Table 1. Data bases, Tools and Software used in this study for prediction of potential fungal drug targets

Tool/Software/Database	Accessibility	Utility
Database of Essential Genes (DEG)	http://tubic.tju.edu.cn/deg/	For screening of essential genes
STRING database version 8.3	http://string-db.org/	For known and predicted protein/COGs interaction
Cytoscape version 2.8.1	http://www.cytoscape.org/	For network analysis and visualization
Human Microbiome Project (HMP)	http://www.hmpdacc.org/	Comparative human microbiome genetic analysis
JGI Genome Portal database	http://genome.jgi-psf.org/	Integrated Genomic Information
MEGA 4.0.2 package	http://www.megasoftware.net/	Molecular evolutionary genetic analysis software
Kyoto Encyclopedia of Genes and Genomes (KEGG)	http://www.genome.jp/	Pathways analysis and comparison
CELLO v.2.5	http://cello.life.nctu.edu.tw/	Subcellular localization prediction for bacteria and Eukaryotes
Uniprot	http://www.uniprot.org/	Resource of protein sequence and functional information
National Centre for Biotechnology information	http://blast.ncbi.nlm.nih.gov/	Biomedical and genomic information source
David Bioinformatic tool	http://david.abcc.ncifcrf.gov	Functional Annotational tool

Screening of essential genes. The Database of Essential Genes (DEG) was used for screening of essential genes (Luo et al., 2014; Zhang & Lin, 2009; Zhang et al., 2004) andsequence alignments were done by using the BLASTp function of the database of essential genes against each gene and protein sequence of the pathogen. The cut off parameter was set at a minimum 100 bit score (> 80% identity), along with 0.0001 expectation value-for short listing the essential genes (Barh et al., 2011).

Comparative analysis against human genome. Essential protein sequences of Candida albicans were queried to NCBI for finding human homolog and non-human homolog proteins. The BLASTp algorithm of NCBI was used forhuman-homologs based on E=1 cutoff and sequence similarity of less than 50% at the NCBI server (Muhammad et al., 2014).

Comparative analysis against human microbiota. Many different kinds of organisms are present to in the gut of normal healthy individuals and they perform a very important function in the health and disease of an individual (Garcı, 2004). They not only participate in a commensal relationship between the gut flora and humans,but their role is more than mutualism (Arias & Murray, 2009). The microorganisms perform many useful functions such as fermentation, enhancing the immune system, preventing harmful species growth (Guarner & Malagelada, 2003), gut developmental regulation, vitamins production and defending host against various diseases (Arias & Murray, 2009). If proteins of these microorganisms wereinhibited, then there was a number of side effects (Riesbeck, 2013). To overrule this possibility and to screen out the matched proteins the short listed NHHG (non-human homolog genes) of Candida albicans were compared to proteins of the gut flora by sequence analysis with cutoff value (E=0.01) and bit score (>100) at the Human Microbiome Project Database server (Lewis, 2013).

Protein-protein interaction (PPI) studies. The functional annotation and topology analysis was done using protein-protein interactions in the Candida albicans system to examine the role of each proteins in a cellular system. For this analysis, the interacting proteins of selected non-human and non-gut flora homologs (NHNGH) were retrieved from the STRING database version 10 with confidence score of >7.0 (Szklarczyk et al., 2011). The PPI-network was constructed by using the Cytoscape software (Bauer-Mehren, 2013).

Prediction of sub-cellular localization. NHNGHwerefound to be the potential fungal targets and their sub-cellular localization (Cytoplasmic membrane, secreted inside cell or secreted extracellularly) were important due to access of drug ligand to the putative target. This localization of NHNGHwere predicted for biological significance and distribution of these target genes by using CELLO v 2.5 sub-cellular localization prediction tool (Lu et al., 2004).

Pathway analysis. The interaction between integrated metabolic and protein network of Candida albicans was analyzed and it was found that a correlation existed between these pathways. The identified sequences, which were essential, non-human and non-gut flora homolog genes, were mapped to metabolic pathways. Metabolic pathways were prepared using KEGG database (Altschul et al., 1997). For individual genesanalysis, gene ontology (GO) network was created for cataloging the homologous pathways with in fungus. This analysis also showed those pathways which were absent in the host but were present in the Candida albicans.

Protein structure modeling and posttranslational modifications (PTMs). For structure based drug design, there is a need ofprotein structure with accurate conformation (Rausch et al., 2014). The selected essential non-human and non-gut flora homolog protein sequences whose structures were not available in the existing protein databases were comparatively modelled with maximum homology (Arnold et al., 2006) using online SWISS-MODEL server (Bordoli et al., 2009). The stereo-chemical quality of these protein models were based on the Q-means and Z-score (Benkert et al., 2009; Benkert et al., 2011). Further, we predicted the posttranslational modifications (PTMs) of these protein models by sequencebased predictors. We used different statistical tools of the ExPASY resource server (https://www.expasy.org/, n.d.). The protein sequences of these models were analyzed for N-glycosylation, acetylation, O-glycosylation with N-Acetylglucosamine (GlcNAc) or NAcetylgalactosamine (GalNAc) and phosphorylation with NetAcet (Kiemer et al., 2005), NetNGlyc (Gupta, et al., 2002) NetOGlyc (Steentoft et al., 2013), YinOYang (Gupta et al., 2002), and NetPhos (Blom et al., 1999; Jensen et al., 2002), respectively. In this study the probability of palmitoyl chains bound to free cysteine side chains was also examined CSS-Palm (Ren et al., 2008).

