Gonorrhea caused due to antimicrobial-resistant bacteria Neisseria gonorrhoeae treated using probiotic peptide

Abstract

Neisseria gonorrhea is a sexually transmitted disease from gonorrhea that lacks treatment; despite the urgency, the absence of adequate drugs, lack of human correlates of protection, and inadequate animal models of infection have delayed progress toward the prevention of gonococcal infection. Lactobacillus crispatus is a lactic acid bacterium typically found in the human vaginal microbiota. Peptides from L. crispatus have shown a potential therapeutic option for targetting N. gonorrhea. Bioinformatics analysis is important for speeding up drug target acquisition, screening refinement, and evaluating adverse effects and drug resistance prediction. Therefore, this study identified an antimicrobial peptide from the bacteriocin immunity protein (BIP) of L. crispatus using the bioinformatics tool and Collection of Antimicrobial Peptide (CAMPR3). Based on the AMP score and highest ADMET properties, the peptide SM20 was chosen for docking analysis. SM20 was docked against multiple proteins from the genome of the AMR bacterium N. gonorrhea using an online tool; protein-peptide interactions were established and visualized using the PyMol visualizing tool. Molecular docking was carried out using the CABSdock tool, and multiple conformations were obtained against the membrane proteins of N. gonorrhoea. The peptide SM20 exhibited higher docking scores and ADMET properties. Therefore, SM20 could be further encapsulated with cellulose; it can be applied topically to the genital tract to target N. gonorrhea infection. The controlled release of the antimicrobial peptide from the gel can provide sustained delivery of the treatment, increasing its efficacy and reducing the risk of resistance development.

Introduction

Neisseria gonorrhoeae is a low cause of the sexually transmitted infectious disease (STI) gonorrhea- and mid-disease with a high morbidity rate and an estimated 87 million cases yearly on a global scale (Kirkcaldy et al. 2019). Infections with N. gonorrhea in men are often symptomatic urethritis, whereas gonorrhea affecting women is usually asymptomatic, progressing significant reproductive tract issues like pelvic inflammatory disease, ectopic pregnancy, infertility, and disseminated gonococcal infections. The emergence of fluoroquinolone resistance and growing levels of resistance to the latest FDA-approved drug, cefixime, have hampered existing therapeutic and pharmacological strategies for gonorrhea infections (Lahsoune et al. 2007; Reddy et al. 2004). The Centre for Disease Control and Prevention (CDC) currently recommends ceftriaxone and azithromycin for treatment, although resistance to cefixime and ceftriaxone has already emerged outside of the United States, perhaps leading to the spread of untreatable gonorrhea or evolved resistance till the last antibiotics being used empirical therapy for treating the disease and therefore become one of the top worldwide concerns in the battle towards antimicrobial resistance (AMR) (Brook 2015; Yeaman and Yount 2003).

To effectively address the problem of managing the rising worldwide threat of multi-drug resistance to N. gonorrhea, an inter approach including diagnostic kits and laboratories, attending physicians, public health policy, the pharmaceutical sector, and fundamental research is necessary (Mikelsaar and Zilmer 2009; Salminen et al. 2021). Therefore, this research study focused on antimicrobial peptides, which would show extreme effects on many pathogens (Brook 2015; Yeaman and Yount 2003; Golparian et al. 2014; Riley and Wertz 2002). Antimicrobial peptides are host defense molecules and small, positively charged oligopeptides as effective as commercially available antibiotics against many bacteria, viruses, and fungi. Antimicrobial peptides specifically target the bacterial cytoplasmic membrane but can also target the cell nucleus and protein synthesis. Naturally produced peptides are a promising research approach despite the growing desire for new therapies to combat the issue of antimicrobial resistance (Sathyamoorthi et al. 2019; Jenssen et al. 2006; Casadei et al. 2015).

A study published in 2014 investigated the activity of peptides produced by lactobacillus crispatus against N. gonorrhea. The researchers found that the peptides could inhibit the bacterium's growth in vitro at concentrations similar to those in the human vagina. L. crispatus is frequently one of the most prevalent species in a healthy vaginal microbiome where Lactobacillus species predominate (Amabebe and Anumba 2018). Lactic acid, produced by L. crispatus, helps maintain the vagina's acidic pH and inhibits the development of harmful bacteria and viruses. Hydrogen peroxide, another substance produced by L. crispatus with antibacterial characteristics, can effectively prevent the growth of harmful bacteria. This bacteria also supports the creation of mucus, which acts as a barrier against infections and preserves the integrity of the vaginal epithelium (Mitchell et al. 2015).

Bioinformatics analysis is important for speeding up drug target acquisition, screening refinement, and evaluating adverse effects and drug resistance prediction. The expansion of homology modeling and protein structure simulation, as well as large structure databases of biomolecules and metabolic products, combined with the accumulation of protein and RNA structures, helped pave the way for even more feasible protein–ligand docking experimentation and more instructive silico (Sathyamoorthi et al. 2017; Velayutham et al. 2022a). Therefore, in this study, we focus on screening peptides from L.crispatus and further screened a specific peptide, SM20, based on its ADMET properties and peptide ranking as a possible drug target against the membrane proteins of N. gonorrhoea.

