Peptide Derived from Bungarus caeruleus Proteome Binds with Higher Affinity to Ethionamide Resistance Regulator of Mycobacterium tuberculosis than Isoniazid

doi:10.21203/rs.3.rs-5298284/v1

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Peptide Derived from Bungarus caeruleus Proteome Binds with Higher Affinity to Ethionamide Resistance Regulator of Mycobacterium tuberculosis than Isoniazid

https://doi.org/10.21203/rs.3.rs-5298284/v1

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Antimicrobial resistance has become a serious health concern worldwide because of high morbidity and mortality. An increase in multi-drug and extensively drug-resistant (MDR and XDR) strains of Mycobacterium tuberculosis has trigged the finding of effective therapeutic alternatives. A computational approach has been utilized to predict and prediction of novel antimycobacterial peptides using the proteome sequences of Bungarus caeruleus (Indian Krait). In-silico digestion of proteome sequences using five different enzymes yielded more than 1000 shorter peptides sequences. Further, the antibacterial peptide was predicted using DBAASP server. Based on the various physiochemical properties (including stability, half-life and ADMET) 11 peptides were taken for molecular docking study. Ethionamide resistance regulator (EthR)-peptide docking was performed using HADDOCK server, and the peptide sequence HGATVAVKQVNRCSKNHL shows the maximum binding affinity with EthR. The binding score was found to be maximum for the peptide with -9.3 kcal/mol in comparison with the standard drug (Isoniazid) with -5.69 kcal/mol when analysed with PRODIGY server. The complex structure and the interactions were found to be stable during the 100 ns molecular dynamics simulations using Gromacs-2023.1. The stability of the complex was analysed in terms of RMSD, RMSF, radius of gyration, H-bond, and SASA. MMPBSA analysis indicated that the free energy of interaction of EthR with peptide and Isoniazid was found to be -36.15 kcal/mol and -6.95 kcal/mol respectively. The results indicate the potential anti-mycobacterial property of this 18-mer peptide which can be validated further through in-vitro and in-vivo studies.

Antimicrobial peptide

Mycobacterium tuberculosis

Bungarus caeruleus

docking and dynamic simulation.

With the emergence of multi-drug-resistant (MDR) and extensively drug-resistant tuberculosis (XDR-TB), there is a growing need to explore alternative treatment methodologies to address infections with high morbidity and mortality rates [1]. In 2022, an estimated 10.6 million people fell ill with tuberculosis (TB) worldwide, including 5.8 million men, 3.5 million women and 1.3 million children. TB is present in all countries and age groups. TB is curable and preventable (WHO, 2023).

The Ethionamide resistance regulator (EthR) protein plays a crucial role in Mycobacterium tuberculosis (Mtb) infection. Ethionamide resistance regulator (EthR) serves as a transcriptional regulator within Mycobacterium tuberculosis (Mtb), controlling the activity of the Ethionamide Activator (ethA) gene responsible for activating the antitubercular medication ethionamide. Ethionamide resistance regulator (EthR) belonging to the tetracycline repressor (TetR) family, EthR binds to the promoter of ethionamide activator (ethA), suppressing its expression. Ethionamide relies on EthA's activation to exert its antimycobacterial effects. Suppressing EthR expression or function can elevate EthA levels, enhancing ethionamide activation and thereby boosting the drug's efficacy against tuberculosis. EthR is taken as target which involves in the repression of the monooxygenase EthA which is responsible of the formation of the active metabolite of ethionamide (Eth) [2]. Binding of inhibitors to EthR induces a conformational change in this repressor, which is then unable to bind its DNA operator, consequently allowing for increased transcription of EthA and bioactivation of ethionamide (Eth) [3]. The enoyl-acyl carrier protein reductase (InhA) is a crucial target for tuberculosis treatment, targeted by first- and second-line drugs isoniazid and ethionamide, respectively. Both drugs need activation within M. tuberculosis cells by specific proteins: KatG for isoniazid and EthA for ethionamide. Resistance often stems from these activation processes. Ethionamide resistance mechanisms involve increased expression of EthR, a transcriptional regulator, which lowers EthA levels. Targeting EthR to control EthA expression could enhance ethionamide's bactericidal effect, reducing resistance and toxicity. Identifying compounds binding to EthR could aid in this approach [4].

The development of new antimicrobial drugs is of great importance [5]. Therefore, antimicrobial peptides (AMPs) represent potent and efficient candidates for the development of a new drug generation [6]. As a result, some AMPs have already reached clinical trial levels, and currently some are already available in the market, such as polymyxins B and E, bacitracins, and gramicidins [7, 8]. AMPs are low molecular weight compounds, ranging from 12 to 50 amino acid residues [9]. They can be naturally occurring, usually cationic and hydrophobic [10], are encoded by genes that remain conserved in the genome during evolution, and can be isolated from virtually all forms of living organisms [7]. The cathelicidin-related antimicrobial peptides (CRAMPs) from natural snake venom constitute an important family involved in the immune system defence, presenting not only antibacterial for Gram positive and Gram negative but also immunomodulatory properties [11].

Bungarus caeruleus, also known as the common krait or Bengal krait, is a highly venomous elapid snake belonging to the genus Bungarus, native to the Indian subcontinent. Bungarus caeruleus snake venom of has been studied for its potential therapeutic applications, particularly in the development of antivenom for snakebite treatment. The venom's composition, including presynaptically-acting toxins and potent postsynaptic neurotoxins, contributes to its biological effects and potential therapeutic uses [12]. Snake venom peptides have been explored for their antimicrobial activity, particularly against Staphylococcus epidermidis. These peptides, such as pseudonajide, have been derived from snake venom sequences and have shown significant antibiofilm and antimicrobial properties. The selection of snake venom peptides for antimicrobial activity is based on their ability to interact with bacterial cell envelopes, disrupting cell walls and membranes, and leading to morphological defects in prokaryotes.

