Structural and molecular insight into the disintegration of BRPs released by massive wasp stings using serratiopeptidase

doi:10.21203/rs.3.rs-2352098/v1

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Structural and molecular insight into the disintegration of BRPs released by massive wasp stings using serratiopeptidase

https://doi.org/10.21203/rs.3.rs-2352098/v1

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Objective and design: Serratiopeptidase a multifaceted therapeutic enzyme renowned for its anti-inflammatory, analgesic, anti-biofilm, fibrinolytic and anti-edemic properties. It is vital to uncover more about the assets of such efficacious enzyme in order to facilitates their contribution in all health-related issues, notably inflammatory ailments. The current study sought to determine whether serratiopeptidase would disintegrate bradykinin related peptides (BRPs) from wasp venom in the same manner as it does with human bradykinin.

Methods: To accomplish this objective, we used molecular modeling, docking, MD simulation, MMG/PBSA along with the SMD simulations.

Results: We docked selected BRPs on to the binding pocket of wild and previously identified mutant (N412D) of serratiopeptidase. Based on their docked scores, top two BRPs were selected and their conformational behavior was analyzed employing molecular dynamics studies. Additionally, thermodynamics end-state energy analysis reported that both the complexes exhibited higher stability and analogous ∆G values when compared to the reference complex. Further, to understand the unbinding mechanism, we condemned external pulling force on both peptides and observed that BRP-7 peptide was tightly anchored and laid out the highest pulling force to get detach from the active pocket of serratiopeptidase.

Conclusion: The current study endorses up the current findings and paves the way for serratiopeptidase to be used as an anti-angioedemic as well as fixed dose combination in hypotensive drugs.

Serratiopeptidase

Bradykinin related peptides

MMG/PBSA

Steered molecular dynamics

Wasp stings

Inflammation acts as a protective measure against infection and injury. Inflammation is therefore viewed as the body's natural purifying function that maintains homeostasis. Generally, inflammation is characterized by excessive localized edema brought on by increased vascular infiltration at the inflammatory site [1]⁠. The severity of the trigger and the pathological condition of the tissues are used to classify acute and chronic inflammation. Numerous factors, including infectious agents, short peptides, proteins, hormones, enzymes, etc., are involved in the molecular biology of inflammation, which is highly complicated [2]⁠. Dysregulation of inflammation can occasionally cause a variety of clinical manifestations, from a simple skin reaction (urticaria, pruritus, anaphylaxis and angioedema) to symptoms of the respiratory system, gastrointestinal tract, cardiovascular dysfunction, or in exceptional instances, even death [3]⁠. Stings from Hymenoptera (wasp and bee) constitute one of the most common causes of anaphylaxis and angioedema [4]⁠. Life threatening stings usually happen in the neck or head region, and within an hour, hypotension, nasopharyngeal fluid retention, or pulmonary obstruction usually leads to death [5, 6]⁠. Wasp venom is primarily comprised of protein, with high molecular weight proteins (phospholipases, antigens, and hyaluronidases) and low molecular weight peptides like wasp kinin (bradykinin related peptide), as well as bioactive compounds including serotonin, histamine, and catecholamines etc. [7]⁠. In this paper we are targeting bradykinin related peptides (BRP) of wasps venom that are primarily accountable for anaphylaxis and angioedema disorders. BRPs potentiates the expression of the inflammatory amine bradykinin in the vicinity of the inflamed area, contributing to systemic inflammation, cellular hyper-reactivity, and hypotension. Bradykinin functions as a key mediator in the kinin-kallikrein system. An interruption in Bk homeostasis causes activation of kallikrein system, which breaks down high-molecular-weight kininogen (HMWK) to produce bradykinin [7, 8]. Factor XII, which is regulated by plasmin via the fibrinolytic pathway, is accountable for activating kallikrein through the contact system. Bradykinin induces substance P secretion and causes angioedema by binding to the bradykinin B2 receptor on the endothelial surface (Fig. 1) [9, 10]⁠. There are several ways to treat bradykinin-mediated angioedema, but serratiopeptidase enzyme is the powerful protease to break down the inflammatory amines like serotonin, histamine, and bradykinin with the fewest side effects like NSAIDs [11]⁠. Serratiopeptidase or serrapeptase a metalloendoprotease isolated from Serratia E15 strain found in silkworm Bombyx mori gut. Serratiopeptidase has been used to treat chronic inflammatory conditions like sinusitis, bronchitis, carpal tunnel syndrome, breast engorgement, post-traumatic swelling, atherosclerosis, wound debridement, and fibrocystic breast disease [12–15]⁠. Serrapeptase emerges to be an ideal substitute for hazardous non-steroidal anti-inflammatory drugs (NSAIDs) in severe inflammatory conditions [11]⁠.

In our previously published study, extensive in-silico research on wild and mutant serratiopeptidase was done to determine how likely this protease would've been able to withstand acid in the stomach's acidic pH in order to utilize it as a potent anti-inflammatory medication [16]⁠. In this study, we broadened the application of serratiopeptidase as an antiedemic therapeutic in cases of severe angioedema by using the application of previously identified mutant (N412D) of serratiopeptidase. To accomplish the goal of our present work we have taken wild-type and mutant (N412D) serratiopeptidase to see if they both break down BRPs (wasp venom) in the same way as it does in case of human bradykinin. We have employed molecular docking, dynamic simulations studies to get in-depth understanding of protein-peptide complexes. Additionally, to illustrate the mechanism of binding and unbinding in all complexes, Molecular Mechanics Generalized/Poisson-Boltzmann surface area (MMG/PBSA) and steered molecular dynamics simulations (SMD) studies were performed.

