Identification of potential targets in Staphylococcus aureus N315 using computer aided protein data analysis

Staphylococcus aureus is a gram positive bacterium, responsible for both community-acquired and hospital-acquired infection, resulting in a mortality rate of 39%. 43.2% resistance to methicilin and emerging resistance to Fluroquinolone and Oxazolidinone, have evoked the necessity of the establishment of alternative and effective therapeutic approach to treat this bacteria. In this computational study, various database and online software are used to determine some specific targets of Staphylococcus aureus N315 other than those used by Penicillin, Quinolone and Oxazolidinone. For this purpose, among 302 essential proteins, 101 nonhomologous proteins were accrued and 64 proteins which are unique in several metabolic pathways of S. aureus were isolated by using metabolic pathway analysis tools. Furthermore, 7 essentially unique enzymes involved in exclusive metabolic pathways were revealed by this research, which can be potential drug target. Along with these important enzymes, 15 non-homologous proteins located on membrane were identified, which can play a vital role as potential therapeutic targets for the future researchers.     

Protein interaction network analysis—Approach for potential drug target identification in Mycobacterium tuberculosis

In host-parasite diseases like tuberculosis, non-homologous proteins (enzymes) as drug target are first preference. Most potent drug target can be identified among large number of non-homologous protein through protein interaction network analysis. In this study, the entire promising dimension has been explored for identification of potential drug target. A comparative metabolic pathway analysis of the host Homo sapiens and the pathogen M. tuberculosis H37Rv has been performed with three level of analysis. In first level, the unique metabolic pathways of M. tuberculosis have been identified through its comparative study with H. sapiens and identification of non-homologous proteins has been done through BLAST similarity search. In second level, choke-point analysis has been performed with identified non-homologous proteins of metabolic pathways. In third level, two type of analysis have been performed through protein interaction network. First analysis has been done to find out the most potential metabolic functional associations among all identified choke point proteins whereas second analysis has been performed to find out the functional association of high metabolic interacting proteins to pathogenesis causing proteins. Most interactive metabolic proteins which have highest number of functional association with pathogenesis causing proteins have been considered as potential drug target. A list of 18 potential drug targets has been proposed which are various stages of progress at the TBSGC and proposed drug targets are also studied for other pathogenic strains.As a case study, we have built a homology model of identified drug targets histidinol-phosphate aminotransferase (HisC1) using MODELLER software and various information have been generated through molecular dynamics which will be useful in wetlab structure determination. The generated model could be further explored for insilico docking studies with suitable inhibitors.     

Strategic genome-scale prioritization of unique drug targets: A case study of Streptococcus gordonii

The current reach of genomics extends facilitated identification of microbial virulence factors, a primary objective for antimicrobial drug and vaccine design. Many putative proteins are yet to be identified which can act as potent drug targets. There is lack and limitation of methods which appropriately combine several omics ways for putative and new drug target identification. The study emphasizes a combined bioinformatic and theoretical method of screening unique and putative drug targets, lacking similarity with experimentally reported essential genes and drug targets. Synteny based comparison was carried out with 11 streptococci considering S. gordonii as reference genome. It revealed 534 non-homologous genes of which 334 were putative. Similarity search against host proteome, metabolic pathway annotation and subcellular localization predication identified 16 potent drug targets. This is a first attempt of several combinational approaches of similarity search with target protein structural features for screening drug targets, yielding a pipeline which can be substantiated to other human pathogens.     

Core proteome mediated therapeutic target mining and multi-epitope vaccine design for Helicobacter pylori