This study employs diverse computational tools to design and forecast antimicrobial peptides from Bungarus caeruleus (Indian Krait) using machine learning algorithms. Additionally, molecular docking, dynamics simulation, and MMPBSA analysis were conducted for the target protein to assess the efficacy of the predicted AMPs against drug-resistant M. tuberculosis.

In silico hydrolysis and prediction of AMP from the parent protein

Proteome sequences were retrieved from the Uniprot database using the keyword “Bungarus caeruleus”. It was further subjected to an Expasy peptide cutter server, and digested with five different enzymes Trypsin, Chymotrypsin-high specificity (C-term to [FYW], not before P), Chymotrypsin-low specificity (C-term to [FYWML], not before P), Pepsin (pH 1.3), and Pepsin (pH > 2) for the collection of shorter peptide sequences; which were further evaluated for the AMP property. These enzymes digest the proteome sequences at specific regions resulting in different lengths of peptide sequences and the benchmark was set for the sequence range from 6–25 amino acids length [13]. The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) server was used for the prediction of AMP from the collected shorter peptide sequences. Based on machine learning algorithm and Moon and Fleming scale, it predicts the antibacterial property of the linear peptide in general and against specific bacterial strain [14].

Physicochemical properties of the predicted AMPs

Physicochemical properties of the predicted peptides were calculated using two web servers such as APD3 and Expasy PROTPARAM, which predicted the various physiochemical properties such as molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (GRAVY) [15]. The Toxicity property of the predicted AMP was calculated using ToxIBTL (https://server.wei-group.net/ToxIBTL/Server.html), which works on the principle of a novel deep learning framework by using the principle of bottleneck and transfer learning algorithm for the toxicity prediction of the peptides [16].

ADMET property for the predicted AMPs

To evaluate the ADMET properties of the predicted peptides were predicted using ADMETlab 2.0 server (https://admetmesh.scbdd.com/), we converted the amino acid residues to SMILES (Simplified Molecular Input Line Entry System) format using the PepSMI server (https://www.novoprolabs.com/tools/convert-peptide-to-smiles-string) [17]. The key parameter that was inferred for the ADMET profile is Human Intestinal Absorption (HIA), Caco-2 permeability, volume distribution, mutagenicity, carcinogenicity, Central nervous system (CNS) permeability, Drug-Induced liver injury (DILI), different cytochrome enzymes inhibition and substrate, and skin sensitization. Overall, the AMPs with the best ADMET profiles were then, prepared for the molecular docking exercises.

Evaluation of Cell Penetrating, Hemolytic, Toxicity, and Allergenicity Property

Cell penetrating property of the peptides were predicted using Support Vector Machine based algorithm based web server CellPPD (https://webs.iiitd.edu.in/raghava/cellppd/index.html). Peptide sequences were subjected to amino acid residues scanning tool with the default SMV threshold and the results were obtained as CPP and Non-CPP on the basis of the prediction score (Gautam et al., 2013; Gautam et al., 2015). Hemolytic prediction for the peptides sequences were carried out using SVM based web server HemoPI (https://webs.iiitd.edu.in/raghava/hemopi/design.php). PROB score is the normalized SVM score and ranges between 0 and 1, i.e. 1 very likely to be hemolytic, 0 very unlikely to be hemolytic [20]. Toxicity and allergenecity for the peptide were predicted using ToxinPred (https://webs.iiitd.edu.in/raghava/toxinpred/algo.php) and AllerTop server (https://www.ddg-pharmfac.net/AllerTOP) (Gupta et al., 2013; Dimitrov et al., 2014; Gupta et al., 2015) pH-dependent folded and unfolded states were predicted using SVM-based DispHScan (http://disphscan.ppmclab.com/) [24].

Structure Prediction

The generated sequences for the predicted antimicrobial peptides were modelled using PEPFOLD2.0 (https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD2/), for the 3D- dimensional structure prediction [25]. It works on the principle of HMM model (Hidden Markov Model) to predict the 3D- structure of the peptides and the peptide length should not be less than 9 amino acid residues [26]. The best ranked model from the results were further validated using PROCHECK server of SAVES v6.0 with the help of Ramachandran plot by the positioning of amino acids residues in the different regions such as allowed, favoured and disallowed regions in the plot Laskowski [27].

Molecular Docking

HADDOCK (High Ambiguity Driven protein-protein DOCKing) is an information-driven flexible docking approach for the modelling of biomolecular complexes. Protein structure for the HTH-type transcriptional regulator EthR of Mycobacterium tuberculosis protein (UniProt Id - P9WMC1) protein structures were prepared for the docking by removing the water molecules and other heteroatom molecules from the protein file and hydrogen bonds and charges were added using AutoDock tool. and peptide were prepared using AutoDock tools for hydrogen, charges and energy minimization. Then prepared structures were uploaded to the HADDOCK server and protein-peptide docking was performed specific to the active site residues [28, 29]. The active site for the target protein was predicted from the Computed Atlas of Surface Topography of Proteins (CASTp 3.2 version), and from the literature previously reported with the binding site. The output results were recorded for different parameters such as HADDOCK score, Van der Waals energy, Electrostatic energy, Desolvation energy, and Z- score. Interaction visualization between protein-peptide complex were studied using BIOVIA Discovery Studio and LigPlot tools.

