- Open Access
FungalRV: adhesin prediction and immunoinformatics portal for human fungal pathogens
© Chaudhuri et al; licensee BioMed Central Ltd. 2011
- Received: 21 September 2010
- Accepted: 15 April 2011
- Published: 15 April 2011
The availability of sequence data of human pathogenic fungi generates opportunities to develop Bioinformatics tools and resources for vaccine development towards benefitting at-risk patients.
We have developed a fungal adhesin predictor and an immunoinformatics database with predicted adhesins. Based on literature search and domain analysis, we prepared a positive dataset comprising adhesin protein sequences from human fungal pathogens Candida albicans, Candida glabrata, Aspergillus fumigatus, Coccidioides immitis, Coccidioides posadasii, Histoplasma capsulatum, Blastomyces dermatitidis, Pneumocystis carinii, Pneumocystis jirovecii and Paracoccidioides brasiliensis. The negative dataset consisted of proteins with high probability to function intracellularly. We have used 3945 compositional properties including frequencies of mono, doublet, triplet, and multiplets of amino acids and hydrophobic properties as input features of protein sequences to Support Vector Machine. Best classifiers were identified through an exhaustive search of 588 parameters and meeting the criteria of best Mathews Correlation Coefficient and lowest coefficient of variation among the 3 fold cross validation datasets. The "FungalRV adhesin predictor" was built on three models whose average Mathews Correlation Coefficient was in the range 0.89-0.90 and its coefficient of variation across three fold cross validation datasets in the range 1.2% - 2.74% at threshold score of 0. We obtained an overall MCC value of 0.8702 considering all 8 pathogens, namely, C. albicans, C. glabrata, A. fumigatus, B. dermatitidis, C. immitis, C. posadasii, H. capsulatum and P. brasiliensis thus showing high sensitivity and specificity at a threshold of 0.511. In case of P. brasiliensis the algorithm achieved a sensitivity of 66.67%. A total of 307 fungal adhesins and adhesin like proteins were predicted from the entire proteomes of eight human pathogenic fungal species. The immunoinformatics analysis data on these proteins were organized for easy user interface analysis. A Web interface was developed for analysis by users. The predicted adhesin sequences were processed through 18 immunoinformatics algorithms and these data have been organized into MySQL backend. A user friendly interface has been developed for experimental researchers for retrieving information from the database.
FungalRV webserver facilitating the discovery process for novel human pathogenic fungal adhesin vaccine has been developed.
- Invasive Aspergillosis
- Hydrophobic Amino Acid
- Compositional Property
- Pneumocystis Jirovecii
- Negative Dataset
As cases of immunosuppression rise, the spectrum of fungal pathogens is increasing thus posing a serious threat to human health. In the USA and in most European countries infection due to Candida species have become very common . Amongst the Candida spp, C. albicans and C. glabrata account for approximately 70-80% of Candida species recovered from patients with candidemia or invasive candidiasis [2, 3]. Another pathogenic fungi, A. fumigatus is the most common life-threatening aerial fungal pathogen which primarily affects the lungs. In severe invasive aspergillosis caused mainly in immunocompromised individuals, the fungus can transfer from lungs through blood stream to brain and other organs. This condition of invasive aspergillosis is often associated with significant mortality and morbidity [4, 5]. In addition, certain non-life-threatening superficial and respiratory infections caused by dimorphic pathogenic fungi like C. immitis, H. capsulatum, P. brasiliensis and B. dermatitidis impose significant restrictions on patients, resulting in a reduced quality of life. In some cases these infections may turn to life threatening specially in immunocompromised patients, where the infection spreads beyond the respiratory system to other parts of the body [6–10]. Another fungal infection Pneumocystis pneumonia (PCP) or pneumocystosis caused by unusual unicellular fungi Pneumocystis jirovecii (formerly called Pneumocystis carinii) is the most common opportunistic infection in persons with HIV infection .
It is challenging to identify candidates for vaccines in case of fungal infections because of their occurrence in immunocompromised or otherwise debilitated host. Yet it is being realized that either a preventive or therapeutic vaccine could be useful for at-risk patients [12, 13].
Adhesins are important virulence factors used by pathogens during establishment of infection. Therefore, targeting the adhesins in vaccine development can help efficiently combat fungal infections by blocking their function and preventing adherence to host cell . A few vaccine formulations using adhesins as immunizing agents and are under evaluation include agglutinin-like sequence proteins in Candida albicans[15, 16], BAD-1(WI adhesin) protein in Blastomyces dermatitidis[17, 18], 43 kDa glycoprotein in Paracoccidioides brasiliensis[19, 20] and spherule outer wall glycoprotein in Coccidioides immitis[21, 22]. Among these, the spherule outer wall glycoprotein in Coccidioides immitis has undergone trial in humans, while others have proved their efficacy in mouse experimental models.
