Multi-epitopes Vaccine Design against Klebsiella pneumoniae based on Outer Membrane Protein using Immunoinformatics Approaches

Indira Prakoso¹, Alfero Putra Iryanto², Tiara Rahayu³, Anzillina Rahma⁴,

Muhammad Nur Aziz Ar Rizqi⁵, Viol Dhea Kharisma^6,7,

Arif Nur Muhammad Ansori^8,9, Maksim Rebezov^10,11,12, Pavel Burkov¹³,

Marina Derkho¹³, Belyakova Natalia¹⁴, Rybakova Anna¹⁵,

Vikash Jakhmola⁹, Rahadian Zainul^16,17*

¹Department of Food Science and Biotechnology, Brawijaya University, Malang, Indonesia.

²Department of Biotechnology, Esa Unggul University, Jakarta Barat, Indonesia.

³Faculty of Military Mathematics and Natural Sciences,

The Republic of Indonesia Defense University, Bogor, Indonesia.

⁴Department of Electrical Engineering, University of Indonesia, Depok, Indonesia.

⁵Department of Biology, Diponegoro University, Semarang, Indonesia.

⁶Department of Biology, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia.

⁷Division of Molecular Biology and Genetics, Generasi Biologi Indonesia Foundation, Gresik, Indonesia.

⁸Faculty of Veterinary Medicine, Universitas Airlangga, Surabaya, Indonesia.

⁹Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, India.

¹⁰Department of Scientific Research, V. M. Gorbatov Federal Research Center for Food Systems,

Moscow, Russian Federation.

¹¹Faculty of Biotechnology and Food Engineering, Ural State Agrarian University,

Yekaterinburg, Russian Federation.

¹²Department of Scientific Research, Russian State Agrarian University –

Moscow Timiryazev Agricultural Academy, Moscow, Russian Federation.

¹³Institute of Veterinary Medicine, South Ural State Agrarian University, Troitsk, Russian Federation.

¹⁴Moscow University of Technology and Management named after K.G. Razumovsky,

Moscow, Russian Federation.

¹⁵Moscow Institute of Physics and Technology, Moscow, Russian Federation.

¹⁶Department of Chemistry, Faculty of Mathematics and Natural Sciences,

Universitas Negeri Padang, Padang, Indonesia.

¹⁷Center for Advanced Material Processing, Artificial Intelligence, and Biophysic Informatics (CAMP-BIOTICS), Universitas Negeri Padang, Padang, Indonesia.

*Corresponding Author E-mail: rahadianzmsiphd@fmipa.unp.ac.id

ABSTRACT:

Klebsiella pneumoniae is a gram-negative of bacteria that are known to cause a variety of nosocomial respiratory tract infections including pneumonia. K. pneumoniae is also included in the ESKAPE bacteria group which has high resistance to antibiotics. Therefore, alternative treatment for K. pneumoniae infection is needed, one of which is by developing a vaccine. The aim of this study was to design a vaccine against K. pneumoniae by targeting the outer membrane protein using immunoinformatics approaches. 1,708 protein of K. pneumoniae was then screened using signalP, pred-TMBB2, and Blastp to select outer membrane proteins. The selected protein, PA1_KLEPN and BAMA_KLEP7 were then predicted using T-and B-cell Epitope Prediction on IEDB to obtain epitope regions. Vaccine design of K. pneumoniae consists of 1 BCL epitope, 2 CTL epitopes, 1 HTL epitope, an adjuvant and PADRE sequences constructed with linkers using Benchling. This vaccine construction is predicted to be non-toxic/allergenic and have a strong binding affinity with human TLR-4 with the HADDOCK score of -93.2kcal/mol, RMSD 0.5 and Z-score -2.5. According to the computer-aided studies conducted for this study, the chosen epitopes may provide excellent vaccine candidates to stop K. pneumoniae infections in people. However, in order to further confirm the efficacy of this suggested vaccine candidate, in vitro and in vivo validation is required.

KEYWORDS: Bioinformatic, Immunoinformatics, In silico, Klebsiella pneumoniae, Multi-epitope vaccine.

INTRODUCTION:

A gram-negative, harmful bacteria called Klebsiella pneumoniae can grow at human mucous membranes; this can enter the tissue and cause several disorders¹. One of the illnesses brought on by K. pneumonia when the bacteria leave the human intestines and enter the respiratory system (breathing system)². According to studies, pneumonia is the leading infectious illness killer of children, accounting for 14% of all pediatric fatalities and killing 740 180 kids in 2019 alone³. In Indonesia, the minister of health reported that pneumonia was to blame for 5% of child deaths and 14.5% of infant deaths⁴.