Protein-peptide interface interaction analysis

To study the protein-peptide interface interactions, PRODIGY web server were employed, it gives the binding energy for the docked protein-peptide complexes in Kcal/mol unit. The binding energy evaluated at the PRODIGY server, was calculated using following equation [30],

∆𝑮𝒄𝒂𝒍𝒄 = 𝟎. 𝟎𝟗𝟒𝟓𝟗𝑰𝑪𝒔 𝒄𝒉𝒂𝒓𝒈𝒆𝒅/𝒄𝒉𝒂𝒓𝒈𝒆𝒅 + 𝟎. 𝟏𝟎𝟎𝟎𝟕𝑰𝑪𝒔 𝒄𝒉𝒂𝒓𝒈𝒆𝒅/𝒂𝒑𝒐𝒍𝒂𝒓 − 𝟎. 𝟏𝟗𝟓𝟕𝟕𝑰𝑪𝒔 𝒑𝒐𝒍𝒂𝒓/𝒑𝒐𝒍𝒂𝒓 + 𝟎. 𝟐𝟐𝟔𝟕𝟏𝑰𝑪𝒔 𝒑𝒐𝒍𝒂𝒓/𝒂𝒑𝒐𝒍𝒂𝒓 − 𝟎. 𝟏𝟖𝟔𝟖𝟏%𝑵𝑰𝑺𝒂𝒑𝒐𝒍𝒂𝒓 − 𝟎. 𝟏𝟑𝟖𝟏𝟎%𝑵𝑰𝑺 𝒄𝒉𝒂𝒓𝒈𝒆𝒅 + 𝟏𝟓. 𝟗𝟒𝟑𝟑

Where, ICs (Inter-residue contacts) and NIS (Non-interacting surface) terms represent the importance of different types of residues interaction in defining the overall binding energy for the interacted complex.

Molecular dynamics (MD) simulation

Molecular dynamics simulation for the protein alone, and protein – peptide and protein-INH complex, which has the maximum binding energy after the docking process was performed for 100 ns. The MD simulations were performed to evaluate the conformational variability and stability during dynamics simulation using GROMACS – 2023.1 version (academic) [31]. The peptide with maximum binding energy against one target protein was prepared using Charmm-GUI force field for the refinement of amino acid interactions during simulation. Meanwhile, the solvation for the protein was done in an orthorhombic box using the TIP3P (Transferable Intermolecular Potential with 3 points) water molecules. Salt ions (Na⁺) were added to neutralise the system prepared for the solvation. The water box was prepared in such a manner that it completely covered both the protein and peptide complex. Energy minimization and equilibration for the system were carried out through the procedure mentioned in the Desmond suite. The final MDS run was performed for 100 ns with temperature relaxation at 300K, with bar pressure 1 bar for 24 psi in the NPT ensemble, after performing the minimization and equilibration of the solvated protein-peptide complex system. Similarly, the system was generated for the protein-INH complex and MD simulation was performed for 100 ns. The stability and other properties of the interactions during the simulation process were studied using RMSD, RMSF, SASA and radius of gyration other profiles that were interpreted.

Estimation of free energy binding

The interaction free energies of protein-peptide complex were determined using the gmx MMPBSA technique, which is a quantitative calculation of the binding free energy used to examine biomolecular complexes. The binding energy was calculated for the known standard and the predicted peptide bound at the active site of the target protein. The MD simulation trajectories for the 100 ns simulation were utilized to calculate the binding energy of the complexes. Other interaction analysis such as Van der Waals energy (ΔVDWAALS), Electrostatic molecular energy (ΔEEL), polar contribution to the solvation energy (ΔEPB), nonpolar contribution of repulsive solute–solvent interactions to the solvation energy (ΔENPOLAR), total gas phase molecular energy (ΔGGAS), and total solvation energy (ΔGSOLV) respectively.

In silico hydrolysis and prediction of AMP from the parent proteins

Protein sequences were retrieved from the UniProt database for the Bungarus caeruleus (Indian Krait) and subjected to Expasy peptide cutter tool for cleavage of larger protein sequences into shorter peptide sequences using different enzymes (Table 1) (Fig. 1). Totally five enzymes which were used to cleave the 27 protein sequences of B. caeruleus. Initially, when different enzymes were used for the cleavage of protein sequences into shorter peptides, more than 1000 shorter sequences were obtained, of which 300 peptides had the peptide chain length of six to twenty-five. Further, when those shorter peptide sequences were given as input to the DSAABP server for the prediction of AMP property, 48 peptide sequences were predicted to have antimicrobial peptide property. When protein sequences were cleaved with trypsin enzyme, 9 shorter peptide sequences were obtained that showed AMP property. Similarly, when cleaved using chymotrypsin with low specificity, 12 shorter peptide sequences; using high specificity 12 shorter peptide sequences, using Pepsin pH 1.3, 5 shorter peptide sequences; and using pepsin pH > 2, 10 shorter peptide sequences were obtained after analysing through the DSAABP server for the prediction of antimicrobial peptide property. The prediction was performed based on the machine learning algorithm and uses the Moon and Fleming scale [32].