Helicobacter pylori is a Gram-negative spiral-shaped bacterium that infects half of the human population worldwide and causes chronic inflammation. In the present study, we used the art of computational biology for therapeutic drug targets identification and a multi-epitope vaccine against multi-strains of H. pylori. For drug target identification, we used different tools and softwares to identify human non-homologous but pathogen essential proteins, with virulent properties and involved in unique metabolic pathways of H. pylori. For this purpose, the core proteome of 84 strains of H. pylori was retrieved from EDGAR 2.3 database. There were 59,808 proteins sequences in these strains. Duplicates and paralogous protein sequence removal was followed by human non-homologous protein miningPathogen essential and virulent proteins were subjected to pathway analysis Subcellular localization of the virulent proteins was predicted and druggability was also checked, leading to 30 druggable targets based on their similarity with the approved drug targets in Drugbank. For immunoinformatics analysis, we selected two outer membrane proteins (HPAKL86_RS06305 and HPSNT_RS00950) and subjected to determined immunogenic B and T-Cell epitopes. The B and T-Cell overlapped epitopes were selected to design 9 different vaccine constructs by using linkers and adjuvants. Least allergenic and most antigenic construct (C-8) was selected as a promiscuous vaccine to elicit host immune response. Cloning and in silico expression of the constructed vaccine (C-8) was done to produce a clone having the desired (gene) vaccine construct. In conclusion, the prioritized therapeutic targets for 84 strains of H.pylori will be useful for future therapy design. Vaccine design may also prove useful in the quest for targeting multi-strains of H. pylori in patients.     

Interaction between Paracoccidioides brasiliensis conidia and the coagulation system: involvement of fibrinogen

The infectious process starts with an initial contact between pathogen and host. We have previously demonstrated that Paracoccidioides brasiliensis conidia interact with plasma proteins including fibrinogen, which is considered the major component of the coagulation system. In this study, we evaluated the in vitro capacity of P. brasiliensis conidia to aggregate with plasma proteins and compounds involved in the coagulation system. We assessed the aggregation of P. brasiliensis conidia after incubation with human serum or plasma in the presence or absence of anticoagulants, extracellular matrix (ECM) proteins, metabolic and protein inhibitors, monosaccharides and other compounds. Additionally, prothrombin and partial thromboplastin times were determined after the interaction of P. brasiliensis conidia with human plasma. ECM proteins, monosaccharides and human plasma significantly induced P. brasiliensis conidial aggregation; however, anticoagulants and metabolic and protein inhibitors diminished the aggregation process. The extrinsic coagulation pathway was not affected by the interaction between P. brasiliensis conidia and plasma proteins, while the intrinsic pathway was markedly altered. These results indicate that P. brasiliensis conidia interact with proteins involved in the coagulation system. This interaction may play an important role in the initial inflammatory response, as well as fungal disease progression caused by P. brasiliensis dissemination.     

A partial proteome reference map of the wine lactic acid bacterium Oenococcus oeni ATCC BAA-1163

Oenococcus oeni is the main lactic acid bacterium that carries out the malolactic fermentation in virtually all red wines and in some white and sparkling wines. Oenococcus oeni possesses an array of metabolic activities that can modify the taste and aromatic properties of wine. There is, therefore, industrial interest in the proteins involved in these metabolic pathways and related transport systems of this bacterium. In this work, we report the characterization of the O. oeni ATCC BAA-1163 proteome. Total and membrane protein preparations from O. oeni were standardized and analysed by two-dimensional gel electrophoresis. Using tandem mass spectrometry, we identified 224 different spots corresponding to 152 unique proteins, which have been classified by their putative function and subjected to bioinformatics analysis.     

Core Proteomic Analysis of Unique Metabolic Pathways of Salmonella enterica for the Identification of Potential Drug Targets

Background

Infections caused by Salmonella enterica, a Gram-negative facultative anaerobic bacteria belonging to the family of Enterobacteriaceae, are major threats to the health of humans and animals. The recent availability of complete genome data of pathogenic strains of the S. enterica gives new avenues for the identification of drug targets and drug candidates. We have used the genomic and metabolic pathway data to identify pathways and proteins essential to the pathogen and absent from the host.

Methods

We took the whole proteome sequence data of 42 strains of S. enterica and Homo sapiens along with KEGG-annotated metabolic pathway data, clustered proteins sequences using CD-HIT, identified essential genes using DEG database and discarded S. enterica homologs of human proteins in unique metabolic pathways (UMPs) and characterized hypothetical proteins with SVM-prot and InterProScan. Through this core proteomic analysis we have identified enzymes essential to the pathogen.

