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Citation: Alshamrani, S.; Mashraqi,
M.M.; Alzamami, A.; Alturki, N.A.;
Almasoudi, H.H.; Alshahrani, M.A.;
Basharat, Z. Mining
Autoimmune-Disorder-Linked
Molecular-Mimicry Candidates in
Clostridioides difficile and Prospects of
Mimic-Based Vaccine Design: An In
Silico Approach. Microorganisms 2023,
11, 2300. https://doi.org/10.3390/
microorganisms11092300
Academic Editors: Amedeo Amedei
and Gwendolyn Barceló-Coblijn
Received: 1 July 2023
Revised: 7 September 2023
Accepted: 7 September 2023
Published: 12 September 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
microorganisms
Article
Mining Autoimmune-Disorder-Linked Molecular-Mimicry
Candidates in Clostridioides difficile and Prospects of
Mimic-Based Vaccine Design: An In Silico Approach
Saleh Alshamrani 1, Mutaib M. Mashraqi 1, * , Ahmad Alzamami 2, Norah A. Alturki 3,
Hassan H. Almasoudi 1, Mohammed Abdulrahman Alshahrani 1and Zarrin Basharat 4, *
1Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University,
Najran 61441, Saudi Arabia; saalshamrani@nu.edu.sa (S.A.); hhalmasoudi@nu.edu.sa (H.H.A.);
maalshahrani@nu.edu.sa (M.A.A.)
2Clinical Laboratory Science Department, College of Applied Medical Science, Shaqra University,
AlQuwayiyah 11961, Saudi Arabia; aalzamami@su.edu.sa
3Clinical Laboratory Science Department, College of Applied Medical Science, King Saud University,
Riyadh 11433, Saudi Arabia; noalturki@ksu.edu.sa
4Alpha Genomics (Private) Limited, Islamabad 45710, Pakistan
*Correspondence: mmmashraqi@nu.edu.sa (M.M.M.); zarrin.iiui@gmail.com (Z.B.)
Abstract:
Molecular mimicry, a phenomenon in which microbial or environmental antigens resemble
host antigens, has been proposed as a potential trigger for autoimmune responses. In this study, we
employed a bioinformatics approach to investigate the role of molecular mimicry in Clostridioides
difficile-caused infections and the induction of autoimmune disorders due to this phenomenon.
Comparing proteomes of host and pathogen, we identified 23 proteins that exhibited significant
sequence homology and were linked to autoimmune disorders. The disorders included rheumatoid
arthritis, psoriasis, Alzheimer’s disease, etc., while infections included viral and bacterial infections
like HIV, HCV, and tuberculosis. The structure of the homologous proteins was superposed, and
RMSD was calculated to find the maximum deviation, while accounting for rigid and flexible regions.
Two sequence mimics (antigenic, non-allergenic, and immunogenic) of
≥
10 amino acids from these
proteins were used to design a vaccine construct to explore the possibility of eliciting an immune
response. Docking analysis of the top vaccine construct C2 showed favorable interactions with
HLA and TLR-4 receptor, indicating potential efficacy. The B-cell and T-helper cell activity was also
simulated, showing promising results for effective immunization against C. difficile infections. This
study highlights the potential of C. difficile to trigger autoimmunity through molecular mimicry and
vaccine design based on sequence mimics that trigger a defensive response.
Keywords: Clostridioides difficile; molecular mimicry; autoimmunity; vaccine design; bioinformatics;
immune response; docking; pathogenesis
1. Introduction
Molecular mimicry refers to the phenomenon in which microbial or environmental
antigens share structural or sequence similarities with host antigens [
1
]. This similarity
can lead to a cross-reactive immune response, in which the immune system mistakenly
targets self-tissues, resulting in autoimmune disorders [
2
]. The onset of autoimmune
disorders due to molecular mimicry by pathogenic proteins or antigens presents an intrigu-
ing research area that investigates the possible connection between microbial infections
and the onset of autoimmune responses [
3
,
4
]. Understanding the mechanisms by which
molecular mimicry contributes to autoimmune disorders is crucial for developing tar-
geted therapies and preventive strategies [
5
–
7
]. Numerous studies have investigated the
molecular mimicry between human proteins associated with autoimmune disorders and
Microorganisms 2023,11, 2300. https://doi.org/10.3390/microorganisms11092300 https://www.mdpi.com/journal/microorganisms
Microorganisms 2023,11, 2300 2 of 25
pathogen-derived proteins [
8
–
13
]. These investigations have revealed several mechanisms
that contribute to the initiation and perpetuation of autoimmune responses [
14
–
16
]. Specific
pathogen proteins have been found to share sequence motifs with host proteins involved
in autoimmune disorders, enabling the activation of autoreactive T cells [
14
,
17
]. Strepto-
coccus pyogenes has been implicated in autoimmune diseases, such as rheumatic fever [
18
],
glomerulonephritis [
19
], and multiple sclerosis [
20
]. Epstein–Barr virus has been connected
with molecular-mimicry-mediated autoimmune disorders [
21
], like systemic lupus ery-
thematosus [
22
,
23
], hepatitis [
24
], and multiple sclerosis [
25
]. These pathogens possess
proteins that mimic self-antigens, leading to cross-reactivity with host tissues. Additionally,
molecular mimicry has been observed in viral infections, such as the hepatitis C virus, in
which viral proteins share sequence homology with host proteins involved in autoimmune
liver diseases [26].
Techniques that can be employed to study molecular-mimicking peptides include
phage display [
27
], and bioinformatics approaches include sequence alignment and molec-
ular modeling [
28
], etc. Sequence alignment algorithms can identify regions of similar-
ity or shared motifs between the two protein sequences [
29
]. Homology modeling and
comparative protein structure prediction can be used to analyze the three-dimensional
structures [
30
] of pathogen and host proteins. By comparing the structural features and
folding patterns, potential mimicking regions can be identified. Predictive algorithms, such
as NetMHC [
31
] and the Immune Epitope Database (IEDB) [
32
], can be utilized to identify
potential epitopes within pathogen proteins that resemble host epitopes associated with
autoimmune disorders [
33
]. To validate the cross-reactivity of pathogen and host proteins,
enzyme-linked immunosorbent assay (ELISA) assays [
34
], Western blotting [
35
], and flow
cytometry [
36
], etc., can be employed. These methods measure the binding of antibodies or
T cells to specific antigens and can confirm the presence of molecular mimicry. Apart from
these, animal models, such as transgenic mice expressing human proteins associated with
autoimmune disorders, can be used to study the effects of pathogen infections and eval-
uate the development of autoimmune responses [
37
,
38
]. Disease models, such as
in vitro
models of tissue inflammation can also be employed to investigate the consequences of
molecular mimicry [
8
,
39
]. By utilizing a combination of these techniques, researchers can
gain insights into the occurrence and mechanisms of molecular mimicry, contributing to
a better understanding of its role in autoimmune disorders and potentially guiding the
development of therapeutic interventions.
Bioinformatics is a swift approach to identifying and characterizing the molecular-
mimicry interactions between human proteins and pathogens [
40
–
42
]. Various bioinfor-
matics tools and databases are utilized to analyze protein sequences, identify shared motifs
or structural similarities, and predict antigenicity and immunogenicity [43]. Comparative
genomics and proteomics approaches are employed to identify pathogen proteins that
mimic host antigens associated with autoimmune disorders [
44
–
46
]. Previous studies
have utilized bioinformatics approaches to uncover potential molecular-mimicry mecha-
nisms between pathogens and host proteins by employing sequence alignment algorithms,
structural modeling, and epitope prediction tools to assess the extent of mimicry and
the potential immunological consequences [
15
,
41
,
47
]. Additionally, database-mining tech-
niques have been used to establish links between identified mimicry interactions and
autoimmune disorders [
9
,
48
–
50
]. Herein, we analyzed mimicry prediction and association
with autoimmune disorders in Clostridioides difficile using in silico methods. By integrating
bioinformatics analyses with experimental validation, this information contributes to our
understanding of the complex interactions between pathogens and the human immune sys-
tem, shedding light on the role of molecular mimicry in the development and progression
of autoimmune disorders.