Table 1

Peptide sequences derived from *Bungarus caeruleus* with anti-mycobacterial property
Source protein with UniProt Id	Trypsin	Pepsin (pH 1.3)	Pepsin (pH > 2)	Chymotrypsin-low specificity (C-term to [FYWML], not before P	Chymotrypsin-high specificity (C-term to [FYW], not before P)
Acidic phospholipase A2 2 (Fragment) - Q6SLM2			FKNMIQCAGTRT YNKAANIPGCNP	NKAANIPGCNPL
Basic phospholipase A2 KPA2 - Q9DF52	NMIQCAGTRPWTAYVNYGCYCGK		KNMIQCAGTRP		KNMIQCAGTRPW
Basic phospholipase A2 3 (Fragment) - Q6SLM0				CGKGGSGTPVDKL	CGKGGSGTPVDKLDRCCY
Basic phospholipase A2 beta-bungarotoxin A2 chain - Q8QFW4					KLTKRTIICY
Neurotrophin-3 (Fragment) - A0A7L5TX42	QAKPAK			GPSPVKQY
Ubinuclein 1 (Fragment) - A0A7L5TY62	SSPSSMVK		QQQVKAPAKVPTF
V(D)J recombination-activating protein 1 (Fragment) - A0A7L5TXZ9	VIFHMSQR TMLNSQLSK	CHNCWKAMQRKVSNAPHEAH RGVKRQKTML	KAMQRKVSNAPHEAH RGVKRQKTML LKVMGS	QRKVSNAPH VSPVKSF	KAMQRKVSNAPHEAH LLEKDPVKW CPSCRYPCFPTDLVSPVKSF
Cytochrome b (Fragment) - A1IML4	IPLHPYHTYK	ICIYTHIARG RSIPNK	THIARG	RSIPNKL	THIARGLY GILRSIP
Prolactin receptor (Fragment) - A0A7L5TXF2	HLPQLASR			SSHDAIRICKRVL KATNVKRSPIL NSQRNRGASF KSIKPM	KSIKPMD
non-specific serine/threonine protein kinase - A0A7L5TXC6	NHLASR	RIRRLPPHL	HGATVAVKQVNRCSKNHL	GATVAVKQVNRCSKNH KPANIF	RIRRLPPHLAW THRAPELLKG

Physicochemical properties of the predicted AMPs

Physicochemical properties for the predicted antimicrobial peptides were evaluated using different webservers such as APD3 antimicrobial database and PROTPARAM. The properties include Peptide mass, charge, pI (isoelectric point), hydrophobicity value, hydropathy value, Boman index, instability index, and half lifetime. Lowest and highest peptide length obtained were 9 and 18 amino acid residues with the peptide sequence TMLNSQLSK, QRKVSNAPH and HGATVAVKQVNRCSKNHL and peptide mass of 1021.20, 1036.16, and 1962.26 respectively. For peptide sequence HGATVAVKQVNRCSKNHL, net charge + 3.5 is maximum, whereas the pI value 12.00 is maximum for NSQRNRGASF among all the predicted AMP sequences. Hydrophobicity value for the peptide sequence KAMQRKVSNAPHEAH is maximum with 7.08 value and IPLHPYHTYK has lowest value with − 0.38. Hydrophobicity value is the sum of whole-residue free energy of transfer of the peptide from water to POPC interface and termed as Wimley-white whole residues value.

Boman index estimates the potential for a protein to bind with other proteins [33]. In other words, a high Boman index value indicates that an AMP will be multifunctional or play a variety of different roles within the cell due to its ability to interact with a wide range of proteins. Peptide sequence with NSQRNRGASF has highest Boman index with 4.97 (Kcal/mol) and sequence ICIYTHIARG has lowest Boman index value with 0.35 (Kcal/mol). Further, instability index and the half lifetime of the predicted AMP were determined using Expasy PROTPARAM server. The stable peptides were taken for the further analysis and the stability index for the prediction was based on the values less than 40 is given as stable, whereas more than 40 is given as unstable peptide. One predicted peptide sequence has a half-life of 30 hours or more in mammalian reticulocytes in vivo, three peptide sequences have a half-life of 20 hours or more in yeast in vivo, and eight peptide sequences have a half-life of 10 hours or more in Escherichia coli in vitro. Peptide sequence QRKVSNAPH has lowest half-life time in mammalian reticulocytes with 0.8 hours (Table 2).

Table 2

Physicochemical properties for predicted AMP of Peptide mass, charge, pI, hydrophobicity value, hydropathy value, boman index, instability index and the half-life time
Peptide Sequence	Peptide Length	Peptide mass (Daltons)	Charge	pI	Hydrophobicity (Wimley-White whole-residue)	Hydropathy value	Boman Index (kcal/mol)	Instability Index	Half Life Time
Peptide Sequence	Peptide Length	Peptide mass (Daltons)	Charge	pI	Hydrophobicity (Wimley-White whole-residue)	Hydropathy value	Boman Index (kcal/mol)	Instability Index	Mammalian reticulocytes (in-vitro)	Yeast (in-vivo)	Escherichia coli (in-vivo)
KLTKRTIICY	10	1238.55	+ 3	9.79	0.71	0.03	1.52	33.11	1.3 hours	3 min	3 min
FKNMIQCAGTRT	12	1369.62	+ 2	9.51	1.35	-0.30833	1.95	-16.72	1.1	3 min	2 min
ICIYTHIARG	10	1146.37	+ 1.25	8.23	-0.81	0.77	0.35	35.96	28	30	> 10 hours
IPLHPYHTYK	10	1268.48	+ 1.5	8.51	-0.38	-0.85	0.78	8.04	20	30	> 10 hours
TMLNSQLSK	9	1021.20	+ 1	8.41	1.04	-0.4111	1.65	30.29	7.2	> 20 hours	> 10 hours
QRKVSNAPH	9	1036.16	+ 2.25	11.00	3.79	-1.666	3.87	20.86	0.8	10 min	10 hours
NSQRNRGASF	10	1136.19	+ 2	12.00	2.35	-1.69	4.97	23.62	1.4	3 min	> 10 hours
GATVAVKQVNRCSKNH	16	1711.96	+ 3.25	10.06	4.97	-0.575	2.34	12.36	30	> 20 hours	> 10 hours
KAMQRKVSNAPHEAH	15	1703.94	+ 2.5	9.99	7.08	-1.34	3.06	5.83	1.3	3 min	3 min
THRAPELLKG	10	1121.30	+ 1.25	8.44	3.64	-0.84	2.19	28.66	7.2	> 20 hours	> 10 hours
HGATVAVKQVNRCSKNHL	18	1962.26	+ 3.5	10.06	4.58	-0.4777	2.06	6.33	10	10 min	> 10 hours

ADMET property for the predicted AMPs

Evaluating the pharmacokinetic properties of the predicted peptides is important for investigating its efficacy and indemnity. In the current evaluation of ADMET property analysis, we have considered several parameters such as Human Intestinal Absorption (HIA), mutagenicity, carcinogenicity, Central Nervous System (CNS) permeability, drug induced liver injury, Cytochrome P450 enzymes inhibition, etc. Human Intestinal Absorption is one the important parameter that needs to be considered, because while administrating drug orally, HIA influences the bioavailability [34].