2.1. Dataset preparation

The crystal structure of the serratiopeptidase enzyme was obtained from the RCSB PDB database with PDB ID 1SAT (471 residues) [17]⁠. The protein structure (1SAT) was refined through the Discovery studio software by using its “protein prepare” methodology. The earlier reported substrate bradykinin (inflammatory amine) has been selected as a reference peptide [18]⁠. Further, Expasy VenomZone, a free web resource, was used, which provides information on venoms from six taxa (snakes, scorpions, spiders, cone snails, sea anemones, and insects), as well as on their targets (http://venomzone.expasy.org/). But Here, we are targeting bradykinin-related peptides, so keeping that thing, we have selected taxa Insecta as our study objective for further analysis. This web server provides different information pages for taxonomy, venom proteases, and protein family activity. These pages are linked to the manually curated UniProtKB/Swiss-Prot database and give detailed information on the respective venom protein. Then from UniProtKB/Swiss-Prot database, we selected all the 14 bradykinin-related peptides (BRPs) among insects (13 wasps and 1spider) Table 1 [19]⁠. Then for further selection, we have attempted multiple sequence alignment with the tool Clustal Omega [20]⁠.

Table 1

A detailed data-set of all the, bradykinin-related peptides (BRPs) among insects (13 wasps and 1spider) by using UniProtKB/Swiss-Prot database.
Bradykinin related peptides (BRPs)	Uniprot IDs	Organism (scientific name)	Protein/Peptide	Organism (common name)	Sequence length
BRP-1	P58396 · KLPS_LYCER	Lycosa erythrognatha	Kinin-like peptide-S	Wolf spider	14
BRP-2	P0DV01 · NBRKB_SCODV	Scolia decorata ventralis	Beta-scoliidine	Solitary wasp	14
BRP-3	P83660 · BRK6_CYPDO	Cyphononyx dorsalis	Bradykinin-related peptide Cd-146	Spider wasp	16
BRP-4	P0DL32 · BRKF_CYPDO	Cyphononyx fulvognathus	Fulvonin	Spider wasp	11
BRP-5	P0DM71 · BRK2_PROEX	Protopolybia exigua	Protopolybiakinin-2	Neotropical social wasp	19
BRP-6	P12797 · BRK_MEGFL	Megascolia flavifrons	Megascoliakinin	Garden dagger wasp	11
BRP-7	P85873 · BRK_POLMJ	Polistes major	Wasp kinin PMM1	Neotropical social wasp	11
BRP-8	P0DM70 · BRK1_PROEX	Protopolybia exigua	Protopolybiakinin-1	Neotropical social wasp	21
BRP-9	P83659 · BRKC_CYPDO	Cyphononyx fulvognathus	Cyphokinin	Spider wasp	11
BRP-10	C0HLT7 · BRK_POLOC	Polybia occidentalis	Thr6-bradykinin	Paper wasp	9
BRP-11	Q7M3T2 · BRK_VESXA	Vespa xanthoptera	Vespakinin-X	Japanese yellow hornet	12
BRP-12	Q7M3T3 · BRK_VESMA	Vespa mandarinia	Vespakinin-M	Asian giant hornet	12
BRP-13	P57672 · BRK_VESMC	Vespula maculifrons	Vespulakinin-1	Eastern yellow jacket (Wasp)	17
BRP-14	P42717 · BRK_PARID	Parapolybia indica	Waspkinin	Lesser paper wasp	13

2.2. Molecular modeling

Model generation for all the peptides were taken into account by using Alpha fold2 jupyter notebook (AlphaFold-Colab or ColabFold)Ref. AlphaFold Colab is a fast and reliable software for protein structure modeling that replaces AlphaFold2's homology search methods (HMMer and Hhblits methods) with 40–60 times faster Mmseqs2 (Many-against-Many sequence searching) server [21]⁠. ColabFold comprises of three main sections. The first is a homology search server based on Mmseqs2 method to create variety of MSAs (multiple sequence alignment) and template identification. The seconds section is a Python library that prepares input parameters for structure prediction by interacting with Mmseqs2 server [22]⁠. The final section contains the jupyter notebooks that employ the Python library for basic, advance, and batch use. In ColabFold the pipeline for structure building comprises of five steps. The methodology for these steps starts with the query sequence submission and then MSA options for server selection in paired and unpaired modes of sequences. In the advance settings parameters were kept by default for the protein structure prediction. In the 4th step we hit the install dependencies run to further run the program. Then in the final step we hit the run prediction, which later shows the 3D structures with plots of model quality and MSA coverage.

2.3. Molecular docking

All the eight BRP models and reference human bradykinin were then subjected to molecular docking with the target protein serratiopeptidase. We used HADDOCK v2.4 (High Ambiguity Driven Protein-Protein DOCKing) an information-driven docking method that can leverage a variety of information regarding two molecules binding interfaces to facilitate docking [23]⁠. In order to predict the interactive or binding site residues from both wild and mutant structures, we employed CPORT (Consensus Prediction Of interface Residues in Transient complexes) webserver [24]⁠. Furthermore, quantitative study of intermolecular contacts (< 5.0Å) is used to automatically determine semi-flexible residues. For each docking trial fifty percent of the AIRs were randomly excluded by default with number of random exclusion partition set to 2.0.