Materials and methods

Quantum computational studies

In this study, we followed a structured protocol for selecting bacteriocin immunity protein (BIP) sequences to identify potential antimicrobial peptides (AMPs). The objective was to establish clear criteria and a methodology for this selection process. We initiated the protocol by obtaining the genomic sequence of our target microorganism, L. crispatus, alongside gathering information on the presence of BIPs within the genome. The criteria for BIP selection included their presence in the genomic sequence, guided by their functional relevance to host–microbe interactions, particularly concerning AMP production or regulation. An evolutionary context supporting a role in antimicrobial defense further informed the selection process. To identify AMPs, we employed the widely recognized bioinformatics tool, CAMP (Collection of Antimicrobial Peptides), which can be accessed at (http://www.camp.bicnirrh.res.in/prediction.php). This tool is renowned for its ability to predict and analyze AMPs. Selected BIP sequences were input into the CAMP tool for analysis using its integrated algorithms and databases. The computational analysis followed the instructions provided by the CAMP tool. Results from the CAMP analysis were diligently collected, and in pursuit of accuracy and reliability, the predicted AMPs underwent validation through additional analyses as necessary (Li et al. 2018; Prabhu et al. 2021).

Peptide structure prediction

I-TASSER (Iterative Threading Assembly Refinement) (https://zhanggroup.org/I-TASSER/) is a powerful tool for predicting protein structure using comparative/homology, threading, or ab initio modeling. The Normalized B-factor for Solvent Accessibility is anticipated. The top 10 threading templates utilized by I-TASSER predict the top five models based on C-score, Estimated TM-score, and Estimated RMSD. The number of enzyme commissions and active sites is also estimated (Passari et al. 2018; Raju et al. 2021).

Bioactive prediction

The PeptideRanker system (http://distilldeep.ucd.ie/PeptideRanker/) calculates the likelihood of these peptides being identified. With the PeptideRanker tool, the peptide's predicted value surpasses 0.5, indicating that it is active. PeptideRanker assigns a score to peptides based on their likelihood of being bioactive.

Solubility and toxicity prediction

ToxinPred (http://crdd.osdd.net/raghava/toxinpred/) is a distinctive in-silico method for predicting peptide toxicity (Gupta et al. 2013). The ToxinPred tool may detect highly toxic or non-toxic peptides from many peptides submitted. The peptides in single-letter codes were pasted in this experiment, and their physicochemical attributes were exhibited (http://crdd.osdd.net/raghava//toxinpred/) (Gupta et al. 2015).

ADMET prediction

The pkCSM ADMET (https://biosig.lab.uq.edu.au/pkcsm/) descriptors algorithm was used to profile drugs' PK characteristics such as absorption, distribution, metabolism, excretion, and toxicity (ADMET). The 2D polar surface area (PSA 2D, a major predictor of fractional absorption) and lipophilicity levels in the form of atom-based LogP are two significant chemical descriptors that correlate well with PK characteristics (AlogP98). Drug absorption is influenced by membrane permeability [as determined by the Caco-2 colon cancer cell line], intestinal absorption, skin permeability levels, and P-glycoprotein substrate or inhibitor. Drug distribution is influenced by variables such as the blood–brain barrier (logBB), CNS permeability, and distribution volume (VDss). The CYP models for substrate or inhibitor are used to predict metabolism (CYP2D6, CYP3A4, CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4). The entire clearance model and renal OCT2 substrate are used to predict excretion. AMES toxicity, hERG inhibition, hepatotoxicity, and skin sensitivity are used to predict drug toxicity. These parameters were computed and verified to ensure that they were within their normal limits (Sudhakaran et al. 2022a; Pires et al. 2015).

Homology modelling

The experimental crystal structure of membrane proteins of N. gonorrhoea is not available in the Protein Data Bank (PDB); hence, its 3D structure was modeled. The SWISS-MODEL (https://swissmodel.expasy.org/) platform created a homology model with sufficient query sequence coverage and identity. Based on the Global Model Quality Estimation (GMQE) and Qualitative Model Energy Analysis (QMEAN) values, the most stable 3D structure was determined. The GMQE values are typically between 0 and 1; the higher the number, the more reliable the predicted structure, whereas a value below 4.0 indicates reliability for QMEAN (Sathyamoorthi et al. 2019).

