibitor and water molecules by using PyMol: Orange and blue colours are representing chain a and chain b, respectively; while yellow and green colours are representing binding and active sites, respectively

Auto dock tools (ADT) were utilized to add polar hydrogens and Kollman united charges to increase susceptibility towards electronegative atoms [19]. Grid box (Dimensions: X = 72, Y = 56, Z = 58) was implemented over the active sites of receptor protein to locate ligand.

Ligand preparation

Several synthetic compounds were spotted from PubMed literature as ligand against enzyme commission (EC: 4.2.1.11) of alpha-enolase and the PubChem database was used to retrieve structures of the ligand. ADT was adopted to check Gasteiger charges for selected ligands, and a torsion count widget was chosen to classify rotatable and non-rotatable bonds of ligands [19].

Enrichment analysis (EA)

With so many ligands, virtual screening (VS) methods are considered the best strategy for the particular target(s) under the analysis of considerable significance for consumers. Objective tests are indispensable for this function for any workable method [20–22]. Normally, ligand enrichment is calculated by retrospective small-scale VS with a benchmark set for each approach, as shown by several pieces of literature. Ligand enhancement is a metric of how many decoys are assumed inactive, not likely to bond to the target, to position genuine ligands at the top rank in the screening list [23]. The benchmarking set is a mixture of true ligands and related decoy [24]. The number and quality of the active compounds, included in the test range, are an important problem in determining enrichment rates. In this respect, we want to explain the distinction between active compounds, other actives that directly refer to the receptor, decoys, and compounds randomly picked. Enrichment factor calculation was done by selecting decoys from directory of useful decoys (DUD) [23].

A random collection of 2566 compounds from a wider dataset of over 900 thousand compound structures, provided a subset of commercially available chemicals. The compounds have also been chosen on the following conditions that must be satisfied:

Specific characteristics

• Molecular weight from 150 to 750 Da (Da)

• log D value of −6 to +6

• Number of less than 7 rotation bonds

• A minimum of one (n, O, S, or P) polar atom

To mitigate the well-known inclination of a scoring element to favour larger molecules in the action, decoys or compounds that have no similarity to the active compounds, except for the action, were used with the same distribution of Molecular Weight, to reduce active compounds (refer to below). The collection of decoys was biased to drugs, used filters for functional groups and excluded both molecular weight and the number of rotational bonds to ensure that discrimination by a scoring function was a real challenge. When we merged the chosen random subset with 500 receptor-specific ligands and eliminated duplicate structures, we achieved several 2566 compounds ready for further experiments in computer-processing and docking.

For particular targets and the chosen compounds of the screening database with molecular properties, including logD, MW, rotary bonds, and PSA, were calculated before carrying out virtual screening experiments, and the number of hydrogen bond donors and accepters (HBD and HBA) had been measured. This was done to determine whether an apparent systemic discrepancy between the known active ligand collection and the randomly chosen compounds occurred, to eliminate the possibility of possible distortions in favour of the former. This was a cautionary review. Figure 5 indicates the results. As drug-like guidelines have been applied for the initial random sorting of the compounds, there were no clear discrepancies between known active substances and randomly chosen compounds in aspects of all properties estimated around the specified ranges.

Fig. 5.

ADME parameters distributions of ligands (observing + decoys): based on the threshold, light blue bars are showing active ligands and a pink colour bars are representing in-active ligands in our benchmarking sets for S. pneumoniae alpha-enolase

In-silico docking

In-silico-based drug discovery and designing have expanded with the advanced knowledge-based algorithm developed by a plethora of docking tools including Glide, Auto dock, MOE, Surflex, PyRX, FITTED, and Gold. The computer-aided drug designing (CADD) approach assists to predict bond confirmation with a binding affinity of the receptor (macromolecule) and ligand (small compound) structures acquired from homology modelling and MD simulation, etc. [16]. In this study, we used AutoDock vina for molecular docking and BIOVIA Discovery Studio to visualize results. AutoDock Vina implements sophisticated optimization and PeptoGrid algorithm for rescoring the rate of ligand atom’s appearance at a given grid box with specific characteristics [25, 26]. The results are evaluated using X-score implemented by AutoDock Vina, it is a weighted sum of stearic interactions, the interaction of hydrophobic atoms, and hydrogen bonding [25]. Swift, adequate scoring, and binding algorithm give rise to confident results that make it valuable in the spectrum range of docking tools. The top six ligands were selected based on a significant binding affinity for pharmacokinetics screening.