Results

Finding of essential genes

The database of essential gene was used for homologous sequence search of individual fungal proteins using BLASTp options. Based on the cutoff parameters, we shortlisted 538 essential proteins which are associated with various structural and physiological functions.

Orthology analysis with Homo sapiens

Each essential protein sequence of Candida albicans was analyzed for sequence homology with human genome using standard human BLASTp at the NCBI server with an E-value cutoff of 1. The comparative analysis was performed to exclude the human homolog proteins and to retain non-human homolog protein sequences. 464 genes out oftotal 538 protein sequences were found to be non-human homologous sequences, while 160 genes of these 464 were found to be conserved.

Comparative analysis with human microbiota

The most commonly used antibiotics kill pathogenic microbiota and healthy microbial flora non-selectively. This had a profound and prolonged harmful impact on normal gut flora (Jernberg, Löfmark, Edlund, & Jansson, 2010), so here efforts to design novel drugs that specifically targetthe fungus proteins. The short listed 464 essential proteins were compared with the gut flora by sequence analysis. The outcomes of sequence homolog analysis revealed that 20 (4.4%) were non-homologous to the gut flora (Fig. 2), and these may be further used for interactomic analysis and designing of drug ligand to cure the disease. The complete list of putative targets is provided as a supporting document (Supplementary Table 1 at www.actabp.pl).

Interactomic network

To understand and analyze the topology and functional annotation of protein-protein interaction in Candida albicansa protein networks was constructed (20-proteins) that contained 111-nodes and 122-edges. The entire protein network consisted of high confidence scoring partners (STRING relevant confidence score >0.8). The main component of this network connected nodes and edges (nodes represent protein and edges represent interaction). In Fig. 3, the interaction network was largely segregated into three neighborhoods: one enriched for modelled proteins shown in dark green among light green nodes representing potential drug targets and blue nodes indicates the interacting proteins with source proteins. The network was analyzed by Cytoscape network statistics with undirected score based on the centrality relationship among nodes.

The topological analysis of this network revealed the direct interaction of these critical and essential proteins that can be probable drug targets. 20 proteins were mapped to the PPI network. The visual analysis of this network showed that not only these essential proteins interact with each other and but were involved also in biosynthesis of amino acids signal transduction systems, nucleic acid synthesis, ribosomal proteins-functioning and other physiological transportation of chemicals and neurotransmitter.

Prediction of sub-Cellular localization

We predicted sub-cellular localization of the putative 20 target proteins. Among these targets, 80% of the proteins belonged to the cytoplasmic compartment, followed by the membrane associated protein (10%) and extracellular proteins (10%) (Fig. 4).

Identifying pathways of potential drug targets

The role of the 20 sorted proteins in the metabolic pathways of Candida albicans was studied and correlated by KEGG database (Fig. 5). An illustrated map of the metabolic network was created showing system level investigations of this fungus’cell machinery. The 20 inputs genes encode enzymes important in various biochemical reactions related to 40 pathways (KEGG database). These reactions include carbohydrate metabolism, protein synthesis, and its degradation, two component systems, phosphotransferase system, cofactor biosynthesis, DNA synthesis and others.

Protein modeling and analysis of posttranslational modification

Among the potential drug targets of Candida albicans, four proteins including RNA polymerase I, orotidine 5 phosphate decarboxylase, ubiquitin-like protein-conjugating E2 enzyme and serine-theonine phosphatase5 were selected based on the maximum homology of protein models and critical analysis of the pathways. The protein models of these potential targets were validated by the Q-Mean and Z-score which shows that the geometry and conformation of these protein models are stable (Fig. 6).