Molecular docking

Molecular docking is an in-silico method that predicts protein-small molecule interactions based on geometry and scores. Modeling the molecular docking of peptides to proteins is challenging (Sudhakaran et al. 2022b; Velayutham et al. 2022b). Modeling the conformational flexibility of a protein–peptide system is one of the most difficult tasks. CABS-dock (http://biocomp.chem.uw.edu.pl/CABSdock) is a standalone Python program for protein-peptide docking with backbone flexibility that runs on several platforms. The CABS-dock technique is unique in that it can quickly mimic the considerable backbone flexibility of the complete protein-peptide system. In-silico molecular docking studies of peptides that bind to particular receptors offer information on the interaction's shape, pattern, and affinity. The peptide (SM20) was investigated against N. gonorrhoea membrane proteins.

Results

Peptide structure prediction

The I-TASSER webserver predicted the peptide's tertiary 3D structures using ten threading templates with Z and confidence score (C-score) values. The C score ranges from −5 to 2, with higher scores indicating greater certainty. Each peptide's best structure with a C value of less than 2.01 was chosen for further investigation.

Bioactive prediction

PeptideRanker was used to forecast the relationship between peptide amino acid content and bioactivity. PeptideRanker assigns a score to each peptide; the closer the value is to 1 (between 0 and 1), the more likely the peptide is bioactive. In practice, we established a higher threshold of 0.72 to limit the number of false positives. In this investigation, a sequence of 14 peptides from the Lactobacillus family was chosen, and the active score in each projected peptide sequence is shown in Table 1.

Table 1.

The likelihood of the peptides calculated using the PeptideRanker system

No	Peptide	Peptide ranker
1	KFHYAITSYARPVSAKAGAC	0.2
2	ITSYARPVSAKAGACGGAAL	0.4
3	YARPVSAKAGACGGAALGMA	0.7
4	ARPVSAKAGACGGAALGMAF	0.9
5	VSAKAGACGGAALGMAFSGL	0.92
6	SAKAGACGGAALGMAFSGLM	0.96
7	AKAGACGGAALGMAFSGLMG	0.95
8	KAGACGGAALGMAFSGLMGK	0.95
9	AKAGACGGAALGMAFSGLMG	0.95
10	KAGACGGAALGMAFSGLMGK	0.95
11	LAKHKDHPVGKNRKMHVNRQ	0.33
12	MQVNPTKRYYRAYKRYERKH	0.12
13	QVNPTKRYYRAYKRYERKHY	0.1
14	VNPTKRYYRAYKRYERKHYP	0.09

Solubility and toxicity and ADMET prediction

Adsorption

The high adsorption score of 6 suggests that the peptide is likely to bind strongly to surfaces or interfaces when it comes into contact with them. This property may be desirable or undesirable depending on the intended use of the peptide. The negative water solubility score of −2.892 indicates that the peptide is likely poorly soluble in water, making it difficult to administer as a drug or eliminate from the body. The negative Caco2 permeability score of −0.317 suggests that the peptide may have low intestinal permeability, which could limit its bioavailability as a drug. The neutral score of 0 for intestinal absorption suggests that the peptide may have moderate absorption in humans, but additional testing or modeling may be needed to confirm this. The negative skin permeability score of −2.735 indicates that the peptide is unlikely to penetrate the skin easily, which may be desirable or undesirable depending on the intended use of the peptide. A “Yes” value for the P-glycoprotein substrate suggests that the protein may recognize and transport the peptide, which could affect its absorption, distribution, and elimination in the body. The absence of a “Yes” value for P-glycoprotein I inhibitor indicates that the peptide is not predicted to inhibit the activity of P-glycoprotein I, which may affect the pharmacokinetics and efficacy of other drugs that are substrates of this protein. The absence of a “Yes” value for the P-glycoprotein II inhibitor indicates that the peptide is not predicted to inhibit the activity of P-glycoprotein II, which is similar to P-glycoprotein I but may have different substrate specificities. These results suggest that your peptide may have some limitations regarding its solubility, permeability, and potential interactions with P-glycoprotein Table 2. Further studies or modeling may be needed to evaluate its suitability as a drug or other application.

Table 2.

The adsorption parameters of the peptides calculated using pkCSM ADMET descriptors algorithm

Adsorption	1	2	3	4	5	6	7	8	9	10	11	12	13	14
Water solubility	−2.892	−2.892	−2.892	−2.892	−2.892	−2.892	−2.892	−2.892	−2.892	−2.892	−2.892	−2.892	−2.892	−2.892	Numeric (log mol/L)
CaCo2 permeability	−1.229	−0.545	−0.568	−0.422	−0.346	−0.317	−0.267	−0.287	−0.267	−0.287	−1.287	−1.618	−1.777	−2.892	Numeric (log Papp in 10–6 cm/s)
Intestinal absorption (human)	0	0	0	0	0	0	0	0	0	0	0	0	0	0	Numeric (% absorbed)
Skin permeability	−2.735	−2.735	−2.735	−2.735	−2.735	−2.735	−2.735	−2.735	−2.735	−2.735	−2.735	−2.735	−2.735	−2.735	Numeric (log Kp)
P-glycoprotein substrate	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Categorical (yes/no)
P-glycoprotein I inhibitor	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
P-glycoprotein II inhibitor	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)

