In Silico identification of angiotensin-converting enzyme inhibitory peptides from MRJP1.

Hypertension is considered as one of the most common diseases that affect human beings (both male and female) due to its high prevalence and also extending widely to both industrialize and developing countries. Angiotensin-converting enzyme (ACE) has a significant role in the regulation of blood pressure and ACE inhibition with inhibitory peptides is considered as a major target to prevent hypertension. In the current study, a blood pressure regulating honey protein (MRJP1) was examined to identify the ACE inhibitory peptides. The 3D structure of MRJP1 was predicted by utilizing the threading approach and further optimized by performing molecular dynamics simulation for 30 nanoseconds (ns) to improve the quality factor up to 92.43%. Root mean square deviation and root mean square fluctuations were calculated to evaluate the structural features and observed the fluctuations in the timescale of 30 ns. AHTpin server based on scoring vector machine of regression models, proteolysis and structural characterization approaches were implemented to identify the potential inhibitory peptides. The anti-hypertensive peptides were scrutinized based on the QSAR models of anti-hypertensive activity and the molecular docking analyses were performed to explore the binding affinities and potential interacting residues. The peptide "EALPHVPIFDR" showed the strong binding affinity and higher anti-hypertensive activity along with the global energy of -58.29 and docking score of 9590. The aromatic amino acids especially Tyr was observed as the key residue to design the dietary peptides and drugs like ACE inhibitors.

Introduction

Pulmonary arterial hypertension (PAH) affects the small pulmonary arterioles, which lead to a progressive disease of the lung vascular system. The progressive narrowing of the blood vessels is a collective effect of increased contractility of the small pulmonary arteries, remodeling, and proliferation of endothelial smooth muscle cells and endothelial dysfunction [1].

PAH is transmitted through an autosomal dominant trait with mitigated trenchancy. The mutations in the bone morphogenetic protein receptor type-II (BMPR2) elucidate 70% of the hereditary cases while 20% of the cases have unknown reasons [2, 3]. BMPR2 belongs to the super-family of TGFb/BMP [4] and its heterozygous alterations occur in the transmissible PAH [5, 6] leads to the illness [7]. The hereditary PAH is localized at chromosome 2q33 [8, 9]. The nonsense, frameshift and missense mutations in BMPR2 lead to change the bone morphogenetic protein and TGF-b1/SMAD signaling pathways, which ultimately cause escalation instead of apoptosis of the vascular cells [6, 7, 10–12]. The system modifications entailed in the cardiovascular attunement are possibly associated with the commencement and conservation of the blood pressure elevation [13].

Angiotensin-converting enzyme (ACE) is a vital constituent of the renin-angiotensin system (RAS), arbitrating various systemic and local effects in the cardiovascular system. The ACE synthesis in somatic tissues endothelium as a transmembrane protein comprising of two active domains which are inhibited by ACE inhibitors [14]. ACE peptides as inhibitors are extensively studied in different bioactive peptides [15-18] for therapeutic purposes. The conversion of ACE transmutes angiotensin I to angiotensin II is a dynamic vasoconstrictor and a vital enzyme in the modulation of blood pressure and body fluids. It is also involved in the anatomization of bradykinin to dilate the blood vessels [19].

The ACE function could induce the vasoconstriction and progression of hypertension and related pathological manifestations. ACE suppression is considered as an essential approach in regulating hypertension [20]. The synthetic or celluloid ACE inhibitor drugs have side effects including a dry cough, skin rashes or erythema, taste turbulences and the modifications in serum lipid metabolism [21]. The commercially available ACE inhibitor drugs are discouraged and food protein-derived ACE inhibitory peptides are preferred [18, 22, 23] for effective therapies. The amino acid residues determine the inhibitory potency of ACE inhibitory peptides such as the existence of hydrophobic and positively charged amino acids [24, 25]. The purpose to assess the food proteins from primary food products as precursors in producing ACE inhibitory peptides facilitates to develop a principle for proper selection of substrate protein. The high-potential food and the sedentary lifestyle are known to trigger hypertension [26].

The current work demonstrates the in silico identification of potential anti-hypertensive peptides from honey protein MRJP1. Computational approaches have shown considerable success in research methodologies to solve biological problems [27]. After the successful identification of computational drugs and drug targets in neurological disorders [27-32] and cancer [33-36], researchers also utilized the computational approaches to design epitope-based peptide vaccines through immunoinformatic approaches [37]. The 3D model was built by using homology modeling and threading based approaches followed by the Molecular Dynamic (MD) simulations to optimize and analyze the structural features of a model for protein-peptide docking analyses. The screening for ACE inhibitory peptides was performed to identify the potential anti-hypertensive peptides. The observed anti-hypertensive peptide-protein interactions may serve to replace the drugs by dietary peptides and to narrow down the diverse combinatorial search space.

Results and discussion

The objective of the current research was to identify the potential anti-hypertensive peptides derived from MRJP1. The retrieved sequence of MRJP1 was used to identify the appropriate templates but the query coverage and sequence identity against suitable templates were not satisfactory to build a model through a comparative modeling approach. The top-ranked template belongs to Salivary protein having only 25% identity and 61% query coverage was observed, therefore the threading based approach was utilized through I-Tasser for structure prediction of MRJP1.

The top-ranked five models were predicted by using the templates with higher similarity identified through the threading alignments. It was observed that the template protein (PDB ID: 3q6k) has a resolution of 2.52 Å structure (Salivary protein) and showed the highest confidence score of 0.54. The salivary protein belongs to the MRJP protein family [38] and the first structurally characterized member of the family that is being utilized in MRJP1 structure prediction. The homologous templates for evolutionarily related proteins are identified through the sequence profile analyses [39] and considered as reliable for the prediction of high-resolution structures. The non-homologous proteins may also have the similar structures, and threading approaches [40, 41] have ability to match the query sequences onto the available structures with the aim of identifying the similar folds to the query even though there is no evolutionary relationship among the template protein and the query sequence. The models predicted through homology modeling and threading approaches with the RMSD range of 2–5 Å from distant templates that can be utilized for functional analyses and the identification of the active site residues [42-45]. MD simulation has been utilized for the ab initio structure prediction [46] to simulate the folding of the protein, while the template-based structure prediction is considered as one of the most reliable approaches [47-54].

Numerous models were predicted by utilizing a homology modeling and threading based approach and all the predicted models were evaluated critically. The model showed 78.53% quality factor and further subjected for MD simulations to optimize and extract the structural fluctuations throughout the 30 ns and it was observed that the quality factor was improved up to 92.43% (S1 Fig) and while 98.3% residues appeared in favorable regions.