Microorganisms 2023,11, 2300 3 of 25
2. Material and Methods
2.1. Homology Analysis
The entire set of proteins from the human and C. difficile S-0253 (reference strain
ASM1888508v1) samples was obtained from Uniprot (https://www.uniprot.org/proteomes/
UP000005640 (accessed on 31 May 2023)) and the NCBI database (GenBank accession:
CP076401.1; accessed 31 May 2023), respectively. To identify potential homologous proteins,
a local installation of BLAST was utilized, applying a threshold of
≥
50% identity and
≥100-bit score to retain proteins for further analysis [16].
2.2. Mimic Region Identification
To identify potential regions involved in mimicry, the proteins were aligned to un-
cover regions of similarity with a minimum length of 10 amino acids [
16
]. RMSD was
employed as a scoring metric to assess the structural similarity of the peptides. To ob-
tain the 3D structures of these proteins, the state-of-the-art-predicted protein structures
from the AlphaFold database were utilized [
51
,
52
]. To compare and align the obtained
structures, both iPBA [
53
] and TM-align [
54
] algorithms were employed. These tools
are widely recognized tools in the field of structural biology, known for their accuracy
in comparing protein structures and determining alignment based on various structural
features. iPBA is a sequence-independent method that uses a fragment-based approach (for
capturing large protein fold changes) [
53
], while TM-align superimposes 3D coordinates
and aligns protein structures by dynamic programming method (for capturing small fold
changes) [
54
]. The superposed structures diagram was generated through the TM-align
module of the RCSB PDB structural alignment tool (https://www.rcsb.org/alignment
(accessed on 21 June 2023)).
2.3. Autoimmunity Elucidation
In order to identify homologous protein pathways associated with autoimmune
disorders or infection, relevant databases, such as pathDIP [
55
] and PHAROS (https:
//pharos.nih.gov/targets/ (accessed on 15 June 2023)) [
56
], were surveyed, along with a
thorough review of the literature. PHAROS provides preprocessed data from the Target
Central Resource Database (TCRD) on the input of the human gene name, Uniprot ID, etc.
Linked disorders can be manually checked for autoimmunity. For pathDIP, all databases
were selected with a minimum confidence level set to 0.99. The data type selected was
extended pathway associations. The protein interaction set considered for analysis included
both experimentally detected and computationally predicted protein–protein interactions
(PPIs) using the full IID dataset. pathDIP serves as a comprehensive reference for signaling
cascades across various species, consolidating key pathways sourced from major curated
pathway databases [
57
]. The associations in pathDIP are based on a combination of com-
putational predictions, experimentally confirmed interactions, orthology mapping, and
inference of physical protein interactions. This database provides a valuable resource for
exploring and understanding signaling pathways associated with autoimmune disorders
and infection. Apart from this, a literature search was conducted to identify infection or
autoimmune pathways linked with these homologs. A BLAST search (
≥
90% homology) of
epitopes was also carried out against IEDB [
32
], and the relevant literature was identified
for the listed infection or autoimmune disorder in the database.
2.4. Mimic-Based Vaccine Design
Identified mimics were subjected to antigenic analysis using VaxiJen server [
58
]. Apart
from this, properties like allergenicity, toxicity, and other parameters useful in finalizing
peptides for vaccine design were studied. ProtParam was used for physicochemical pro-
filing [
59
], while AllerCatPro (https://allercatpro.bii.a-star.edu.sg/, accessed on 16 June
2023) and ToxinPred (https://webs.iiitd.edu.in/raghava/toxinpred/multi_submit.php,
accessed on 16 June 2023) were used for allergenicity and toxicity analysis, respectively.
IEDB was used for immunogenicity prediction [
32
]. Mimics were prioritized for vac-
Microorganisms 2023,11, 2300 4 of 25
cine design based on these parameters, the ability to induce immune response, and con-
servation across the strains. Conservations was determined by the ConSurf webserver
(https://consurf.tau.ac.il/consurf_index.php; accessed on 17 June 2023).
The vaccine construct was designed according to the previously described methodol-
ogy [
16
], using suitable linkers, adjuvants, and binders. They were subjected to another
round of evaluation according to properties like antigenicity, toxicity, allergenicity, etc.
The best antigenic and non-allergenic, non-toxic construct was tested for immune reaction
incitation and cloned in a pET-28(a)+ vector (available at https://www.snapgene.com/
plasmids/pet_and_duet_vectors_(novagen)/pET-28a(%2B); accessed on 18 June 2023) after
reverse translation and codon optimization through the JCat tool [
60
]. C-ImmSimm [
61
]
was used for the simulation of immune reaction. The parameters were as follows: Simu-
lation_volume = 10; Num_steps = 1000; HLA = A0101, A0102, B0702, B0704, DRB1_0101,
DRB1_0102; No_of_injections = 3; Time of injection (in days) = 1, 30); Adjuvant = 100.
The first two injections on day 1 and 30 were of the vaccine. At day 240, the proteins
phosphoribosylaminoimidazolecarboxamide formyltransferase and adenylosuccinate lyase
were injected to test the reaction.
2.5. Immune Receptor Binding Study
To elicit protection, the vaccine protein should bind the immune receptors with good
affinity [
62
,
63
]. To analyze this property, we constructed a 3D model of the vaccine construct
with SWISS-MODEL [
64
], AlphaFold [
51
], and I-TASSER [
65
]. The best model was selected
based on Ramachandran plot statistics from the assess module of SWISS-MODEL (https:
//swissmodel.expasy.org/assess (accessed on 20 June 2023)) and energy-minimized using
Molecular Operating Environment (MOE) v2016 software. It was then docked with immune
receptors of importance like TLR-4 receptor (PDB ID: 3FXI), HLA-A (PDB ID: 3OX8), and
HLA-B (PDB ID: 4JQX), using the ClusPro server [
66
]. ClusPro focuses on predicting the
overall shape and orientation of the protein–protein complex [
67
]. Prodigy [
68
] was used
to predict the thermodynamic properties and binding affinities of the obtained docked
complexes. An experimentally determined protein–protein interacting complex (PDB
ID:4GIQ) was employed as a control to compare predicted values. This comparison allowed
us to assess whether the binding scores were superior or inferior to the control, providing
valuable insights into the efficacy and specificity of our vaccine design approach.
3. Results
3.1. Homologous Sequence Identification
In total, 23 proteins were obtained with significant similarity between human and
C. difficile. F0F1 ATP synthase subunit beta had the highest number of peptide mimics
(n= 11), followed by F0F1 ATP synthase subunit alpha (n= 7) and heat-shock protein
DnaK (n= 6) (Table 1). These were superposed (Figure 1), and RMSD after structural
superposition varied between iPBA and TM-align prediction. A possible reason for this is
the algorithm difference, in which iPBA is tailored for flexible proteins or regions within
a protein. Thus, it gave overall lower RMSD values compared to TM-align. However,
both servers gave the lowest RMSD of 0.51 for the ATP-dependent Clp endopeptidase
proteolytic subunit ClpP. This suggests a high degree of structural similarity between the
human and bacterial homologs of this protein.
Microorganisms 2023,11, 2300 5 of 25
Table 1. RMSD of C. difficile homologs in humans. Molecular mimics with length ≥10 are also shown.