The docking protocol consists of three stages: rigid-body docking/minimization semi-flexible refinement, and explicit water refinement. A total of 10000 confirmations were produced during stage one of the docking of the protein and peptide complex. In order to reduce the likelihood of false positives, 400 rotating solutions were periodically sampled during the rigid-body docking phase. In the second stage of semi-flexible refinement, the top 20 percent of rigid-body docking stage structures were utilized. The final refinement in explicit solvent was performed (1000 MD steps at 300, 200, and 100 K) with position restraints on the backbone atoms of the protein complex, with exception of the interface atoms. After that, a cluster analysis was performed using the pairwise backbone root mean square deviation (RMSD) with threshold value of 5.0. Final ranking of these clusters was carried out on the basis of their interaction energy, buried surface area (BSA), electrostatic and van der waal energies. In order to enhance the reliability of the docking findings, we additionally plot 2D interaction plots using Discovery Studio to determine the interaction patterns and bonding both in mutant and wild protein peptide complexes [25]⁠.

2.4 Molecular dynamics simulation

All simulations were conducted using the Charmm36 force field in GROMACS version 2020 [26, 27]⁠. The topologies for protein and peptide were generated through the in-built ‘pdb2gmx’ script of GROMACS. To effectively accommodate the complexes inside water box, simulations were carried out in a periodic box with a dodecahedron unit cell shape along with the single point charge (SPC) solvent model. In the next step, we have added ions (Na⁺ and Cl⁻) to the solvated system to neutralize the total charge of the system by using the ‘gmx genion’ script. All the complexes were minimized using the steepest decent minimization algorithm with an energy step size of 0.01 and 50000 minimization steps.

Furthermore, under Particle Mesh Ewald (PME) coloumb type and periodic boundary conditions, cut-off parameters for calculating interactions such as electrostatic and van der Waal were set to 1.2 nm. The equilibration of protein-peptide complexes was carried out by applying restraints to the peptide with a Berendsen thermostat as a temperature coupling parameter. LINCS (Linear Constraint Solver) constraint algorithm was employed to retain the hydrogen bond length constrained between the complexes [28]⁠. The equilibration of all the systems was then accomplished in two steps. In the first step under the NVT ensemble, 1000 ps steps and a time frame of 2.0 fs were used with no pressure coupling across all MD systems. Thereafter, all atoms simulations were conducted for 100 ns without any constraints as an NPT ensemble for each protein-peptide complex. The Parrinello-Rahman method and Berendsen's V-rescale weak coupling methodology [29]⁠ were used to equilibrate the system's pressure and temperature to 310 K and 1 atm, respectively [30]⁠. The solvated and equilibrated complexes of the protein-peptide complexes were then run through the production MD phase to generate plots of the radius of gyration (Rg), root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible surface area (SASA), and hydrogen bond (H-bond) trajectories using the built-in GROMACS commands.

2.5. MMG/PBSA free energy calculations

Using the gmx _MMG/PBSA v1.5.6 module based on the MMPBSA.py module, we were able to calculate the free energy of binding for all simulated protein-peptide systems [31]⁠. This binding energy assessment offers a quantitative biomolecular interaction between selected peptides and the target protein. The electrostatic energy, van der Waals energy, solvation energy (polar and non-polar), net gas phase, net solvation energy, and net system energy are all components of this binding energy. The binding free energy of a complex (protein-peptide) was calculated using the following equation:

ΔGbind = ΔGcomplex − ΔGreceptor − ΔGligand (1)

Whereas, Gcomplex represents the complex's Gibbs free energy, Greceptor and Gligand represent the total energy of the protein and ligand, respectively. We have also derived additional parameters to gain a deep understanding of the stability of complexes, interaction entropy, and differences in complexes' binding energies in linear and non-linear PB solver.

2.6. Steered molecular dynamics

All-atom steered molecular dynamics (SMD) simulations of all the protein-peptide complexes were performed using Gromacs [32]⁠. In SMD simulations, to ensure the effect of strain or pull rates, a time-dependent external force is applied to the peptide to facilitates its unbinding from the target protein. All the systems were solvated using SPC water box in 0.15 M NaCl solution by ensuring that the complex system should be at least 10 Å away from the edge of the water box. The complex's centre of mass was set at (6.880, 24181, 3.4775) in a box of dimensions 12.460 x 8.362 x 30. All system preparation in SMD was done using the same method as discussed above in the MD simulation section. All systems go through 50000 minimization steps, which are then followed by three phases of equillibration with harmonic time-dependent potential restraints positioned across all heavy atoms to let the the solution to undertake specific positions all around protein fold. After that each system was then equilibrated using NPT ensemble for 100 ps, with the V-rescale and Berendsen [29]⁠ and Parinello Rahman algorithms [30]⁠ used to keep the system at temperature 310 K and 1 bar pressure, respectively. An external pulling force was exerted across the simulation pathway on the z-axis to facilitate the unbinding of all peptides from the serratiopeptidase protein. Force-controlled simulations were performed for 1000 ps at a pull rate of 10 nm ps^− 1 and a pull force constant of 1000 kJ/mol/nm².