2.1 Molecular dynamics simulation

The predicted structure of MRJP1 was subjected to MD simulations applying ensemble, temperature and appropriate solvent molecules. The constant temperature for 300K, 1atm pressure and heating for 500 ps were applied for simulation experiments in initial equilibration. The steric energy constraints were eliminated or reduced through energy minimization. Newtonian’s dynamics equilibrated the system to locate a thermally bound state, which leads to the production runs and simulations also deliver ensembles of structure to analyze the results. The conformational changes in the MRJP1 structure have been concluded from macroscopic features. The conformational variations of the MRJP1 structure were analogously determined at 0 ns, 10 ns, 20 ns, and 30 ns. Three major physical properties comprising RMSD, RMSF, and B-factor of the simulated system were calculated to analyze the conformational changes in the hydrated environment.

2.1.1 Root mean square deviation (RMSD)

The atomic position of RMSD was calculated by considering the predicted structure of MRJP1 as a foremost model to find out the sustainability and convergence of the MD simulations. The 30 ns runs of molecular dynamics denoted that the RMSD of Cα-atoms as a function of simulation time (Fig 1). The results indicated that the RMSD values showed minimal fluctuation throughout the simulations studies. The high variation of atoms along with the residues close to NTD and CTD were observed. Overall, the stability of the structure was observed in 30 ns particularly at the end of the simulation, thus the simulated model was utilized for further processes. RMSD analyses of MRJP1 have shown no major fluctuations throughout the 30 ns simulations. The protein showed some higher fluctuations only at the start of the simulation while the stability of the structure was observed at the end of the simulation system.

Fig 1

Root mean square deviation graph vs. time; the graph showed the minimal fluctuations throughout the simulation runs and structural stability and optimization were achieved with respect to time.

2.1.2 Root mean square fluctuation (RMSF)

The RMSF analysis of a protein about their conformations is a significant mark of many biological processes which includes complex recognition, protein activity and macromolecular recognition [55]. The RMSF graph was computed for each residue of Cα-atoms, while the overall MRJP1 structure exhibited an advanced fluctuation level. The RMSF graph demonstrated the residual fluctuations of the MRJP1 model over 30 ns timeframe (Fig 2) and four major fluctuation peaks were observed. The first major residual fluctuation was observed from 30–67 (37 residues) amino acids, while second, third and fourth were 134–153 (19 residues), 228–256 (28 residues), and 374–405 (29 residues) amino acids respectively.

Fig 2

RMSF fluctuation graph showed the variations of individual residues from 0–30 ns.

2.1.3 B-Factor

The applications of computational advances are to anticipate the thermal motion that examines the obscure structure of the proteins with dynamic attributes. The polypeptide backbones and side chains of MRJP1 structure were persistent in motion owing to kinetic energy and thermal motion of atoms. The fluctuations of the atoms regarding their average positioning were reflected by B-factors of protein structure and provided significant evidence about the protein dynamics. RMSD and PMSF plots indicated the stability of the model and only a few structural fluctuations were observed at residues level. It has also been verified through secondary structure analysis that there were few coils (irregular) elements along helices and sheets. Moreover, the observed B-factor analyses were in favor of higher values at corresponding positions anticipating that the MRJP1 structure is reliable for further analyses (Fig 3).

Fig 3

The B-factor analysis represents the fluctuations of the atoms regarding their average positioning.

2.2 Structural analyses

The structural analyses were performed of simulated MRJP1 model at varying degrees of MD simulations such as 0 ns, 10 ns, 20 ns, and 30 ns. The structural fluctuations along with differences in the number of helices and sheets were observed in structural analyses (Fig 4). The most prominent difference in terms of improvement was the quality factor and the structural stability from 0 ns, 10 ns, 20 ns, and 30 ns structures as 78.53%, 85.37%, 89.14%, and 92.43% respectively. The number of alpha-helices at 0 ns and 30 ns were same as ten (10) helices but vary in residues length from 33 to 39 residues respectively, while the structure at 10 ns and 20 ns contains 14 and 11 helices respectively. On the other hand, 23 beta-sheets were observed at 20 ns and 30 ns while the structure at 0 ns and 10 ns comprise 21 and 24 beta-sheets respectively. The fluctuations in the number and lengths of the secondary structural elements were observed in the simulated model that greatly influenced the structural quality. The terminal directions of the structure have changed during the simulation analyses. The N and C terminals in the unrefined structure were embedded in the structure and projected inwards. The refined structure has terminals projected out of the protein structure with clear ends. It was also seen that the pattern similarity in overall structure and protein model stability incremented with MD simulations.

Fig 4

Structural details of the simulated structure of MRJP1 at 0 ns, 10 ns, 20 ns, and 30 ns.

2.3 Derived peptides

The peptides were manually derived based on the properties of the interacting residues and structural characterization of the amino acids; the peptides considered for the current study were specifically including di-peptides. The criteria for the selection of di-peptides includes both of the amino acids either belong to a hydrophobic group or bulky hydrophobic. The peptides were derived by using the peptide cutter with two enzymes pepsin and trypsin individually, structural characterization and AHTpin server based on support vector machine score (SVM). The peptides derived from the applied techniques are mentioned in Table 1 with the cleavage site, peptide length, and SVM score.

Table 1

Derived peptides having anti-hypertensive activity.

Technique		Cleavage Site	Peptide	Peptide Length	SVMScore	Anti-hypertensive Peptide inhibitor
						Cleavage Site	Peptide	Peptide Length	SVMScore
Proteolysis	Pepsin	110	LLQPYPDW	8	1.47	265	LYYSPVASTSLYY	13	1.74
		294	QQNDIH	6	0.35	264	NLYYSPVASTSLY	13	1.66
		135	AIDKCDRL	8	0.04	266	YYSPVASTSLYYV	13	1.52
		369	PHVPIF	6	1.72	262	TNNLYYSPVASTS	13	1.49
		237	YDPKF	5	0.76	102	PLLQPYPDWSFAK	13	1.48
	Trypsin	114	VGDGGPLLQPYPDWSFAK	18	0.39	101	GPLLQPYPDWSFA	13	1.41
		62	QDAILSGEYDYK	12	0.65	151	SPKLLTFDLTTSQ	13	1.38
		166	LLTFDLTTSQLLK	13	0.30	268	SPVASTSLYYVNT	13	1.38
		371	EALPHVPIFDR	11	1.52	100	GGPLLQPYPDWSF	13	1.36
Structural Characterization		175	AV	2	3.10 (pIC50)	96	KVGDGGPLLQPYP	13	1.36
		53	AI	2	5.47 (pIC50)	263	NNLYYSPVASTSL	13	1.33
		212	GL	2	2.60 (pIC50)	267	YSPVASTSLYYVN	13	1.30
		255	GM	2	2.85 (pIC50)	97	VGDGGPLLQPYPDWS	15	1.20
		85	GV	2	2.34 (pIC50)	99	DGGPLLQPYPDWS	13	1.16
		137	VL	2	4.89 (pIC50)	258	LSPMTNNLYYSPV	13	1.11
						257	ALSPMTNNLYYSP	13	1.05
						164	LLQPYPDWSFAKY	13	1.03

