An Immunoinformatics Study to Explore HTL Epitopes for Fungal Pathogen Aspergillus lentulus

doi:10.21203/rs.3.rs-1746294/v1

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Research Article

An Immunoinformatics Study to Explore HTL Epitopes for Fungal Pathogen Aspergillus lentulus

https://doi.org/10.21203/rs.3.rs-1746294/v1

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published 23 Jan, 2024

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Purpose

Aspergillus lentulus causes invasive aspergillosis and responsible for harmful impact on human health, as targets pulmonary sites and causes impaired respiration. Aspergilli mycotoxins too have a significant negative influence on human and animal health. Here, we found T-cell epitopes from proteome of Aspergillus lentulus and targeted them against MHC-Class II HLA determinants to predict putative immunogenic nature. Here we used best immunoinformatics tools like PatchDock, NetMHCII server, Molprobity, ProSA, ToxinPred and ProtParam to conduct the epitopes screening and to construct a novel scheme of epitope structural and functional working. The immunoinformatics study was found to be very novel method in rapid designing of vaccines; it is an efficient way to reduce hit and trial approaches. Here we finalized four epitopes GENGVNQLQGTTLGR, ITNYFSTQKLGSSSQ, YITNYFSTQKLGSSS, and PADYTSTARALSLPM, that interacts with MHC Class II alleles and were found to show better population coverage and stability in the docked complexes. In current study we targeted membrane proteins of Aspergillus lentulus fungal pathogen, it was very less studied but found to be involved in producing harmful pulmonary impacts on human and animals. The immunoinformatics study was found to be very novel method in rapid designing of vaccines; it is an efficient way to reduce hit and trial approaches. Here we finalized four epitopes against this harmful pathogen that can elicit immune responses in host.

Aspergillus lentulus

Molecular Docking

Ramachandran plot

Epitopes

Immunoinformatics

Aspergillus lentulus induced invasive aspergillosis is linked with a significant death rate. It belongs to Ascomycota division of kingdom fungi under class Eurotiomycetes. Aspergillus lentulus, in fact, has emerged as a major disease in a number of nations, including the United States, European countries, Brazil, Japan, and Korean peninsula. In comparison to its close relative A. fumigatus, it has smaller conidial heads with smaller vesicles, and it cannot live above 118°F (Balajee et al., 2005). Since its discovery as a new species in 2005, it has been assumed that it reproduces entirely asexually. Swilaiman et al., 2013 discovered that A. lentulus has an operational sexual cycle in 2013. Aspergillus fumigatus, formerly thought to be a single species, is really a group of phenotypically similar but molecularly separate "cryptic" species. A. lentulus and A. thermomutatus, among others, have been identified thanks to molecular typing. Cryptic species are responsible for 3–15 percent of invasive aspergillosis infections; however they go unnoticed by laboratories that rely only on phenotypic identification (Sugui et al., 2014, Takeda et al., 2021). Because some of these species are resistant to triazoles and amphotericin B, proper identification is critical (Takeda et al., 2021).

In the immunosuppressed sarcoidosis individual, this microorganism was linked to aspergillosis and found as a coloniser in the airway. This is consistent with the pattern documented for all Aspergillus species, which produce illness along a range from airway colonisation to tissue invasion, depending on the patient's risk. Aspergillus lentulus has been linked to disease in a variety of people, including transplant and oncology patients, as well as people with chronic respiratory disease, such as cystic fibrosis (Alhambra et al., 2008). In the many recent studies it was found that immunoinformatics study was successful in determining epitopes against Nervous necrosis virus (Joshi et al., 2021), Human cytomegalovirus (Akhtar et al., 2021a), Canine circovirus (Jain et al., 2021), Tropheryma whipplei (Joshi et al., 2022a), Ortho hanta virus (Joshi et al., 2022b) and Ebola virus (Saraswat et al., 2012). Here, we found T-cell epitopes from proteome of Aspergillus lentulus and targeted them against MHC-Class II HLA determinants to predict putative immunogenic nature.

Protein selection

Membrane proteins for Aspergillus lentulus was retrieved from NCBI-Genbank database. The allergenicity for these selected proteins was calculated by using AllergenFP tool (Dimitrov et al., 2014), the allergenicity was calculated by TSI (tanimoto similarity index).