Pneumonia can be categorized into two types: hospital-acquired pneumonia (HAP) and community-acquired pneumonia (CAP), depending on the site of previous exposure. K. pneumoniae is a CAP pathogen in various countries in the Asia-Pacific region⁵. CAP is a leading source of morbidity and mortality worldwide. Since epidemiological changes brought on by antimicrobial resistance are making choosing an adequate antibiotic treatment for CAP more and more challenging⁶.

Multidrug Resistant (MDR) having evolved resistance to at least one agent from three or more antimicrobial groups, K. pneumoniae is said to be in this condition⁷. Antimicrobial resistance has increased over time, complicating therapy and harming patient care. Effective prevention measures for community-acquired pneumonia are therefore urgently neede⁸. Therefore, there is an urgent need for effective strategies to prevent community-acquired pneumonia.

The field of bioinformatics is rapidly advancing, bringing forth a new age for developing vaccines using immunoinformatics⁹. Immunoinformatics has paved the way for improved comprehension of some etiology, diagnostics, immune system response, and computational vaccination¹⁰. Additionally, immunoinformatic-based vaccinations are more affordable, do not require microbial culturing, outperform many wet lab trials, and are a safer alternative because they omit the complete pathogen¹¹.

Epitope-based, viral vector, and nucleic acid-based mechanisms have been used in most new vaccine design efforts. The target antigen must possess B-cell receptor epitopes to elicit humoral (antibody reactions) and cellular immunity through these pathways¹².

Reverse vaccinology has been used to create multi-subunit vaccines against numerous pathogens that are based on epitopes9. vaccination with multiple epitopes made up of a number of overlapping peptides. Multi-epitope vaccines differ from classical and single-epitope vaccines in that they (a) contain multiple MHC-restricted epitopes that can be recognized by TCRs of multiple clones from different T-cell subsets; (b) contain CTL (Cytotoxic T Lymphocyte), Th (T-helper), and B-cell epitopes so the vaccines can induce strong cellular and humoral immune responses; and (c) consist of several epitopes from various antigens that can broaden the targets; (d) add certain adjuvant components that can improve immunogenicity and long-lasting immune responses; and (e) remove undesirable components that can provoke pathological immunological reactions.By considering the benefits of multi-epitope-based vaccines, the shots can be effective preventative and therapeutic treatments for a variety of infections¹³.

MATERIALS AND METHODS:

Protein retrieval:

A total of 1,708 proteins from Klebsiella pneumoniae were retrieved fromUniversal Protein Resource (UniProt) database (http://www.uniprot.org/uniprot), which Swiss-Prot has reviewed. The sequences written in this FASTA format will later be analyzed.

Signal peptide, homology, and topology prediction:

The selected protein is a protein that has a signal peptide and is non-homological to human protein. SignalP 5.0 (https://services.healthtech.dtu.dk/service.php?SignalP-5.0) was used to predict used to predict the presence of signal peptides in these proteins and determine their cleavage sites¹⁴. The signal peptide sequence is then cut from the original protein, leaving only mature protein to be exported outside the cell via the secretory pathway system. Blastp (https://blast.ncbi.nlm.nih.gov/) was used to predict homology of selected proteins with human proteins. Potential trans-membrane β-strands were predicted using PRED-TMBB2 (http://www.compgen.org/tools/PRED-TMBB2). PRED-TMBB2 will predict the trans-membrane beta-strand using the Hidden Markov Model method¹⁵.

Prediction of B-cell linear epitopes:

Linear B cell epitopes were screened using five methods: Bepipred Linear Epitope Prediction 2.0, Bepipred Linear Epitope Prediction, Emini Surface Accessibility Prediction, Kolaskar and Tongaonkar Antigenicity, and Parker Hydrophilicity Prediction from Immune-Epitope-Database and Analysis-Resource (IEDB) at http://tools.iedb.org/bcell/. BepiPred 2.0 uses the Random Forest Algorithm to predict B-cell epitopes with a threshold value of 0.5¹⁶. In contrast, BepiPred 1.0 uses the Hidden Markov model and a propensity scale method with a threshold value of 0.35¹⁷. The sequence of proteins with an accessibility value above 1.0 indicates a high probability of being found in the surface membrane¹⁸. The antigenicity scale of Kolaskar and Tongaonkar has 75% accuracy in predicting antigenic determinants on proteins based on the revealed data¹⁹. Hydrophilicity analysis of the surface proteins increases the understanding of interactions between proteins and other molecules. In other words, the hydrophilicity properties were correlated with the antigenicity of protein sequences²⁰. The predicted epitopes were further screened using Vaxijen 2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) for antigenicity test with a default threshold of 0.4. Vaxijen 2.0 uses the auto cross-covariance (ACC) transformation method to predict protein sequences. The models have performed well with an accuracy of 70% - 89%²¹. The predicted epitopes were also subjected to AlgPred 2.0 (https://webs.iiitd.edu.in/raghava/algpred2/) and Toxin Pred 2 (https://webs.iiitd.edu.in/raghava/toxinpred/) for allergenicity and toxicity test. Epitope, which is predicted to be antigenic, not an allergen, and has no toxicity, will be selected for vaccine construction.