3.1. Three-dimensional model generation using AlphaFold-Colab

Following the dataset of all peptides in Table 1 and the alignment data of aligned sequences in comparison to the reference human bradykinin in Fig. S1, only eight peptides out of 14 were chosen for model generation. The criteria we used to select the peptides were based on a matched sequence length of at least six residues, in comparison to the reference bradykinin's sequence length of nine residues. Since there are no crystal structures for any of the selected peptides (BRP-6, BRP-7, BRP-8, BRP-9, BRP-10, BRP-11, BRP12, and BRP-13) in the PDB database. All of the peptide sequences were available in the UniProt database, and we modeled all of them using the Alpha fold2 jupyter notebook (AlphaFold-Colab or ColabFold). Structural modeling facilitates the functional characterization of the protein sequence for the prediction of three-dimensional (3D) structure, when only sequence data are available. AlphaFold 2 provides five different models of query sequence by ranking them on the basis of pLDDT score. The higher the pLDDT score, the better the model. In terms of the confidence index, if the pLDDT score is less than 70 (70 > pLDDT), the structure is considered unconfident and poorly modeled. In another scenario, if the model's pLDDT score is between 70 and 90, it is considered a good model, and pLDDT scores greater than 90 are considered highly accurate models (pLDDT > 90) [33]⁠. In our case, all eight models had confidence indices ranging from 77 to 89. In comparison to other BRPs, BRP-10, BRP-6, and BRP-7 had higher pLDDT values (88.95, 84.16, and 87.49, respectively) Figs. 2 and 3. Although all of the other BRPs are above the 75 percent confidence level, this simply means that each of the structure are modeled as confident models. Thereby further, all eight modeled structures were chosen for molecular docking simulations.

3.2. Molecular docking score and interaction analysis

After model generation the molecular docking technique was used to induce the best matched binding mode of a peptide to the biological target. To support our main objective of understanding how serratiopeptidase can disintegrate other peptides in comparison to human bradykinin, we used the HADDOCK server to dock wild and mutant serratiopeptidase with all eight modeled BRPs, along with human bradykinin, a reference peptide. HADDOCK is a flexible docking approach for designing biomolecular complexes based on the different type of data about binding interfaces of two molecules. HADDOCK is distinct from previous ab-initio docking techniques in that it employs ambiguous interaction restraints (AIRs) to direct the docking procedure by encoding information from known or predicted potential protein interface binding sites. By submitting the PDB format of individual structures (serratiopeptidase and all peptides) to the CPORT webserver, we obtained the active site residues. In the docking run, we have chooses those active residues (E177, H176, H180, H186, and Y216 ) which were involved in the active region of serratiopeptidase. Subsequently, we have selected all the predicted residues in the peptides sequences of BRPs to achieve the good interaction energy between the docked complexes. To confirm the disintegration mechanism of serratiopeptidase towards wasp BRPs we, took serratiopeptidase and bradykinin complex’s (wild and mutant) docking values from previous studies as the reference values (HADDOCK, Z-score, and binding energy) [34]. The HADDOCK server's findings demonstrated the clusters of water refined models of the submitted complexes, and the clusters were being listed in the order of their haddock score. The mean values of the top-four best-scoring structures within each PDB cluster were represented by in-depth matrices for each cluster. Further, we chose the top cluster for each complex from among the top ten best clusters. Each cluster contain four best structure, among those we have chooses the structure with higher binding affinity. The cluster with the higher HADDOCK score is the most reliable of all. In our case, all the wild protein-peptide complexes except for BRP9, BRP10 and BRP12 showed good HADDOCK score in comparison to the reference wild protein-peptide complex. We have selected complexes BRP-6 and BRP-7 with their haddock score − 106.0+/-4.4 and − 114.0+/-7.5, which is higher among all the BRPs and the reference bradykinin complex too. In case of mutant protein serratiopeptidase and BRP, reference peptide complexes, we have followed the same scenario and selected those BRPs which showed the higher docking score in comparison to the all other BRPs and the reference bradykinin peptide. Thus we selected the BRP-6 and BRP-7 again in case of mutant BRP complexes because both these peptide complexes exhibited higher HADDOCK score in contrast to other BRPs. The highest and comparable docking score in both wild and mutant complexes of BRPs and reference peptide is the the evident that all the complexes docked perfectly even more than the reference peptide bradykinin Table 2. In addition we have taken into account the electrostatic energy which contributes in the steric clashes and electrostatic charges. Here in our study all the mutant complexes showed higher electrostatic energy in comparison to wild type complexes where negative score indicates the lesser repulsion and steric clashes. The statistics of the all complexes electrostatic energy are presented in Table 2. The binding energy score was also calculated to verify the correctness of molecular docking-based extrapolations about the binding affinity of serratiopeptidase protein and BRPs in comparison to the reference peptide bradykinin. Binding affinity score for all the docked complexes exhibited lowest binding energy score in case of all the mutants when compared to their wild complexes. Highest binding energy score exhibited by BRP-6 and BRP-7 i.e. -313.46 and − 321.19 which is higher than the wild BRP-6 and BRP-7 binding energy score i.e. -308.95 and − 304.03. In addition, the Z-score, which indicates how many standard deviations from the overall mean the cluster is positioned in aspects of scoring system, was calculated. Where a higher negative score is preferred over a lower negative score. Thus, when compared to the wild type, all mutant complexes showed a significant increase in Z-score (negative). In Table 2 statistical analysis on Z-scores showed that among all mutant complexes BRP-6, scored highest Z-score − 2.3, compared to wild BRP-6 (-1.7). Furthermore, we have performed the interaction analysis using Discovery Studio to ensure the binding of the active site residues of serratiopeptidase (wild and mutant) to the selected BRPs in comparison to reference bradykinin (Fig. 4 and Fig. S2-S3). As a result, we discovered that all the BRP complexes (wild and mutant) interacted with said active region residues (Table S1 and S2). Based on HADDOCK score, electrostatic energy, binding energy, Z-score, and interacting key residues, we chose the best participating BRPs (BRP-6 and BRP-7). Further, we used MD simulations to examine the dynamic expression of protein-peptide conformational modification.

