Increasing fungal infections in immunocompromised hosts are a growing concern for global public health. Along with treatments, preventive measures are required. The emergence of reverse vaccinology has opened avenues for using genomic and proteomic data from pathogens in the design of vaccines. In this work, we present a comprehensive collection of various computational tools and databases with potential to aid in vaccine development. The ongoing pandemic has directed attention toward the increasing number of mucormycosis infections in COVID-19 patients. As a case study, we developed a computational pipeline for assisting vaccine development for mucormycosis. We obtained 6 proteins from 29,447 sequences from UniProtKB as potential vaccine candidates against mucormycosis, fulfilling multiple criteria. These criteria included potential characteristics, namely adhesin properties, surface or extracellular localization, antigenicity, no similarity to any human proteins, nonallergenicity, stability in vitro, and expression in fungal cells. These six proteins were predicted to have B cell and T cell epitopes, proinflammatory inducing peptides, and orthologs in several mucormycosis-causing species. These data could aid in vaccine development against mucormycosis for at-risk individuals.
WalshTJ, GamaletsouMN, McGinnisMR, HaydenRT, KontoyiannisDP: Early clinical and laboratory diagnosis of invasive pulmonary, extrapulmonary, and disseminated mucormycosis (zygomycosis). Clin Infect Dis, 2012; 54(Suppl 1):S55–S60.
9.
Fungal Diseases and COVID-19 | CDC [Internet]. 2021 [cited August 31, 2021]. https://www.cdc.gov/fungal/covid-fungal.html
10.
MahalaxmiI, JayaramayyaK, VenkatesanD, SubramaniamMD, RenuK, VijayakumarP, NarayanasamyA, GopalakrishnanAV, KumarNS, SivaprakashP, Sambasiva RaoKRS, VellingiriB: Mucormycosis: An opportunistic pathogen during COVID-19. Environ Res, 2021; 201:111643.
11.
SelarkaL, SharmaS, SainiD, SharmaS, BatraA, WaghmareVT, DileepP, PatelS, ShahM, ParikhT, DarjiP, PatelA, GoswamiG, ShahA, ShahS, LathiyaH, ShahM, SharmaP, ChopraS, GuptaA, JainN, KhanE, SharmaVK, SharmaAK, ChanACY, OngJJY: Mucormycosis and COVID-19: An epidemic within a pandemic in India. Mycoses, 2021; 64:1253–1260.
12.
RodenMM, ZaoutisTE, BuchananWL, KnudsenTA, SarkisovaTA, SchaufeleRL, SeinM, SeinT, ChiouCC, ChuJH, KontoyiannisDP, WalshTJ: Epidemiology and outcome of zygomycosis: A review of 929 reported cases. Clin Infect Dis, 2005; 41:634–653.
13.
Mucormycosis [Internet]. [Cited February 14, 2022]. https://www.who.int/india/emergencies/coronavirus-disease-(covid-19)/mucormycosis
14.
Mucormycosis in COVID-19: A Clinico-Microbiological Dilemma [Internet]. Kauvery Hospital. [Cited February 14, 2022]. https://www.kauveryhospital.com/kauverian-scientific-journal/mucormycosis-in-covid-19-a-clinico-microbiological-dilemma
15.
PilmisB, AlanioA, LortholaryO, LanternierF: Recent advances in the understanding and management of mucormycosis. F1000Res, 2018; 7:F1000 Faculty Rev-1429.
16.
KatragkouA, WalshTJ, RoilidesE: Why is mucormycosis more difficult to cure than more common mycoses?. Clin Microbiol Infect, 2014; 20(Suppl 6):74–81.
17.
GallettaK, AlafaciC, D'AlcontresFS, MariaME, CavallaroM, RicciardelloG, VinciS, GrassoG, GranataF: Imaging features of perineural and perivascular spread in rapidly progressive rhino-orbital-cerebral mucormycosis: A case report and brief review of the literature. Surg Neurol Int, 2021; 12:245.
18.
KainzK, BauerMA, MadeoF, Carmona-GutierrezD: Fungal infections in humans: The silent crisis. Microb Cell Graz Austria, 2020; 7:143–145.