Prediction of CD8+ T cell epitopes:

T cell epitopes were predicted using the NetMHCpan 4.1 server (https://services.healthtech.dtu.dk/service.php?NetMHCpan-4.1), a web server with a model of artificial neural networks. The Human Leukocyte Antigens (HLA) used in this study were HLA-A1101, HLA-A2402, HLA-B1502, and HLA-B4403 because they have the highest frequency in Indonesia²². This server will then divide two types of epitopes: strong binder epitope (threshold 0.5) and weak binder epitope (threshold 2%). The epitopes with the most and strongest bonds will be selected and subjected to an immunogenicity test using IEDB (http://tools.iedb.org/immunogenicity/). The higher the immunogenicity value, the higher the probability of the epitope being able to induce the immune system. Same as the previous epitope prediction, the selected epitope was then subjected to Vaxijen 2.0, AlgPred 2.0, and ToxinPred 2. Epitope, which is predicted to be antigenic, not an allergen, and has no toxicity, will be selected for vaccine construction.

Prediction of CD4+ T cell epitopes:

NetMHCIIpan 4.0 server (https://services.healthtech.dtu.dk/service.php?NetMHCIIpan-4.0) was used to predict any MHC II molecules of given sequences using artificial neural networks. The Human Leukocyte Antigens (HLA) used in this study were DRB1_1502, DRB1_1202, HLA-DPA10103-DPB10501, HLA-DPA10103-DPB10101, HLA-DPA10103-DPB11301, HLA-DQA10601-DQB10301, HLA-DQA10601-DQB10502 because they have the highest frequency in Indonesia²². This server will then divide two types of epitopes: strong bond epitope (threshold 1%) and weak bond epitope (threshold 5%). The epitopes with the most and strongest bonds will be selected and predicted as inducer of interferon-gamma using IFNepitope (https://webs.iiitd.edu.in/raghava/ifnepitope/design.php), inducer of interleukin-4 using IL4pred (https://webs.iiitd.edu.in/raghava/il4pred/design.php), and inducer of interleukin-10 using IL10pred (https://webs.iiitd.edu.in/raghava/il10pred/predict3.php). Same as the previous epitope prediction, the selected epitope was then subjected to Vaxijen 2.0, AlgPred 2.0, and ToxinPred 2. Epitope, which is predicted to be antigenic, not an allergen, and has no toxicity, will be selected for vaccine construction.

Population coverage analysis:

Selected epitopes from CD8+ T cells and CD4+ T cellswere subjected to the IEDB Population Coverage tool (http://tools.iedb.org/population/) with their respective HLA. IEDB will predict the percentage of population coverage by epitopes and HLA alleles that will be compatible with the selected populations. In this research, we predict the distribution of epitopes-HLA in the entire population that was provided in the IEDB tools.

Construction of vaccine:

The vaccine was constructed using Benchling (https://www.benchling.com/academic). Selected epitopes from linear B-cell, CD8+ T cell and CD4+ T cell were constructed together using KK, GGGS, and GPGPG linkers, respectively. Adjuvant peptide was incorporated to the vaccine construction to increase probability that the vaccine can attach to immune cell receptors, namely Toll-like receptors (TLRs).We used cholera toxin B, the nontoxic portion of cholera toxin, as an adjuvant in this final vaccine construct to increase optimal access to the immune system²³. To overcome the problems caused by highly polymorphic HLA class-2 alleles, the Universal Pan HLA DR Sequence (PADRE) was added after the adjuvant²⁴.

Protein modelling and validation:

The 3D structure of the vaccine construct was predicted using tr-Rosetta (https://yanglab.nankai.edu.cn/trRosetta/), a prediction tool of protein structure and function based on a web server. It allows users to obtain 3D model predictions from a given amino acid sequence²⁵. Online platform, ProSA was used to validate the quality of the 3D model protein of vaccine²⁶. The physiochemical properties of the vaccine sequence were predicted using online platform, Protparam (https://web.expasy.org/protparam/)²⁷.