Most fungal adhesins have a general structure consisting of an N-terminal carbohydrate or peptide-binding domain, central Ser-Thr rich glycosylated domains and C-terminal region mediating covalent cross-linking to the wall through modified glycosylphosphatidylinositol (GPI) anchors [23, 24]. Others such as WI-1/Bad1 adhesin (from B. dermatiditis), Int1p adhesin (from C. albicans) do not conform to this general structure thereby causing difficulty in their identification. Using similarity search approach, Weig et al. (2004) and Butler et al. (2009) identified adhesins and GPI-anchored proteins in certain fungal pathogens [25, 26]. These efforts can be complemented using machine learning techniques trained on compositional properties in the identification of novel adhesins because in principle, this approach allows development of a non-homology composition based method. The similarity based approach in principle enable identifying members of related family whereas the non-homology composition based method has potential to identify other novel members. Algorithms based on compositional properties for adhesin identification in different pathogenic species such as Plasmodium and bacteria have been useful [27, 28], encouraging us to attempt to develop a similar method for fungal species. Here, we present an algorithm developed by using Support Vector Machine trained through a combination of 3945 compositional properties for classifying human pathogenic fungal adhesins and adhesin like proteins. The predictions from these algorithms can be integrated with the immunoinformatics algorithms to facilitate rational vaccine development using reverse vaccinology [29, 30]. The immunoinformatics data on the predicted fungal adhesins and adhesin like proteins are also organized for easy analysis and retrieval. These resources are made available through a user friendly interface FungalRV.
Through literature survey we collected known human pathogenic fungal adhesin protein sequences from C. albicans, C. glabrata, A. fumigatus, B. dermatitidis, C. immitis, C. posadasii, H. capsulatum, P. brasiliensis, P. jirovecii and P. carinii. In C. glabrata proteins having PA14 and GLEYA adhesin domain were also included [31, 32]. Sequences were collected from the National Center for Biotechnology Information (NCBI) , Candida Genome Database (CGD)  and Swiss-Prot Databases .
Protein sequences which are not likely to be on the surface, or associated with adhesion were collected from NCBI, CGD and Swiss-Prot using keywords 'dehydratase', 'ribosomal protein', 'kinase', 'polymerase', 'acyl-CoA synthase', 'decarboxylase', and 'hydrolase'. Poorly annotated sequences were not considered. Pfam domain search was performed on negative dataset sequences. The results were analyzed exhaustively and any extracellular location associated domain containing protein sequence in the negative dataset was excluded. 'See additional file 1: Pfam domain search result of negative dataset'.
List of databases from which the human pathogenic fungal proteomes were sourced.
Candida albicans (21st assembly)
Candida Genome Database
J. Craig Venter Institute
Coccidioides immitis RMSCC 2394
Coccidioides posadasii Silveira
Histoplasma capsulatum Nam1
Paracoccidioides brasiliensis Pb01
Blastomyces dermatitidis SLH14081
Rendering datasets nonredundant
The stringent criterion (S = 100, L = 1, b = T) specified in the BLASTCLUST computer program was used to identify redundancy. Redundant entries were removed using Shell scripts. The final positive dataset had 101 non redundant adhesin protein sequences and the negative dataset had 2644 non redundant protein sequences.
Compositional Attributes Used
After several attempts using different combinations of compositional properties, we finally settled on the following:
Amino acid frequencies
Xi is the counts of ith amino acid in the sequence, i = 1, ..., 20 for each of the amino acid type and L is the length of the protein. There are 20 possible values for fi(a) for 20 amino acids.
Xmi is the counts of ith amino acid occurring as multiplet. There are 20 possible values for fi(m) for each of the 20 amino acids; and L is the length of the protein.
where i, j = 1...20 for each of the 20 amino acids and L is the length of the sequence. The best dipeptides discriminators between positive and negative sets were identified with the help of Welch's t test in R statistical software (ver 2.9.2) . Top 247 dipeptides were selected at cutoff significance at P-value < 0.001.
where i, j, k = 1-20. The best tripeptides discriminators between positive and negative sets were identified with the help of Welch's t test in R statistical software (ver 2.9.2) . Top 3653 tripeptides were selected at cutoff significance at P-value < 0.001.
where L is the length of the protein. Furthermore, information on the characteristics of the distribution of these amino acids in a given protein sequence was obtained by computing the moments of the positions of the occurrences of these amino acids. The general expression to compute moments of a given order; say 'r' is,
X m is the mean of sequence positions of all hydrophobic amino acids, Xi is the sequence position of the ith hydrophobic amino acid where i is any of the 7 hydrophobic amino acids A, M, C, F, L, V, I; and N is the total number of hydrophobic amino acids in the sequence and r is from 2-5. The values of the rth order moments were downscaled to smaller decimal values by dividing by (1000)r while preparing the feature input to SVM.