Results

The identification of 73 enzymes common in 42 strains of S. enterica is the real strength of the current study. We proposed all 73 unexplored enzymes as potential drug targets against the infections caused by the S. enterica. The study is comprehensive around S. enterica and simultaneously considered every possible pathogenic strain of S. enterica. This comprehensiveness turned the current study significant since, to the best of our knowledge it is the first subtractive core proteomic analysis of the unique metabolic pathways applied to any pathogen for the identification of drug targets. We applied extensive computational methods to shortlist few potential drug targets considering the druggability criteria e.g. Non-homologous to the human host, essential to the pathogen and playing significant role in essential metabolic pathways of the pathogen (i.e. S. enterica). In the current study, the subtractive proteomics through a novel approach was applied i.e. by considering only proteins of the unique metabolic pathways of the pathogens and mining the proteomic data of all completely sequenced strains of the pathogen, thus improving the quality and application of the results. We believe that the sharing of the knowledge from this study would eventually lead to bring about novel and unique therapeutic regimens against the infections caused by the S. enterica.     

A combined computational-experimental analyses of selected metabolic enzymes in Pseudomonas species

Comparative genomic analysis has revolutionized our ability to predict the metabolic subsystems that occur in newly sequenced genomes, and to explore the functional roles of the set of genes within each subsystem. These computational predictions can considerably reduce the volume of experimental studies required to assess basic metabolic properties of multiple bacterial species. However, experimental validations are still required to resolve the apparent inconsistencies in the predictions by multiple resources. Here, we present combined computational-experimental analyses on eight completely sequenced Pseudomonas species. Comparative pathway analyses reveal that several pathways within the Pseudomonas species show high plasticity and versatility. Potential bypasses in 11 metabolic pathways were identified. We further confirmed the presence of the enzyme O-acetyl homoserine (thiol) lyase (EC: 2.5.1.49) in P. syringae pv. tomato that revealed inconsistent annotations in KEGG and in the recently published SYSTOMONAS database. These analyses connect and integrate systematic data generation, computational data interpretation, and experimental validation and represent a synergistic and powerful means for conducting biological research.     

Predictive and comparative analysis of Ebolavirus proteins

Ebolavirus is the pathogen for Ebola Hemorrhagic Fever (EHF). This disease exhibits a high fatality rate and has recently reached a historically epidemic proportion in West Africa. Out of the 5 known Ebolavirus species, only Reston ebolavirus has lost human pathogenicity, while retaining the ability to cause EHF in long-tailed macaque. Significant efforts have been spent to determine the three-dimensional (3D) structures of Ebolavirus proteins, to study their interaction with host proteins, and to identify the functional motifs in these viral proteins. Here, in light of these experimental results, we apply computational analysis to predict the 3D structures and functional sites for Ebolavirus protein domains with unknown structure, including a zinc-finger domain of VP30, the RNA-dependent RNA polymerase catalytic domain and a methyltransferase domain of protein L. In addition, we compare sequences of proteins that interact with Ebolavirus proteins from RESTV-resistant primates with those from RESTV-susceptible monkeys. The host proteins that interact with GP and VP35 show an elevated level of sequence divergence between the RESTV-resistant and RESTV-susceptible species, suggesting that they may be responsible for host specificity. Meanwhile, we detect variable positions in protein sequences that are likely associated with the loss of human pathogenicity in RESTV, map them onto the 3D structures and compare their positions to known functional sites. VP35 and VP30 are significantly enriched in these potential pathogenicity determinants and the clustering of such positions on the surfaces of VP35 and GP suggests possible uncharacterized interaction sites with host proteins that contribute to the virulence of Ebolavirus.     

Homology modeling, comparative genomics and functional annotation of Mycoplasma genitalium hypothetical protein MG_237

Mycoplasma genitalium is a human pathogen associated with several sexually transmitted diseases. The complete genome of M. genitalium G37 has been sequenced and provides an opportunity to understand the pathogenesis and identification of therapeutic targets. However, complete understanding of bacterial function requires proper annotation of its proteins. The genome of M. genitalium consists of 475 proteins. Among these, 94 are without any known function and are described as 'hypothetical proteins'. We selected MG_237 for sequence and structural analysis using a bioinformatics approach. Primary and secondary structure analysis suggested that MG_237 is a hydrophilic protein containing a significant proportion of alpha helices, and subcellular localization predictions suggested it is a cytoplasmic protein. Homology modeling was used to define the three-dimensional (3D) structure of MG-237. A search for templates revealed that MG_237 shares 63% homology to a hypothetical protein of Mycoplasma pneumoniae, indicating this protein is evolutionary conserved. The refined 3D model was generated using (PS)(2)-v2 sever that incorporates MODELLER. Several quality assessment and validation parameters were computed and indicated that the homology model is reliable. Furthermore, comparative genomics analysis suggested MG_237 as non-homologous protein and involved in four different metabolic pathways. Experimental validation will provide more insight into the actual function of this protein in microbial pathways.     