Table 2

Table showing docking statistics of binding complexes wild and mutant serratiopeptidase to the wasp (BRPs) bradykinin related peptides in reference to bradykinin peptide.
Variant features		Human Bradykinin	BRP-6	BRP-7	BRP-8	BRP-9	BRP-10	BRP-11	BRP-12	BRP-13
HADDOCK score	Wild	-95.2 +/- 4.4	-106.0 +/- 4.3	-114.0 +/- 7.5	-98.6 +/- 8.0	-94.3 +/- 1.1	-93.0 +/- 4.2	-105.0 +/- 3.5	-90.2 +/- 6.5	-103.9 +/- 4.1
HADDOCK score	Mutant	-90.3 +/- 2.7	-109.7 +/- 4.8	-112.9 +/- 7.3	-103.3 +/- 5.7	-98.0 +/- 4.2	-99.8 +/- 4.9	-103.0 +/- 3.4	-106.1 +/- 4.9	-106.1 +/- 8.7
Electrostatic energy	Wild	-134.3 +/- 2.9	-249.9 +/- 46.0	-205.5 +/- 10.2	-295.7 +/- 33.5	-173.1 +/- 17.5	-146.6 +/- 28.2	-181.4 +/- 11.7	-156.2 +/- 34.5	-225.1 +/- 33.7
Electrostatic energy	Mutant	-142.1 +/- 9.1	-269.5 +/- 19.6	-223.3 +/- 58.0	-350.2 +/- 64.7	-193.3 +/- 16.0	-197.5 +/- 20.3	-167.8 +/- 29.8	-157.1 +/- 15.0	-235.6 +/- 63.0
Binding energy	Wild	-190.528	-308.95	-304.03	-271.57	-208.62	-193.82	-217.39	-190.43	-268.85
Binding energy	Mutant	-211	-313.46	-321.19	-280.86	-214.15	-236.88	-237.04	-205.58	-219.148
Z-Score	Wild	-2.2	-1.7	-1.8	-1.3	-1.5	-1.5	-1.6	-1.0	-1.6
Z-Score	Mutant	-1.2	-2.3	-1.8	-1.3	-1.8	-1.8	-1.5	-1.7	-1.6

HADDOCK score: Wild-bradykinin -95.2 +/- 4.4*, Mutant-bradykinin -90.3 +/- 2.7*

Binding energy: Wild-bradykinin -190.528*, Mutant-bradykinin -211*

3.3. Molecular dynamics simulations

Molecular dynamics simulations were conducted on the top two protein-peptide docked complexes in wild and mutant forms, including the reference complexes. The top docked complexes with the highest haddock score and binding energy were picked as the initial structure for the simulation study. Though molecular docking provides useful information about static binding poses, MD simulation provides a robust in-depth structure-based assessment of docking-generated poses by mimicking the natural environment for protein-peptide complexes. As a result, both the chosen BRP complexes (Wild and Mutant-BRP-6, and Wild and Mutant-BRP-7), as well as the reference peptide complex (Wild-bradykinin and Mutant-bradykinin), were subjected to long term (100 ns) MD simulation. In total, three complexes encompassing both mutant and wild complexes in their comparative trajectories were investigated for structural compactness, flexibility, and stability.

3.3.1 Structural deviation and compactness of protein-peptide complexes in mutant and wild type serratiopeptidase

To analyze the spatial/structural deviations between simulated and initial structure of the opted complexes trajectories for root mean square deviation (RMSD) of the backbone C-alpha atoms were examined. Among the serratiopeptidase complexes (Wild and Mutant) with BRP-6, BRP-7, and bradykinin, the highest and lowest values were exhibited by Wild and Mutant BRP-6 (~ 0.55 nm) and Wild and Mutant-BRP-7 (~ 0.5 nm) respectively, which was lesser than the RMSD value of the reference complex (Wild-bradykinin and Mutant-bradykinin) showing ~ 0.6 nm. The low RMSD values of the BRP complexes in comparison to the reference peptide indicated the minor deviations and stronger conformational stability throughout the trajectory. The low RMSD values validated that all the selected protein-peptide complexes trajectories acquired stable and lesser perturbations. This signifying that after entry of peptides in serratiopeptidase protein, there were no major disturbances found in the protein backbone, in whole protein structure remains undisturbed with lesser fluctuation and stable trajectories Fig. 5–7 (b). In addition, to investigate the alteration in the compactness of all the simulated systems, the radius of gyration (Rg) was utilized. In general, smaller and higher Rg values estimates the folded and extended shapes of protein upon ligand binding. Where higher Rg values accountable for unfolding and lower values bestows folding (higher compactness). In our findings the average Rg values for Mutant-BRP-6 complex were between ~ 2.45 to ~ 2.55, which showed significant compactness in contrast to Wild-BRP-6 complex (~ 2.55 to ~ 2.7). The other BRP complex (Wild-BRP-7 and Mutant-BRP-7) in comparison to the reference complex (Wild-bradykinin and Mutant-bradykinin) presented approximately comparable trajectories Fig. 5–7 (b). This implied that both the complexes (Wild and Mutant-BRP-6 and Wild and Mutant-BRP-7) showed higher compactness in contrast to the reference peptide complex.