All the derived peptides were evaluated by the regression models of SVM score and the leading peptides were docked with ACE to identify the high binding affinities. SVM regression model was built for di- and tri-peptides, while SVM classification models for peptides have more than three residues. The applied methods were based on the nature of amino acids, atomic composition and chemical descriptors (15,537) while trained by the machine learning techniques to evaluate through regression and classification methods.

Regression analyses were conducted to correlate the chemical descriptors and biological activity (pIC50) of small peptides for the pIC50 prediction of novel peptides.

Di-peptides and tri-peptides belong to a small class of peptides but separate regression models were implemented for each method to predict the biological activity. The classification models predicted the special type of peptides either AHT or non-AHT based on the descriptors of the training set. Mainly, PubChem, CDK-fingerprint, XLogP, electrotopological state atom type, and auto-correlation descriptors were implemented to develop the di-peptide QSAR model while tri-peptide QSAR model was developed primarily by KlekotaRoth fingerprint count, PubChem fingerprint, CDK graph only fingerprint and extended fingerprint descriptors [56]. The biological activity of di-peptides including AV, AI, GL, GM, GV & VL from MRJP1 was validated and evaluated by AHTpin. The reliability and bioactivity of all the derived peptides from MRJP1 were validated by AHTpin.

The lead anti-hypertensive peptides were selected on the basis of SVM scores for molecular docking analyses. The protein-peptide molecular docking analyses were performed and ACE was utilized as receptor against all the derived peptides to determine the binding position and orientation (S1 File). The docking analyses were performed by using the segmentation technique to identify and scrutinize the patches to evaluate the binding conformations and give a score to geometric complementary shapes. The docking complexes were ranked by the observed docking score and top ten ranked peptides having highest binding affinities were selected (Table 2) for further binding interactional studies through PyMol and UCSF Chimera (Fig 5). It was observed that the peptide “EALPHVPIFDR” from all the scrutinized peptides showed docking score of 9590 and effective binding affinity. The interesting fact was observed that the scrutinized top-ranked peptide was embedded in the receptor surface and engaged the binding domain. The anti-hypertensive peptides EALPHVPIFDR, NLYYSPVASTSLY, PHVPIF, and LYYSPVASTSLYY showed least binding energies may have the potential to behave as ACE inhibitors. The binding interactions of the selected peptides revealed that the Tyr residue is the most common interacting residue that behaved as an ACE inhibitor and has the potential to be a potent drug target.

Table 2

Protein-peptide interactions along with docking scores and binding residues.

Peptide	SVM Score	PatchDock Score	Global Energy(kcal/mol)	ACE Binding Residues
EALPHVPIFDR	1.52	9590	-58.29	Tyr62, Ala63, Asn66, Asn70, Ile88, Lys118, Glu123, Met223, Val351, His353, Ala354, Ser355, Ala356, Trp357, Asp358, Tyr360, Lys368, Glu384, Phe391 Arg402, Glu403, Phe512, His513, Ser516, Ser517, Val518, Tyr520, Arg522, Tyr523, Zn701
NLYYSPVASTSLY	1.66	11060	-52.04	Trp59, Tyr62, Asn66, Asn70, Leu81, Lys118, Val119, Gln120, Asp121, Glu123, Arg124, Leu139, Leu140, Tyr213, Met223, Val351, His353, Ser355, Trp357, Lys368, Arg402, Glu403, Phe512, Ser516, Ser517, Val518, Pro519, Phe570
PHVPIF	1.72	6968	-47.47	Trp59, Tyr62, Ile88, Thr92, Lys118, Glu123, Arg124, Tyr360, Arg402, Glu403, Pro519, Arg522
LYYSPVASTSLYY	1.74	10388	-33.24	Trp59, Tyr62, Asn66, Asn70, Lys118, Asp121, Glu123, Arg124, Ser219, Trp220, Ser222, Tyr213, Met223, Ser355, Ala356, Trp357, Tyr360, Glu403, Asn406, Pro407, Ser516, Ser517, Val518, Pro519, Arg522, Phe570, Zn701

Fig 5

Interacting residues of the ACE-peptides are represented in different colors.

The crystal structure of human ACE (PDB ID: 1O8A) protein is divided into two domains as Domain I (N-terminal) (37–291 amino acids) represented in cyan color while Domain II as C-terminal domain is presented in orange color (292–625 amino acids). The N-terminal lid appeared as the α1, α2, and α3 exhibiting the active site of protein along with the Zn binding site. The scrutinized peptides showed the interactions at binding sites and represented in different colors along with interacting residues.

Majority of the therapeutic agents attain their outcomes by binding and modify the functions of the target proteins. Traditionally, the binding within small cavities and catalytic sites inhibition exhibit the high affinity and successful therapeutics by compounds [57].

Food is considered as a source of nutrients and energy essentials to sustain the appropriate functions of the body. Now, scientists are trying to identify the novel characteristics of food constituents that may assist to overcome the numerous ‘diseases of civilization’. The mutual objectives of nutritionists, food manufacturers and researchers are to focus the proteins that have the origins of ACE inhibitors, to enhance their bioactivity and formulating those as commercial food to improve the human health [58]. In recent years, peptides have gained demanding attention in the pharmaceutical research for being highly efficacious, selective and relatively safe. More than a few hundreds of novel peptide therapeutics are currently being evaluated in pre-clinical and clinical trials while over 60 peptides have reached the market for different therapies [59].

Various side effects such as cough, headache, dizziness, and angioedema of synthetic anti-hypertensive drugs have been reported [60]. Therefore, the identification of potential anti-hypertensive biopeptides from foods gained attention [61]. The peptides of anti-hypertension have been reported in various dietary sources including egg, milk, meat, potato, wheat, soya beans, and vegetables. The synthetic compounds also occur as ACE inhibitors for hypertension therapies, although synthetic drugs contain adverse effects. So, the inclination towards nature-derived anti-hypertensive molecules is highly desired. The in silico identification of ACE inhibitory peptides from honey protein was performed which is considered as a source of anti-hypertension in the form of the ACE inhibitor [17].