The post translation modifications showed that according to the NetAcet server, for Sequence 5 (Orotidine 5-phosphate decarboxylase), sequence 17 (ubiquitin-like protein-conjugating E2 enzyme) and sequence 20 (delta14-sterol reductase), acetylationis not possible as there is no alanine, glycine threonine and serine at this position. The sequence 12 (serine/threonine-protein phosphatase 5) and sequence 14 (RNA polymerase I) show positive results as acetylation is possible in bothproteins. Similarly, According to NetAcet server, Sequence 5 (hexokinase), sequence 17 (ubiquitin-like protein-conjugating E2 enzyme) and sequence 20 (delta14-sterol reductase), the acetylation of this protein is not possible as there of no alaline, glycine threonine and serine at this position. The sequence 12 (serine/threonine-protein phosphatase 5) and sequence 14 (RNA polymerase I) show positive results as acetylation is possible in both proteins.For each sequence, potential glycosylation sites or location are identified by using prediction confidence of predicted score higher than 0.5. The NetPhos prediction indicated about the presence of serine threonine and tyrosine phosphorylation sites in our protein for specific and general kinases. This site predicts for following kinases: ATM CKI CKII CAMII DNAPK EGFR GSK3 INSR PKA PKB PKC RSK SRC cdc2 cdk5 p38MAPK. We predicted that serine/ threonine residues of protien are used to predict it O-GlcNAcylated as well as phosphorylated in YinOYang prediction, these sites are acylated reversibly at different time for the different function performed in the cell. None of the proteins in our scheme showed YinOYong positive results (Table 3).

Discussion/Conclusion

Alternative drug targets are needed for nosocomial Candida albicans infections, as this fungus has become resistant to all therapies currently available (Arias & Murray, 2009; Boyanova et al., 2007). One innovative way is to target genes essential for fungal survival. The product of such essential gene could become a new antifungal drug target (Materi & Wishart, 2007). In this study, we found that in Candida albicans 20 proteins were potential targets which were non-homologous to human genes as well as the gut flora. These proteins are recommended as drug targets having minimum side effects. By having a deep knowledge on a biological system by using genomic databases and system biological studies we can formulate better drugs. These systems help us to explore various pathways to find the most appropriate drugs by exploration of pathways and whole cellular systems which are involved in given disease. Computational systems help us in assessing the criticalness of any individual protein by studying the involvement of that proteins in alternative pathways or to the alternative pathway mechanismsthat compensate for that protein. System level studies are making it clearer day by day that a whole new plethora of techniques may be used for simulation and analysis of biochemical systems (von Mering et al., 2007; Szklarczyk et al., 2011).

The protein network map shows the behavior of essential and non-essential proteins. Protein-protein interaction are comprehensive due to availability of experimentally mapped interactionsin the integrated databases (Perumal et al., 2007). Along with the essentiality of a selected protein target for Candida albicans. The other most common requirement is non-similarity with human proteins. Inhibition of the human proteins could lead to adverse effects. Querying the selected proteins against human proteome data at NCBI and UNIPROT databases was simplest and efficient way to achieve this. Sequence information for hundreds of eukaryotes/prokaryotes is available and this kind of analysis has been reported for Mycobacterium tuberculosis,Pseudomonas and Helicobacter species (Singh et al., 2006; Beaugerie & Petit, 2004).

The role of normal human flora is ecological balance of the body system along with controlling the human mood and immunity (O’Hara & Shanahan, 2006; Hugot, 2004; Payne et al., 2003; Carman et al., 2004). The normal flora usually resides in digestive tract, upper respiratory tract and otic region of the human body and perform immunity control, mood regulation, synthesis of vitamins and prevention of superinfections. The metabolic activities performed by microflora of any species is now considered equal to a virtual organ which might be named as a forgotten organ (Guarner & Malagelada, 2003). Broad spectrum antibiotics affect the host’s health by changing composition of the gut flora (Carman et al., 2004). Due to this reason, non-human homologous sequences could not be our targets as many microbes are needed for balancing of ecology of the human body. The short listed 238 protein sequences which were non-human homologs were then compared with the gut flora and it was found that 20 of them were non-human and non-gut floral sequence homologs, leaving 87% of the non-homologous sequences to gut flora as our confident drug targets. The products of these selected genes were mapped to biological pathwaysand it was found these putative target proteins were involved in processes. The involvement of putative proteins in biological make them an essentiality for structure and survival ability of Candida. The gene ontology of twenty selected proteins indicated thatthese proteins weremostly cytoplasmic in origin, followed by mitochondrial and cell wall proteins (Muhammad et al., 2014). Out of 20 proteins, 4were further modelled for the docking studies using a Swiss model template. The template with the highest quality has been identified and selected for the model building (Remmert et al., 2011; Guex & Peitsch, 1997; Bordoli et al., 2009; Sali & Blundell, 1993; Benkert et al., 2011; Benkert et al., 2009; Biasini et al., 2014). The QMEAN scoring function was used for the assessment of the global and per-residue model. These homology models may be used for used for computational analysis of new ligand, ligand designing and ligand modification. The present antifungal drug can also be studied for their bonding affinities with these putative target proteins. These homology model are already being used for some antifungal docking studies (unpublished data). These protein models provide useful template for designing of novel drugs, as well as studies of old ligands against these putative targets. The homology models provide a good template to identify the selective targets in any species and selective toxicity of drug/ligand toward that species.

The selective toxicity of compound is very important as the natural human flora act as an organ of human body, providing nutrients like folic acid, are responsible for mood stabilization and have a commensal function to not allowing pathogenic species to grow. Further studies and a refined modeling algorithm will help us to design a tailor made drug towards a selected target of a given species

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