Epitope selection

Epitopes selected for all these proteins by using the server NetMHCII pan ver.4.0 (Reynisson et al., 2020), this server predicts epitopes on the basis scores generated on the basis of neural network algorithms. Here, we considered DRB01_0101, DRB01_0301, and DRB01_1101 MHC class II allelic determinants and cross-verified binding of predicted epitopes by strong binding indicators at binding level section.

Antigenicity Prediction

Antigenicity was determined for all epitopes by using VaxiJen Ver 2.0 server (Doytchinova & Flower., 2007), the threshold for the fungal pathogen was considered as greater than equal to 0.5.

Physiochemical properties Evaluation

ToxinPred tool was used to calculate SVM scores for 15 residue epitopes to check toxicity (Gupta et al., 2013). ProtParam tool was used to calculate instability index, GRAVY score, isoelectric point, and half life of epitopes. This assisted in further screening of epitopes on the basis of stability.

3D-Structure designing of epitopes

Three dimensional structures of epitopes were designed by deploying ab-initio approaches, where we used the Pepfold ver 3.5 server (Thévenet et al., 2012). Here individual amino acid propensity was determined by hidden markov model algorithms that determine individual residue coil, helix, and sheet patterns for epitope structure.

Retrieval of Structure for HLA DRB allelic determinants

Structure of HLA-DRB allelic determinants were retrieved from RCSB-PDB database. HLA-DRB01_0101, DRB01_0301, and DRB01_1101 PDB ID’s were:

Molecular Docking

Docking between selected epitopes and MHC class II HLA determinants was conducted by using PatchDock server (Schneidman-Duhovny et al., 2005), and further screening of best docked model was conducted by using FireDock tool.

Best docked complexes structure validations

Structural validations were conducted by using ProSA webserver and Molprobity server. Z-score for docked complexes was calculated by ProSA server (Wiederstein & Sippl., 2007) and Ramachandran plot for the docked complexes was calculated by using Molprobity tool (Chen et al.,2010).

Population Coverage analysis

Population coverage analysis was conducted for selected epitopes for Worldwide, Europe, Asia, North America, Africa, South America and Indian subcontinent populations by using IEDB tool.

Allergenicity for selected proteins

Allergenicity was calculated on the basis of tanimoto similarity scores for the retrieved membrane proteins for the fungi Aspergillus lentulus (see Table 1).

Epitopes screening

Epitopes from membrane protein were screened by deploying NetMHCII Pan server, here 15 residue length epitopes showing strong binding to MHC class II molecules were selected on the basis of scores. Also VaxiJen tool was used to calculate antigenicity of these screened epitopes, where threshold was set to > 0.5 (see Table 2).

Table 1

List of proteins and there antigenicity analysis (TSI: Tanimoto Similarity index)
Name of protein	Accession number	Amino acids	TSI/ Allergenicity
pheromone-regulated membrane protein 10 [Aspergillus lentulus]	GAQ09275.1	776	0.86 (non-allergen)
mitochondrial inner membrane protein OXA1	GAQ08952.1	643	0.84 (non-allergen)
peroxisomal membrane protein pex16	XP_033417300.1	382	0.8 (non-allergen)
ER membrane protein complex subunit 3	XP_033415737.1	268	0.81 (non-allergen)
golgi apparatus membrane protein tvp38	XP_033410381.1	418	0.83 (non-allergen)