The functional and nutritional features of dietary proteins have been studied over decades. The physiological consumption of amino acids after digestion and protein composition exhibit the nutritional characteristics [62]. Glycine was found as a predominant amino acid in AHTs server analysis and possesses two residues, revealed through amino acid composition investigation [56].

The proteolytic processing of food proteins leads to the production of active and bioactive peptides that performs various physiological functions of the body. These bioactive peptides may act as an opioid antagonist, agonists, anti-hypertensive agents, and moreover anti-cancer, anti-thrombotic, anti-microbial, immune-modulating and anti-oxidative activity have been reported. The bioactive peptides may be utilized in functional food components due to their therapeutic potentials [63].

The peptides are preferred over the small compounds due to their structural compatibility, small size and ability to interrupt protein-protein interfaces. The rational methods have a key hindrance to design effective peptide ligands for the development of potential drugs. However, numerous computational techniques have evidenced the structural and functional insights into the architecture of protein-peptide interfaces for the rational peptide design approach. These methods help to fulfill the vision of computationally designed peptides for therapies through the high-resolution structures of protein-peptide complexes [64-66].

In vivo studies have found that anti-hypertensive effects can be attained in humans through peptides especially di- and/or tri-peptides [67NR, 68]. Hata et al., [68] demonstrated the effectiveness of Ile-Pro-Pro and Val-Pro-Pro on blood pressure (BP) regulation. They hypothesized that stimulation in aortas along with circulatory ACE inhibition would be the reason for that effect [69]. It has also been reported that the intake of bioactive Val-Tyr di-peptide led to a significant reduction of systolic BP after 1 week on mildly hypertensive subjects [67]. These discoveries strongly recommended that the renin-angiotensin system suppression by bioactive smaller peptides play a significant role in the regulation of BP.

The ACE inhibition was greatly enhanced through gastrointestinal protease hydrolysis of royal jelly by trypsin followed by pepsin and chymotrypsin [70]. These analyses reported that the inactive royal jelly proteins might be an effective ACE inhibitor to regulate the BP and new peptide inhibitors in gut formed through gastrointestinal proteases would be more significant. Uno et al. [71] documented that consumption of royal jelly hydrolysate by trypsin and pepsin amplified the hemoglobin levels and reduced the higher cholesterol levels in human beings. Therefore, the royal jelly is considered as a beneficiary to improve the homeostasis.

Ohashi et al. [72], derived peptides from the royal jelly glycoproteins and demonstrated that most of the isolated peptides have aromatic amino acid residues as Phe and Tyr at C-terminus exhibited the strong inhibitory activity. Cheung et al., [73] confirmed the inhibition potential of these aromatic peptides in their research and observed the additional ACE inhibition for peptides with Trp-Tyr-Phe at the C-terminus. It has also been reported that the peptides having Ile-Val-Tyr residues extracted from the royal jelly hydrolysate with the highest ACE inhibitory contribution rate of 16.9% in addition to wheat germ hydrolysate [74].

Okunishi et al. [75] elaborated the long-term oral therapeutic drug, spirapril that suppresses the ACE activities in blood vessels and induce the extended depressor effects. Their analyses showed that few of the natural inhibitory peptides, specifically royal jelly peptides could gather at the vessels and exert a regulation of secretion for active elements including prostaglandins or endothelin and nitric oxide [76]. The royal jelly protein has the ability to produce plenty of ACE inhibitory peptides throughout the digestion to reduce the depressor effect and it was a latent natural source along with vivo anti-hypertensive effects.

The development of peptide-based therapeutics is of great interest and has rapid growth [77-79]. Currently, a robust approach has been evolved that incorporates topographical, conformational, dynamic and structural considerations to design the peptides for drugs, drug molecules, and biological tools. Current developments to understand the chemistry of life, specifically molecular biophysics, proteomics, genomics, and molecular biology have described that the macromolecular-peptide interactions establish the key physiochemical processes whereby living mechanisms are modulated and controlled [80]. In this modern era, bioinformatics approaches play a vital role in the discovery of novel peptides [81]. Traditionally, the peptide design utilizes the homology models or structures along with the docking methods to design the peptides with high affinity against the target proteins [80].

The current findings focused to reveal the potent anti-hypertensive peptides as ACE inhibitors from royal jelly protein (MRJP1) through in silico approaches. RMSD and RMSF graphs described the structural stability of MRJP1 in MD simulations at 30 ns along with the quality factor of 92.43%. Peptides were derived by protease hydrolysis, structural characterization /physiological properties, and AHTpin server approaches. The anti-hypertensive potential of scrutinized peptides was measured by QSAR methods of the AHTpin program and the preferable anti-hypertensive candidates with SVM scores ranges from 0.04 to 1.74 were determined. Protein-peptide docking analyses were further carried out to reveal the binding conformations, binding affinities, and potential binding residues. It has also been analyzed that the peptides were embedded with ACE receptor protein and top-ranked 4 peptides were selected having strong binding affinities i.e. 10388 to 9590 docking scores. Top four peptides mainly encompass aromatic amino acid residues including Tyr-Trp-Phe while Tyr was observed as the most abundant amino acid in the selected peptides. Various in vivo studies have reported the strong anti-hypertensive activity of aromatic amino acids, particularly Tyr amino acid [82]. It has been suggested that the protease hydrolysis of the royal jelly protein produces many effective ACE inhibitors that would regulate BP.

The molecular docking analyses have the significance of elucidating the interacting residues between the receptor proteins and ligands [83]. Generally, there are three modes of ACE inhibitory peptides as competitive, non-competitive and mixed. The competitive inhibitory peptides possess 2–12 amino acid residues in length and attached at the binding site of ACE. The non-competitive inhibitory peptides showed that the binding other than substrate binding site and affect the ACE enzyme activity. Zn is considered as the significant component of the active site of ACE and ACE activity also depends on Zn [84]. The ACE active site is divided into three binding pockets as S1 (Ala354, Glu384, and Tyr523), S1´ (Glu162) and S2 (Gln281, His353, Lys511, His513, and Tyr520) [85]. The binding stability of peptides at the binding site of the ACE enzyme depends upon hydrogen bonding [86]. Additionally, the involvement of His353, Ala354, Ser355, Glu384, His513, and Pro519 residues are significant for the stability of peptide and enzyme complex while numerous effective peptides have been reported for their interactions at the specified binding sites [87, 88].