Serial
no. Name
UniProt ID
of Human
Homolog
NCBI Accession
of Bacterial
Homolog
Bacterial Protein
Structure
AlphaFold ID
No. of Similar
Peptides
(Length ≥10)
Molecular Mimic Region (Length ≥10)
Superposed
Protein RMSD
(iPBA)
TM-Align
RMSD
1Molecular chaperone DnaK P38646 QWS53804.1 Q182E8 6
GIDLGTTNSCVAV
RTTPSVVAFT
INEPTAAALAYG
LLLDVTPLSLGIET
RNTTIPTKKSQ
PQIEVTFDIDANGIV
1.13 2.0
2Translation elongation factor 4 Q8N442 QWS53810.1 Q182F4 3 DHGKSTLADRL
LNLIDTPGHVDF
VLAKCYGGDI 1.25 1.55
3Uracil-DNA glycosylase P13051 QWS53824.1 Q182G9 0 - 1.01 1.13
4Acetyl-CoA C-acetyltransferase Q9BWD1 QWS54012.1 Q18AR0 1 NASGINDGAA 0.70 0.75
5
3-oxoacid CoA-transferase subunit B
E9PDW2 QWS54014.1 Q183B1 0 - 1.00 1.74
6UDP-glucose 4-epimerase GalE Q14376 QWS54050.1 Q183E8 3 GGAGYIGSHT
VFSSSATVYG
DGTGVRDYIHV 0.79 0.79
7V-type proton ATPase subunit B P21281 QWS54278.1 Q184E3 2 YAEALREVSAA
THPIPDLTGYITEGQI 0.81 0.91
8
V-type ATP synthase catalytic unit A
P38606 QWS54279.1 Q184E7 2 MPVAAREASIYTGIT
MADSTSRWAEALRE 0.98 1.03
9Phosphopyruvate hydratase P09104 QWS54488.1 Q181T5 3 LDSRGNPTVEV
QEFMILPVGA
VGDEGGFAPN 0.87 1.39
10 ATP-dependent Clp endopeptidase
proteolytic subunit ClpP Q16740 QWS54701.1 Q180F0 1 VVEQTGRGER 0.51 0.51
11 ATP-dependent Clp endopeptidase
proteolytic subunit ClpP Q16740 QWS54730.1 Q180J6 0 - 0.56 0.56
12 F0F1 ATP synthase subunit beta P06576 QWS54823.1 Q184E3 11
TGIKVVDLLAPY
KGGKIGLFGGAGVGKTVLI
G VGERTREGNDLY
GQMNEPPGAR
DNIFRFTQAGSEVSALLGR
PSAVGYQPTLAT
TKKGSITSVQA
YVPADDLTDPAPATTF
LGIYPAVDPL
LQDIIAILGMDELS
RARKIQRFLSQ
2.11 2.73
Microorganisms 2023,11, 2300 6 of 25
Table 1. Cont.
Serial
no. Name
UniProt ID
of Human
Homolog
NCBI Accession
of Bacterial
Homolog
Bacterial Protein
Structure
AlphaFold ID
No. of Similar
Peptides
(Length ≥10)
Molecular Mimic Region (Length ≥10)
Superposed
Protein RMSD
(iPBA)
TM-Align
RMSD
13 F0F1 ATP synthase subunit alpha P25705 QWS54825.1 Q184E7 7
IKEGDIVKRTG
PIGRGQRELIIGDRQTGKTSI
CIYVAIGQKRST
YTIVVSATAS
YDDLSKQAVAYR
MSLLLRRPPGREAYPGDVFYLHSRLLERAAK
IETQAGDVSAYIPTNVISITDGQI
1.52 2.69
14 Elongation factor Tu P49411 QWS55098.1 Q18CE4 3 GTIGHVDHGKTTLTAAITK
DCPGHADYVKNMITG
DGPMPQTREH 0.98 1.12
15 Elongation factor Tu P49411 QWS55112.1 Q18CE4 3 GTIGHVDHGKTTLTAAITK
DCPGHADYVKNMITG
DGPMPQTREH 0.98 1.12
16 Methionine adenosyltransferase P31153 QWS55174.1 Q18CL7 4
EGHPDKICDQISD
RFVIGGPQGD
HGGGAFSGKD
TKVDRSAAYAAR
0.72 0.93
17 Chaperonin GroEL P10809 QWS55239.1 Q18CT5 3 AGDGTTTATVLA
VVAVKAPGFGD
DALNATRAAVEEGIV 1.22 3.61
18 Isocitrate/isopropylmalate
dehydrogenase family protein P50213 QWS55807.1 Q18A33 5
VTLIPGDGIGPE
VMPNLYGDILSDL
AGDGTTTATVLA
VVAVKAPGFGD
DALNATRAAVEEGIV
0.89 1.48
19 Phosphoribosylaminoimidazole-
carboxamide formyltransferase P31939 QWS55813.1 Q18A34 5
WQLVKELKEA
SFKHVSPAGAAVG
REVSDGIIAPGY
KYTQSNSVCYAK
GAGQQSRIHCTRLAG
0.73 1.23
20 Acyl-CoA dehydrogenase P16219 QWS55947.1 Q18AQ1 2 LIFEDCRIPK
ITEIYEGTSE 0.72 0.99
21 Acetyl-CoA C-acetyltransferase Q9BWD1 QWS55952.1 Q18AR0 2 NASGINDGAA 0.70 0.75
22 Fe-S cluster assembly scaffold
protein NifU Q9H1K1 QWS56138.1 Q18BE3 2 GCGSAIASSS 1.01 1.39
23 Adenylosuccinate lyase P30566 QWS56192.1 Q18BJ9 2 RGVKGTTGTQASFL
YKRNPMRSER 0.81 0.94
Microorganisms 2023,11, 2300 7 of 25
Microorganisms 2023, 11, x FOR PEER REVIEW 7 of 25
Figure 1. Superposed structures of the C. difficile and human homologous proteins. (A) Molecular
chaperone DnaK; (B)Translation elongation factor 4; (C) Uracil-DNA glycosylase; (D) Acetyl-CoA
C-acetyltransferase; (E) 3-oxoacid CoA-transferase subunit B; (F) UDP-glucose 4-epimerase GalE;
(G) V-type proton ATPase subunit B; (H) V-type ATP synthase catalytic unit A; (I) Phosphopyruvate
hydratase; (J) ATP-dependent Clp endopeptidase proteolytic subunit ClpP; (K) ATP-dependent Clp
endopeptidase proteolytic subunit ClpP; (L) F0F1 ATP synthase subunit beta. Due to space con-
straints, the first 12 (Table 1) of the 23 proteins are shown here. Human homologs are shown in
brown and bacterial proteins are shown in blue.
3.2. Autoimmunity Prediction
PHAROS and PATHDIP database scan revealed several autoimmune diseases linked
with the human homologous sequences of C. difficile (Table 2). ATP-dependent Clp prote-
ase proteolytic subunit and elongation factor Tu had two copies, so homologs were re-
moved from database mining. For the rest of the homologs, the most commonly identified
infection was tuberculosis (for DnaK, V-type ATP synthase, and ClpP) and the most com-
monly identified autoimmune disease was rheumatoid arthritis (for chaperones DnaK
and GroEL, elongation factor tu, Translation elongation factor 4, 3-oxoacid CoA-transfer-
ase subunit B, V-type proton ATPase, phosphopyruvate hydratase, Phosphoribosylami-
noimidazolecarboxamide formyltransferase, and NifU).
Figure 1.
Superposed structures of the C. difficile and human homologous proteins. (
A
) Molecular
chaperone DnaK; (
B
) Translation elongation factor 4; (
C
) Uracil-DNA glycosylase; (
D
) Acetyl-CoA
C-acetyltransferase; (
E
) 3-oxoacid CoA-transferase subunit B; (
F
) UDP-glucose 4-epimerase GalE;
(
G
) V-type proton ATPase subunit B; (
H
) V-type ATP synthase catalytic unit A; (
I
) Phosphopyruvate
hydratase; (
J
) ATP-dependent Clp endopeptidase proteolytic subunit ClpP; (
K
) ATP-dependent
Clp endopeptidase proteolytic subunit ClpP; (
L
) F0F1 ATP synthase subunit beta. Due to space
constraints, the first 12 (Table 1) of the 23 proteins are shown here. Human homologs are shown in
brown and bacterial proteins are shown in blue.