Protein-protein docking

Molecular docking was done to predict the binding energy between vaccine construct and immune receptor. We assessed HADDOCK server (https://wenmr.science.uu.nl/haddock2.4/) to analyze protein-protein interaction between vaccine and TLR4 (PDB: 4G8A). Visualization of interaction was done using BIOVIA Discovery Studio 2021 and PyMol.

RESULTS AND DISCUSSION:

Selection of protein target:

To design the vaccine, literature studies were conducted about signaling system in K. pneumoniae, antigen and antimicrobial presentation, and the ways to design the vaccines. Screening of protein sequences of K. pneumoniae is conducted by accessing UniProt website. Total 1,708 reviewed protein sequences are selected and chosen. 13 proteins are selected which their output plots from signalP have “S”, “C”, or “Y” symbols. AA sequences that contain a signal peptide as an indicator of secretion are predicted using the SignalP program. In signalP, there are three distinct score types. A raw cleavage site score called the C-score is utilized to identify the existence of a signal peptide cleavage inside the AA sequence. A way to distinguish places in signal peptides from positions in the remaining AA sequence is to use the S-core. The C-score and steepest slope are combined to create the Y-score²⁸.

To avoid autoimmunity, the vaccines target should not be similar to human proteins, therefore, blastp was used to verify if the proteins have >30% identity with human proteins²⁹. From the result of blastp, all of 13 proteins are not homology with the human proteins. All of 13 proteins were screened using PRED_TMBB2 to predict whether the outer membrane proteins are present in transmembrane regions³⁰. From the result of PRED_TMBB2, there are two proteins which have parts in their outer membranes (PA1 and BAMA proteins).

Linear B-cell epitopes:

From each protein, one epitope with the most potential was taken based on its antigenicity and the number of recognizable HLA alleles. This epitope was selected based on the consensus predictions of Bepipred Linear Epitope Prediction 2.0, Bepipred Linear Epitope Prediction, Emini Surface Accessibility Prediction, Kolaskar and Tongaonkar Antigenicity, and Parker Hydrophilicity Prediction from Immune-Epitope-Database and Analysis-Resource (IEDB). After being subjected in AlgPred, only one peptide ‘VHDKPAVRG’ from PA1 protein met the qualification to be selected as linear B-cell epitopes candidates as it was predicted to be non-allergen. This epitope is potential to be antigen with antigenicity value of 1.1343²¹. (Table-1).

CD8+ T-cell (CTL) epitopes:

Before vaccine design can be implemented in wet lab research, vital allergenicity prediction is the possibility of designing vaccine candidates eliciting a type II reaction. The peptides were selected based on their percentage rankings binder epitope to induce the immune system and immunogenicity value³¹. CD8+ T-cells are activated by Th1 response⁹. T cytotoxic cells (Tc cells) are CD8+ T cells that have the ability to detect the peptides shown by MHC I. T cytotoxic cells have cytolytic activity and caused virally infected cells to apoptose. The result from NetMHCpan 4.1 prediction for the most and strongest bonds (threshold 0.5%) selected were ‘TLRDIEMGY’ from protein PA1 with allele of HLA-B*15:02 and ‘SFSANDFTF’ from protein BAMA with alleles of HLA-A*24:02 and HLA-B*15:02. These proteinswere predicted to benon-allergen and non-toxicmaking it safe as candidate of vaccine. The peptide length selected as the epitope is the 9-mer peptideas there are more epitopes with this length have the ability to bind to the MHCI receptor than epitopes with other lengths such as 8-, 10-, or 11-mer³². The selected epitope was then subjected to Vaxijen 2.0 and it showed that the two peptides are potential to be antigen with antigenicity value above threshold of 0.4²¹. Immunogenicity prediction of the CD8+ T cell epitopes using IEDB showed positive value which means these epitopes are possible to trigger immune respons³³.

Construction of the peptide vaccine:

This multi-epitope peptide vaccine hasfinal construction of 219 amino acids made up from 4 chosen antigenic B and T cells epitopes that are covalently bonded with an immunoadjuvant (Figure 2). Additionally, the multiple epitope vaccine's tertiary structure was obtained (Figure 3). The vaccine obtained value of 0.8114 antigenicity with Vaxijen prediction tool means that the vaccine was predicted to be antigen.

Figure 1: Construction of vaccine

Figure 3: 3D structure of vaccine construction

EAAAK linker function as rigid linkers and was used to linked between adjuvant and PADRE sequences, it would efficiently assure in vivo separation of distinct epitopes in a natural environment³⁵. Invasin sequence of Yersinia was used to end the construction vaccine by added it at the C-terminal. Invasin C-terminal was known could enhance the immunogenicity³⁶.EGGE linker act as a linker between B-cell epitopes and invasin. Each of the several linkers plays a unique purpose. However, the GGGS, GPGP, KK, and EGGE allow flexibility to fuse the vaccine candidates³⁷.