The molecular interactional studies of ACE inhibitory peptides are beneficial for the designing and screening of potential novel inhibitory peptides. The reported peptides also present the binding interactions at binding pockets and behave as competitive inhibitory peptides. The top-ranked peptide (EALPHVPIFDR) showed binding interactions in S1 and S2 binding pocket of the ACE enzyme and engaged the significant interacting residues through hydrogen bonding leading to the stability of the complex. The utilized in silico approaches provide a novel and potential ACE inhibitors through various distinctive techniques that have the potential to analyze the large-scale conformations through protein-peptide interactions. This leads to an initial step of reducing and eliminating hypertension without drug usage and not to bear their side effects. This could probably be happening only by using those food sources and dietary components, which improves human health and act as preventive measures of these sorts of diseases.

Conclusions

Contemporary research methods including bioinformatics and proteomic tools applied in current research on peptides from honey protein as a food source and identified the potential anti-hypertensive peptides. It has been demonstrated that the scrutinized peptides EALPHVPIFDR, NLYYSPVASTSLY, PHVPIF, and LYYSPVASTSLYY may have the potential to reduce hypertension with minimal side effects. The reported peptides comprise of aromatic amino acids particularly Tyr and its strong anti-hypertensive activity made the selected peptides a better choice after an extensive in silico studies. Even if such food and peptides of proteins are not being able to replace drugs in acute hypertension, they still may have the potential to prevent hypertension.

Material and methods

3.1 Functional information and canonical sequence

The present studies demonstrate the identification of ACE inhibitory peptides from MRJP1 honey protein against hypertension by employing the in silico approaches comprising computational 3D modeling, MD simulations, peptides designing, molecular docking analyses, and anti-hypertensive activity predictions. The utilized methodology of the current study is presented in a flow chart (Fig 6). The functional information and canonical sequence of MRJP1 in FASTA format were retrieved from UniProt Knowledgebase (http://www.uniprot.org/) having the accession number O18330. The MRJP1 protein sequence was subjected to the protein-protein basic local alignment search tool (BLASTp) [89] against PDB to identify the possible templates. The homology modeling (Modeller 9.14 [54]) and threading based approaches (I-Tasser [50]) were implemented to build the 3D structure of MRJP1. The 3D crystal structure of human ACE was retrieved from PDB (ID: 1O8A) having a resolution of 2 Å determined by the X-ray diffraction method. Errat [90] and Rampage [91] evaluation tools were utilized to evaluate the predicted structure before and after the simulations analyses.

Fig 6

The methodology of current research work.

3.2 MD simulations

The selected MRJP1 model was subjected to MD simulations by employing AMBER v14 [92] with an ff14SB force field. The simulation analyses were executed in explicit solvent and three-point transferable intermolecular potential (TIP3P) water molecules implemented to solvate the initial structures of a modeled system. Pre-equilibrated elementary cubic box of 78.672 Å* 84.370 Å * 79.589 Å was applied to cover the target protein completely that appended 12,397 water molecules. This system amplified the total mass up to 272215.674 amu accompanied by a density of 0.856 g/cm3. The system was neutralized by incorporating the 15 Na+. The comprehensive energy minimization of the solvated protein was carried out for MD simulation experiments. The energy minimization comprising 1500 cycles of conjugate gradient and steepest descent algorithm was executed to eliminate or reduce the energy constraints. SHAKE algorithm was implemented to constrain the hydrogen atoms and bond lengths [93]. A non-bonded cutoff of 10.0 Å with a time step of 0.002 ps was employed by the Berendsen coupling algorithm. Ewald summation method was used to execute the MD simulations for comprehensive electrostatic interactions [94]. The simulation experiments were simulated in initial equilibration at 1 atm pressure, constant temperature for 300 K and heating time for 500 ps. The simulations for 30 ns were performed and coordinate files were saved after every 5 ns time frame for the structural analyses. PTRAJ module of AMBER generated the output files for the analyses and then visualized by using UCSF Chimera [95]. The obtained results were analyzed by considering various factors including B-Factor, RMSD, and RMSF. The graphs for B-factor, RMSD and RMSF were generated by Microsoft Excel.

3.3 Preparation of peptides

The preparation of peptides was performed by three different approaches as structural characterization, proteolysis and AHTpin server [56]. The peptides were manually derived on the basis of anti-hypertensive properties and structural attributes characterizing di-peptides and tri-peptides ACE inhibitors. Di-peptides were composed of amino acids with bulky and hydrophobic side chains, while in tri-peptides, the 1st residue at N-terminal was aromatic, 2nd one was positively charged and the 3rd residue at C-terminal was hydrophobic [25].

3.3.1 Proteolysis

Proteolysis was conducted by employing the Peptide Cutter software (http://www.expasy.ch/tools/peptidecutter/) with pepsin and trypsin enzymes individually.

3.3.2 Peptide derivation

The anti-hypertensive peptide inhibitors (AHTpin), an online server was used to derive the peptides having anti-hypertensive inhibitory activity by submitting the sequence of MRJP1 to the server. The anti-hypertensive peptides extracted from the above-mentioned techniques were prepared for docking experiments with the receptor protein ACE. Protein-peptide docking analyses were carried out through PatchDock [96] with the parameter of clustering RMSD as 4 to identify the binding affinities of securitized peptides. The top-ranked analyzed complexes were further refined by the Fast Interaction REfinement in the molecular DOCKing (FireDock) server [97] and scrutinized the effective complexes on the basis of their global energy. UCSF Chimera visualization tool was implemented to critically analyze and visualize the peptide interactions and binding pockets accompanied by the bond lengths.

ERRAT quality factor analyses of the predicted structure.

(TIF)

Click here for additional data file.

Table 1: Manually derived peptides with docking scores.

Table 2: Docking Scores for Peptides Generated Using Pepsin Enzyme. Table 3: Docking Scores for Peptides Generated Using Trypsin Enzyme. Table 4: AHTpin peptides docking scores.

(DOCX)

Click here for additional data file.

8 Aug 2019

PONE-D-19-16606

In silico Identification of Angiotensin Converting Enzyme inhibitory Peptides from MRJP1

PLOS ONE

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Reviewer #2: Partly

**********

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Reviewer #1: Yes

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**********

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: The manuscript submitted by Rana Tahir et al depicted "In silico Identification of Angiotensin Converting Enzyme inhibitory Peptides from MRJP1", established a systematic computational workflow to screen ACE inhibitor by Homology modeling, Molecular dynamic and docking, and predicted peptide “EALPHVPIFDR” has higher anti-hypertensive activity, and we looking forward to some deeper and new-finding based on that. This work is innovative and interesting. And the methods are also standard. In conclusion, this paper can be accepted, but there are still some places need to be revised:

1:Is the time of 30ns too short for protein structure stabilization and optimization using molecular dynamics?