Thus, a total of 3945 compositional properties included amino acid frequencies of 20 from amino acids, 247 selected dipeptide frequencies, 3653 selected tripeptide frequencies, 20 amino acid multiplets frequencies, frequency of the hydrophobic amino acids and moments of hydrophobic amino acid distribution of order from 2-5.
Each sequence is represented by 3945 features. Programs in C language were written to calculate these compositional properties. These compositional properties will serve as an input for the machine learning algorithm SVM.
SVM is a supervised machine learning algorithm first introduced by Vapnik used for problems involving classification and regression. In this study SVM was implemented using SVMlight package written and distributed by Thorsten Joachims. This package has two modules svm_learn and svm_classify.
svm_learn: svm_learn is used prepare models(classifiers) built by learning from the training sets- positively and negatively labeled datasets labeled +1 and -1 respectively.
svm_classify: svm_classify is used by the models(classifiers) generated by svm_learn to classify the test set sequences (labeled 0).
Training and Testing process
The model (classifiers) are built using svm_learn module of SVMlight. The training set was a file containing positively and negatively labeled samples labeled +1 and -1 respectively mixed in alternating order. Each positive sample corresponding to a positive protein sequence had +1 label followed by 3945 compositional properties. Similarly each negative sample has -1 label followed by 3945 compositional properties.
We have used two types of kernel functions, the polynomial function and the radial basis function (RBF). For polynomial kernel, all the SVM parameters were set to default, except d and C, the trade-off between training error and margin. The scalable memory parameter (m) was fixed to 120. The values for d and C were incremented stepwise through a combination of 1, 2, 3, 4. . .to . . . 9 for d, and 10-5 to. . .1015 for C. For the RBF kernel, the parameter gamma g and C were incremented stepwise through a combination of 10-15 to . . .103 for g, and 10-5 to. . .1015 for C. Svm_light was provided with these parameters along with the input training set and by varying these parameter values total 588 models are generated.
Subsequently each model was input to svm_classify to classify the test set sequences. The test set is a file containing positively and negatively labeled samples labeled 0 mixed in alternating order. The 3945 features of these samples were classified and the result is a numerical value for every sample. This numerical value above set threshold value of 0.0 is indicative of the sequence being classified as positive label or negative. This prediction is compared to our known knowledge of test set and performance of the model is evaluated.
Threefold Cross Validation
where TP is true positives; TN is true negatives; FP is false positives; FN is false negatives.
All evaluations were carried out at a base cutoff value of 0.0 as discriminator between positive and negative samples. This entire process was automated using perl scripts. Subsequently, coefficient of variation (CV) of MCC of each model across the three subsets was also calculated. In the next step, the models were arranged in descending order of MCC in each of the three subsets and the models with high average MCC value [0.831-0.919 (maximum)] and low CV (≤5%) were shortlisted.
Performance Check on Human pathogenic fungal species
Parameter Sets and Performances of three Selected Models to Identify Fungal Adhesins and Adhesin-Like Proteins in human pathogenic fungal species.
Performance of best
model (MCC) in the
Mean MCC for
CV for parameters
g = 0.01
C = 100
g = 0.01
C = 100
g = 0.001
C = 100
Receiver operating characteristic Curve
Summary of predictions by FungalRV adhesin predictor using optimal threshold of 0.511.
Number of Proteins
Number of Known
Adhesins in proteome
Number of adhesins
Number of hypothetical
Number of false
Performance Check on other fungal species
Parameter Sets and Performances of three Selected Models to Identify Fungal Adhesins and Adhesin-Like Proteins in other fungi (not pathogenic to human).
Performance of best model (MCC)
in the selected subset
Mean MCC for parameters
accoss three subsets
CV for parameters
accross three subsets
d = 2
c = 0.1
g = 0.01
c = 100
d = 1
c = 1
Algorithms used to analyse predicted adhesins for Immunoinformatics.
Clusters protein or DNA sequences based on pairwise matches found using the BLAST algorithm in case of proteins or Mega BLAST algorithm for DNA.