ProSA-webwas used to evaluate the structural validation by providing structural quality in z-score. Structures with poor quality are indicated by z-score values that are far from the characteristic range of the native protein³⁸. The vaccination projected model had a Z-score of -5.44, which indicates that it is a rather decent model (Figure 4).

Figure 4: Overall model quality calculation by ProSA

Physicochemical properties:

The effectiveness and efficacy of a vaccine can also be seen from its physicochemical properties.The vaccine was predicted to have molecular weight of 23887.20 Da and theoretical pI of 9.25, with an estimated half-life of 30 hours.With a molecular weight below <110kD and a high pI value, this vaccine is expected to work well in the human body³⁹. The instability index was 27.02, means that the vaccine is stable in a solvent environment because the value still below 40 instability index⁴⁰. The aliphatic index waspredicted to be 70.09, with a GRAVYscore of -0.269 means that the vaccine is hydrophilic since the GRAVY score below 0⁴¹.

Docking between vaccine and Toll-like Receptor 4 (TLR4):

Multi epitope vaccine construct was then docked with humas TLR4 receptor. The TLR4 was chosen because it has immunomodulatory properties that can cause IFN-g and activate type I IFN responses⁴². Binding site for docking was determined based on native ligan position in TLR4 receptor. The top ten docked models were ranked by the HADDOCK server according to the protein's surface and electrostatic complementarity. The TLR-4 and vaccine complex was discovered to have the best docking, with a HADDOCK score of -93.2 kcal/mol, RMSD 0.5, and Z-score -2.5. The lowest score was chosen since it shows that the docked complex has the maximum binding affinity for the TLR-4 portion of the vaccine⁴³. RMSD (root-mean square deviation) was used to determine the difference of docking orientation pose of the same molecule. The lower the RMSD value indicates the better the docking validity⁴⁴. The Z-score shows the standard deviation of the clusters given to the average of all clusters so that the smaller or more negative the z-score value, the better the docking results⁴⁵.

Figure 2: Visualization of docking between TLR-4 receptor (blue)with vaccine candidate (yellow)

CONCLUSION:

The K. pneumoniae PA1 and BAMA proteins were considered the most promising candidate vaccines because they contained a signal peptide, were non-homologous to human proteins and had a place in transmembrane cells. It is anticipated that the vaccine construct's low binding energy interaction with TLR4 will be able to stimulate immunogenicity reaction. The majority of HLA alleles in Europe, North America, Russia, the United States, and some Asian countries have also been predicted to be compatible with a subset of MHC class I and class II epitopes. The findings of this study still require validation through additional research, such as molecular dynamics, in vitro or in vivo.

DISCLOSURE STATEMENT:

The authors have no conflicts of interest to declare.

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Received on 18.01.2023 Modified on 30.03.2023

Research J. Pharm. and Tech 2024; 17(1):11-18.

DOI: 10.52711/0974-360X.2024.00003

Protein	Start	End	Peptide	Length	Antigenicity	Toxicity	Allergenicity
PA1	26	34	VHDKPAVRG	9	1.1343	Non-toxic	Non-allergen
BAMA	213	227	VVGDRKYQKQKLAG	14	0.6553	Non-toxic	Allergen

Protein	Start	End	Peptide	Alleles	Antigenicity	Immunogenicity	Allergenicity	Toxicity
PA1	152	161	TLRDIEMGY	HLA-B*15:02	0.5184	0.1346	Non-allergen	Non-toxic
BAMA	543	552	SFSANDFTF	HLA-A24:02, HLA-B15:02	0.9065	0.1217	Non-allergen	Non-toxic

Protein	Start	End	Core peptides	Peptides	Alleles	Antigenicity	IFN-γ	IL-4	IL-10	Allergenicity
PA1	152	161	YTQIYSGYG	HVRAYTQIYSGYGES	HLA-DPA101:03, HLA-DPB113:01, HLA-DQA106:01, HLA-DQB105:02, HLA-DPB1*05:01	0.674	-	-	+	Non-allergen
BAMA	543	552	VFYNDFDAN	GGRVFYNDFDANDAD	HLA-DQA106:01, HLA-DQB105:02, HLA-DPA101:03, HLA-DPB101:01, HLA-DPB105:01, HLA-DPB113:01	0.528	-	+	-	Allergen