19.
ArvasA: Vaccination in patients with immunosuppression. Turk Arch Pediatr Pediatri Arş, 2014; 49:181.
RubinLG, LevinMJ, LjungmanP, DaviesEG, AveryR, TomblynM, BousvarosA, DhanireddyS, SungL, KeyserlingH, KangI, Infectious Diseases Society ofAmerica: 2013 IDSA clinical practice guideline for vaccination of the immunocompromised host. Clin Infect Dis, 2014; 58:309–318.
22.
CDC: COVID-19 Vaccination [Internet]. Centers for Disease Control and Prevention. 2020. [Cited February 14, 2022]. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/immuno.html
BambiniS, RappuoliR: The use of genomics in microbial vaccine development. Drug Discov Today, 2009; 14:252–260.
25.
MaríaRAR, ArturoCVJ, AliciaJA, PaulinaMLG, GerardoAO: The Impact of Bioinformatics on Vaccine Design and Development [Internet]. Vaccines IntechOpen; 2017 [Cited August 31, 2021]. https://www.intechopen.com/chapters/55711
PubMed [Internet]. PubMed. [Cited August 31, 2021]. https://pubmed.ncbi.nlm.nih.gov/
28.
UniProt [Internet]. [Cited August 31, 2021]. https://www.uniprot.org/
29.
CharifD, LobryJR: SeqinR 1.0–2: A contributed package to the R project for statistical computing devoted to biological sequences retrieval and analysis. In: Bastolla U, Porto M, Roman HE, and Vendruscolo M (eds.). Structural Approaches to Sequence Evolution: Molecules, Networks, Populations [Internet]. Springer, Berlin, Heidelberg, 2007, pp. 207–232. (Biological and Medical Physics, Biomedical Engineering).
30.
ChaudhuriR, AnsariFA, RaghunandananMV, RamachandranS: FungalRV: Adhesin prediction and immunoinformatics portal for human fungal pathogens. BMC Genomics, 2011; 12:192.
31.
RamanaJ, GuptaD: FaaPred: A SVM-based prediction method for fungal adhesins and adhesin-like proteins. PLoS One, 2010; 5:e9695.
32.
SavojardoC, MartelliPL, FariselliP, ProfitiG, CasadioR: BUSCA: An integrative web server to predict subcellular localization of proteins. Nucleic Acids Res, 2018; 46:W459–W466.
33.
EmanuelssonO, BrunakS, von HeijneG, NielsenH: Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc, 2007; 2:953–971.
34.
VaxiJen [Internet]. [Cited February 14, 2022]. www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html
35.
Protein BLAST: Search protein databases using a protein query [Internet]. [Cited February 14, 2022]. https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins
KringelumJV, LundegaardC, LundO, NielsenM: Reliable B cell epitope predictions: Impacts of method development and improved benchmarking. PLoS Comput Biol, 2012; 8:e1002829.
42.
KimDE, ChivianD, BakerD: Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res, 2004; 32:W526–W531.
43.
PaulS, SidneyJ, SetteA, PetersB: TepiTool: A pipeline for computational prediction of T cell epitope candidates. Curr Protoc Immunol, 2016; 114:18.19.1–18.19.24.
44.
WeiskopfD, AngeloMA, de AzeredoEL, SidneyJ, GreenbaumJA, FernandoAN, BroadwaterA, KollaRV, De SilvaAD, de SilvaAM, MattiaKA, DoranzBJ, GreyHM, ShrestaS, PetersB, SetteA: Comprehensive analysis of dengue virus-specific responses supports an HLA-linked protective role for CD8+ T cells. Proc Natl Acad Sci U S A, 2013; 110:E2046–E2053.
45.
GreenbaumJ, SidneyJ, ChungJ, BranderC, PetersB, SetteA: Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes. Immunogenetics, 2011; 63:325–335.
46.
NielsenM, LundegaardC, WorningP, LauemøllerSL, LamberthK, BuusS, BrunakS, LundO: Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci, 2003; 12:1007–1017.
47.
PetersB, SetteA: Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics, 2005; 6:132.
48.