Drug-likeness analysis

At the onset of the twenty-first century with the evolution of CADD, few adequate methods had designed to predict the ligand potential as an orally active drug. Lipinski’s rules of five (LRo5) [27] and Egan’s BOILED-Egg [28] methods were adopted to evaluate the favourability of ligands for biological systems. Absorption, distribution, metabolism, and excretion (ADME) were predicted by SwissADME [29], a web-based accessible tool available to compute physiochemical properties, pharmacokinetic properties, ADME parameters, drug-likeness, and medicinal chemistry.

Results

Physiochemical properties and secondary structure analysis

S. pneumoniae alpha-enolase sequence was perceived to exhibit 47,102.94 Da molecular weight with an aliphatic index of 87.95. Instability index of 28.63 (less than 40) and − 0.214 GRAVY values demonstrated that the protein is stable with hydrophilic nature (Table 1). Secondary structure predicted by PSIPRED, exhibited that 14 strands (E), 15 helixes (H), and 29 coils (C) are present in alpha-enolase (Fig. 2).

Table 1.

Physiochemical properties of S. pneumoniae alpha-enolase calculated by ProtParam

Serial no.	Feature	Value
1.	Amino acids	434
2.	Molecular weight	47,102.94 Da
3.	Theoretical pI	4.69
4.	*Extinction coefficient	39,310
5.	Instability index	28.63
6.	Aliphatic index	87.95
7.	Grand Average of Hydropathicity (GRAVY)	−0.214

*extinction coefficients are in units of M-1 cm-1, at 280 nm measured in water

Fig. 2.

Secondary structure prediction of alpha-enolase by PSIPRED: Grey, pink, and yellow colours are representing coils, helixes, and strands, respectively

Molecular docking and virtual screening

In-silico molecular docking of S. pneumoniae alpha-enolase was accomplished by using AutoDock Vina and MOE. We used cross-docking method to validate our docking scores and ranking. The results demonstrated that adopted inhibitors were inside the pocket site of alpha-enolase and inhibited successfully with possible interactions, but some of them showed a negligible binding affinity with active site residues. The results ranked based on binding affinity, an inhibitor with a lower value of binding affinity is supposed to be established strong interaction on a specific active site (Table 2).

Table 2.

Ranked inhibitors based on their binding affinity

Serial no.	Name	Compound ID	Binding affinity (KD) (Auto Dock)	Energy score (kcal/mol) (MOE)
1.	Sodium (1,5-dihydroxy-2-oxopyrrolidin-3-yl)-hydroxy-dioxidophosphanium	102,188,018	−7.2	−18.3658
2.	((3 s,5 s)-1,5-Dihydroxy-3-Methyl-2-Oxopyrrolidin-3-Yl)phosphonic Acid	122,362,338	−6.7	−16.7458
3.	[(3S)-1-Hydroxy-2,5-Dioxopyrrolidin-3-Yl]Phosphonic Acid	140,430,625	−6.7	−15.2235
4.	(1 s)-1-Fluoro-2-(Hydroxyamino)-2-Oxoethyl]phosphonic Acid	17,754,240	−6.2	−13.9173
5.	[(3 s)-1-Hydroxy-2-Oxopiperidin-3-Yl]phosphonic Acid	122,540,887	−6.2	−13.2112
6.	Phosphono-acetohydroxamate	445,375	−6.1	−12.2411
7.	D-Tartronate semialdehyde 2-phosphate	21,123,090	−6.0	−12.1869
8.	Citric acid	311	−5.9	−10.4750
9.	2-phospho-D-glycerate	439,278	−5.9	−9.4117
10.	Nonaethylene Glycol	4867	−5.6	−9.3848
11.	2-(N-Morpholino)ethanesulfonic Acid	78,165	−5.5	−7.8519
12.	(2R)-2-phosphonooxypropanoic Acid	444,348	−5.5	−7.6745
13.	2-Phosphoglycol Acid	529	−5.2	−7.5933
14.	Phosphoenolpyruvate	1005	−5.2	−7.4094
15.	Tromethamine	3,777,159	−4.7	−7.4021
16.	(R)-2-methyl-2,4-pentanediol	5,288,845	−4.6	−7.3734
17.	(4S)-2-Methyl-2,4-Pentanediol	5,288,834	−4.4	−7.3698
18.	Triethylene Glycol	8172	−4.2	−7.3663