Table 2

Epitopes selection for MHC Class II HLA allelic determinants (S: Selected, NS: Not Selected)
Membrane ProteinAccession no.	HLA-Allele Type MHC Class II	Epitopes	NetMHC Score	Binding Level	VaxiJen Score (Threshold: Greater than equal to 0.5)	Inference
GAQ09275.1	DRB1_0101	LEEYMQMTARVLDIE	0.955525	Strong	1.1966	S
		RLEEYMQMTARVLDI	0.924136	Strong	0.5053	S
		EEYMQMTARVLDIEA	0.910441	Strong	1.0458	S
		HRLEEYMQMTARVLD	0.891315	Strong	-0.1777	NS
		PGIFTMVPSGLASSG	0.877098	Strong	-0.6869	NS
		EQSNLHMAVAAEAQE	0.826153	Strong	0.6041	S
		IPGIFTMVPSGLASS	0.818861	Strong	0.2281	S
		QSNLHMAVAAEAQEL	0.792673	Strong	1.1084	S
	DRB1_1101	ITNYFSTQKLGSSSQ	0.958569	Strong	2.0371	S
		YITNYFSTQKLGSSS	0.939975	Strong	1.9883	S
		TNYFSTQKLGSSSQV	0.903268	Strong	2.2594	S
		GYITNYFSTQKLGSS	0.871328	Strong	2.1383	S
		GGGVLSALLRLQASQ	0.83079	Strong	0.5757	S
		PGIFTMVPSGLASSG	0.808024	Strong	-0.6869	NS
GAQ08952.1	DRB1_0101	GELFAHEAAFRLGWS	0.950392	Strong	0.031	NS
		PPSIWGILRGAAINL	0.936982	Strong	0.5521	S
		GENGVNQLQGTTLGR	0.93138	Strong	0.9847	S
		KPPSIWGILRGAAIN	0.924481	Strong	0.0368	NS
		PSIWGILRGAAINLI	0.923732	Strong	-0.1004	NS
		FGELFAHEAAFRLGW	0.922147	Strong	-0.1999	NS
	DRB1_0301	DLKLAADYERRRKEE	0.876569	Strong	1.7764	S
		KDLKLAADYERRRKE	0.845791	Strong	1.7804	S
		EKDLKLAADYERRRK	0.776732	Strong	2.2177	S
		LKLAADYERRRKEEE	0.716656	Strong	1.3457	S
	DRB1_1101	EEDWKREERNHARRE	0.903557	Strong	0.0344	NS
		EEEDWKREERNHARR	0.879801	Strong	0.0456	NS
		SLFMYLSIRKGGENG	0.834108	Strong	1.1529	NS
		LFMYLSIRKGGENGV	0.804056	Strong	0.9978	NS
XP_033417300.1	DRB1_1101	TELLWEMIARRRGEK	0.982591	Strong	1.592	NS
		ELLWEMIARRRGEKV	0.981921	Strong	1.1897	NS
		YTELLWEMIARRRGE	0.964971	Strong	1.866	NS
		LLWEMIARRRGEKVR	0.941881	Strong	1.0071	NS
XP_033415737.1	DRB1_0101	KERLSLLRGVNLRNN	0.971874	Strong	0.3445	NS
		SKERLSLLRGVNLRN	0.959346	Strong	0.4674	S
		ESKERLSLLRGVNLR	0.900876	Strong	0.8977	S
		ERLSLLRGVNLRNNA	0.891784	Strong	0.0615	S
	DRB1_0301	VEQTILRDPALFYWI	0.899373	Strong	0.5561	NS
		GVEQTILRDPALFYW	0.883677	Strong	0.0119	NS
		EQTILRDPALFYWIL	0.871181	Strong	0.6722	NS
XP_033410381.1	DRB1_0101	KGWILYATATVLGST	0.827857	Strong	0.0956	NS
	DRB1_0101	WKGWILYATATVLGS	0.777526	Strong	0.1067	NS
	DRB1_0301	RRSLQADHAAHRPHG	0.9745	Strong	0.7799	S
	DRB1_0301	IRRSLQADHAAHRPH	0.970025	Strong	0.4108	NS
	DRB1_1101	MPADYTSTARALSLP	0.956813	Strong	1.4613	S
	DRB1_1101	PADYTSTARALSLPM	0.954347	Strong	1.4738	S

Physiochemical screening of epitopes

Epitopes were screened further by deploying physiochemical analysis, which includes instability index, toxicity analysis, isoelectric point calculation and Half-life prediction. Here we selected 9 epitopes (see Fig. 1) from 22 epitopes that were selected in previous steps on the basis of stability criterion (see Table 3).