The peptide SM20 has been highlighted in bold

Distribution

The high distribution score of 6 suggests that the peptide will distribute widely throughout the body, potentially reaching tissues beyond the bloodstream. The negative value of −0.297 for VDss (volume of distribution at steady state) suggests that the peptide may have a limited distribution within the body and may be more confined to the bloodstream. The value of 0.37 for fraction unbound (Fu) suggests that only 37% of the peptide may be available to interact with target proteins or to be eliminated from the body, while the remaining 63% may be bound to plasma proteins. The negative BBB permeability score of −3.063 suggests that the peptide is unlikely to cross the blood–brain barrier easily, which could limit its ability to target central nervous system (CNS) disorders. The very low CNS permeability score of −8.841 suggests that the peptide is unlikely to penetrate the CNS easily, further confirming its limited ability to target CNS disorders. These results suggest that the peptide may have limited distribution within the body and may have difficulty crossing biological barriers such as the blood–brain barrier and the CNS (Table 3). Additionally, a relatively high percentage of the peptide may be bound to plasma proteins, potentially limiting its efficacy and bioavailability.

Table 3.

The distribution parameters of the peptides calculated using pkCSM ADMET descriptors algorithm

Distribution	1	2	3	4	5	6	7	8	9	10	11	12	13	14
VDss (human)	−0.006	−.0.155	−0.24	−0.229	−0.306	−0.297	−0.346	−0.292	−0.346	−0.292	0.011	0.011	0.011	0.011	Numeric (log L/kg)
Fraction unbound (human)	0.382	0.377	0.378	0.379	0.37	0.37	0.369	0.371	0.369	0.371	0.381	0.381	0.381	0.381	Numeric (Fu)
BBB permeability	−4.614	−3.606	−3.472	−3.276	−2.831	−3.063	−2.875	−2.928	−2.875	−2.928	−5.378	−6.407	−6.356	−6.183	Numeric (log BB)
CNS permeability	−10.55	−9.658	−9.23	−9.071	−8.774	−8.841	−8.682	−8.813	−8.682	−8.813	−12.009	−12.132	−12.151	−11.788	Numeric (log PS)

The peptide SM20 has been highlighted in bold

Metabolism

The definite “No” result for the CYP2D6 substrate suggests that the CYP2D6 enzyme will not likely metabolize the peptide. The definite “Yes” result for the CYP3A4 substrate suggests that the peptide is likely to be metabolized by the CYP3A4 enzyme. The definite “No” result for the CYP1A2 inhibitor suggests that the peptide is not likely to inhibit the CYP1A2 enzyme, which metabolizes many drugs and environmental chemicals. The definite result of “No” for the CYP2C19 inhibitor suggests that the peptide is not likely to inhibit the CYP2C19 enzyme, which is involved in the metabolism of many drugs. The definite result of “No” for the CYP2C9 inhibitor suggests that the peptide is not likely to inhibit the CYP2C9 enzyme, which is involved in the metabolism of many drugs. The definite “No” result for the CYP2D6 inhibitor suggests that the peptide is not likely to inhibit the CYP2D6 enzyme. The definite result of “No” for the CYP3A4 inhibitor suggests that the peptide is not likely to inhibit the CYP3A4 enzyme, which is involved in the metabolism of many drugs. Overall, these results suggest that the peptide is not likely to interfere with the activity of several major drug-metabolizing enzymes, except for CYP3A4, which is likely to metabolize the peptide (Table 4). This information is important for predicting potential drug-drug interactions and determining the appropriate dosing regimen for SM20.

Table 4.

The metabolism parameters of the peptides calculated using pkCSM ADMET descriptors algorithm

Metabolism	1	2	3	4	5	6	7	8	9	10	11	12	13	14
CYP2D6 substrate	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
CYP3A4 substrate	No	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	No	Yes	Categorical (yes/no)
CYP1A2 inhibitor	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
CYP2C19 inhibitor	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
CYP2C9 inhibitor	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
CYP2D6 inhibitor	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
CYP3A4 inhibitor	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)