UniRule annotated S. pneumoniae alpha-enolase, provided by UniProt Knowledgebase (UniProt KB), revealed that 155 (HIS), 164 (GLU), 205 (GLU), 242 (ASP), 291 (GLU), 318 (ASP), 343 (LYS) and 394 (LYS) residues are the binding sites, while 205 (GLU) and 343 (LYS) residues act as active sites. UniRule is a method that integrates related systems, created by the members of the UniProt consortium, for protein annotation and curation. UniRule method manipulates protein family signatures to choose sets of reviewed proteins from protein databases that have similar functional characteristics carried by experimental confirmation [30]. Sodium (1, 5-dihydroxy-2-oxopyrrolidin-3-yl)-hydroxy-dioxidophosphanium (SF-2312) showed the minimum value of the binding affinity of −7.2. Therefore, it displayed the strongest interaction with alpha-enolase among all docked ligands discussed in this study. It constructed conventional hydrogen bonds with binding residues of GLN-163, GLU-164, GLU-205, ASP-242, ASP-318, HIS-371, SER-373, and LYS-394 (Fig. 3). Moreover, LYS-343 and ARG-372 residues fabricated strong attractive charges that assist to bind the ligand more strongly inside the pocket of alpha-enolase to inhibit its activity.

Fig. 3.

SF-2312 interactions with alpha-enolase residues of the active pocket sites: a 3D interactions between ligand and protein with bond length; and b 2D interactions between ligands and protein, where attractive charges and conventional hydrogen bonds are representing as orange and green dotted lines, respectively

((3 s, 5 s)-1, 5-Dihydroxy-3-Methyl-2-Oxopyrrolidin-3-Yl)phosphonic acid (5TX) and [(3S)-1-Hydroxy-2,5-Dioxopyrrolidin-3-Yl]phosphonic acid (KVM) ranked on second and third positions, while both showed −6.7 binding affinity that is higher than SF-2312 ligand. Lower the value of binding affinity results in stronger bonding between ligand and protein. 5TX placed on second because it not only constructed conventional hydrogen bonds with GLN-163, GLU-164, ASP-242, GLU-291, ASP-318, SER-373, and LYS-394 but also fabricated strong bond with active site residue LYS-394 (Fig. 4a). KVM fabricated hydrogen bonds with protein residues of GLN-163, GLU-164, ASP-242, SER-370, ARG-372, SER-373, and LYS-394. However, it did not construct any bond with active site residues that arise doubts on the potential of ligand to inhibit the activity of alpha-enolase. (1 s)-1-Fluoro-2-(Hydroxyamino)-2-Oxoethyl]phosphonic acid (FSG) placed on the fourth number with a binding affinity of −6.2, and it constructed hydrogen bonds with the residues of HIS-155, GLN-163, GLU-164, GLU-205, ARG-372, and SER-373. Moreover, ARG-372 residue also constructed carbon-hydrogen (C-H) bonds that escalated the stability of ligand on the binding site (Fig. 4b).

Fig. 4.