3.2. Autoimmunity Prediction
PHAROS and PATHDIP database scan revealed several autoimmune diseases linked
with the human homologous sequences of C. difficile (Table 2). ATP-dependent Clp protease
proteolytic subunit and elongation factor Tu had two copies, so homologs were removed
from database mining. For the rest of the homologs, the most commonly identified infec-
tion was tuberculosis (for DnaK, V-type ATP synthase, and ClpP) and the most commonly
identified autoimmune disease was rheumatoid arthritis (for chaperones DnaK and GroEL,
elongation factor tu, Translation elongation factor 4, 3-oxoacid CoA-transferase subunit B,
V-type proton ATPase, phosphopyruvate hydratase, Phosphoribosylaminoimidazolecar-
boxamide formyltransferase, and NifU).
Microorganisms 2023,11, 2300 8 of 25
Table 2. Autoimmune pathways of the selected homologs.
Serial
no. Protein Homolog
PHAROS PATHDIP Literature
Autoimmunity
Pathway Infection Pathway Autoimmune
Pathway Infection Pathway Autoimmune Pathway Infection Pathway
1 P38646 Molecular chaperone DnaK Autoimmune
disease, Parkinson’s
disease
Perinatal necrotizing
enterocolitis, HIV,
Tuberculosis
Parkinson’s disease,
Huntington’s
disease, Diabetes
mellitus,
Alzheimer’s
Tuberculosis, HIV,
Papillomavirus,
E. coli,
Cytomegalovirus,
Staphylococcus sp.,
Legionellosis,
Chagas,
Leishmaniasis,
Measles
Guillain–Barrésyndrome [69],
Multiple sclerosis [70],
Vitiligo [71], Systemic lupus
erythematosus [72],
Ankylosing spondylitis [73],
Type I Diabetes mellitus [74],
Rheumatoid arthritis [75]
Trypanosoma
cruzi [76],
Mycobacterium
leprae [77]
2 Q8N442 Translation elongation factor 4 - - Tuberculosis,
Rheumatoid arthritis
- -
3 P13051 Uracil-DNA glycosylase ---HIV, Viral
carcinogenesis - -
4 Q9BWD1 Acetyl-CoA C-acetyltransferase ---HBV, Viral
carcinogenesis Systemic lupus
erythematosus [78]HCV [79]
5 E9PDW2 3-oxoacid CoA-transferase subunit B Crohn’s disease ---Rheumatoid arthritis [80]-
6 Q14376 UDP-glucose 4-epimerase GalE Psoriasis, Interstitial
cystitis Tinea corporis,
Tinea pedis - - Type I Diabetes mellitus [81]Hemophilus
influenzae [82]
7 P21281 V-type proton ATPase subunit B IgA
glomerulonephritis -Huntington’s
disease, Rheumatoid
arthritis
Helicobacter pylori
infection, HPV,
Tuberculosis, Viral
carcinogenesis, Vibrio
chloerae, HIV
-Tuberculosis [83],
SARS-CoV-2 [84]
8 P38606 V-type ATP synthase catalytic unit A Psoriasis, Myopathy -
Alzheimer’s disease,
Huntington’s
disease, Parkinson’s
disease, Rheumatoid
arthritis,
HPV, H. pylori,
Tuberculosis, Vibrio
cholerae, Viral
carcinogenesis, HIV
Thyroid eye disease [85]
Influenza H1N1 [86],
Salmonellosis [87],
Rabies virus [88],
SARS-CoV-2 [89],
Tuberculosis [90]
9 P09104 Phosphopyruvate hydratase Psoriasis - - - Autoimmune
encephalomyelitis [91],
Rheumatoid arthritis [92]
Cytomegalovirus
[93]
10 Q16740 ATP-dependent Clp endopeptidase
proteolytic subunit ClpP Psoriasis -
Alzheimer’s disease,
Huntington’s
disease, Parkinson’s
disease
- - Tuberculosis [94]
Microorganisms 2023,11, 2300 9 of 25
Table 2. Cont.
Serial
no. Protein Homolog
PHAROS PATHDIP Literature
Autoimmunity
Pathway Infection Pathway Autoimmune
Pathway Infection Pathway Autoimmune Pathway Infection Pathway
11 P06576 F0F1 ATP synthase subunit beta - -
Alzheimer’s,
Huntington’s,
Parkinson’s,
Non-alcoholic
fatty-acid liver
diseases
Epstein–Barr virus
infection, HBV, HCV,
HPV, Measles,
Legionellosis, E. coli
Autoimmune myocarditis [95]
MERS
coronavirus [96],
Echinococcus
granulosus [97]
12 P25705 F0F1 ATP synthase subunit alpha Alzheimer’s disease ---Sjogren’s syndrome [98],
Crohn’s disease [98],
Ankolysing spondolytis [73]-
13 P49411 Elongation factor Tu - - Huntington’s,
Parkinson’s
HCV, HBV,
Legionellosis,
E. coli,V. cholerae
Sjogren’s syndrome [99],
Crohn’s disease [98],
Ankolysing spondylitis [73]
Streptococcus
pneumoniae [100],
bacteria like Bacillus
anthracis,Francisella
talurensis,
Staphylococcus sp.,
E. coli,H. pylori,
etc. [101]
14 P31153 Methionine adenosyltransferase
Type 2 diabetes
mellitus,
demyelinating
diseases, MODY,
Psoriasis, fatty liver,
or non-alcoholic
steatohepatitis
---Rheumatoid arthritis [102],
Uveitis [103]
Herpes simplex
type 1 [104],
Poxvirus [105], West
Nile virus [106]
15 P10809 Chaperonin GroEL Allergic rhinitis Tuberculosis, HIV - -
Type I Diabetes [103], Juvenile
chronic arthritis [107],
Atherosclerosis [108], Crohn’s
disease [109], Rheumatoid
arthritis [110], Systemic lupus
erythematosus [111], Sjogren
syndrome [112], Hashimoto
thyroiditis [113,114], and
myasthenia gravis [115],
Autism [116]
H. pylori [117],
P. aeruginosa and
S. aureus [118]
16 P50213 Isocitrate dehydrogenase [NAD]
subunit alpha Psoriasis - Huntington’s
disease, Parkinson’s
disease
Epstein–Barr virus,
HCV, Legionellosis Atherosclerosis [119],
Type I diabetes [120]H. pylori [117]
Microorganisms 2023,11, 2300 10 of 25
Table 2. Cont.
Serial
no. Protein Homolog
PHAROS PATHDIP Literature
Autoimmunity
Pathway Infection Pathway Autoimmune
Pathway Infection Pathway Autoimmune Pathway Infection Pathway
17 P31939 Phosphoribosylaminoimidaz-
olecarboxamide formyltransferase
Rheumatoid arthritis,
Psoriatic arthritis,
Erythrodermic
psoriasis, Pustular
psoriasis, Plaque
psoriasis, Diabetes
mellitus, Juvenile
idiopathic arthritis
- - - - C. neoformans [121]
18 P16219 Acyl-CoA dehydrogenase Allergic rhinitis,
Ulcerative colitis,
Crohn’s disease - - - - -
19 Q9BWD1 Acetyl-CoA C-acetyltransferase - - Parkinson’s disease HBV, Viral
carcinogenesis - -
20 Q9H1K1 Fe-S cluster assembly scaffold
protein NifU - - Parkinson’s disease Influenza, HIV -Human respiratory
syncytial virus [122],
SARS-CoV-1 [96]
21 P30566 Adenylosuccinate lyase Psoriasis - - - - Schistosomiasis [123],
Chlamydia sp. [124]
Microorganisms 2023,11, 2300 11 of 25
3.3. Sequence Mimics and Vaccine Design
Out of 68 mimics
≥
10 aminoacids in length, 31 were antigenic (Table 3), with four being
allergenic. Among the 31 antigenic sequences, 14 mimics were identified as IL-4 inducers
(RTTPSVVAFT, DHGKSTLADRL, GGAGYIGSHT, DGTGVRDYIHV, LGIYPAVDPL, IKEGDI-
VKRTG, CIYVAIGQKRST, IETQAGDVSAYIPTNVISITDGQI, EGHPDKICDQISD, TKVDR-
SAAYAAR, GAGQQSRIHCTRLAG, GCGSAIASSS, RGVKGTTGTQASFL, YKRNPMRSER).