PINAT1.0: Protein interaction network analysis tool

Cellular processes are regulated by interaction of various proteins i.e. multiprotein complexes and absences of these interactions are often the cause of disorder or disease. Such type of protein interactions are of great interest for drug designing. In host­parasite diseases like Tuberculosis, non-homologous proteins as drug target are first preference. Most potent drug target can be identifying among large number of non-homologous protein through protein interaction network analysis. Drug target should be those non-homologous protein which is associated with maximum number of functional proteins i.e. has highest number of interactants, so that maximum harm can be caused to pathogen only. In present work, Protein Interaction Network Analysis Tool (PINAT) has been developed to identification of potential protein interaction for drug target identification. PINAT is standalone, GUI application software made for protein-protein interaction (PPI) analysis and network building by using co­evolutionary profile. PINAT is very useful for large data PPI study with easiest handling among available softwares. PINAT provides excellent facilities for the assembly of data for network building with visual presentation of the results and interaction score. The software is written in JAVA and provides reliability through transparency with user.

Availability

PINAT is available at www.manit.ac.in/pinat     

Acidic Di-leucine Motif Essential for AP-3–dependent Sorting and Restriction of the Functional Specificity of the Vam3p Vacuolar t-SNARE

The transport of newly synthesized proteins through the vacuolar protein sorting pathway in the budding yeast Saccharomyces cerevisiae requires two distinct target SNAP receptor (t-SNARE) proteins, Pep12p and Vam3p. Pep12p is localized to the pre-vacuolar endosome and its activity is required for transport of proteins from the Golgi to the vacuole through a well defined route, the carboxypeptidase Y (CPY) pathway. Vam3p is localized to the vacuole where it mediates delivery of cargoes from both the CPY and the recently described alkaline phosphatase (ALP) pathways. Surprisingly, despite their organelle-specific functions in sorting of vacuolar proteins, overexpression of VAM3 can suppress the protein sorting defects of pep12Δ cells. Based on this observation, we developed a genetic screen to identify domains in Vam3p (e.g., localization and/or specific protein–protein interaction domains) that allow it to efficiently substitute for Pep12p. Using this screen, we identified mutations in a 7–amino acid sequence in Vam3p that lead to missorting of Vam3p from the ALP pathway into the CPY pathway where it can substitute for Pep12p at the pre-vacuolar endosome. This region contains an acidic di-leucine sequence that is closely related to sorting signals required for AP-3 adaptor–dependent transport in both yeast and mammalian systems. Furthermore, disruption of AP-3 function also results in the ability of wild-type Vam3p to compensate for pep12 mutants, suggesting that AP-3 mediates the sorting of Vam3p via the di-leucine signal. Together, these data provide the first identification of an adaptor protein–specific sorting signal in a t-SNARE protein, and suggest that AP-3–dependent sorting of Vam3p acts to restrict its interaction with compartment-specific accessory proteins, thereby regulating its function. Regulated transport of cargoes such as Vam3p through the AP-3–dependent pathway may play an important role in maintaining the unique composition, function, and morphology of the vacuole.     

A generalized computational framework to streamline thermodynamics and kinetics analysis of metabolic pathways