Interactions of ligands with alpha-enolase residues of active pocket sites: a 5TX created conventional hydrogen bonds and unfavourable bond with the receptor, indicating as green and red dotted lines, respectively; b FSG fabricated conventional hydrogen bonds and carbon-hydrogen bond (C-H) with receptor pocket residues of the protein, representing by the green and cyan dotted lines, respectively; c 6BM generated conventional hydrogen bonds and pi-alkyl bond with protein residues, indicating by green and pink dotted lines, respectively; and d PhAH constructed conventional hydrogen bonds and unfavourable bond with residues of alpha-enolase, representing as green and red dotted lines, respectively

[(3 s)-1-Hydroxy-2-Oxopiperidin-3-Yl] phosphonic acid (6BM) indicated the binding affinity of −6.2; therefore, placed on the fifth number against all selected ligands. Like others, it also made hydrogen bonds with residues of GLN-163, ASP-318, LYS-343, ARG-372, and SER-373. It also fabricated one pi-alkyl bond with HIS-371 that helps the ligand in intercalating the binding site of the receptor by transferring of charge (Fig. 4c). Phosphono-acetohydroxamate (PhAH) also showed adequate binding affinity (−6.1) by constructing conventional hydrogen bonds with active site residues GLU-205 and LYS-343 of enolase (Fig. 4d), while the rest of the ligands evinced ineffectual binding affinity score and bond formation. Therefore, the top six ligands were processed further to evaluate the pharmacokinetics that helps to identify potential ligand for biological systems.

Both docking tools (Auto dock and MOE) are showing the same ranking of the receptor-ligand complex based on energy scores. This ranking homogeneity cross-validated our results that Sodium (1,5-dihydroxy-2-oxopyrrolidin-3-yl)-hydroxy-dioxidophosphanium is showing significant potential to block active sites of enolase protein of S. pneumoniae with predicted binding affinity −7.2 by Auto dock and energy score (S-score) -18.3658 by MOE.

Enrichment assessment

Enrichment assessment analysis representing the behaviour of active ligands vs in -active ligands. We used two thresholds to decide actives and in -actives: one is IC50, if IC50 value of any chemical compound is greater than 10 nM then that compound is inactive while chemical compounds having IC50 range from 1- to 10 nM are supposed to be actives against our target protein; second is a violation of Lipinski’s rule of five (LRo5), if chemical compound violating any 2 conditions of rule of five then it is supposed to be inactive for our body and chemical compounds no violation or below one violation is considered as active as shown in Fig. 5.

Pharmacokinetics of ligands

The antagonistic interactions of ligands with receptor protein do not express the favourability of inhibitors in a biological system as a potential drug. Therefore, ADME and drug-likeness is a major key to examine the credentials of ligands in biological systems. LRo5 and Egan’s BOILED-Egg methods were implemented on ligands to interpret pharmacokinetics (ADME). LRo5 depends on threshold values of four physicochemical parameters: molecular weight (MW) should be less than equal to 500 g/mol, lipophilicity (less than equal to 5), and several hydrogen bond donors and acceptors should be less than equal to 5 and 10, respectively [27]. It is a thumb rule to estimate drug-likeness and biological or pharmacological activity of a chemical compound that would create it probably orally active drug. All six selected ligands fulfilled the parameters of LRo5 that demonstrates the suitability of these chemical compounds (ligands) for bioavailability (Table 3).

Table 3.

Drug-likeness evaluation of inhibitors through Lipinski’s rule of five by using Swiss-ADME