The peptide SM20 has been highlighted in bold

Excretion and toxicity

Regarding excretion, the total clearance value of 0.459 (log mL/min/kg) suggests that SM20 may be slowly eliminated from the body. Additionally, SM20 is not a substrate for renal OCT2. In terms of toxicity, SM20 is not predicted to have AMES toxicity, hERG I or II inhibition, or skin sensitization. However, it is predicted to be hepatotoxic. The Max. tolerated dose (human) value of 0.44 (log mg/kg/day) indicates the highest dose that is safe for human consumption, while Oral Rat Acute Toxicity (LD50) value of 2.482 (log mol/kg) suggests that the peptide is relatively safe for rats at high doses. The Oral Rat Chronic Toxicity (LOAEL) value of 6.214 (log mg/kg_bw/day) indicates the lowest observed adverse effect level for long-term exposure to the peptide. Regarding distribution, SM20 has a relatively low BBB permeability (−3.063, log BB) and CNS permeability (−8.841, log PS), suggesting that it may have limited ability to cross the blood–brain barrier and enter the central nervous system. Additionally, the VDss value of −0.297 (log L/kg) suggests that the peptide may have a relatively small volume of distribution. Regarding metabolism, SM20 is predicted to be a substrate for CYP3A4 but not CYP2D6. It is not predicted to inhibit any of the listed cytochrome P450 enzymes (CYP1A2, CYP2C19, CYP2C9, CYP2D6, or CYP3A4). Finally, SM20 is predicted to be toxic to T. pyriformis, with a toxicity value of 0.285 (log μg/L), but not to minnows. The latter has a high toxicity value of 18.504 (log mM), indicating that the peptide may be toxic to fish at high concentrations (Tables 5, 6, and 7).

Table 5.

The excretion parameters of the peptides calculated using pkCSM ADMET descriptors algorithm

Excretion	1	2	3	4	5	6	7	8	9	10	11	12	13	14
Total clearance	−0.405	0.043	0.445	0.284	0.261	0.459	0.48	0.531	0.48	0.531	−0.174	−2.054	−2.653	−2.812	Numeric (log mL/min/kg)
Renal OCT2 substrate	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)

The peptide SM20 has been highlighted in bold

Table 6.

The toxicity parameters of the peptides calculated using pkCSM ADMET descriptors algorithm

Toxicity	1	2	3	4	5	6	7	8	9	10	11	12	13	14
AMES toxicity	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
Max. tolerated dose (human)	0.438	0.439	0.439	0.438	0.44	0.44	0.441	0.439	0.441	0.439	0.438	0.438	No	0.438	Numeric (log mg/kg/day)
hERG I inhibitor	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
hERG II inhibitor	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
Oral rat acute toxicity (LD50)	2.482	2.482	2.483	2.483	2.482	2.482	2.483	2.482	2.483	2.482	2.482	2.482	2.482	2.482	Numeric (mol/kg)
Oral rat chronic toxicity (LOAEL)	10.123	6.822	5.798	5.863	6.095	6.214	6.231	6.643	6.231	6.643	8.985	18.392	20.64	20.418	Numeric (log mg/kg_bw/day)
Hepatotoxicity	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	No	No	Categorical (yes/no)
Skin sensitisation	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Categorical (yes/no)
T. pyriformis toxicity	0.285	0.285	0.285	0.285	0.285	0.285	0.285	0.285	0.285	0.285	0.285	0.285	0.285	0.285	Numeric (log μg/L)
Minnow toxicity	28.07	23.035	19.092	19.378	18.392	18.504	17.197	17.66	17.197	17.66	34.094	45.195	48.674	47.588	Numeric (log mM)

The peptide SM20 has been highlighted in bold

Table 7.

The toxicity and solubility parameters of the peptides calculated using Toxinpred algorithm

	Peptide Sequence	SVM Score	Prediction	Hydrophobicity	Steric hindrance	Sidebulk	Hydropathicity	Amphipathicity	Hydrophilicity	Net Hydrogen	Charge	pI	Mol wt
1	KFHYAITSYARPVSAKAGAC	−0.91	Non-toxin	−0.09	0.57	0.57	0.03	0.56	−0.26	0.70	3.50	9.65	2141.74
2	ITSYARPVSAKAGACGGAAL	−1.08	Non-toxin	0.01	0.59	0.59	0.55	0.31	−0.26	0.50	2.00	9.36	1864.42
3	YARPVSAKAGACGGAALGMA	−0.94	Non-toxin	0.03	0.60	0.60	0.57	0.31	−0.26	0.40	2.00	9.36	1822.40
4	ARPVSAKAGACGGAALGMAF	−0.97	Non-toxin	0.06	0.60	0.60	0.77	0.31	−0.27	0.35	2.00	9.55	1806.40
5	VSAKAGACGGAALGMAFSGL	−1.21	Non-toxin	0.16	0.61	0.61	1.12	0.18	−0.47	0.20	1.00	8.57	1739.32
6	SAKAGACGGAALGMAFSGLM	−1.11	Non-toxin	0.15	0.61	0.61	1.00	0.18	−0.45	0.20	1.00	8.57	1771.38
7	AKAGACGGAALGMAFSGLMG	−1.07	Non-toxin	0.17	0.62	0.62	1.02	0.18	−0.47	0.15	1.00	8.57	1741.36
8	KAGACGGAALGMAFSGLMGK	−1.17	Non-toxin	0.10	0.63	0.63	0.73	0.37	−0.30	0.25	2.00	9.36	1798.46
9	AKAGACGGAALGMAFSGLMG	−1.07	Non-toxin	0.17	0.62	0.62	1.02	0.18	−0.47	0.15	1.00	8.57	1741.36
10	KAGACGGAALGMAFSGLMGK	−1.17	Non-toxin	0.10	0.63	0.63	0.73	0.37	−0.30	0.25	2.00	9.36	1798.46
11	LAKHKDHPVGKNRKMHVNRQ	−0.32	Non-toxin	−0.48	0.57	0.57	−1.71	1.26	0.67	1.30	6.50	11.17	2394.11
12	MQVNPTKRYYRAYKRYERKH	−0.92	Non-toxin	−0.59	0.63	0.63	−2.15	1.24	0.55	1.65	6.50	10.38	2688.39
13	QVNPTKRYYRAYKRYERKHY	−0.69	Non-toxin	−0.60	0.62	0.62	−2.31	1.24	0.50	1.70	6.50	10.21	2720.37
14	VNPTKRYYRAYKRYERKHYP	−0.43	Non-toxin	−0.57	0.61	0.61	−2.21	1.18	0.49	1.60	6.50	10.21	2689.35