All the 11 predicted peptides have the HIA value more than 0.5, and it can be easily absorbed in the human intestine. An additional property such as Blood brain barrier permeability index, BBB value more than 0.3 can result in causing the CNS toxicity, therefore BBB value less than 0.3 can be considered safe. In that consideration, all the 11 peptides have BBB index less than 0.3 value under optimal distribution volume for the peptides were in the range of 0.207–0.534 L/kg, which has to be in the range of 0.04-20 L/Kg value. None of the peptides exhibited inhibitory activity against the cytochrome P450 enzymes, as their values remained below the threshold of 0.5, which is typically considered indicative of no inhibition. For the cytochrome P450 enzymes, none of the peptides were able to inhibit the activity, thereby falling below the range of 0.5 value; which is considered as range for the no inhibition activity. Also, the clearance level in the excretion section for all the peptides are below 5 ml/min/Kg, which is considered as low clearance level therefore will not accumulate in the intestine. For toxicity property, hERG (human ether-a-go-go-related gene) value is less than 0.5 for all the peptides, similarly for other parameters such as carcinogenicity, mutagenicity, and skin sensitization, all the peptides fall below the value which is considered as harmful for these parameters. When these peptides were subjected for PAINS (Pan Assay Interference Compounds) and BMS (Bristol-Meyers Squibb’s) reactions for screening process, there were no reactions alerts were detected resulting in inferring that these peptides are safer to use as therapeutic agents (Table 3).

Table 3

Predicted ADMET scores for the antimicrobial peptides
Category	Property	KLTKRTIICY	FKNMIQCAGTRT	ICIYTHIARG	IPLHPYHTYK	TMLNSQLSK	QRKVSNAPH	NSQRNRGASF	GATVAVKQVNRCSKNH	KAMQRKVSNAPHEAH	THRAPELLKG	HGATVAVKQVNRCSKNHL
Absorption	HIA/Human Intestinal absorption (%)	0.998	0.918	0.901	0.967	0.984	0.998	0.398	0.825	0.981	0.733	0.093
Absorption	Papp/Caco-2 permeability (cm/s)	-6.702	-6.847	-6.889	-7.17	-6.502	-6.621	-6.337	-6.914	-6.945	-7.359	-6.408
Distribution	VD/Volume distribution (L/Kg)	0.497	0.207	0.268	0.463	0.391	0.534	0.517	0.353	0.382	0.442	0.456
	BBB/Blood brain barrier penetration (%)	0.043	0.04	0.059	0.059	0.031	0.016	0.113	0.044	0.043	0.037	0.102
	PPB/Plasma protein binding (%)	48.80%	18.79%	17.43%	14.57%	14.34%	18.85%	16.18%	20.87%	36.24%	11.99%	17.39%
Metabolism	CYP1A2 – Inhibitor	0	0	0	0	0	0	0	0	0	0	0
	CYP1A2 – substrate	0.011	0	0	0.001	0	0	0.002	0	0.004	0	0
	CYP3A4 – Inhibitor	0.24	0	0	0.011	0	0.012	0.002	0	0.035	0.001	0
	CYP3A4 - substrate	0.002	0	0	0	0	0.001	0	0	0.001	0	0
Excretion	CL/Clearance (ml/min/Kg)	1.632	-0.236	-0.229	1.375	0.692	1.39	0.94	0.084	1.678	1.043	0.455
Toxicity	hERG blockers	0.079	0	0.001	0.026	0.008	0.032	0.016	0.002	0.033	0.001	0.009
	DILI/Drug induced liver Injury	0.655	0.002	0.002	0.015	0.001	0.002	0.014	0.008	0.012	0.002	0.001
	AMES (Ames mutagenicity)	0.005	0.022	0.021	0.007	0.01	0.007	0.017	0.012	0.007	0.062	0.021
	Carcinogenicity	0.009	0.005	0.012	0.015	0.028	0.037	0.056	0.01	0.01	0.275	0.047
	Skin sensitization	0.113	0.048	0.063	0.18	0.05	0.049	0.17	0.119	0.049	0.176	0.154
	Log P value	-0.021	-4.875	-5.424	-2.872	-2.417	0.238	-5.052	-6.228	0.071	-2.783	-5.74
	H acceptor	29	54	48	30	35	29	30	47	28	27	35
	H donor	17	38	35	20	26	23	22	32	19	20	28

Cell Penetrating, Hemolytic, Toxicity, and Allergenicity Property

Peptides were analysed for their cell penetrating property, which can help them to penetrate through the cells and kill the bacteria. Other important property to be investigated for the peptides are hemolytic, toxicity and allergenicity property to avoid hemolysis, neglecting out toxicity nature of the peptides and not having any allergenicity towards the immune system. From the results, it has been observed that the three peptides sequences have cell penetrating property whereas other peptides do not possess the CPP property. For hemolytic property, all the peptides have the value less than 0.5, which indicates that they are non-hemolytic peptides and will not hemolyse the blood cells. For toxicity and allergenicity, it has been found that all the peptides are non-toxic and 5 peptides are non-allergen, thereby they will not have any toxic effects and also can be used for drug delivery application without any allergenicity effect. Similarly, the pH dependent folding and unfolding property in the range of pH 3–10 were predicted and it has been found that 7 peptides sequences are in folding states, whereas 7 peptides sequences are in conditional folding states, and one peptide sequences is in non-confident transition states (Table 4).