2. It is suggested to incorporate the flow chart of the applied methodology for better understanding.

3: Authors stated that "The values of RMSD and RMSF denoted that the MRJP1 has rare loop structures at corresponding residues", What is the basis of this argument? References need to be updated.

4. The label of Figure 5 is difficult to see particularly yellow color. Change it to visible labeling and yellow color to any other one

5. Authors stated that "Mainly, PubChem, CDK-fingerprint, XLogP, electrotopological state atom type and auto-correlation descriptors were implemented..." and "while tri- peptide QSAR model was developed primarily by KlekotaRoth fingerprint count, PubChem fingerprint, CDK graph only fingerprint and extended fingerprint descriptors....". What is the basis of selecting these different descriptors for di-peptide or tri-peptide? References need to be updated. It is recommended to add a detailed description for modelling process of SVM, including feature engineeing (how to select or exclude features) and relevant diagrams.

6. Auther stated that "The peptide “EALPHVPIFDR” showed strong binding affinity and higher anti-hypertensive activity along with the global energy of -58.29 and docking score of 9590". The authors are off to a good start, however, this study requires additional experiments, such as implementing MD on complex structure from docking and then computing

binding free energy by MMPBSA. It would be better if auther supplement the simple biological test in vitro.

Reviewer #2: The authors present a purely computational study to identify Novel Angiotensin‐Converting Enzyme Inhibitory Peptides Derived from honey protein MRJP1. They tried to make a reliable structural model of MRJP1. Later, the top ranked derived peptides was docked against MRJP1 to analyse the molecular interactions. The fact that several modeling services have been employed, as well as their combination with MD refinement denote considerable care. However, the underlying problem remains the lack of independent confirmation of the docking poses, which is possibly the weakest link.

Major issues:

1) My main problem with this manuscript is that I do not understand who might take advantage of the reported findings: i) Biochemists/Molecular biologists might be tempted to validate In vitro the top ranked peptides if they have the system in place already, and peptides need to be synthesized and to make it proteolytic resistant which is the major challenge in peptide designing. ii) Computational fellows might want the presented pipeline to be validated before applying the method to other systems and iii) Chemists as well will not start any optimization before an experimental evidence.

2) The other major concern relates with their homology modeling approach. When they had an x-ray resolved crystal structure of Major Royal Jelly Protein 1 Oligomer readily available (PDB ID: 5YYL against their mentioned uniprot sequence ID: O18330) then there was absolutely no need to perform homology modeling, MD refinement and later model validation analysis. They should revise the study by taking into account the crystal structure and perform further analysis. Later, they should perform MD simulation of MRJP1 with/without bound peptide to examine the considerable influence of bound peptide. They should also perform the actual MM-GBSA calculations to better explore the binding free energy calculations in the presence of explicit solvent. The only docking conformations are not enough to estimate the most plausible interactions.

3) Another comment on their work relates with a suggestion if one considers a homology model. On what basis a 30ns timescale was selected? A careful reflection on RMSD revealed a gradual expansion even after 30 ns. In general, especially to estimate the protein’s stability, a simulation must be long enough to converge the dynamics of interest and exhibit equilibrium sampling. Everything depends on what you're trying to study, and depends again on the size of the system. For example, two small proteins with only one domain could perhaps be studied in a matter of tens or hundreds of nanoseconds. Complexes of multi-domain proteins may require microseconds or milliseconds depending on the time scale of domain motion. If there was a need of homology modeling (provided the template with >70% identity), they should have examined a backbone stability unless RMSD show convergence up to 10 ns at least. Later, they should perform clustering analysis and take the most representative conformation (with RMSD < 1Å) from the largest cluster to declare it a reasonable model. To better understand the timescale, author should consider this article:

Zwier, M.C. and Chong, L.T., 2010. Reaching biological timescales with all-atom molecular dynamics simulations. Current opinion in pharmacology, 10(6), pp.745-752.

4) I really enjoyed reading their discussion. After considering all above points in their revised study, the authors should compare their findings with the previously reported ACE inhibitory peptides. Quite a few articles are already published which have the same methodology as authors presented in their study. For example:

Yu, Z., Fan, Y., Zhao, W., Ding, L., Li, J. and Liu, J., 2018. Novel Angiotensin‐Converting Enzyme Inhibitory Peptides Derived from Oncorhynchus mykiss Nebulin: Virtual Screening and In Silico Molecular Docking Study. Journal of food science, 83(9), pp.2375-2383.

Yu, Z., Chen, Y., Zhao, W., Li, J., Liu, J. and Chen, F., 2018. Identification and molecular docking study of novel angiotensin‐converting enzyme inhibitory peptides from Salmo salar using in silico methods. Journal of the science of food and agriculture, 98(10), pp.3907-3914.

Vukic, V.R., Vukic, D.V., Milanovic, S.D., Ilicic, M.D., Kanuric, K.G. and Johnson, M.S., 2017. In silico identification of milk antihypertensive di-and tripeptides involved in angiotensin I–converting enzyme inhibitory activity. Nutrition research, 46, pp.22-30.

Others include:

Yu, Z., Wu, S., Zhao, W., Ding, L., Shiuan, D., Chen, F., Li, J. and Liu, J., 2018. Identification and the molecular mechanism of a novel myosin-derived ACE inhibitory peptide. Food & function, 9(1), pp.364-370.

Wang, C., Tu, M., Wu, D., Chen, H., Chen, C., Wang, Z. and Jiang, L., 2018. Identification of an ACE-Inhibitory Peptide from Walnut Protein and Its Evaluation of the Inhibitory Mechanism. International journal of molecular sciences, 19(4), p.1156.

Tu, M., Wang, C., Chen, C., Zhang, R., Liu, H., Lu, W., Jiang, L. and Du, M., 2018. Identification of a novel ACE-inhibitory peptide from casein and evaluation of the inhibitory mechanisms. Food chemistry, 256, pp.98-104.

Yu, Z., Fan, Y., Zhao, W., Ding, L., Li, J. and Liu, J., 2018. Novel Angiotensin‐Converting Enzyme Inhibitory Peptides Derived from Oncorhynchus mykiss Nebulin: Virtual Screening and In Silico Molecular Docking Study. Journal of food science, 83(9), pp.2375-2383.

By providing a comparison with the previously reported ACE inhibitory peptides, a fruitful discussion can be presented.