The peptide SM20 has been highlighted in bold

Homology modelling and molecular docking

The DNA Mimic Protein and Multi-Drug Efflux Protein models exhibit GMQE scores close to 1, suggesting good quality, while slightly negative QMEAN scores indicate minor structural deviations. Conversely, the YjPd model hints at lower quality and substantial structural variations with its low GMQE score and significantly negative QMEAN score. The Type 2 Toxin model boasts a high GMQE score, signifying reliability, while its slightly negative QMEAN score suggests modest structural discrepancies. For the Adhesion and Penetration Protein Autotransporter, the GMQE score is moderate, indicating fair quality, and the QMEAN score hints at some structural variations. The Type 4 Pillin Protein model demonstrates a fair GMQE score and a QMEAN score near zero, suggesting moderate quality with relatively minor structural deviations. In contrast, the Transcriptional Regulator exhibits a low GMQE score, signifying lower quality, while the strongly negative QMEAN score indicates significant structural deviations, raising concerns about reliability. The Ton B Protein model boasts a high GMQE score, indicating decent quality, though the slightly negative QMEAN score implies some structural discrepancies. The Outer Membrane Beta Barrel Protein model offers a reasonable GMQE score, suggesting moderate quality, while the negative QMEAN score implies some structural variations in Table 8.

Table 8.

GMQE and QMEAN of the membrane proteins identified using SWISS MODEL

Protein name	Template	GMQE	QMEAN
DNA mimic protein	3vjz.1.A	0.91	−1.03
Multi-drug efflux protein	6vks.1.A	0.80	−2.24
YjPd	6zmq.1.A	0.17	−6.85
Type 2 toxin	2h1o.1.E	0.93	−0.32
Adhesion and penetration protein autotransporter	3syj.1.A	0.47	−1.03
Type 4 pillin protein	5v0m.1.A	0.55	0.41
Transcriptional regulator	5ca5.1.A	0.12	−4.72
Ton B protein	3v8x.1.A	0.82	−2.03
Outer membrane beta barrel protein	1p4t.1.A	0.79	−1.68

Interactions between SM20 and DNA mimic protein

PRO-7 contains a phenol functional group that forms hydrogen bonds with other molecules. SER-83 contains an amide functional group that can participate in other interactions. This interaction could help reduce inflammation-induced severity during the diseased state caused by N. gonorrhea. This interaction may also provide insights into potential mechanisms by which LC and other probiotic bacteria can protect against pathogenic infections; however, further research is required to understand the implication of this interaction (Fig. 1A).

Fig. 1.

Superimposed images of SM20 peptide with DNA mimic protein, Multi drug efflux transportor protein, and Yjpd protein of N. gonorrhea with multiple amino acid interactions

Interactions between SM20 and multi-drug efflux transporter protein

The interactions between peptide SM20 and multi-drug efflux transporter protein could inhibit the activity of this bacterial protein. The multi-drug efflux transport protein is a membrane protein that pumps out various antibiotics and other drugs from the bacterial cells, making the bacteria resist the cell. ASP-773 and ASP-187 contain negative charges carboxylate groups that can interact with positively charged amino acids on the transporter protein, and GLN-192 contains an amide group that forms hydrogen bonds with other molecules (Fig. 1B). However, the efficacy of the peptide as a treatment must be further evaluated.

Interactions between SM20 and Yjpd protein

Interactions with ILE 119 might indicate that SM20 could affect the structure or function of the protein in a way that interferes with its normal activity. This could potentially impact the protein's role in the pathogen's survival and replication. Interactions with MET 125 might be particularly significant, as methionine residues are often involved in essential biological processes. These interactions could potentially disrupt critical functions within the protein or its interactions with other molecules. LEU 12 interactions suggest that SM20 may influence the structural conformation of the protein, potentially affecting its stability or function (Fig. 1C).