SidneyJ, AssarssonE, MooreC, NgoS, PinillaC, SetteA, PetersB: Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res, 2008; 4:2.
49.
JensenKK, AndreattaM, MarcatiliP, BuusS, GreenbaumJA, YanZ, SetteA, PetersB, NielsenM: Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology, 2018; 154:394–406.
50.
NielsenM, LundO: NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics, 2009; 10:296.
51.
NielsenM, LundegaardC, LundO: Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics, 2007; 8:238.
52.
SturnioloT, BonoE, DingJ, RaddrizzaniL, TuereciO, SahinU, BraxenthalerM, GallazziF, ProttiMP, SinigagliaF, HammerJ: Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol, 1999; 17:555–561.
53.
NielsenM, LundegaardC, BlicherT, PetersB, SetteA, JustesenS, BuusS, LundO: Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comput Biol, 2008; 4:e1000107.
BibiS, UllahI, ZhuB, AdnanM, LiaqatR, KongWB, NiuS: In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology. Sci Rep, 2021; 11:1249.
62.
WieczorekM, AbualrousET, StichtJ, Álvaro-BenitoM, StolzenbergS, NoéF, FreundC: Major histocompatibility complex (MHC) class I and MHC class II proteins: Conformational plasticity in antigen presentation. Front Immunol, 2017; 8:292.
63.
SchmidtS, TramsenL, PerkhoferS, Lass-FlörlC, HanischM, RögerF, KlingebielT, KoehlU, LehrnbecherT: Rhizopus oryzae hyphae are damaged by human natural killer (NK) cells, but suppress NK cell mediated immunity. Immunobiology, 2013; 218:939–944.
64.
AreitioM, Martin-VicenteA, ArbizuA, AntoranA, Aparicio-FernandezL, BuldainI, Martin-SoutoL, RementeriaA, CapillaJ, HernandoFL, Ramirez-GarciaA: Identification of Mucor circinelloides antigens recognized by sera from immunocompromised infected mice. Rev Iberoam Micol, 2020; 37:81–86.
65.
SatoK, OinumaKI, NikiM, YamagoeS, MiyazakiY, AsaiK, YamadaK, HirataK, KanekoY, KakeyaH: Identification of a novel Rhizopus-specific antigen by screening with a signal sequence trap and evaluation as a possible diagnostic marker of mucormycosis. Med Mycol, 2017; 55:713–719.
UpadhyaR, LamWC, MaybruckB, SpechtCA, LevitzSM, LodgeJK: Induction of protective immunity to cryptococcal infection in mice by a heat-killed, chitosan-deficient strain of Cryptococcus neoformans. mBio, 2016; 7:e00547-16.
MouynaI, FontaineT, VaiM, MonodM, FonziWA, DiaquinM, PopoloL, HartlandRP, LatgéJP: Glycosylphosphatidylinositol-anchored glucanosyltransferases play an active role in the biosynthesis of the fungal cell wall. J Biol Chem, 2000; 275:14882–14889.
72.
SoltanMA, EldeenMA, ElbassiounyN, KamelHL, AbdelraheemKM, El-GayyedHA, GoudaAM, ShehaMF, FayadE, AliOAA, GhanyKAE, El-DamasyDA, DarwishKM, ElhadySS, SileemAE: In silico designing of a multitope vaccine against Rhizopus microsporus with potential activity against other mucormycosis causing fungi. Cells, 2021; 10:3014.
73.
NovaDigm Therapeutics, Inc. Phase I double-blind, placebo-controlled dose escalation study to evaluate the safety, tolerability and immunogenicity of NDV-3, a recombinant alum-adjuvanted vaccine for Staphylococcus aureus and Candida infections, administered intramuscular to healthy adults [Internet]. clinicaltrials.gov; 2012 May [Cited February 10, 2022]. Report No.: NCT01273922. https://clinicaltrials.gov/ct2/show/NCT01273922
74.
ShuklaM, RohatgiS: Vaccination with secreted aspartyl proteinase 2 protein from Candida parapsilosis can enhance survival of mice during C. tropicalis-mediated systemic Candidiasis. Infect Immun, 2020; 88:e00312–e00320.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