OrthoMCL software was used to cluster proteins based on sequence similarity, using an all-against-all BLAST search of each species' proteome, followed by normalization of inter-species differences, and Markov clustering.
Predicts the right-handed parallel beta-helix supersecondary structural motif in primary amino acid sequences by using beta-strand interactions learned from non-beta-helix structures.
Predicts potentially antigenic regions of a protein sequence, based on occurrence frequencies of amino acid residue types in known epitopes.
Predicts the subcellular location of eukaryotic proteins based on the predicted presence of any of the N-terminal presequences: chloroplast transit peptide (cTP), mitochondrial targeting peptide (mTP) or secretory pathway signal peptide (SP).
5. SignalP 3.0
Predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks and hidden Markov models.
6. TMHMM Server v. 2.0
Predicts the transmembrane helices in proteins based on Hidden Markov Model.
7. Conserved Domain Database and Search Service, v2.22
The Database is a collection of multiple sequence alignments for ancient domains and full-length proteins. It is used to identify the conserved domains present in a protein query sequence.
It uses the BLAST algorithm to compare an amino acid query sequence against a protein sequence database.
Predict B cell epitope(s) in an antigen sequence, using artificial neural network.
Predicts linear B-cell epitopes, using physico-chemical properties.
11. Discotope 1.2
Predicts discontinuous B cell epitopes from protein three dimensional structures utilizing calculation of surface accessibility (estimated in terms of contact numbers) and a novel epitope propensity amino acid score.
BEPro, uses a combination of amino-acid propensity scores and half sphere exposure values at multiple distances to achieve state-of-the-art performance.
Predicts MHC Class-II binding regions in an antigen sequence, using quantitative matrices derived from published literature. It assists in locating promiscous binding regions that are useful in selecting vaccine candidates.
14. IEDB-AR (Average Relative Binding Method)
Predicts IC(50) values allowing combination of searches involving different peptide sizes and alleles into a single global prediction.
Ranks potential 8-mer, 9-mer, or 10-mer peptides based on a predicted half-time of dissociation to HLA class I molecules. The analysis is based on coefficient tables deduced from the published literature by Dr. Kenneth Parker, Children's Hospital Boston.
16. NetMHC 3.0
Predicts binding of peptides to a number of different HLA alleles using artificial neural networks (ANNs) and weight matrices.
Predicts allergens in query protein based on similarity to known epitopes, searching MEME/MAST allergen motifs using MAST and assign a protein allergen if it have any motif, search based on SVM modules and search with BLAST search against 2890 allergen-representative peptides obtained from Bjorklund et al 2005 and assign a protein allergen if it has a BLAST hit.
Predicts the potential allergenicity of proteins by bioinformatics approaches as recommended by the Codex alimentarius and FAO/WHO Expert consultation on allergenicity of foods derived through modern biotechnology.
The Webserver is built on Apache version 2.0. Server side scripting was done in PHP version 5.1.4. The programs running at back-end for compositional property calculation are written in C programming language. These C programs were compiled using the GNU gcc compiler 3.4.3 in the Itanium 2, 64-bit dual processor server running on Red Hat Linux Enterprise version 4. The client side scripting was prepared in HTML and AJAX. FungalRV can be best viewed with Mozilla Firefox and Internet Explorer. The database was developed using MySQL version 4.1.20 at back end and runs in Red Hat Enterprise Linux ES release 4. The database web interfaces have been developed in HTML and PHP 5.1.4, which dynamically execute the MySQL queries to fetch the stored data and is run through Apache2 server.
The "Known Vaccines" tab takes user to the page containing the list of known vaccine candidates provided in tabular form.
Adhesin prediction for human fungal pathogens
User interface -
A user friendly interface was developed for using the "Fungal RV adhesin predictor" algorithm. Users can paste the sequence in FASTA format or even upload a file. A threshold of 0.511 was set as the optimal threshold (Figure 2). However, users can set a threshold of their own choice. The results are displayed in a colour coded tabular format. 'See additional file 2: Adhesins and adhesin like proteins predicted by "FungalRV adhesin predictor" in 8 human fungal pathogens'. Results can be exported in tab delimited text format.