3.3.2 Residue flexibility of both reference bradykinin and BRPs bound to wild and mutant serratiopeptidase

Moreover, trajectories for the root mean square fluctuation (RMSF) were constructed in all the simulated systems to examine the fluctuation of Cα atoms from their respective positions. In all the simulated systems, we have generated graphs for RMSF values to explore the difference in fluctuations (along Y-axis) of residues (along X-axis) before and after mutation. The selected mutated complexes in comparison to the reference mutated complexes showed lesser fluctuation throughout the structure. Where Mutant-BRP-6 throughout the structure showed lesser perturbations (0.1 to ~ 0.4 nm) in comparison to their Wild-BRP-6 complex, which fluctuates in the range of 0.1 to ~ 0.06 nm. In the second BRP complex, Mutant-BRP-7 lies between 0.1 to ~ 0.49 nm region, where Wild-BRP-7 ranges upto ~ 0.53 nm. However, we have observed the higher peaks of RMSF values in case of reference complex (Wild-bradykinin and Mutant-bradykinin) ranges between ~ 0.1 to ~ 0.6 nm region of fluctuation. These comparative analysis of RMSF suggested that both the BRP complexes showed more rigidity in comparison to the reference simulated system Fig. 5–7 (c). Furthermore, we have performed RMSF analysis to understand the flexibility of active site residues involved in the binding of the serratiopeptidase to reference and selected BRPs. On analyzing the RMSF trajectory of Mutant-BRP-7 complex, we found that all the key residues showed less fluctuations with smaller RMSF values (0.08 to ~ 0.19 nm) in contrast to Wild-BRP-7 complex, exhibited ~ 0.11 to ~ 0.26 nm RMSF value as shown in extracted trajectory Fig. 7 (C). While in case of Mutant-BRP-6 complex the key residues showed smaller perturbations with lower RMSF value (~ 0.07 nm to ~ 0.12 nm), while in Wild-BRP-6 system these residues inferred higher RMSF value (~ 0.08 nm to ~ 0.20 nm) respectively. Further, the RMSF values for Wild-bradykinin exhibited value in the range of ~ 0.05 nm to ~ 0.15 nm, whereas Mutant-bradykinin showed values for key residues in the range of ~ 0.08 nm to ~ 0.18 nm. On the basis of above indicated RMSF findings, Mutant-BRP-7 complex exhibited higher conformational stability in comparison to the Wild-BRP-7 and reference complex (Wild-bradykinin and Mutant-bradykinin). The RMSF values for Mutant-BRP-6 complex was comparable to the reference complex as shown in Fig. 6 (C). This analysis supported the stability of interactions shown in molecular docking analysis Table S1 and S2.

3.3.3 SASA and H-bond analysis of BRP complexes

The solvent accessible surface area (SASA) describes the receptor's binding accessibility with the peptide to ensure the protein stability changes within its solvent molecule. SASA explores for accessible residues and locates the buried region in the proteins. It is the fraction of contact area in a protein of interest which is accessible for a solvent. We have performed comparative interpretation of SASA for selected BRP complexes in comparison to the reference complex. Our analysis showed that SASA values for both the BRP complexes exhibited similar accessibility area approximately ~ 180 nm² to ~ 210 nm² when compared to the reference peptide complex Fig. 5–7 (d). Data interpretation from all the SASA trajectories depicted that all the mutant complexes showed significant increase in surface accessibility area from ~ 196 nm² (all wild-type complexes) to ~ 210 nm² (all mutant complexes). The reason behind this transformation of accessibility in case of mutant complexes, is the contribution of single mutant residue which shows 88% surface accessibility on surface of protein [16]⁠.

Moreover, we have also evaluated the no. of H-bonds formed between all the protein-peptide complexes to ensure the stability, functionality and proper orientation of the structure. Both BRP-6 and BRP-7 complexes in their wild and mutant forms demonstrated an average number ~ 3.18 to ~ 4.3 hydrogen bonds. Whilst, in case of reference wild and mutant complexes, the no. of hydrogen bonds approaches to the range of ~ 2.26 to ~ 2.51 respectively Fig. 5–7 (e). These findings enabled us to substantiate the C-H bond interactions established during molecular interaction analysis (Table S1 and S2). These results revealed that each of the complexes were satisfactorily equilibrated and robust. Both the complexes showed significant and comparable findings for all the trajectories, but to get more insight into their binding affinities, we subjected these systems to ∆G binding analysis using MMGBSA approach.