Only three peptides (PQIEVTFDIDANGIV, CIYVAIGQKRST, GAGQQSRIHCTRLAG), be-
longing to DnaK, F0F1 ATP synthase subunit alpha, and Adenylosuccinate lyase, were pre-
dicted as non-inducers of IL-6, while all the rest were inducers. Mimics LLLDVTPLSLGIET,
DGTGVRDYIHV, VGERTREGNDLY, EGHPDKICDQISD, and RGVKGTTGTQASFL, be-
longing to DnaK, GalE, F0F1 ATP synthase subunit beta, methionine adenosyltransferase,
and adenylosuccinate lyase, respectively, were predicted as inducers of IL-10.
Table 3. Immunogenicity, allergenicity, and other properties of the antigenic mimics.
Serial
no.
Antigenic
Score Sequence Length
Immuno-
genicity
Score
SVM Score
for Toxicity Hydroph-
obicity
Hydropath-
icity
Hydroph-
ilicity Charge Mol Wt Allergenicity
1. 1.09 GIDLGTTNSCVAV 15 0.65 0.07 0.85 −0.42 −1 1249.59 0.07 No
2. 0.58 RTTPSVVAFT 12 0.35 −0.07 0.4 −0.39 1 1078.36 −0.07 No
3. 0.89 LLLDVTPLSLGIET 12 0.31 0.15 1.18 −0.49 −2 1483.99 0.15 yes
4. 1.39 PQIEVTFDIDANGIV 11 0.29 0.04 0.42 −0.16 −3 1631.04 0.04 yes
5. 0.80 DHGKSTLADRL 12 0.26 −0.33 −1.01 0.66 0.5 1212.48 −0.33 No
6. 1.22 NASGINDGAA 12 0.26 −0.05 −0.22 0.04 −1 889.02 −0.05 No
7. 0.65 GGAGYIGSHT 24 0.21 0.08 −0.13 −0.52 0.5 919.11 0.08 No
8. 1.47 DGTGVRDYIHV 10 0.18 −0.15 −0.42 0.09 −0.5 1231.49 −0.15 No
9. 1.20 LDSRGNPTVEV 10 0.18 −0.23 −0.57 0.39 −1 1186.44 −0.23 yes
10. 1.48 VVEQTGRGER 11 0.14 −0.42 −1.26 0.88 0 1130.37 −0.42 No
11. 0.54 TGIKVVDLLAPY 10 0.12 0.1 0.91 −0.47 0 1288.73 0.1 No
12. 1.25 VGERTREGNDLY 11 0.12 −0.41 −1.48 0.77 −1 1408.66 −0.41 No
13. 1.03 PSAVGYQPTLAT 10 0.11 0.03 0.08 −0.58 0 1204.51 0.03 No
14. 1.04 TKKGSITSVQA 14 0.11 −0.19 −0.38 0.2 2 1119.44 −0.19 no
15. 0.54 LGIYPAVDPL 10 0.09 0.19 0.97 −0.67 −1 1057.4 0.19 yes
16. 0.65 IKEGDIVKRTG 10 0.06 −0.29 −0.69 0.86 1 1215.58 −0.29 No
17. 0.70 CIYVAIGQKRST 12 0.05 −0.13 0.2 −0.23 2 1338.76 −0.13 No
18. 0.53 YTIVVSATAS 10 0.02 0.14 1.22 −0.83 0 1011.27 0.14 No
19. 0.84 IETQAGDVSAYIP
TNVISITDGQI 12 0.01 0.02 0.25 −0.26 −3 2506.13 0.02 No
20. 0.56 DGPMPQTREH 13 −0.05 −0.41 −2.06 0.7 −0.5 1167.4 −0.41 No
21. 1.41 EGHPDKICDQISD 10 −0.05 −0.28 −1.22 0.8 −2.5 1456.73 −0.28 No
22. 1.22 HGGGAFSGKD 12 −0.06 −0.1 −0.84 0.28 0.5 932.1 −0.1 No
23. 0.57 TKVDRSAAYAAR 15 −0.12 −0.35 −0.65 0.51 2 1308.6 −0.35 No
24. 1.75 AGDGTTTATVLA 12 −0.13 0.06 0.52 −0.28 −1 1077.32 0.06 No
25. 1.07 VTLIPGDGIGPE 10 −0.16 0.11 0.41 −0.11 −2 1167.51 0.11 No
26. 1.76 AGDGTTTATVLA 14 −0.17 0.06 0.52 −0.28 −1 1077.32 0.06 No
27. 1.62 GAGQQSRIHCTRLAG 13 −0.18 −0.23 −0.5 0.01 2.5 1554.97 −0.23 No
28. 1.22 NASGINDGAA 10 −0.19 −0.05 −0.22 0.04 −1 889.02 −0.05 No
29. 1.42 GCGSAIASSS 10 −0.20 0.06 0.66 −0.26 0 839.01 0.06 No
30. 1.65 RGVKGTTGTQASFL 11 −0.22 −0.14 −0.24 −0.07 2 1422.81 −0.14 No
31. 0.55 YKRNPMRSER 11 −0.25 −0.77 −2.62 1.19 3 1336.66 −0.77 No
Two sequence mimics (GAGQQSRIHCTRLAG and RGVKGTTGTQASFL) belonging
to the phosphoribosylaminoimidazolecarboxamide formyltransferase and adenylosucci-
nate lyase proteins, respectively, were selected for vaccine design based on antigenicity and
other values. RGVKGTTGTQASFL was predicted as an inducer of IL-4, IL-6, and IL-10,
while GAGQQSRIHCTRLAG was predicted as only an IL-4 inducer. Evolutionary analysis
revealed majority of residues of both sequence mimics are highly conserved (Figure 2).
Microorganisms 2023,11, 2300 12 of 25
Microorganisms 2023, 11, x FOR PEER REVIEW 12 of 25
(A)
(B)
Figure 2. Conservation of sequence mimics from (A) phosphoribosylaminoimidazolecarboxamide
formyltransferase and (B) adenylosuccinate lyase used for vaccine design underlined by red (and
star symbol). Yellow color indicates insufficient data for conservation inference.
Mimic-based vaccine design is an innovative approach that utilizes synthetic pep-
tides or proteins to mimic specific antigens of pathogens [16]. By presenting these mimics
to the immune system, it can generate targeted immune responses against the actual path-
ogen. Here, in total, nine constructs were made, and the non-allergenic ones were retained
for analysis (Supplementary Table S1). Among these, a stable, highly antigenic one, i.e.,
construct C2 was chosen for further downstream processing. It was reverse-translated and
cloned in a pET-28(a)+ vector (Figure 3).
Figure 2.
Conservation of sequence mimics from (
A
) phosphoribosylaminoimidazolecarboxamide
formyltransferase and (
B
) adenylosuccinate lyase used for vaccine design underlined by red (and
star symbol). Yellow color indicates insufficient data for conservation inference.
Mimic-based vaccine design is an innovative approach that utilizes synthetic peptides
or proteins to mimic specific antigens of pathogens [
16
]. By presenting these mimics to the
immune system, it can generate targeted immune responses against the actual pathogen.
Here, in total, nine constructs were made, and the non-allergenic ones were retained
for analysis (Supplementary Table S1). Among these, a stable, highly antigenic one, i.e.,
construct C2 was chosen for further downstream processing. It was reverse-translated and
cloned in a pET-28(a)+ vector (Figure 3).
3.4. Immune Response Simulation
Immune response simulation analysis utilized Parker’s propensity scale to predict
potential epitopes within the vaccine sequence [
125
], which may be recognized by the
immune system, particularly by T cells. Six B-cell epitopes (EQIG, STRGRKCCRRKKEA,
AGGGSRGVKGTTGT, AGGGSGAGQ, GGSHEY, AGGGS) were identified using the Parker
propensity scale. For MHC-I, no binding epitope was present for the A0101 and B0702
allele, while two binding epitopes (IINTLQKYY and AGGGGSHEY) were identified for the
A0102 allele and one (RVRGGRCAV) was identified for B0704. For MHC-II binding, seven
epitopes were predicted for DRB1_0101 (YCRVRGGRC, FVAAWTLKA, WTLKAAAGG,
LKAAAGGGS, VKGTTGTQA, FLGGGSAKF, LERAGAKFV) and two were predicted for
DRB1_0102 (LKAAAGGGS and LERAGAKFV).