Interactions between SM20 and Type-2 toxin protein

T2TP is a virulence factor that plays an important role in the pathogenesis of infections caused by the bacterium; by TYR-106, ARG-90 interactions with the protein SM20 may inhibit the activity of N.gonorrhea (Fig. 2A).

Fig. 2.

Superimposed images of SM20 peptide with Type 2 toxin, Adhesion penetration protein and Type 4 pilli protein of N. gonorrhea with multiple amino acid interactions

Interactions between SM20 and Type-4 pillin

The interactions between SM20 and Type-4 pillin protein can potentially disrupt the ability of Type-4 pilli protein to form hair-like pilli structures. ASP-112 and THR-115 interact with the protein surface, destabilizing the protein structure. Interactions between SM20 and Neisseria gonorrhoeae's Type 4 Pillin involving HIS-89, LYS-93, and SER-83 suggest SM20's potential to disrupt the protein's function, possibly impeding the pathogen's attachment and infection capabilities (Fig. 2B).

Interactions between SM20 and adhesion penetration protein

A potential interaction that interferes with the bacteria's ability to attach and penetrate the host cells is observed. GLU-16, ASP-862, and ASP-843 have negatively charged amino acids that interact with the positively charged residues on the adhesion membrane protein of N. gonorrhea. TYR-9 is a hydrophobic amino acid destabilizing the structure and forming van der Waals interactions on nearby residues. ASN-684, THR-634, ASN-609, and GLN-1 form hydrogen bonds and can penetrate the host cells (Fig. 2C).

Interactions between SM20 and transcriptional regulator

SM20 and the transcriptional regulator protein of N. gonorrhea suggest a potential binding between these two molecules. In particular, the amino acids GLY-73, ALA-75, ASP-104, and PHE-100 in SM20 interact with specific regions on the transcriptional regulator protein. These interactions can indicate specific binding sites, which could be further investigated to understand the potential functional implications of this interaction. These results suggest a possible interaction between SM20 and the transcriptional regulator protein of N.gonorrhea, which could affect how this bacterium interacts with L.crispatus and other microorganisms (Fig. 3A). Further studies would be needed to confirm the significance of this interaction and its potential functional implications.

Fig. 3.

Superimposed images of SM20 peptide with transcriptional regulator, TonB protein, and outer membrane protein of N.gonorrhea with multiple amino acid interactions

Interactions between SM20 and Ton B protein

Ton B is a protein transporting nutrients, such as iron, across the outer membrane of Gram-negative bacteria. SM20 binding to Ton B protein might affect nutrient uptake by N. gonorrhea. Alternatively, SM20 binding to Ton B protein blocks its function or interferes with its activity (Fig. 3B).

Interactions between SM20 and outer membrane proteins (OMPs)

OMPs are important for the survival and virulence of many Gram-negative bacteria and are often targets for drug development. The discovery of a potential binding interaction between SM20 and the outer membrane protein of N. gonorrhea could be significant in developing new treatments for N. gonorrhea infections. Specifically, the interactions between the ASN-161, ASN-3, ARG-163, TYR-15, THR-94, and ASP-58 in SM20 and the OMPsof N.gonorrhea indicate that these amino acids may be involved in the binding process. These interactions could indicate that SM20 can potentially interfere with the outer membrane protein (Fig. 3C).

Discussion

For almost two decades, the fast rise and spread of multidrug-resistant bacterial infections have prompted researchers to look for other ways to battle infection. Numerous bacteriocins have revealed a distinctive mode of action compared to conventional antibiotics, which may reduce the possibility of cross-resistance development and enable them to be evaluated as potential antibiotic alternatives. Establishing drugs to prevent gonococcal infections is critical (Cao and Li 2014; Chow et al. 2017; Chitsaz et al. 2019). Despite the urgency, the absence of adequate drugs, the lack of human correlates of protection, and inadequate animal models of infection have delayed progress toward preventing gonococcal infection (Gupta et al. 2013; Wang et al. 2012). However, the worldwide spread of genetic lineages with Multi-drug resistance phenotypes and the recent finding of dual-resistant azithromycin–ceftriaxone-resistant strain might be developed as a superbug, resistant to all currently available antibiotics and will almost undoubtedly be seen around the world. Due to their severe impact on numerous infections, AMPs were the focus of this research study. AMPs are host defense molecules and short, positively charged oligopeptides that are as potent against many bacteria, viruses, and fungi as commercially available antibiotics. Although they can also target the cell nucleus and protein production, AMPs specifically target the bacterial cytoplasmic membrane. Despite the growing demand for new treatments to address the problem of antimicrobial resistance, naturally generated peptides represent a viable research strategy (Cao and Li 2014; Chow et al. 2017; Chitsaz et al. 2019; Zielke et al. 2014; Arvidson et al. 1999).