Our algorithm "FungalRV adhesin predictor" predicted many cell surface GPI anchored proteins as novel adhesins from the 8 fungal pathogens. 'See additional file 3: GPI anchored proteins predicted as adhesin by FungalRV adhesin Predictor'. GPI anchor proteins in fungi are known to be either covalently incorporated into the cell wall network or remain attached to the plasma membrane. The predicted amino acid sequences of GPI proteins conform to a general pattern. Their N-termini has a hydrophobic signal sequence that directs the protein to the ER and their C-termini has a second hydrophobic domain, which is cleaved off and replaced with a GPI anchor (a preformed lipid in the membrane of the endoplasmic reticulum) by a transamidase enzyme complex. The GPI anchored proteins are linked to plasma membrane via this preformed GPI anchor . These proteins may have roles in cell wall biosynthesis, cell wall remodeling, determining surface hydrophobicity and antigenicity and in adhesion and virulence [49, 50].
In C. albicans "FungalRV adhesin predictor" predicted proteins proposed to be involved in the process of adhesion to host such as SUN41, IFF4 [51, 52]. These proteins were not included in the training set due to absence of evidence on their direct involvement in adhesion process. However, their eventual prediction as adhesins by "FungalRV adhesin predictor" suggests their potential role in mediating adhesion. "FungalRV adhesin predictor" at optimal threshold of 0.511 predicts all the members of ALS and Hyr/iff (GPI family 17 and 18), proposed to be involved in modulating adhesion and biofilm formation in C. albicans. The ALS family in C. albicans is characterized as the main class of adhesins [53, 54]. Another protein RBT1 showing similarity to HWP1 and may have adhesion property  is also predicted by "FungalRV adhesin predictor".
In C. glabrata, several proteins showing similarity to flocculins and STA1 glucoamylase homologue of S. cerevisiae were predicted. 'See additional file 4: Predicted adhesins from C. glabrata with similarity to either flocculins or STA1'. The flocculins are involved in adhesion process in S. cerevisiae[56, 57] and therefore it is probable that these proteins have functional similarity in their role as adhesins in C. glabrata as well. When compared to the predicted in-silico adhesins by Weig et al , the new release of C. glabrata proteome by Genolevures (Sep. 2009) retains 28 orfids of the 51 orfids predicted as adhesins in the older proteome release by Genolevures (June 2004). "FungalRV adhesin predictor" could predict 24 of the 28 in-silico predicted adhesins at optimal threshold value of 0.511. 'See additional file 5: "FungalRV adhesin predictor" scores of In-silico predicted adhesins by Weig et al'.
"FungalRV adhesin predictor" run on proteomes of some of the human pathogenic fungi with low incidence of occurrence- Candida dubliniensis, Candida tropicalis, Candida parapsilosis, Candida lusitaniae and Candida guilliermondii has been provided as supplementary data. 'See additional file 6: Adhesins and adhesin like proteins predicted by "FungalRV adhesin predictor" in other pathogenic fungi with low occurrence of incidence'.
Our algorithm FungalRV adhesin predictor uses highly accurate SVM models (greater than 99%) and therefore it achieves a good MCC of 0.8702 at a positive threshold of 0.511 in comparison to FAAPred , which uses SVM models of lower accuracy (86%) and achieves a MCC of 0.610 at a relatively high negative threshold of -0.8. FAAPred misses identifying integrins (a class of known adhesins) from C. albicans and P. carinii and in some cases identifies known adhesins with low score in the range (-0.06 to - 0.74) indicating low confidence predictions in contrast to our algorithm.
First level of searching and retrieval of data is possible either through ORF ID or keywords. Multiple ORF IDs can be submitted using comma separation. Keywords can be used singly. If multiple keywords are used then the search is implemented using the AND Boolean. In the case of searching for epitope data, due to their huge size, data are conveniently retrieved in a singular mode for each ORF ID specifically. All data can be exported conveniently as a text file.
A Web server aiding in novel human pathogenic fungal adhesin vaccine prediction and development has been prepared .
Sever can be accessed at http://fungalrv.igib.res.in. The server is best viewed with Explorer 8.0 or later and Mozilla firefox version 3.0 or later
SR is a Bioinformatics scientist with focus on infectious diseases at the Institute of Genomics and Integrative Biology (CSIR), Delhi 110 007, India. RC is a Ph.D. student carrying out her thesis work at the Institute of Genomics and Integrative Biology (CSIR), Delhi 110 007, India. MVR is a systems scientist at the Institute of Genomics and Integrative Biology (CSIR), Delhi 110 007, India
Acknowledgements and Funding
This work was supported in part by grants to SR "Integrated in silico analysis of Surface proteins from selected Microbial Pathogens: Identification, structure modeling, scanning for active & binding sites, docking analysis of ligands and small molecules", from the Department of Science and Technology, Govt. of India, and a fellowship from The Indian Council of Medical Research. We thank Shri Vijay Kumar Nalla, for discussions.
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