Microorganisms 2023,11, 2300 13 of 25
Microorganisms 2023, 11, x FOR PEER REVIEW 13 of 25
Figure 3. The 5083 bp cloned vector of the vaccine construct.
3.4. Immune Response Simulation
Immune response simulation analysis utilized Parker′s propensity scale to predict
potential epitopes within the vaccine sequence [125], which may be recognized by the im-
mune system, particularly by T cells. Six B-cell epitopes (EQIG, STRGRKCCRRKKEA,
AGGGSRGVKGTTGT, AGGGSGAGQ, GGSHEY, AGGGS) were identified using the Par-
ker propensity scale. For MHC-I, no binding epitope was present for the A0101 and B0702
allele, while two binding epitopes (IINTLQKYY and AGGGGSHEY) were identified for
the A0102 allele and one (RVRGGRCAV) was identified for B0704. For MHC-II binding,
seven epitopes were predicted for DRB1_0101 (YCRVRGGRC, FVAAWTLKA,
WTLKAAAGG, LKAAAGGGS, VKGTTGTQA, FLGGGSAKF, LERAGAKFV) and two
were predicted for DRB1_0102 (LKAAAGGGS and LERAGAKFV).
The IgM + IgG population was 140,000 cells/mm3, and only a slight difference was
observed after the second injection (Figure 4A). This suggests that the primary immune
response, characterized by the production of IgM antibodies followed by IgG antibodies
[126,127], was already established after the first injection. The second injection did not
result in a significant increase in the overall IgM + IgG population. IgG1 + IgG2 count was
below 80,000 cells/mm3 but increased to more than 90,000 cells/mm3 after the second in-
jection. This indicates that the secondary immune response, mediated by IgG antibodies
[128], was robustly triggered by the second injection. The overall immune cell counts plat-
eaued after around 200 days, indicating stabilization of the immune response. The B-cell
population remained active after the vaccine injection, up to 100 cells/mm3 (Figure 4B).
Their sustained presence suggests ongoing immune surveillance and the potential for
long-term immune memory [129]. T-helper (TH) cells increased after each vaccine injec-
tion and remained active with a count of 4000 cells/mm3 even after 300 days (Figure 4C).
Figure 3. The 5083 bp cloned vector of the vaccine construct.
The IgM + IgG population was 140,000 cells/mm
3
, and only a slight difference
was observed after the second injection (Figure 4A). This suggests that the primary
immune response, characterized by the production of IgM antibodies followed by IgG
antibodies [126,127]
, was already established after the first injection. The second injection
did not result in a significant increase in the overall IgM + IgG population.
IgG1 + IgG2
count was below 80,000 cells/mm
3
but increased to more than 90,000 cells/mm
3
after the
second injection. This indicates that the secondary immune response, mediated by IgG
antibodies [
128
], was robustly triggered by the second injection. The overall immune cell
counts plateaued after around 200 days, indicating stabilization of the immune response.
The B-cell population remained active after the vaccine injection, up to 100 cells/mm
3
(Figure 4B). Their sustained presence suggests ongoing immune surveillance and the
potential for long-term immune memory [
129
]. T-helper (TH) cells increased after each
vaccine injection and remained active with a count of 4000 cells/mm3even after 300 days
(
Figure 4C
). TH cells play a crucial role in coordinating the immune response by facilitating
communication between various immune cells, and their persistent presence indicates their
continued involvement in supporting and regulating the immune response [
130
,
131
]. The
count of cytotoxic T (TC) non-memory cells fluctuated, possibly indicating their active
participation in eliminating target cells (Figure 4D). In contrast, TC memory cells remained
consistently higher than 100 cells/mm
3
, suggesting the establishment of immunological
memory. Memory cells enable a rapid and specific response upon re-exposure to the anti-
gen, contributing to long-term immunity [
132
]. Natural killer (NK) cells have a role in
innate immune defense [
133
], and their population remained at more than 300 cells/mm
3
for the whole period (Figure 4E). No significant changes were observed in response to
the stress of bacterial proteins. This suggests that the immune system reached a state of
equilibrium and was no longer strongly influenced by the presence of bacterial proteins.
Microorganisms 2023,11, 2300 14 of 25
Microorganisms 2023, 11, x FOR PEER REVIEW 14 of 25
TH cells play a crucial role in coordinating the immune response by facilitating commu-
nication between various immune cells, and their persistent presence indicates their con-
tinued involvement in supporting and regulating the immune response [130,131]. The
count of cytotoxic T (TC) non-memory cells fluctuated, possibly indicating their active
participation in eliminating target cells (Figure 4D). In contrast, TC memory cells re-
mained consistently higher than 100 cells/mm3, suggesting the establishment of immuno-
logical memory. Memory cells enable a rapid and specific response upon re-exposure to
the antigen, contributing to long-term immunity [132]. Natural killer (NK) cells have a
role in innate immune defense [133], and their population remained at more than 300
cells/mm3 for the whole period (Figure 4E). No significant changes were observed in re-
sponse to the stress of bacterial proteins. This suggests that the immune system reached a
state of equilibrium and was no longer strongly influenced by the presence of bacterial
proteins.
Figure 4. Immune system cells released after the C2 vaccine and C. difficile protein (phosphoribosyl-
aminoimidazolecarboxamide formyltransferase and adenylosuccinate lyase) stress, including (A)
immunoglobulins, (B) B cells, (C) TH cells, (D) TC cells, and (E) NK cells.
Figure 4.
Immune system cells released after the C2 vaccine and C. difficile protein (phosphoribo-
sylaminoimidazolecarboxamide formyltransferase and adenylosuccinate lyase) stress, including
(A) immunoglobulins, (B) B cells, (C) TH cells, (D) TC cells, and (E) NK cells.
3.5. Vaccine Interaction
The 3D structure of the vaccine construct was modeled using three tools, in which
SWISS-MODEL achieved the highest percentage (89.60%), indicating a larger portion
of residues in favorable conformation compared to I-TASSER (44.32%) and AlphaFold
(62.75%) (Supplementary Table S2). The QMEANDisCo Global score was additionally used
to assess the global quality of the protein structure, with a lower score suggesting better
overall quality. This metric is used to estimate the quality of a protein tertiary structure by
taking distance constraints into account [
134
]. Again, SWISS-MODEL achieved a score of
0.64 ±0.07, followed by AlphaFold (0.35 ±0.07) and I-TASSER (0.32 ±0.07).
The best-modeled structure by SWISS-MODEL (Supplementary Figure S1A) was used
to map interactions with HLA and TLR-4 receptor (Figure 5). Docking revealed that HLA-A
and HLA-B complexes had relatively lower binding scores compared to the TLR-4 complex,
implying stronger binding affinities between the vaccine construct and HLA receptors
(Supplementary Table S3). The PRODIGY server [
68
] was used to map thermodynamic
Microorganisms 2023,11, 2300 15 of 25
changes in these complexes, where
∆
G (kcal mol
−1
) represents the change in free energy
associated with the formation of the protein–protein complex, while K
d
(M) provided the
equilibrium dissociation constant at 25
◦
C.
∆
G is studied to measure the stability of the
complex, while K
d
is studied to measure the binding affinity, with more negative values
suggesting a stronger interaction [
135
,
136
].
∆
G and K
d
are better predictors of binding
than docking score [
137
] and were, therefore, employed for validation. HLA-B and TLR-4
had a highly negative
∆
G value, indicating stable and stronger interaction compared to
the control. TLR-4 indicated the lowest K
d
value, suggesting a strong binding affinity
and a favorable binding interaction in comparison with the control. This suggests that
the interactions between the vaccine construct and TLR-4 receptor are likely to be more
favorable and specific. This also shows that the ClusPro modeling method performed well
in predicting the binding of the HLA and TLR-4 complex with vaccine construct and is a
reliable approach to determine interactions.