Metabolic engineering is a critical biotechnological approach in addressing global energy and environment challenges. Most engineering efforts, however, consist of laborious and inefficient trial-and-error of target pathways, due in part to the lack of methodologies that can comprehensively assess pathway properties in thermodynamics and kinetics. Metabolic engineering can benefit from computational tools that evaluate feasibility, expense and stability of non-natural metabolic pathways. Such tools can also help us understand natural pathways and their regulation at systems level. Here we introduce a computational toolbox, PathParser, which, for the first time, integrates multiple important functions for pathway analysis including thermodynamics analysis, kinetics-based protein cost optimization and robustness analysis. Specifically, PathParser enables optimization of the driving force of a pathway by minimizing the Gibbs free energy of least thermodynamically favorable reaction. In addition, based on reaction thermodynamics and enzyme kinetics, it can compute the minimal enzyme protein cost that supports metabolic flux, and evaluate pathway stability and flux in response to enzyme concentration perturbations. In a demo analysis of the Calvin–Benson–Bassham cycle and photorespiration pathway in the model cyanobacterium Synechocystis PCC 6803, the computation results are corroborated by experimental proteomics data as well as metabolic engineering outcomes. This toolbox may have broad application in metabolic engineering and systems biology in other microbial systems.     

Metabolic pathway analysis of S. pneumoniae: an in silico approach towards drug-design

The emergence of multidrug resistant varieties of Streptococcus pneumoniae (S. pneumoniae) has led to a search for novel drug targets. An in silico comparative analysis of metabolic pathways of the host Homo sapiens (H. sapiens) and the pathogen S. pneumoniae have been performed. Enzymes from the biochemical pathways of S. pneumoniae from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. Five pathways unique to the pathogen S. pneumoniae when compared to the host H. sapiens have been identified. Potential drug targets from these pathways could be useful for the discovery of broad-spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 161 distinct targets identified from these pathways, many are in various stages of progress at the Microbial Genome Database. However, 44 of the targets are new and can be considered for rational drug design. The study was successful in listing out potential drug targets from the S. pneumoniae proteome involved in vital aspects of the pathogen's metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatics approach can be extended to other pathogens of clinical interest.     

Resistome analysis of Mycobacterium tuberculosis: Identification of aminoglycoside 2'-Nacetyltransferase (AAC) as co-target for drug desigining

The emergence of multidrug resistant tuberculosis (MDRTB) highlights the urgent need to understand the mechanisms of resistance to the drugs and to develop a new arena of therapeutics to treat the disease. Ethambutol, isonazid, pyrazinamide, rifampicin are first line of drugs against TB, whereas aminoglycoside, polypeptides, fluoroquinolone, ethionamide are important second line of bactericidal drugs used to treat MDRTB, and resistance to one or both of these drugs are defining characteristic of extensively drug resistant TB. We retrieved 1,221 resistant genes from Antibiotic Resistance Gene Database (ARDB), which are responsible for resistance against first and second line antibiotics used in treatment of Mycobacterium tuberculosis infection. From network analysis of these resistance genes, 53 genes were found to be common. Phylogenetic analysis shows that more than 60% of these genes code for acetyltransferase. Acetyltransferases detoxify antibiotics by acetylation, this mechanism plays central role in antibiotic resistance. Seven acetyltransferase (AT-1 to AT-7) were selected from phylogenetic analysis. Structural alignment shows that these acetyltransferases share common ancestral core, which can be used as a template for structure based drug designing. From STRING analysis it is found that acetyltransferase interact with 10 different proteins and it shows that, all these interaction were specific to M. tuberculosis. These results have important implications in designing new therapeutic strategies with acetyltransferase as lead co-target to combat against MDR as well as Extreme drug resistant (XDR) tuberculosis.

Abbreviations

AA - amino acid, AT - Acetyltransferase, AAC - Aminoglycoside 2''-N-acetyltransferase, XDR - Extreme drug-resistant, MDR - Multidrug-resistant, Mtb - Mycobacterium tuberculosis, TB - Tuberculosis.     

Annotation of hypothetical proteins orthologous in Pongo abelii and Sus scrofa

A hypothetical protein is predicted to be expressed from an open reading frame without known experimental evidence of translation. They constitute a substantial fraction of proteomes. Domain extraction from these hypothetical sequences helps to search for protein coding genes for protein structural and functional annotation. We describe the analysis of prediction data in a sequence dataset of hypothetical protein orthologs of Pongo abelii (orangutan) and Sus scrofa (pig). It should be noted that these orangutan-pig orthologs are also non-homologous to human proteins. These predicted data find application in the genome wide annotation of proteins in poorly understood genomes.

Abbreviations

PDB - Protein Data Bank, DEG - Database of Essential Genes, CDD - Conserved Domain Database, IUCN - International Union for Conservation of Nature.     