Table 3

Physiochemical analysis of selected epitopes for final selection (S: Selected, NS: Not Selected)
EPITOPES	SVM Score/ Toxicity	Half-life	Isoelectric Point	GRAVY-Score	Instability Index	Finalised Selection
LEEYMQMTARVLDIE	-1.71 Non Toxic	5.5 hour	4	-0.14	82.71 Unstable	NS
RLEEYMQMTARVLDI	-0.49 Non Toxic	1 hour	4.68	-0.207	53.41 Unstable	NS
EEYMQMTARVLDIEA	-1.83 Non Toxic	1 hour	4	-0.273	82.71 Unstable	NS
EQSNLHMAVAAEAQE	-0.98 Non Toxic	1 hour	4.24	-0.527	72.53 Unstable	NS
IPGIFTMVPSGLASS	-0.93 Non Toxic	20 hour	5.52	1.093	40.27 Unstable	NS
QSNLHMAVAAEAQEL	-1.01 Non Toxic	0.8 hour	4.51	-0.04	59.69 Unstable	NS
ITNYFSTQKLGSSSQ	-1.19 Non Toxic	20 hour	8.59	-0.64	27.15 Stable	S
YITNYFSTQKLGSSS	-1.03 Non Toxic	2.8 hours	8.5	-0.493	14.31 Stable	S
TNYFSTQKLGSSSQV	-1.09 Non Toxic	7.2 hours	8.26	-0.66	22.12 Stable	S
GYITNYFSTQKLGSS	-1.03 Non Toxic	30 hour	8.5	-0.467	4.19 Stable	S
GGGVLSALLRLQASQ	-1.29 Non Toxic	30 hour	9.75	0.58	73.20 Unstable	NS
PPSIWGILRGAAINL	-1.07 Non Toxic	20 hour	10.18	0.733	36.80 Stable	S
GENGVNQLQGTTLGR	-1.39 Non Toxic	30 hour	6	-0.88	5.33 Stable	S
DLKLAADYERRRKEE	-1.12 Non Toxic	1.1 hour	6.28	-1.927	85.43 Unstable	NS
KDLKLAADYERRRKE	-1.27 Non Toxic	1.3 hour	9.52	-1.953	63.70 Unstable	NS
EKDLKLAADYERRRK	-1.38 Non Toxic	1 hour	9.53	-1.953	63.70 Unstable	NS
LKLAADYERRRKEEE	-0.99 Non Toxic	5.5 hour	6.31	-1.927	101.7 Unstable	NS
SKERLSLLRGVNLRN	-1.60 Non Toxic	1.9 hour	11.71	-0.7	37.58 Stable	S
ESKERLSLLRGVNLR	-1.67 Non Toxic	1 hour	10.74	-0.7	42.19 Unstable	NS
ERLSLLRGVNLRNNA	-1.50 Non Toxic	1 hour	11.7	-0.5	37.58 Stable	S
RRSLQADHAAHRPHG	-0.59 Non Toxic	1 hour	11.7	-1.58	93.15 Unstable	NS
MPADYTSTARALSLP	-1.57 Non Toxic	30 hour	5.59	-0.04	52.99 Unstable	NS
PADYTSTARALSLPM	-1.16 Non Toxic	20 hour	6.26	-0.04	18.67 Stable	S

Molecular Docking Analysis

Docking was conducted between finalized epitopes and MHC Class II molecules and atomic contact energy was calculated for each docked complex. Out of the nine docked complexes, only four complexes show most negative values for atomic contact energy (Table 4). Four best docked complexes were identified on the basis of atomic contact energy (Fig. 2).

Table 4

Molecular docking scores for the interaction complex between ligands and receptors
HLA-DRB Allele	Epitopes	Atomic contact energy (Kcal/mol)
DRB1_0101	SKERLSLLRGVNLRN	-2.7
	ERLSLLRGVNLRNNA	-2.4
	PPSIWGILRGAAINL	-3.1
	GENGVNQLQGTTLGR	-8.6
DRB1_1101	ITNYFSTQKLGSSSQ	-8.4
	YITNYFSTQKLGSSS	-8.1
	TNYFSTQKLGSSSQV	-4.2
	GYITNYFSTQKLGSS	-3.1
	PADYTSTARALSLPM	-7.8

Best docked complexes structure validations

A structural validation for best docked complexes was conducted by calculating Ramachandran plot analysis and Z-Scores by deploying molprobity and ProSA server. Z-Scores for 4 selected docked complexes are as follows a. HLA-DRB01_0101and GENGVNQLQGTTLGR: -5.71 b. HLA-DRB01_1101 and ITNYFSTQKLGSSSQ: -5.66 c. HLA-DRB01_1101 and YITNYFSTQKLGSSS: -5.66 d. HLA-DRB01_1101 and PADYTSTARALSLPM: -5.66 as per the calculated z-plots for the docked complexes (see Fig. 3). Whereas Ramachandran plots for the docked complexes indicates that these structures shows more than 95% residues under favorable and allowed regions (see Fig. 4).

Population Coverage Analysis

Population coverage of these four epitopes mapped with MHC Class II allelic determinants; all populations across the major continents and India shows an average share of 28 percent along with standard deviation of 10.63 (see, Table 5). Figure 5 shows population coverage for selected epitopes GENGVNQLQGTTLGR, ITNYFSTQKLGSSSQ, YITNYFSTQKLGSSS, and PADYTSTARALSLPM.