Microorganisms 2023, 11, x FOR PEER REVIEW 15 of 25
3.5. Vaccine Interaction
The 3D structure of the vaccine construct was modeled using three tools, in which
SWISS-MODEL achieved the highest percentage (89.60%), indicating a larger portion of
residues in favorable conformation compared to I-TASSER (44.32%) and AlphaFold
(62.75%) (Supplementary Table S2). The QMEANDisCo Global score was additionally
used to assess the global quality of the protein structure, with a lower score suggesting
beer overall quality. This metric is used to estimate the quality of a protein tertiary struc-
ture by taking distance constraints into account [134]. Again, SWISS-MODEL achieved a
score of 0.64 ± 0.07, followed by AlphaFold (0.35 ± 0.07) and I-TASSER (0.32 ± 0.07).
The best-modeled structure by SWISS-MODEL (Supplementary Figure S1A) was
used to map interactions with HLA and TLR-4 receptor (Figure 5). Docking revealed that
HLA-A and HLA-B complexes had relatively lower binding scores compared to the TLR-
4 complex, implying stronger binding affinities between the vaccine construct and HLA
receptors (Supplementary Table S3). The PRODIGY server [68] was used to map thermo-
dynamic changes in these complexes, where ΔG (kcal mol−1) represents the change in free
energy associated with the formation of the protein–protein complex, while Kd (M) pro-
vided the equilibrium dissociation constant at 25 ℃. ΔG is studied to measure the stability
of the complex, while Kd is studied to measure the binding affinity, with more negative
values suggesting a stronger interaction [135,136]. ΔG and Kd are beer predictors of bind-
ing than docking score [137] and were, therefore, employed for validation. HLA-B and
TLR-4 had a highly negative ΔG value, indicating stable and stronger interaction com-
pared to the control. TLR-4 indicated the lowest Kd value, suggesting a strong binding
affinity and a favorable binding interaction in comparison with the control. This suggests
that the interactions between the vaccine construct and TLR-4 receptor are likely to be
more favorable and specific. This also shows that the ClusPro modeling method per-
formed well in predicting the binding of the HLA and TLR-4 complex with vaccine con-
struct and is a reliable approach to determine interactions.
Figure 5. Vaccine construct (shown in green) interaction with (A) HLA-A, (B) HLA-B, and (C) TLR-
4. Receptors are shown in cyan. (D) Control Tumor necrosis factor ligand superfamily member 11
(RANK-L) and 11A (RANK) from Mus musculus.
Figure 5.
Vaccine construct (shown in green) interaction with (
A
) HLA-A, (
B
) HLA-B, and (
C
) TLR-4.
Receptors are shown in cyan. (
D
) Control Tumor necrosis factor ligand superfamily member 11
(RANK-L) and 11A (RANK) from Mus musculus.
4. Discussion
C. difficile can cause infections, primarily in the colon or large intestine [
138
,
139
].
Infection usually occurs in the immunocompromised [
140
] and in people who have received
antibiotic therapy, when the natural balance of bacteria in the colon is disrupted [
138
].
This allows the bacterium to multiply and produce toxins that cause inflammation and
damage to the intestinal lining. Molecular mimicry allows C. difficile toxin A to bind
glycosphingolipids [
141
]. Mindur et al. have reported cross-reactive epitopes of myelin
basic protein in the surface layer protein of a sub-species of C. difficile [
142
]. Peptide
EQSLITVEGDKASM from the toxin B protein of the species has also been implicated in an
autoimmune disease, namely primary biliary cirrhosis. Alam et al. have reported a collagen
triple-helix repeat family protein in C. difficile as a mimic of the type II collagen protein of
humans [
143
]. The protein is implicated in reactive arthritis, septic arthritis, and rheumatic
symptoms. However, the sequence identity was less than 45%. This is why this protein
was missed by our analysis, as we followed stringent criteria of identity value ≥50%.
The molecular mimics at the whole proteome scale for C. difficile and their involvement
in autoimmune disorders have not yet been mapped. A bioinformatics-based approach
is a useful method to exploit the publicly available data for this purpose. Mapping the
Microorganisms 2023,11, 2300 16 of 25
molecular-mimicry mechanism employed by C. difficile can provide insights into the viru-
lence and pathogenesis, as well as offer potential targets for the development of therapeutic
interventions, such as vaccines or drugs that can disrupt the interaction between the bacte-
rial mimics and the host receptors, thereby preventing or reducing the severity of C. difficile
infections. For this purpose, we obtained the proteome of the reference strain of C. difficile
S-0253 (n= 3732 proteins). Among these, only 23 proteins were homologous to humans,
having
≥
50% sequence identity. The structural superposition of these proteins revealed
several regions with organizational and fold similarity (Table 1). The ATP-dependent Clp
endopeptidase proteolytic subunit ClpP exhibited the lowest RMSD value, indicating a
high degree of structural similarity between the human and bacterial homologs. It plays
a crucial role in maintaining protein homeostasis in conjunction with chaperones by de-
grading misfolded or damaged proteins. The peptide sequence QIERDTERDRFLSAPEAV
of E. coli ClpP has been previously implicated in autoimmune biliary liver cirrhosis [
144
].
The highest number of peptide mimics were observed in energy-generating F0F1 ATP
synthase and heat-shock protein DnaK. Zhang et al. have reported increased activity of
ATP synthase in the autoimmune neuromyelitis optica spectrum disorder [
145
]. DnaK
has previously been implicated in molecular mimicry of other pathogenic bacteria like
Streptococcus pneumoniae [
16
] and Salmonella typhi [
15
]. DnaK and other molecular chaper-
ones like GroEL have been implicated in multiple autoimmune disorders [
116
,
146
–
151
].
DnaK has been associated with autoimmune atrophic gastritis caused by H. pylori [
152
],
while
Qeshmi et al.
have reported its presence in multiple sclerosis as well [
153
]. Overall,
rheumatoid arthritis, Alzheimer’s disease, psoriasis, Huntington’s disease, and Parkinson’s
disease emerged as the primary autoimmune disease associated with multiple homologs.
Among infectious disease mapping, tuberculosis was the most common infection
linked to the homologous proteins of C. difficile, suggesting a potential role of these proteins
in the immune response against mycobacterial infections. A varying fraction of C. difficile
infection in tuberculosis patients has been reported previously, ranging from ~3 cases
per 1000 adults in Korea [
154
] to ~70 cases per 1000 individuals in South Africa [
155
].
Obuch-Woszczaty´nski reported C. difficile-mediated diarrhea in tuberculosis patients when
rifampicin was used as part of their treatment regimen [
156
]. Rifampicin can contribute to
the development of resistance against this antibiotic in C. difficile, which in turn poses a risk
to the effectiveness of tuberculosis treatment. The rate of C. difficile infection in tuberculosis
patients tends to be higher in aged people compared to younger adults [
157
]. This bacterium
has also been identified as one of the predominant pathogens causing diarrheal illness
in HIV-seropositive individuals, with two times higher prevalence compared to HIV-
seronegative people [158].
Mimic-based vaccine design is an innovative approach that utilizes synthetic pep-
tides or proteins to mimic specific antigens of pathogens [
16
]. By presenting these mimics
to the immune system, targeted immune responses against the actual pathogen can be
generated [159,160]
. Hence, a stable and highly antigenic vaccine construct was designed
using two peptide mimics identified in this study. It was cloned into a pET-28(a)+ vector
and immune response was assessed using in silico simulations. The primary immune re-
sponse, characterized by IgM production followed by IgG production, was established after
the first vaccine injection. The secondary immune response, mediated by IgG antibodies,
was robustly triggered by the second injection. The immune cell counts plateaued after
approximately 200 days, indicating stabilization of the immune response. B cells remained
active, suggesting ongoing immune surveillance and potential long-term immune memory.