**********

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Reviewer #2: Yes: Muhammad Usman Mirza

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3 Oct 2019

We are very thankful to Editor and Reviewers for valuable comments to improve the quality of the manuscript. Authors critically studied the comments by reviewers and solved the comments and improved the manuscript.

Reviewer’s Comments

Reviewer 1:

Comment 1: Is the time of 30 ns too short for protein structure stabilization and optimization using molecular dynamics?

Answer: The major fluctuations were observed only at the initial phases of MD simulations, afterward, structure became stabilized till 30 ns MD simulations. RMSD graph in manuscript presenting the fluctuations and stability of 3D model and there were no such variations in a model at the last frames of simulations and therefore optimized model was obtained at 30 ns.

Comment 2: It is suggested to incorporate the flow chart of the applied methodology for better understanding.

Answer: As per suggestion, flow chat of applied methodology has been incorporated at the appropriate position.

Comment 3: Authors stated that "The values of RMSD and RMSF denoted that the MRJP1 has rare loop structures at corresponding residues", What is the basis of this argument? References need to be updated.

Answer: Thanks for your highlighting, as per suggestion the sentence has been updated to remove the ambiguity and for better understanding.

Comment 4: The label of Figure 5 is difficult to see particularly yellow color. Change it to visible labeling and yellow color to any other one.

Answer: As per suggestion, the yellow color has been replaced with another color for more visibility.

Comment 5: Authors stated that "Mainly, PubChem, CDK-fingerprint, XLogP, electrotopological state atom type and auto-correlation descriptors were implemented..." and "while tri- peptide QSAR model was developed primarily by KlekotaRoth fingerprint count, PubChem fingerprint, CDK graph only fingerprint and extended fingerprint descriptors....". What is the basis of selecting these different descriptors for di-peptide or tri-peptide? References need to be updated. It is recommended to add a detailed description for modelling process of SVM, including feature engineeing (how to select or exclude features) and relevant diagrams.

Answer: The utilized methodology of current research has been incorporated including the peptides derivations and evaluation. AHTpin is an in silico method to predict and design the antihypertensive peptides that is utilized in the current study to derive the antihypertensive peptides followed by the activities (SVM Scores) of already derived peptides through other methods. Here, the descriptors utilized to develop an algorithm of AHTpin tool and QSAR models are described. QSAR models are statistical tools built to correlate the biological bioactivity with descriptors of compounds. The already available QSAR models were utilized in current study and algorithms or working of these methods are discussed at appropriate positions in the manuscript. The references of current method have been updated as per suggestion.

Comment 6: Author stated that "The peptide “EALPHVPIFDR” showed strong binding affinity and higher anti-hypertensive activity along with the global energy of -58.29 and docking score of 9590". The authors are off to a good start, however, this study requires additional experiments, such as implementing MD on complex structure from docking and then computing

binding free energy by MMPBSA. It would be better if auther supplement the simple biological test in vitro.

Answer: The detailed in silico analyses were performed to determine the potential antihypertensive peptides from royal jelly protein verified through docking analysis and regression models (QSAR models) used in AHTpin program. SVM scores are predicted by utilizing the models built on in vitro bioactivities of peptides followed by different docking tools. First, PatchDock identifies the docking transformations and afterwards evaluate each transformation through scoring function based on geometrical fit and atomic solvation energy. The top docking solutions are further refined and optimized by using the FireDock server which delivers the flexible refinement and scoring of docking solutions. It includes optimization of side-chain conformations and rigid-body orientation and allows a high-throughput refinement. Our lab mainly works on computational drug designing and has fewer facilities for wet lab analyses.

Reviewer 2:

The authors present a purely computational study to identify Novel Angiotensin‐Converting Enzyme Inhibitory Peptides Derived from honey protein MRJP1. They tried to make a reliable structural model of MRJP1. Later, the top ranked derived peptides was docked against MRJP1 to analyse the molecular interactions. The fact that several modeling services have been employed, as well as their combination with MD refinement denote considerable care. However, the underlying problem remains the lack of independent confirmation of the docking poses, which is possibly the weakest link.

Comment 1: My main problem with this manuscript is that I do not understand who might take advantage of the reported findings: i) Biochemists/Molecular biologists might be tempted to validate In vitro the top ranked peptides if they have the system in place already, and peptides need to be synthesized and to make it proteolytic resistant which is the major challenge in peptide designing. ii) Computational fellows might want the presented pipeline to be validated before applying the method to other systems and iii) Chemists as well will not start any optimization before an experimental evidence.

Answer: Biochemists, molecular biologists, computational biologists, chemists and other researchers take advantage from the current findings as in detailed in silico analyses as presented in this manuscript has 50-60% chances of success. Every researcher has to perform his task as biochemists are unable to do this extensive in silico study as computational biologists are unable to do in vitro analyses. It is better for the researchers to synthesize and validates the findings through wet lab instead of trying millions of peptides with huge budget and time. The computational biologists who have good expertise in their domain will trust the analyses after reading the detailed findings and researchers have to trust each other work and efforts. Computational biologists will also learn from the utilized methodology in other relevant projects. There is not a single research that has no benefit for others and the researchers always find the benefits from the other researcher’s findings.

Comment 2: Another comment on their work relates with a suggestion if one considers a homology model. On what basis a 30 ns timescale was selected? A careful reflection on RMSD revealed a gradual expansion even after 30 ns. In general, especially to estimate the protein’s stability, a simulation must be long enough to converge the dynamics of interest and exhibit equilibrium sampling. Everything depends on what you're trying to study, and depends again on the size of the system. For example, two small proteins with only one domain could perhaps be studied in a matter of tens or hundreds of nanoseconds. Complexes of multi-domain proteins may require microseconds or milliseconds depending on the time scale of domain motion. If there was a need of homology modeling (provided the template with >70% identity), they should have examined a backbone stability unless RMSD show convergence up to 10 ns at least. Later, they should perform clustering analysis and take the most representative conformation (with RMSD < 1Å) from the largest cluster to declare it a reasonable model. To better understand the timescale, author should consider this article.

Answer: Refer to the comment number 1 of reviewer 1. The mentioned articles have been added to the manuscript at appropriate positions as per suggestion.

Comment 3: I really enjoyed reading their discussion. After considering all above points in their revised study, the authors should compare their findings with the previously reported ACE inhibitory peptides. Quite a few articles are already published which have the same methodology as authors presented in their study.

Answer: Thanks for the appreciation the Discussion section. All the suggested articles are added to the manuscript at appropriate positions as per suggestion.

*Changes are mentioned in colors in a manuscript.