Table 4

Different biological properties of the predicted antimicrobial peptides and their pH dependent folding states
Peptide Sequences	Cell Penetrating	Hemolytic	Toxicity	Allergenicity	Folding states
KLTKRTIICY	CPP	0.49	Non-Toxin	Non-Allergen	Conditional folding
FKNMIQCAGTRT	CPP	0.48	Non-Toxin	Non-Allergen	Non – confident transition
ICIYTHIARG	Non-CPP	0.49	Non-Toxin	Non-Allergen	Folded
IPLHPYHTYK	Non-CPP	0.49	Non-Toxin	Non- Allergen	Conditional folding
TMLNSQLSK	Non-CPP	0.49	Non-Toxin	Non-Allergen	Folded
QRKVSNAPH	Non-CPP	0.48	Non-Toxin	Non-Allergen	Conditional folding
NSQRNRGASF	Non-CPP	0.49	Non-Toxin	Non-Allergen	Conditional folding
GATVAVKQVNRCSKNH	Non-CPP	0.48	Non-Toxin	Non-Allergen	Conditional folding
KAMQRKVSNAPHEAH	Non-CPP	0.48	Non-Toxin	Non-Allergen	Folded
THRAPELLKG	Non-CPP	0.49	Non-Toxin	Non-Allergen	Conditional folding
HGATVAVKQVNRCSKNHL	CPP	0.46	Non-Toxin	Non- Allergen	Folded

Structure Prediction

The 3D- structure of the antimicrobial peptides were predicted using PEPFOLD server. PEP-FOLD is a novel method designed to predict the structure of peptides by analysing amino acid sequences. It employs a structural alphabet called SA letters, characterizing the conformations of four consecutive residues. This approach combines the predicted SA letter sequences with a greedy algorithm and coarse-grained force field to determine the peptide’s structure. It will generate maximum of 5 models for each input sequences, from which we took the best model. The models are generated based on lowest sOPEP force field (a coarse-grain energy) with the maximum melting temperature predicted, and therefore it is taken as a best model for further analysis. Further, the models generated using PEPFOLD were subjected to PROCHECK server for the structure validation using Ramachandran plot. From all the 11 peptide structures, only 1 peptide have all their complete residues in their most favoured regions (100%) and that is HGATVAVKQVNRCSKNHL. Whereas the other peptides have their residues in two regions, that is most favoured and allowed regions, but no peptides sequences have their complete residues in most favoured regions (Supplementary Fig. 1).

Molecular Docking

Molecular docking for the protein target and the predicted antimicrobial peptides were performed using HADDOCK server. Blind docking was performed for the predicted peptides against the target protein of HTH-type transcriptional regulator EthR of Mycobacterium tuberculosis protein (UniProt Id - P9WMC1). Peptide sequence HGATVAVKQVNRCSKNHL has shown the maximum binding affinity with HADDOCK score of -49.9 +/- 11.0 against the protein ethR. Other major docking scores obtained from the HADDOCK server such as Van ader Waals energy, Electrostatic energy, and desolvation energy are − 59.6 ± 2.5, -100.8 ± 25.7, and − 8.9 ± 1.8 respectively (Table 5). Interaction between the protein-peptide complexes were analysed using BIOVIA Discovery studio and LigPlus for various interactions such as hbond, charges, hydrophobicity, ionizability, SAS, and 2-D interactions (Fig. 2) (Supplementary Fig. 2)

Table 5

Docking scores for the predicted antimicrobial peptides against ethR enzyme of *Mycobacterium tuberculosis* protein (UniProt Id - P9WMC1) after molecular docking using HADDOCK server
Peptide Sequences	HADDOCK score	Van der Waals energy	Electrostatic energy	Desolvation energy
KLTKRTIICY	-46.7 ± 4.7	-33.9 ± 10.9	-273.8 ± 31.6	0.3 ± 1.0
FKNMIQCAGTRT	-32.1 ± 5.1	-39.3 ± 1.4	-78.6 ± 56.8	-15.3 ± 3.3
ICIYTHIARG	-22.0 ± 2.4	-43.5 ± 2.5	-50.5 ± 8.8	-14.4 ± 1.5
IPLHPYHTYK	-30.1 ± 10.6	-41.8 ± 3.4	-60.1 ± 33.1	-24.6 ± 1.1
TMLNSQLSK	-17.1 ± 6.9	-35.5 ± 6.0	-124.3 ± 13.3	4.9 ± 0.7
QRKVSNAPH	-16.3 ± 6.1	-20.6 ± 2.7	-181.2 ± 6.6	3.5 ± 0.6
NSQRNRGASF	-20.8 ± 8.8	-43.1 ± 4.0	-95.2 ± 33.4	-3.3 ± 2.7
GATVAVKQVNRCSKNH	-31.0 ± 8.6	-37.7 ± 7.9	-166.4 ± 16.3	7.8 ± 1.3
KAMQRKVSNAPHEAH	-45.1 ± 7.9	-31.3 ± 5.9	-270.7 ± 47.7	3.1 ± 3.1
THRAPELLKG	-24.0 ± 6.1	-29.8 ± 1.3	-161.0 ± 6.9	3.4 ± 0.9
HGATVAVKQVNRCSKNHL	-49.9 ± 11.0	-56.9 ± 7.6	-100.8 ± 21.8	-8.9 ± 2.2

Protein-peptide interface interaction analysis

PRODIGY server was used for the calculation of binding energy between the docked complex of the protein-peptide. For the docking against Mycobacterium tuberculosis HTH-type transcriptional regulator EthR as target of protein (UniProt Id - P9WMC1), the maximum binding energy obtained for the peptides is -9.3 (kcal/mol^− 1) against EthR, which further confirms the results obtained from the HADDOCK docking where the cluster energy being obtained was maximum for the similar peptide sequence HGATVAVKQVNRCSKNHL.