The AMPs in this study were characterized from the BIP of L. Crispatus using various computational tools and techniques in earlier literature. We intend to use quantum computing tools to explore potential new directions for research into treating N. gonorrhea. I-TASSER was used to predict the 3D structure of proteins based on a variety of inputs, including comparative/homology, threading, and ab initio modeling, and predict the structure of the AMPs identified from the BIP of L. crispatus and to estimate the number of enzyme commissions and active sites present in the peptides. The PeptideRanker system was used to calculate the likelihood of active peptides, including their predicted structure and amino acid sequence properties. The PeptideRanker ranked the AMPs identified from the BIP of L. crispatus based on their predicted activity levels, ultimately selecting the SM20 peptide as the most promising candidate for further study and was taken for ADMET screening along with the other peptide sequences (Cao and Li 2014; Chow et al. 2017; Chitsaz et al. 2019; Zielke et al. 2014; Arvidson et al. 1999; Cornelissen and Hollander 2011; Remmele et al. 2014; Winther-Larsen et al. 2001; Leuzzi et al. 2013; Wang et al. 2018) (Fig. 4).

Fig. 4.

An intriguing strategy for treating N. gonorrhoea infection is the use of the SM20 peptide from L. crispatus. Finding promising peptide candidates for antimicrobial therapy can be done with the use of bioinformatics screening. Overall, employing the L. crispatus SM20 peptide as a possible cure for N. gonorrhoea infection

Compared to other peptides, the assessment of SM20 peptide reveals certain constraints concerning its solubility, permeability, and potential interactions with P-glycoprotein. It is conceivable that the peptide's distribution within the body is limited, impeding its ability to traverse vital biological barriers such as the BBB and the CNS. Furthermore, a considerable proportion of SM20 may bind to plasma proteins, possibly hampering its efficacy and bioavailability. While SM20 is not anticipated to impede the function of major drug-metabolizing enzymes, apart from CYP3A4, which is predicted to metabolize the peptide, it is expected to have hepatotoxic properties. Nevertheless, it does not exhibit other toxicities such as AMES toxicity, hERG I or II inhibition, or skin sensitization. The Maximum Tolerated Value (human) denotes the highest safe dosage for human consumption, and the Oral Rat Acute Toxicity (LD50) value suggests that the peptide is relatively safe for rats at elevated doses. Further studies or modeling efforts may be warranted to gauge its suitability for therapeutic or other applications. It is crucial to emphasize that these results are entirely prediction-based, necessitating validation through in-vitro and in-vivo experimentation.

Molecular docking studies were conducted to explore SM20 peptide's interactions with the bacterial cell membrane. The lack of membrane protein data necessitated homology modeling, and quantum computing was used for simulation. These insights can aid researchers in understanding SM20's antimicrobial activity mechanisms.

The investigation extended to the roles of proteins in N. gonorrhea's pathogenesis. SM20's interactions with various membrane proteins were identified, presenting potential strategies for preventing and treating infections. The interactions identified between the SM20 peptide and various proteins in N. gonorrhea reveal potential avenues for combatting this pathogenic bacterium. Notably, the interaction with the DNA mimic protein hints at the ability of SM20 to disrupt the bacterium's immune evasion mechanisms, consequently diminishing its virulence. In addition, the interactions with membrane efflux pumps suggest that SM20 has the potential to address the critical issue of antibiotic resistance in N. gonorrhea, as it could inhibit the efflux of antibiotics, rendering the bacterium more susceptible to treatment.

Furthermore, the interactions with stress-response proteins and toxin–antitoxin systems offer promise for reducing the persistence of the bacterium, potentially making it more susceptible to host defenses. Inhibiting adhesion proteins may prevent the establishment of infection, while targeting the pilin protein could reduce the bacterium's spread within host tissues. The interactions with transcriptional regulators hint at the ability of SM20 to decrease the bacterium's virulence, and the interaction with the TonB protein may inhibit the uptake of essential nutrients, ultimately impacting the bacterium's growth and virulence. Lastly, inhibiting outer membrane proteins can influence the bacterium's physiology and pathogenesis. These findings demonstrate the multifaceted utility of SM20 and its promise in developing innovative strategies to combat N. gonorrhea infections.

Conclusion

Using the SM20 peptide from L. crispatus as a potential treatment for N. gonorrhea infection is a novel approach. Bioinformatics screening can be a useful tool to identify potential peptide candidates for antimicrobial therapy. Overall, using the SM20 peptide from L. crispatus as a potential treatment for N. gonorrhea infection is an interesting approach that warrants further investigation. However, it is important to proceed with caution and conduct rigorous testing in-vitro and in-vivo to ensure the safety and efficacy of the peptide before it is approved for pre-clinical and clinical use.

Author contributions

G.S, D.K, M.S: performed the experiments. A.A, A.G, J.A: wrote the manuscript and supervised this work.

Availability of data and materials

Data will be made available on reasonable request.

Declarations

Conflict of interest

The authors declare no conflict of interest regarding this work.

Ethics statements

Not applicable.