Serial no.	Name	Lipinski’s rule of five					Drug-likeness
		Molecular weight (g/mol)	Lipophilicity (MLOGP)	Hydrogen bond donors	Hydrogen bond acceptors	No. of rule violations
		≤ 500	≤ 4.15	≤ 5	≤ 10	< 2	(Yes/No)
1.	Sodium (1,5-dihydroxy-2-oxopyrrolidin-3-yl)-hydroxy-dioxidophosphanium (SF-2312)	219.07	−2.46	3	6	0	Yes
2.	((3 s,5 s)-1,5-Dihydroxy-3-Methyl-2-Oxopyrrolidin-3-Yl)phosphonic Acid (5TX)	211.11	−2.15	4	6	0	Yes
3.	[(3S)-1-Hydroxy-2,5-Dioxopyrrolidin-3-Yl]phosphonic acid (KVM)	195.07	−1.85	3	6	0	Yes
4.	(1 s)-1-Fluoro-2-(Hydroxyamino)-2-Oxoethyl]phosphonic acid (FSG)	173.04	−2.07	4	6	0	Yes
5.	[(3 s)-1-Hydroxy-2-Oxopiperidin-3-fYl]phosphonic acid (6BM)	195.11	−1.35	3	5	0	Yes
6.	Phosphono-acetohydroxamate (PhAH)	155.05	−2.34	4	5	0	Yes

Drug candidates should have sufficient lipophilicity to penetrate through lipid membranes but not too much that they remain there [31]. However, like lipophilicity, gastrointestinal absorption of a chemical compound (ligand) is also important to exert its effect throughout the body. Egan’s BOILED-Egg method was implemented to predict the permeation of ligands through the gastrointestinal tract and brain. This method uses lipophilicity (WLOGP) and topological polar surface area (TPSA) to predict the permeability of ligand through the gastrointestinal tract and brain. It provides egg-like graphical representation against the threshold values of WLOGP (≤ 5.88) and TPSA (≤ 131.6) [28].

2D graphical representation manifested that all ligands fulfil the parameters of Egan’s BOILED-Egg method (Fig. 6). TPSA values of selected ligands were noticed below the threshold value that reveal their non-toxic nature, the toxicity of the compound is associated with TPSA [32]. Moreover, SF-2312, 5TX, and KVM found on the border of the white region, shows low permeation through the gastrointestinal tract. While, FSG, PhAH, and 6BM felled in the white region that predicts passively absorption of these compounds by the gastrointestinal tract.

Fig. 6.

Evaluation of ligands permeability through the gastrointestinal tract and brain by BOILED-Egg method: yolk region is for those molecules that permeate passively through the blood-brain barrier; white region represents those molecules that can passively be absorbed by the gastrointestinal tract

Discussion

The adherence of pathogen to host cells is responsible to initiate infection and invade over the host body. The mechanism frequently adopts by human pathogens, S. pneumoniae, is the activation of proteolytic form plasmin. Alpha-enolase of S. pneumoniae binds human PLG and converts it into serine protease plasmin, which promotes the migration of pathogens by degrading the extracellular matrix [18]. The existence of alpha-enolase as the only enolase of pneumococci, its identical gene expression in all strains, its conserve nature, and its disruption arises to be lethal suggest that alpha-enolase is a vital glycolytic enzyme [33]. Besides less mass on the cell surface, it shows the highest binding capacity to PLG and it should be considered for the development of a protein-based vaccine. Moreover, S. pneumoniae has acquired a unfold strategy for neutrophil infiltration against innate immune response by inducing and degrading NETs.

In the current study, we predicted six compounds that have the potential to inhibit the activity of S. pneumoniae alpha-enolase, but SF-2312 and PhAH showed promising ligands because both created strong bonds with active sites and covered the binding sites. The results of molecular docking analysis revealed SF-2312 as the best inhibitor among studied ligands because of the lowest binding affinity value (−7.2). However, lipid membranes and gastrointestinal tract allow less quantity of it to diffuse, but it fabricated a strong attractive charge with LYS-343 and conventional hydrogen bond with GLU-205 that makes it a potential therapeutic inhibitor for clinical trials. PhAH passively permeates through lipid membranes and the gastrointestinal tract that makes it suitable for bioavailability but it exhibited more value of binding affinity than SF-2312. SF-2312 is a natural phosphonate antibiotic produced by actinomycetes Micromonospora that becomes active under anaerobic conditions. It inhibits the enolase more strongly at low concentration than PhAH and creates a stable complex against thermal denaturation [34].