24 Oct 2019

PONE-D-19-16606R1

In silico Identification of Angiotensin Converting Enzyme inhibitory Peptides from MRJP1

PLOS ONE

Dear Dr. Sehgal,

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We look forward to receiving your revised manuscript.

Kind regards,

Ghulam Md Ashraf, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript submitted by Rana Tahir et al depicted "In silico Identification of Angiotensin Converting Enzyme inhibitory Peptides from MRJP1", established a systematic computational workflow to screen ACE inhibitor by Homology modeling, Molecular dynamic and docking, and predicted peptide “EALPHVPIFDR” has higher anti-hypertensive activity, and we looking forward to some deeper and new-finding based on that. This work is innovative and interesting. And the methods are also standard. The response is basically reasonable. In conclusion, this paper can be accepted.

Reviewer #2: The authors improved the manuscript but I think authors simply overlooked one major concern as follows (2nd comment in my first revision):

1. "2). The other major concern relates with their homology modeling approach. When they had an x-ray resolved crystal structure of Major Royal Jelly Protein 1 Oligomer readily available (PDB ID: 5YYL against their mentioned uniprot sequence ID: O18330) then there was absolutely no need to perform homology modeling, MD refinement and later model validation analysis. They should revise the study by taking into account the crystal structure and perform further analysis. Later, they should perform MD simulation of MRJP1 with/without bound peptide to examine the considerable influence of bound peptide. They should also perform the actual MM-GBSA calculations to better explore the binding free energy calculations in the presence of explicit solvent. The only docking conformations are not enough to estimate the most plausible interactions"

Once MD simulations has performed then its easy to calculate the MM-GBSA/MM-PBSA. Authors can refer a tutorial from the link below as they used AMBER 14 simulation package:

https://ambermd.org/tutorials/advanced/tutorial3/py_script/section2.htm

2. The advantage of having a crystal structure in this study (as it is readily available) will significantly improved MD simulation analysis in terms of overall stability with bound peptide and binding free energy calculations.

The manuscript should publish after the incorporation of above mentioned suggestions/comments.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Muhammad Usman Mirza

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

12 Dec 2019

Response to Reviewer’s

In silico Identification of Angiotensin-Converting Enzyme inhibitory Peptides from MRJP1

PLOS ONE: PONE-D-19-16606

We are very thankful to the Editor and Reviewers for valuable comments to improve the quality of the manuscript. Authors critically studied the comments by reviewers and solved the comments and improved the manuscript.

Reviewer’s Comments

Reviewer 1: The manuscript submitted by Rana Tahir et al depicted "In silico Identification of Angiotensin Converting Enzyme Inhibitory Peptides from MRJP1", established a systematic computational workflow to screen ACE inhibitor by Homology modeling, Molecular dynamic and docking, and predicted peptide “EALPHVPIFDR” has higher anti-hypertensive activity, and we looking forward to some deeper and new-finding based on that. This work is innovative and interesting. And the methods are also standard. The response is basically reasonable. In conclusion, this paper can be accepted.

Answer: Thanks to the reviewer for accepting the manuscript.

Reviewer 2:

Comment 1:

The other major concern relates with their homology modeling approach. When they had an x-ray resolved crystal structure of Major Royal Jelly Protein 1 Oligomer readily available (PDB ID: 5YYL against their mentioned uniprot sequence ID: O18330) then there was absolutely no need to perform homology modeling, MD refinement and later model validation analysis. They should revise the study by taking into account the crystal structure and perform further analysis. Later, they should perform MD simulation of MRJP1 with/without bound peptide to examine the considerable influence of bound peptide. They should also perform the actual MM-GBSA calculations to better explore the binding free energy calculations in the presence of an explicit solvent. The only docking conformations are not enough to estimate the most plausible interactions". Once MD simulations has performed then its easy to calculate the MM-GBSA/MM-PBSA.

Answer: It is a good suggestion to use the crystal structure for the analyses. We predicted and simulated the model to derive the peptides manually and also through pepfold tool but the whole predicted structure are not utilized in mainstream analyses. Multiple techniques were employed to extract the potential di and tri-peptides from the MRJP1 model. Furthermore, the predicted structure was then used to cross verify the model and peptides (as the x-ray crystallographic structure was online after) and 95% results were similar. As already mentioned in the previous revision, the reported peptides were docked and refined for the verification of analyses.

The extracted peptides were docked and simulated through PatchDock and Firedock and the global free energy was calculated to identify the potential peptides. The hundred poses of each complex were selected from the PatchDock based on scoring function that is combination of pairwise shape complementarity, desolvation and electrostatic energy. The refinement of selected poses was performed by FireDock that refines and scores them according to an energy function, spending about 3.5 seconds per candidate solution. The rearrangement of the side-chains, the relative position of docking partners is refined by Monte Carlo minimization of the binding score function and the refined candidates are ranked by the binding score. FireDock score includes Atomic Contact Energy, softened van der Waals interactions, partial electrostatics and additional estimations of the binding free energy. The final selected protein-peptide complexes are identified through extensive modeling, docking and simulations strategies and it has been reported that these docking and refinement, algorithms based on multiple energy functions are considered as effective approaches to identify the potential docking solutions. Global free energy and MM-GBSA/MM-PBSA have similar algorithms to calculate the free energy after docking and simulation. The suggested experiment was already performed through software instead of AMBER and reliable results were observed.

Comment 2:

2. The advantage of having a crystal structure in this study (as it is readily available) will significantly improved MD simulation analysis in terms of overall stability with bound peptide and binding free energy calculations.

Answer: Thanks for the suggestion as the same analyses were already performed in the analyses and explain briefly in the manuscript.

Hope, these modifications are sufficient to resolve the queries, If Editor or reviewer still feel to incorporate the interactions of peptides extracted from the experimental model in the main manuscript, we can update that results.

*Changes are mentioned in colors in a manuscript.

Submitted filename: Response to Reviewers Review comments.doc

Click here for additional data file.

13 Jan 2020

In silico Identification of Angiotensin Converting Enzyme inhibitory Peptides from MRJP1

PONE-D-19-16606R2

Dear Dr. Sehgal,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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Reviewer #2: All comments have been addressed

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Reviewer #2: Partly

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Reviewer #2: Yes

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Reviewer #2: Although the manuscript lack the important binding free energy calculations after MD simulations but the presented data is still publishable. In future studies, the authors should include MM-GBSA/PBSA calculations as an integral part after simple more unpredictable docking studies.

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Reviewer #2: No

17 Jan 2020

PONE-D-19-16606R2

In silico Identification of Angiotensin-Converting Enzyme inhibitory Peptides from MRJP1

Dear Dr. Sehgal:

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