After performing trajectory analysis, all the complexes subjected to the MM-GBSA method to get the estimation of semi-quantitative interaction affinity with serratiopeptidase protein. We have derived binding free energy of each complex on an average of snapshots retrieved from 100ns of MD trajectories. Binding free energy is the crucial parameter to estimate the potency of binding energy in order to validate the molecular docking findings illustrated in Table 2. The outcomes of ∆G binding data indicated that both the BRP complexes in their mutant forms demonstrated comparable binding free energies with reference Mutant-bradykinin complex as shown in. While both BRP complexes (Wild-BRP-6 and Wild-BRP-7) showed higher values of ∆G (-81.92+/-0.71 and − 83.50+/-0.55) in comparison to the reference Wild-bradykinin, showing least value of ∆G binding (-78.53+/-1.35) Table S3. Moreover, to gain more accuracy in ∆G bind, we have calculated the ∆G binding using interaction entropy approximation Table S4. The outcomes of interaction entropy enhanced the robustness of the MMGBSA data as tabulated in Table S3. To further strengthen the binding energy, we have explored the stability module of the gmx_MMPB/GBSA. After interpretation of stability analysis data, we observed that the average value for Mutant-BRP-6 was higher (-5301.35+/-9.48) in contrast to its wild form (-5275.25+/-16.91). In Mutant-BRP-7 value of stability (-5566.15+/-25.95) was highly significant among all the complexes as demonstrated in the Table 3. The outcomes of stability values were in support with the already calculated ∆G bind and interaction entropy inferences. Additionally, we exploited linear and non-linear PB solver packages to complement the comparative assessment in PBSA and GBSA calculations. In results, of linear and non linear PB analysis we obtained comparable values for both the analysis in mutant and wild complex form with reference protein-peptide complex Table S5 and S6. We discovered favorable results based on the key outputs of MMG/PBSA, justifying the HADDOCK score and binding energy in docking simulations.

Table 3

Stability calculations of selected BRP complexes in reference to the reference complex (Wild-bradykinin and Mutant-bradykinin) using MM-GBSA.
Energy Component	Wild- bradykinin	Mutant- bradykinin	Wild-BRP-6	Mutant-BRP-6	Wild-BRP-7	Mutant-BRP-7
Energy Component	Average +/- SEM	Average +/- SEM	Average +/- SEM	Average +/- SEM	Average +/- SEM	Average +/- SEM
ΔBOND	1474.86+/-5.68	1477.89+/-10.93	1486.80+/-8.47	1484.45+/-5.89	1478.88+/-8.62	1475.89+/-4.15
ΔANGLE	3371.14 +/-11.60	3383.89+/-16.62	3377.32+/-9.26	3417.60+/-9.59	3417.41+/-10.21	3401.48+/-15.86
ΔDIHED	4621.83+/-5.29	4627.24+/-8.37	4659.63+/-9.33	4668.80+/-9.73	4657.27+/-4.65	4655.20+/-7.28
ΔUB	403.89+/-4.01	397.17+/-2.55	400.78+/-1.68	402.06+/-2.37	407.31+/-3.28	403.05+/-2.55
ΔIMP	261.69+/-3.46	264.00+/-3.54	264.34+/-3.42	257.40+/-3.10	259.81+/-2.95	263.60+/-4.17
ΔCMAP	-356.00+/-1.41	-362.79+/-4.36	-375.24 +/-3.79	-373.01+/-4.44	-388.23+/-6.00	-351.84+/-4.47
ΔVDWAALS	-3207.72 +/-7.10	-3244.05+/-7.24	-3200.74+/-7.56	-3211.83+/-8.33	-3233.96+/-7.15	-3248.59+/-5.33
ΔEEL	-27619.76+/-20.66	-27328.87+/-21.90	-27944.69+/-20.69	-27997.38+/-24.26	-28109.51+/-17.04	-27732.70+/-25.31
Δ1–4 VDW	1442.77 +/-6.18	1438.67+/-5.33	1445.59+/-3.91	1449.36+/-6.16	1450.51+/-4.46	1441.24+/-5.71
Δ1–4 EEL	23970.76+/-14.09	23975.65+/-18.49	24026.70+/-16.42	24060.42+/-16.39	23670.08+/-13.39	23664.10+/-13.63
ΔEGB	-9744.26+/-16.17	-10088.36+/-17.65	-9566.09+/-16.43	-9604.78+/-17.80	-9252.95+/-18.32	-9681.97+/-18.74
ΔESURF	145.51+/-0.54	142.64+/-0.41	150.36+/-0.45	145.56+/-0.38	148.82+/-0.42	144.40+/-0.44
ΔGGAS	4363.48+/-13.45	4628.79 +/-27.15	4140.48+/-23.08	4157.86+/-17.23	3609.57+/-28.35	3971.42+/-37.81
ΔGSOLV	-9598.75+/-16.09	-9945.71+/-17.45	-9415.73+/-16.32	-9459.22+/-17.68	-9104.13+/-18.20	-9537.57+/-18.67
ΔTOTAL	-5235.27+/-10.38	-5316.92+/-20.89	-5275.25+/-16.91	-5301.35+/-9.48	-5494.56+/-14.74	-5566.15+/-25.95