Neutrophils are summoned at infection sites from the bloodstream in the response of pathogens, where they eliminate microbial pathogens by phagocytosis. Earlier studies show neutrophil release chromatin fibres, called Neutrophil Extracellular Traps (NETs), to trap large size pathogens which cannot be phagocytosed and kill them by assisting higher concentration of antimicrobial factors at the infection site [35]. Recently, it was shown that S. pneumoniae alpha-enolase induces the formation of NETs by binding on myoblast antigen 24.1D5 expressed on the surface of neutrophil. Myoblast antigen 24.1D5 binding site residues of alpha-enolase range starts from 197 to 211 [11]. The infection comes into existence due to insufficient recruitment of neutrophil in the lungs during the innate immune response that can cause lethal infection. Besides, neutrophils can cause excessive lung injury as a result of inflammation [10]. Therefore, the exemplary therapeutic approach is to enhance the critical role against the antibacterial defence and attenuate the devastating effect of neutrophil.

NETs are abundantly found in liver metastases of breasts and colon cancer patients that reveals the chemotactic factor to entice cancer cells rather than eradicate by trapping them. Transmembrane protein CCDC25, present on cancer cells, senses extracellular DNA that enhances cell motility and transmits cancer cells from distant metastases toward NETs position either in the lungs or liver [36]. However, the role of NETs is still concealed and mysterious during IPD. SF-2312 and PhAH construct a strong hydrogen bond with GLU-205 that could inhibit the binding site of alpha-enolase and restraint the binding of alpha-enolase on myoblast antigen 24.1D5 to induce NETs formation. These proposed ligands will facilitate researchers in the development of vaccines and explicate the role of NETs in the innate immune system.

Conclusion

In a nutshell, our study predicted SF-2312 and PhAH as potential orally active drug to inhibit the activity of alpha-enolase during IPD. Moreover, both compounds are proposed as obstacles over the binding of alpha-enolase on myoblast antigen 24.1D5 that will aid to elucidate the fascinating role of alpha-enolase in the regulation of NETs formation. These proposed compounds would be evinced initial exertion to open the pylon for the development of a protein-based vaccine against S. pneumoniae after structured clinical trials.

Abbreviations

5TX

((3 s,5 s)-1,5-Dihydroxy-3-Methyl-2-Oxopyrrolidin-3-Yl)phosphonic acid

6BM

[(3 s)-1-Hydroxy-2-Oxopiperidin-3-Yl]phosphonic acid

BOILED

Brain Or IntestinaL EstimateD

FSG

(1 s)-1-Fluoro-2-(Hydroxyamino)-2-Oxoethyl]phosphonic acid

IPD

Invasive pneumococcal disease

KVM

[(3S)-1-Hydroxy-2,5-Dioxopyrrolidin-3-Yl]phosphonic acid

LRo5

Lipinski’s rule of five

NETs

Neutrophil Extracellular Traps

PhAH

Phosphono-acetohydroxamate

PLG

Plasminogen

SF-2312

Sodium (1,5-dihydroxy-2-oxopyrrolidin-3-yl)-hydroxy-dioxidophosphanium

S. pneumoniae

Streptococcus pneumoniae

TPSA

Topological polar surface area

Authors’ contributions

Conceptualization: Muhammad Hassan and Atif Amin Baig; Methodology: Muhammad Hassan, Atif Amin Baig, Nordin Bin Simbak and Mohammad Amjad Kamal; Software analysis: Muhammad Hassan, Syed Awais Attique, Shafqat Abbas and Muhammad Usman; Validation: Fizza Khan, Sara Zahid, Qurat Ul Ain; Writing – original draft: Muhammad Hassan, Sara Zahid, Qurat Ul Ain; Writing – review & editing: Atif Amin Baig and Hanani Ahmad Yusof.

Data availability

All data produced during the current study are available from the corresponding author on reasonable request.

Compliance with ethical standards

Conflicts of interest/Competing interests

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethics approval and consent to participate

Not applicable.