Molecular Dynamics Simulation

From the docking studies peptide-protein and protein-INH complex with the best interaction and energy docked conformations results were utilized for molecular dynamic simulation study. A 100 ns molecular simulation was performed for studying the stability of the protein before and after binding with the peptide using GROMACS-2023.1 version.

Molecular Dynamic Simulation of the protein peptide complex

The RMSD (root mean square deviation) value for the backbone of the protein-peptide complex during the simulation, there has been a gradual increase from 0.18 to 0.2 nm till 20 ns with fluctuations at some time periods of 20 ns duration. But after 20 ns period, RMSD value was steady for the protein backbone with minor fluctuations at some time frame of simulation. When simulation was performed for the protein alone (without binding of peptide), the RMSD value were found to be increasing and reached maximum of 0.3 nm, but when ligand is bound with the protein, the RMSD value has been found to be lower than the protein alone for the first 20 ns time period of reaching maximum of 0.2 nm value. This shows that after binding with the ligand (peptide), the RMSD fluctuations in the protein has been reduced highlighting the action of peptide over stabilizing the protein backbone. When analysing RMSD, it serves as an indicator of simulation equilibrium; minor fluctuations towards the simulation’s end align with a thermal average structure. For compact, small proteins alterations in the range of 0.1–0.3 nm are within acceptable limits. However, deviations exceeding the range of 0.3 nm are suggesting significant conformational shifts occurring during the simulation or maybe the protein is a membrane protein (Fig. 3a).

RMSF (root mean square fluctuations) for protein-peptide and protein –drug complex, major fluctuations were at the start of the protein residue, fluctuations around 16–19 amino acid residue, 82–98 amino acid residues, and 180–190 amino acid residues were observed for value of range between 0.24 nm − 0.40 nm. The RMSF attributes helps highlight specific modifications within the protein chain. As depicted in the results figures for the protein peptide complex simulation, fluctuations in RMSF values for the amino acids along the protein backbone are more evident at C- and N- terminal in comparison to other regions of the protein (Fig. 3b).

Other parameters of the protein-peptide and protein-drug complex, during simulation such as H-bond, radius of gyration and SASA were analysed. From SASA analysis, it can be inferred that protein is more susceptible to solvent. For the protein-drug complex, the solvent accessible is less but at the end of the simulation trajectory there has been minor increase in the SASA value, in comparison to protein-peptide complex (Fig. 3c). From radius of gyration plot, it has been observed that for both peptide and drug complex, that protein is undergoing some conformational changes upon the binding. (Fig. 3d). From H bond analysis graph, it has been observed that number of hydrogen bonds is more in protein peptide complex when compared to the protein drug complex (Fig. 3e and f).

Molecular mechanics/Poisson–Boltzmann surface area (MM-PBSA) analysis

MMPBSA analysis were performed to calculate the free energy difference between the bound and unbound states of the complex in different solvation states for the same molecule. For the complex analysis were performed and the ΔG Energy were calculated. The more negative energy the better the interactions and the stability of the protein-peptide complex during the simulation. MMPBSA results also correlating the docking study and the molecular dynamics simulation results. From the graph (Fig. 4) it has been observed that delta energy for the EthR protein from the graph it has been observed that Delta energy for the complex was found to be -36.15 Kcal/mol, whereas for the protein-drug complex, the delta energy was found to be -6.94 Kcal/mol. From this it can be inferred that the higher delta energy for the protein peptide complex is stating that the complex is having more affinity in comparison to the protein drug complex. It is also inferred that the due to the presence of peptide, the delta energy is high for the protein peptide complex.

The emergence and rapid spread of multidrug-resistant strains of Mtb present substantial challenges for clinical interventions. Previous study explores novel drug targets in Mtb as presented by [35], while also reconsidering traditional targets using innovative methodologies [4, 36, 37]. Some Tb drugs, such as ethionamide (ETH) and isoniazid (INH), are structurally analogous and need to be activated before performing their desired biological activity. In this case, both drugs target enoyl-acyl carrier protein reductase (InhA), which is an enzyme necessary for mycolic acid synthesis [38]. ETH is an important second-line drug used in treating Tb, and the mechanism of action is very well understood for ETH, explained the ETH activator Rv3854c and termed it EthA, it is analogous to the various monooxygenases and induces ETH sensitivity on overexpression in mycobacteria. Later, Montellano and coworkers established the EtaA as a FAD-containing enzyme that oxidizes ETA to its corresponding S-oxide. This is further oxidized to 2-ethyl-4-amidopyridine via the formation of intermediate sulfinic acid. EthR is a homodimer with a helical structure and shows similarity to the TetR family members [39].