Ultimately, we inspected both the complexes in steered molecular dynamics to reassert the persistence of our observations. In SMD simulations, we condemned external pulling force on all the systems to observe the force needed in disassociation process in order to endorse the binding affinity findings in terms of unbinding mechanism. We have applied external pulling force to pull off the reference bradykinin, BRP-6 and BRP-7 from the binding pocket of serratiopeptidase. The unbinding of peptide from the binding site of protein could be reckoned as a paradigm for the assortment of significant rigorousness in binding. A virtual damped harmonic spring with a fixed pulling rate of 0.01 nm/ps and a spring force of 1000 kJ/mol/nm² was implemented to simulate SMDs. The peptides slowly moves out of the binding pocket once the maximum force was met. We extracted different time interval snapshots to interpret the dissociation pathway of the peptides movement. Eventually, all complexes witnessed an upsurge in pulling force as shown in Fig S4-S6. We recorded the highest value of pulling force in Wild and Mutant-BRP−7 complex (~ 732.825 and ~ 842.056 kJ/mol/nm²) in comparison to the Wild and Mutant-BRP−6 complex (~ 704.294 and ~ 745.238 kJ/mol/nm²) Fig. 8. The SMD analysis indicated that BRP−7 (wild and mutant variants) interacted more vigorously and endured the highest extrinsic pulling force in order to dissociate from the protein’s binding pocket. The tight binding of BRP−7 to serratiopeptidase protein in comparison to the BRP−6 substantiate the outcomes of MMG/PBSA analysis (Table S3,S4 and Table 3). The BRP−7 (Wasp kinin PMM1) peptide found in Polistes major, a neotropical social wasp. Toxic envenomation from wasp stings (near head or neck region) can result in serious health concerns, such as liver and kidney dysfunction, pulmonary constriction, and nasopharyngeal inflammation [6]⁠. The massive envenomation of hundred of stings attributed to anaphylactic shock (lower blood pressure). The mechanisms involved wasp sting injury often include immediate venom poisoning and an immunological inflammatory response to venom composition, both of which could contribute in organ dysfunction. The peptide present inside the wasp venom potentiate the bradykinin production, which in results, causes arteriole constriction, reduction in flow of blood to downstream blood vessels contributing to drop in blood pressure (hypotension). Based on the research findings, we deduced that serratiopeptidase is not only responsible for the breakdown of human bradykinin, but also a key enzyme in the disintegration of the BRP−7 peptide found in the venom of neotropical wasps. In congruence with all the in-silico analysis, it is evident that serratiopeptidase is the miracle peptidase for the breakdown of BRP−7 peptide among all peptides, even surpassing the human bradykinin. As a consequence, this manuscript affirms the application of our previously identified serratiopeptidase mutant.

In this study, on the basis of alignment score, we have generated three-dimensional structure of only eight BRPs among fourteen. Further, we got insight that how likely the BRPs binds and interacts with the key residues of serratiopeptidase using docking simulation. Then on the basis of higher HADDOCK score and binding energy, we screened out the top two BRP complexes. To better comprehend the BRPs affinity and stability in complex with serratiopeptidase, MD-driven trajectories were analyzed, which appraised stable interactions and trajectories throughout simulations. In major findings we obtained BRP-6 and BRP-7 complexes, showing highest docked score, stable trajectories and good binding energy score. Moreover, to legitimize the docking and molecular dynamics outcomes, binding free energy estimations and steered molecular dynamics studies were accomplished. The adequate binding free energy for both the complexes was stable and comparable to the reference complex. In SMD outcomes, we found that BRP-7 complex took more force and time for rolling out to the active pocket of serratiopeptidase in contrast to BRP-6. Aside from that, in future, this protease could be used to degrade other venomous species’s BPPs (bradykinin potentiating peptides) and BRPs (bradykinin related peptides).

Acknowledgment

We gratefully acknowledge to the Director, CSIR-Institute of Himalayan Bioresource Technology,

Palampur for providing the facilities to carry out this work. The CSIR support in the form of project

MLP: 0201 for bioinformatics studies is highly acknowledged. AD expresses gratitude to the Indian

Council of Medical Research, New Delhi, India for providing Senior Research Fellowship.

Authors contribution

RP conceived of and designed the study. AD and RP analyzed and interpreted the data. RP and AD

critically revised it for important intellectual content.

Consent for publication

All the authors have read and approved the manuscript in all respects for publication.

Supplementary information: Supporting information attached in the additional files with tables and figures.

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No competing interests reported.

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Structural and molecular insight into the disintegration of BRPs released by massive wasp stings using serratiopeptidase

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Abstract

Figures

1. Introduction

2. Material And Methods

2.1. Dataset preparation

2.2. Molecular modeling

2.3. Molecular docking

2.4 Molecular dynamics simulation

2.5. MMG/PBSA free energy calculations

2.6. Steered molecular dynamics

3. Results And Discussion

3.1. Three-dimensional model generation using AlphaFold-Colab

3.2. Molecular docking score and interaction analysis

3.3. Molecular dynamics simulations

3.3.1 Structural deviation and compactness of protein-peptide complexes in mutant and wild type serratiopeptidase

3.3.2 Residue flexibility of both reference bradykinin and BRPs bound to wild and mutant serratiopeptidase

3.3.3 SASA and H-bond analysis of BRP complexes

4. Binding Free Energy, Interaction Entropy And Stability Analysis Using Mmg/pbsa

5. Steered Molecular Dynamics (Smd)

6. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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