TH cells increased after each vaccine injection and remained active even after 300 days, indi-
cating their continued involvement in supporting and regulating the immune response. TC
cells showed fluctuating counts, possibly indicating their active participation in eliminating
target cells, while TC memory cells remained consistently higher, indicating the establish-
ment of immunological memory. NK cells, involved in innate immune defense, maintained
a stable population throughout the study. Hence, a dynamic and robust immune response
occurred following the vaccine injections. The presence of specific antibody populations
Microorganisms 2023,11, 2300 17 of 25
sustained B-cell activity, and a stable count of T cells, NK cells, and EP cells indicated an
effective immune response against the target antigen. The establishment of immunological
memory and the plateauing of immune cell counts suggests a stable and functional immune
system capable of long-term protection. No significant changes were observed in response
to bacterial protein stress, suggesting that the immune system reached an equilibrium state
and was no longer strongly influenced by the presence of bacterial proteins. However,
computational predictions are not without limitations, and although mimic-based vaccine
design and immune response simulation helps accelerate the vaccine-development process,
providing insights into immune responses and generating hypotheses for further exper-
imental investigations, it also has limitations. The foremost limitation is their accuracy
and adverse response mapping due to inadequate input of variables and complexities of
the immune system [
16
]. The local tissue microenvironment and factors such as blood
flow, physical barriers, and cellular interactions can influence immune responses but may
be overlooked or simplified in simulations. Additionally, the pathogens tend to mutate,
and they may not be workable in the real-world scenario due to the altered genetics of the
microbe. To overcome this, we have tried to focus on conserved epitopes of the antigenic
proteins, but the immune evasion mechanism may be altered with time and the epitope may
fail to generate an immune response. Thus, the in silico vaccine design is a valuable tool
for narrowing down potential candidates and reducing research costs and timelines, but it
is just the initial step in the vaccine-development process. Real laboratory-based testing is
essential to validate and refine these designs, ensuring that the vaccine candidates are safe
and effective in a real-world setting. Moreover, the in silico outcomes may not necessarily
mirror those in a real laboratory as they typically do not consider the environmental factors
the laboratory experiments take into account and can be overly optimistic or pessimistic
predictions. Biological systems can also have unexpected interactions and feedback loops
that are difficult to predict computationally. These interactions may only become apparent
through real-world experimentation. The limitations of in silico modeling highlight the
need for a comprehensive and rigorous approach to vaccine development that combines
computational methods with empirical testing.
In summary, using bacterial peptides as structural templates for vaccine design is
a valid approach, but the risk of triggering an autoimmune reaction prevails [
43
]. It is
crucial to assess the risk associated with the potential induction of autoimmune response
after administering the vaccine. It is also important to acknowledge that factors such as
prior exposure to antigens and the presence of known autoantibodies, genetic predisposi-
tion of individuals, and other variables may contribute to the initiation of cross-reactive
responses [161]
. However, the immune system is normally fortified with multiple lay-
ers of protective mechanisms, which work in concordance to prevent the occurrence of
autoimmunity [162]
in response to vaccines. The mammalian immune system has also
evolved an intricate repertoire of mechanisms to discern self- from non-self-antigens, pri-
marily through the establishment of central immune tolerance [
163
,
164
], thus acting as a
safeguard against autoimmunity. Moreover, molecular mimics, such as those utilized in
vaccine design, tend to confer a reduced risk of provoking autoimmune reactions. This
reduced risk can be attributed to the relatively lower immunological pressure imposed
by these mimics when compared to actual pathogens, which inherently exert additional
immune pressure due to the manifestation of the disease itself.
The regulatory T cells (Tregs) also play a pivotal role in modulating the immune
response on encountering the antigen (from the vaccine), ensuring its proportionality and
averting the development of autoimmunity [
161
]. These regulatory processes are further
reinforced by natural checkpoints orchestrated by cytokines and other signaling molecules.
These checkpoints serve as crucial regulators, fine-tuning the intensity and duration of
the immune response [
165
], thus mitigating the risk of overly aggressive reactions that
could harm the body tissues, instead of just the pathogen. Furthermore, cells can release
immunosuppressive signals, including TGF-beta and IL-10, effectively dampening immune
responses and preventing unwarranted reactivity against self-antigens [
166
,
167
]. Antigen-
Microorganisms 2023,11, 2300 18 of 25
presenting cells (APCs) are another player in the immune system that present foreign
antigens to immune cells, while self-antigens are less likely to incite a response [
168
].
As a result, the potential for cross-reactivity leading to autoimmune triggers is notably
diminished in the context of mimic-based vaccine design, thus underscoring the safety and
efficacy of this approach.
Additionally, the prudent approach of pre-vaccination testing for genetically predis-
posed individuals and the adoption of nanocarriers as alternatives to lipid adjuvants hold
promise in mitigating the risk of cross-reactivity and triggering autoimmunity [
163
,
169
].
Furthermore, comparative evaluations of diverse vaccine formulations, concerning their
capacity to induce or exacerbate pathology in relevant models, can yield valuable base-
line data about the efficacy and safety of these vaccines. The inclusion of comprehensive
immunological investigations, including autoimmune serology, within phases I to III of
clinical trials is warranted to holistically assess vaccine responses. Hence, mimic-based
innovative vaccine design, alongside the risk assessment and consideration of the inherent
protective mechanisms of the immune system, offers a promising pathway toward vaccines
that can effectively combat pathogens while sparing the human self-tissues from harm.
5. Conclusions
Investigation of autoimmune pathways associated with the identified human ho-
mologs of C. difficile revealed interesting connections to autoimmune diseases. The iden-
tified associations with autoimmune diseases, particularly rheumatoid arthritis, warrant
further investigation into the underlying mechanisms of autoimmunity and the specific
roles of these homologous proteins in disease pathogenesis. The structural similarity be-
tween human and C. difficile homologs suggests the possibility of using these bacterial
proteins as structural templates for vaccine design and development. Understanding the
conserved regions and functional motifs in these proteins may also aid in the design of
therapeutics targeting C. difficile and related human diseases. We fabricated a vaccine con-
struct using conserved, safe, and immunogenic mimics. It demonstrated good response in
silico, but computational predictions have limitations, and we imply experimental research
to complement or refute our findings.
Supplementary Materials:
The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/microorganisms11092300/s1, Figure S1: (A) 3D structure of
vaccine construct using Swiss-Model (B) Ramachandran plot of the model; Table S1: Non-allergenic
vaccine constructs against C. difficile; Table S2: 3D structure statistics of the vaccine construct, us-
ing various tools; Table S3: HLA and TLR receptor interaction statistics with the designed vaccine
construct. Non-bonded contacts can involve attractive forces, such as van der Waals interactions
and hydrophobic interactions, or repulsive forces, such as steric clashes. No disulphide bond was
detected in any interaction.
Author Contributions:
S.A., Z.B., M.M.M. and A.A. conceptualized the study. Z.B., M.A.A. and
H.H.A. performed the experiments. N.A.A., M.M.M. and S.A. curated the data. S.A., Z.B., M.M.M.
and A.A. wrote and edited the original draft. H.H.A., N.A.A. and M.A.A. reviewed and edited the
draft. A.A., N.A.A. and M.A.A. visualized the study. S.A., Z.B., M.M.M. and H.H.A. obtained the
funding. S.A., Z.B. and M.M.M. administered the project. M.M.M. supervised the study. All authors
have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Deanship of Scientific Research at Najran University
Research Support Program under the grant program code NU/DRP/MRC/12/5.
Data Availability Statement:
All the data used or generated in this study are provided as an accession
number or relevant information as tables in the manuscript.
Acknowledgments:
The authors are also thankful to the Deanship of Scientific Research at Na-
jran University for funding this work, under the General Research Funding program grant code
NU/DRP/MRC/12/5.
Conflicts of Interest: The authors declare that they have no conflict of interest.
Microorganisms 2023,11, 2300 19 of 25
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