Hemolymph proteome changes during worker brood development match the biological divergences between western honey bees (Apis mellifera) and eastern honey bees (Apis cerana)

Background

Hemolymph plays key roles in honey bee molecule transport, immune defense, and in monitoring the physiological condition. There is a lack of knowledge regarding how the proteome achieves these biological missions for both the western and eastern honey bees (Apis mellifera and Apis cerana). A time-resolved proteome was compared using two-dimensional electrophoresis-based proteomics to reveal the mechanistic differences by analysis of hemolymph proteome changes between the worker bees of two bee species during the larval to pupal stages.

Results

The brood body weight of Apis mellifera was significantly heavier than that of Apis cerana at each developmental stage. Significantly, different protein expression patterns and metabolic pathways were observed in 74 proteins (166 spots) that were differentially abundant between the two bee species. The function of hemolymph in energy storage, odor communication, and antioxidation is of equal importance for the western and eastern bees, indicated by the enhanced expression of different protein species. However, stronger expression of protein folding, cytoskeletal and developmental proteins, and more highly activated energy producing pathways in western bees suggests that the different bee species have developed unique strategies to match their specific physiology using hemolymph to deliver nutrients and in immune defense.

Conclusions

Our disparate findings constitute a proof-of-concept of molecular details that the ecologically shaped different physiological conditions of different bee species match with the hemolymph proteome during the brood stage. This also provides a starting point for future research on the specific hemolymph proteins or pathways related to the differential phenotypes or physiology.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-563) contains supplementary material, which is available to authorized users.     

Deciphering unusual uncultured magnetotactic multicellular prokaryotes through genomics

Candidatus Magnetoglobus multicellularis (Ca. M. multicellularis) is a member of a group of uncultured magnetotactic prokaryotes that possesses a unique multicellular morphology. To better understand this organism''s physiology, we used a genomic approach through pyrosequencing. Genomic data analysis corroborates previous structural studies and reveals the proteins that are likely involved in multicellular morphogenesis of this microorganism. Interestingly, some detected protein sequences that might be involved in cell adhesion are homologues to phylogenetically unrelated filamentous multicellular bacteria proteins, suggesting their contribution in the early development of multicellular organization in Bacteria. Genes related to the behavior of Ca. M. multicellularis (chemo-, photo- and magnetotaxis) and its metabolic capabilities were analyzed. On the basis of the genomic–physiologic information, enrichment media were tested. One medium supported chemoorganoheterotrophic growth of Ca. M. multicellularis and allowed the microorganisms to maintain their multicellular morphology and cell cycle, confirming for the first time that the entire life cycle of the MMP occurs in a multicellular form. Because Ca. M. multicellularis has a unique multicellular life style, its cultivation is an important achievement for further studies regarding the multicellular evolution in prokaryotes.     

The Genome Sequence of Leishmania (Leishmania) amazonensis: Functional Annotation and Extended Analysis of Gene Models

We present the sequencing and annotation of the Leishmania (Leishmania) amazonensis genome, an etiological agent of human cutaneous leishmaniasis in the Amazon region of Brazil. L. (L.) amazonensis shares features with Leishmania (L.) mexicana but also exhibits unique characteristics regarding geographical distribution and clinical manifestations of cutaneous lesions (e.g. borderline disseminated cutaneous leishmaniasis). Predicted genes were scored for orthologous gene families and conserved domains in comparison with other human pathogenic Leishmania spp. Carboxypeptidase, aminotransferase, and 3′-nucleotidase genes and ATPase, thioredoxin, and chaperone-related domains were represented more abundantly in L. (L.) amazonensis and L. (L.) mexicana species. Phylogenetic analysis revealed that these two species share groups of amastin surface proteins unique to the genus that could be related to specific features of disease outcomes and host cell interactions. Additionally, we describe a hypothetical hybrid interactome of potentially secreted L. (L.) amazonensis proteins and host proteins under the assumption that parasite factors mimic their mammalian counterparts. The model predicts an interaction between an L. (L.) amazonensis heat-shock protein and mammalian Toll-like receptor 9, which is implicated in important immune responses such as cytokine and nitric oxide production. The analysis presented here represents valuable information for future studies of leishmaniasis pathogenicity and treatment.