Table 5

Population coverage analysis for considered 4 epitopes across the Globe
population/area	Class II
population/area	coverage	average_hit	pc90
Central Africa	27.35%	1.14	0.55
Central America	7.76%	0.31	0.43
East Africa	41.32%	1.77	0.68
East Asia	20.88%	0.87	0.51
Europe	44.87%	1.97	0.73
India	34.75%	1.49	0.61
North Africa	33.40%	1.4	0.6
North America	42.42%	1.84	0.69
Northeast Asia	15.38%	0.63	0.47
Oceania	21.41%	0.86	0.51
South Africa	25.52%	1.02	0.54
South America	17.37%	0.72	0.48
South Asia	35.19%	1.51	0.62
West Africa	26.33%	1.1	0.54
Average	28.14	1.19	0.57
Standard deviation	10.63	0.48	0

Aspergillus fumigatus is abundantly available in the environment, have tiny size that allows for better infiltration in narrow airways, and capacity to thrive at 37 degrees Celsius, Aspergillus fumigatus is the most frequent pathogenic globally. Aspergillus lentulus is a sporulating relative of Aspergillus fumigatus which was first identified in 2005 (Ahmed et al., 2022). It looks similar to Aspergillus fumigatus visually; however they have different mitochondrial cytochrome b (mtcytb) gene sequences and a randomly amplified polymorphic DNA PCR signature (Balajee et al., 2009). Mycotoxins are secondary metabolites produced by fungi that are toxic to both animals and humans. The most prevalent mycotoxin-producing genera are Aspergillus, Fusarium, and Penicillium. Aspergilli mycotoxins have a significant negative influence on human and animal health. Aflatoxin, sterigmatocystin, ochratoxins, fumonisins, patulin, gliotoxin, and cyclopiazonic acid are the most common my-cotoxins generated by Aspergillus species (Varga et al., 2015). In this current study we determine epitopes from this least studied fungi Aspergillus lentulus that can bind to MHC Class II molecules and are capable of inducing immune responses in the host organism. Likewise in many previous studies on fungal pathogens such as Candida aurius (Akhtar et al., 2021b), it was noted here also that epitopes from the membrane proteins were very effective in inducing immune responses in case of Aspergillus lentulus, as these epitopes holds structural stability and show interactions with HLA-DRB allelic determinants of MHC Class II molecules. In recent immunoinformatics studies the similar methods was deployed for determining epitopes against Nervous necrosis virus (Joshi et al., 2021), Human cytomegalovirus (Akhtar et al., 2021a), Canine circovirus (Jain et al., 2021), Tropheryma whipplei (Joshi et al., 2022a), and Ortho hanta virus (Joshi et al., 2022b). We finalized four epitopes GENGVNQLQGTTLGR, ITNYFSTQKLGSSSQ, YITNYFSTQKLGSSS, and PADYTSTARALSLPM in this study that can elicit immune response in host organisms.

In current study we targeted membrane proteins of Aspergillus lentulus fungal pathogen, it was very less studied but found to be involved in producing harmful pulmonary impacts on human and animals. The immunoinformatics study was found to be very novel method in rapid designing of vaccines; it is an efficient way to reduce hit and trial approaches. Here we finalized four epitopes GENGVNQLQGTTLGR, ITNYFSTQKLGSSSQ, YITNYFSTQKLGSSS, and PADYTSTARALSLPM, that interacts with MHC Class II alleles and were found to show better population coverage and stability in the docked complexes.

Acknowledgement

All authors are thankful towards Department of biotechnology, Rama University, Kanpur, for providing efficient computational facility for conduction of research work.

Ethical Statement

Not applicable as study is purely in-silico work.

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No competing interests reported.

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An Immunoinformatics Study to Explore HTL Epitopes for Fungal Pathogen Aspergillus lentulus

Status:

Journal Publication

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Abstract

Purpose

Figures

Introduction

Methodology

Protein selection

Epitope selection

Antigenicity Prediction

Physiochemical properties Evaluation

3D-Structure designing of epitopes

Retrieval of Structure for HLA DRB allelic determinants

Molecular Docking

Best docked complexes structure validations

Population Coverage analysis

Results

Allergenicity for selected proteins

Epitopes screening

Physiochemical screening of epitopes

Molecular Docking Analysis

Best docked complexes structure validations

Population Coverage Analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

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