Research has demonstrated that human cathelicidin exhibits moderate antimicrobial activity against a wide range of Gram-negative and Gram-positive bacteria, including pathogens from genera such as Pseudomonas, Escherichia, Staphylococcus, and Enterococcus [40]. LL-37, a specific cathelicidin peptide, exerts its antibacterial effects through direct mechanisms and immunomodulation. The primary mode of action involves disrupting bacterial membranes, which can lead to increased membrane permeability, membrane lysis, and release of intracellular contents, ultimately resulting in bacterial cell death [41]. Despite their potential, antimicrobial peptides (AMPs) exhibit limitations such as hemolytic activity, sensitivity to proteases, salt, serum, stability issues, and high production costs. To address these challenges, rational design strategies manipulate physicochemical properties to optimize AMPs for desired biological parameters. Sequence optimization or modification can enhance antimicrobial effects while minimizing cytotoxicity [42]. When designing AMPs, consider properties like charge, hydrophobicity, and amphipathicity. Bacterial membranes, rich in negatively charged molecules, significantly influence AMP interactions. Additionally, hydrophobicity plays a crucial role, typically fluctuating between 40% and 60% to achieve a stable, amphipathic AMP structure [43]. Previous studies have explored the design of antimicrobial peptides (AMPs) based on the LL-37 template. Wang et al. designed a novel peptide derived from LL-37 that exhibited stability, selectivity, antimicrobial activity, antibiofilm properties, and anticancer effects [44]. Additionally, a synthetic peptide called CD4-PP, also derived from LL-37, demonstrated activity against clinical and type strains of common pathogens such as E. coli, K. pneumoniae, and P. aeruginosa [45]. LL-37 plays a crucial role in designing peptides with diverse applications, including antibacterial, antibiofilm, antiviral, antifungal, immune modulation, and anticancer properties [46]. In silico approaches have been used to design hybrid AMPs by combining LL-37 with other peptides such as cecropin A and magainin II, while another study proposed a hybrid AMP design involving LL-37 and BMAP-27 [47].

Research has explored the potential of snake venom peptides in combating Mycobacterium tuberculosis (Mtb) infections. Studies have investigated small peptides from snake venom for their in vitro activities against drug-resistant clinical isolates of Mtb, indicating promising antimicrobial properties [48]. From the study it has been observed that peptide sequence has HGATVAVKQVNRCSKNHL cell penetrating property, non-hemolytic and non-allergenicity property. Snake venoms contain antimicrobial peptides that show promise in combating bacterial infections. These peptides, derived from snake venom proteins, have been studied for their antibacterial properties and potential as new drug templates [32]. For example, a peptide named pseudonajide, derived from Pseudonaja textilis snake venom, has shown antimicrobial activity against bacteria like S. epidermidis by disrupting the bacterial cell envelope [49]. Additionally, snake venom proteins and peptides have demonstrated potent antimicrobial activity against various bacteria, such as B. pseudomallei [50]. The cathelicidin-related antimicrobial peptides (CRAMPs) found in snake venoms are structurally diverse and exhibit antibacterial effects, making them valuable for drug development [11]. Overall, the therapeutic potential of snake venoms as a source of antimicrobial agents is being explored due to the presence of bioactive molecules like L-amino acid oxidase (LAAO) and phospholipase A₂ (PLA₂).

Targeting EthR to control EthA expression could enhance ethionamide's bactericidal effect, reducing resistance and toxicity from M. tuberculosis infection. Inhibiting EthR, a regulator of the ethA gene in M. tuberculosis, is essential for activating the prodrug ETH (Ethionamide).

Mycobacterium tuberculosis, a highly adaptable pathogen, presents a spectrum of infections ranging from mild to severe, including life-threatening diseases. To our knowledge, this is the first time we have employed a computational approach to forecast and craft novel antimicrobial peptides using the protein sequences of Bungarus caeruleus (Indian Krait). In summary, the findings from this study highlight the potential of the identified peptide to serve as valuable candidates for further in-vitro and in-vivo studies targeted at combating drug-resistant M. tuberculosis. These peptides hold promise as potential therapeutic agents in the fight against antibiotic-resistant strains, offering new avenues for addressing the growing challenges posed by M. tuberculosis. Till date there is no peptide been utilized from snake venom proteomic database against antimicrobial resistance pathogens especially against M. tuberculosis. Our computational study can pave the way for the further research to be carried out using designed peptide for therapeutic potential since the whole venom protein can be toxic but a shorter peptide sequence will not be that toxic or not be toxic itself, which can be easily used in various therapeutic applications.

Acknowledgments

The authors would like to thank the management of VIT, Vellore, for providing the necessary support for carrying out this research work.

Author contributions

PS: writing–original draft, data curation, formal analysis, investigation, and methodology. JG: resources, supervision, validation, and writing–review and editing.

Funding

This study did not require any specific funding support. This work was supported by the Institutional fund of the host organization.

Availability of Data and Material

The dataset generated and/or analysed during this study are available from the corresponding author upon reasonable request. In silico data and material are present in the manuscript.

Code Availability No code applicable.

Ethics Approval No ethical approval is required in the study.

Consent to Participate Not applicable.

Consent for Publication Not applicable.

Conflict of Interest The authors declare no competing interests

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Peptide Derived from Bungarus caeruleus Proteome Binds with Higher Affinity to Ethionamide Resistance Regulator of Mycobacterium tuberculosis than Isoniazid

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Introduction

Materials and Methods

In silico hydrolysis and prediction of AMP from the parent protein

Physicochemical properties of the predicted AMPs

ADMET property for the predicted AMPs

Evaluation of Cell Penetrating, Hemolytic, Toxicity, and Allergenicity Property

Structure Prediction

Molecular Docking

Protein-peptide interface interaction analysis

Molecular dynamics (MD) simulation

Estimation of free energy binding

Results

In silico hydrolysis and prediction of AMP from the parent proteins

Physicochemical properties of the predicted AMPs

ADMET property for the predicted AMPs

Cell Penetrating, Hemolytic, Toxicity, and Allergenicity Property

Structure Prediction

Molecular Docking

Protein-peptide interface interaction analysis

Molecular Dynamics Simulation

Molecular Dynamic Simulation of the protein peptide complex

Molecular mechanics/Poisson–Boltzmann surface area (MM-PBSA) analysis

Discussion

Conclusion

Declarations

References

Supplementary Files

Status:

Version 1