targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

PocketAlign a novel algorithm for aligning binding sites in protein structures

A fundamental task in bioinformatics involves a transfer of knowledge from one protein molecule onto another by way of recognizing similarities. Such similarities are obtained at different levels, that of sequence, whole fold, or important substructures. Comparison of binding sites is important to understand functional similarities among the proteins and also to understand drug cross-reactivities. Current methods in literature have their own merits and demerits, warranting exploration of newer concepts and algorithms, especially for large-scale comparisons and for obtaining accurate residue-wise mappings. Here, we report the development of a new algorithm, PocketAlign, for obtaining structural superpositions of binding sites. The software is available as a web-service at http://proline.physics.iisc.ernet.in/pocketalign/. The algorithm encodes shape descriptors in the form of geometric perspectives, supplemented by chemical group classification. The shape descriptor considers several perspectives with each residue as the focus and captures relative distribution of residues around it in a given site. Residue-wise pairings are computed by comparing the set of perspectives of the first site with that of the second, followed by a greedy approach that incrementally combines residue pairings into a mapping. The mappings in different frames are then evaluated by different metrics encoding the extent of alignment of individual geometric perspectives. Different initial seed alignments are computed, each subsequently extended by detecting consequential atomic alignments in a three-dimensional grid, and the best 500 stored in a database. Alignments are then ranked, and the top scoring alignments reported, which are then streamed into Pymol for visualization and analyses. The method is validated for accuracy and sensitivity and benchmarked against existing methods. An advantage of PocketAlign, as compared to some of the existing tools available for binding site comparison in literature, is that it explores different schemes for identifying an alignment thus has a better potential to capture similarities in ligand recognition abilities. PocketAlign, by finding a detailed alignment of a pair of sites, provides insights as to why two sites are similar and which set of residues and atoms contribute to the similarity.

Computational approach to identify enzymes that are potential therapeutic candidates for psoriasis

Psoriasis is well known as a chronic inflammatory dermatosis. The disease affects persons of all ages and is a burden worldwide. Psoriasis is associated with various diseases such as arthritis. The disease is characterized by well-demarcated lesions on the skin of the elbows and knees. Various genetic and environmental factors are related to the pathogenesis of psoriasis. In order to identify enzymes that are potential therapeutic targets for psoriasis, we utilized a computational approach, combining microarray analysis and protein interaction prediction. We found 6,437 genes (3,264 upregulated and 3,173 downregulated) that have significant differences in expression between regions with and without lesions in psoriasis patients. We identified potential candidates through protein-protein interaction predictions made using various protein interaction resources. By analyzing the hub protein of the networks with metrics such as degree and centrality, we detected 32 potential therapeutic candidates. After filtering these candidates through the ENZYME nomenclature database, we selected 5 enzymes: DNA helicase (RUVBL2), proteasome endopeptidase complex (PSMA2), nonspecific protein-tyrosine kinase (ZAP70), I-kappa-B kinase (IKBKE), and receptor protein-tyrosine kinase (EGFR). We adopted a computational approach to detect potential therapeutic targets; this approach may become an effective strategy for the discovery of new drug targets for psoriasis.

Comparative genomics of cell envelope components in mycobacteria

Mycobacterial cell envelope components have been a major focus of research due to their unique features that confer intrinsic resistance to antibiotics and chemicals apart from serving as a low-permeability barrier. The complex lipids secreted by Mycobacteria are known to evoke/repress host-immune response and thus contribute to its pathogenicity. This study focuses on the comparative genomics of the biosynthetic machinery of cell wall components across 21-mycobacterial genomes available in GenBank release 179.0. An insight into survival in varied environments could be attributed to its variation in the biosynthetic machinery. Gene-specific motifs like 'DLLAQPTPAW' of ufaA1 gene, novel functional linkages such as involvement of Rv0227c in mycolate biosynthesis; Rv2613c in LAM biosynthesis and Rv1209 in arabinogalactan peptidoglycan biosynthesis were detected in this study. These predictions correlate well with the available mutant and coexpression data from TBDB. It also helped to arrive at a minimal functional gene set for these biosynthetic pathways that complements findings using TraSH.

Structural insights into the novel diadenosine 5',5‴-P¹,P⁴-tetraphosphate phosphorylase from Mycobacterium tuberculosis H37Rv

Rv2613c is a diadenosine 5',5‴-P(1),P(4)-tetraphosphate (Ap(4)A) phosphorylase from Mycobacterium tuberculosis H37Rv. Sequence analysis suggests that Rv2613c belongs to the histidine triad (HIT) motif superfamily, which includes HIT family diadenosine polyphosphate (Ap(n)A) hydrolases and Ap(4)A phosphorylases. However, the amino acid sequence of Rv2613c is more similar to that of HIT family Ap(n)A hydrolases than to that of typical Ap(4)A phosphorylases. Here, we report the crystal structure of Rv2613c, which is the first structure of a protein with Ap(n)A phosphorylase activity, and characterized the structural basis of its catalytic activity. Our results showed that the structure of Rv2613c is similar to those of other HIT superfamily proteins. However, Asn139, Gly146, and Ser147 in the active site of Rv2613c replace the corresponding Gln, Gln, and Thr residues that are normally found in HIT family Ap(n)A hydrolases. Furthermore, analyses of Rv2613c mutants revealed that Asn139, Gly146, and Ser147 are important active-site residues and that Asn139 has a critical role in catalysis. The position of Gly146 might influence the phosphorylase activity. In addition, the tetrameric structure of Rv2613c and the presence of Trp160 might be essential for the formation of the Ap(4)A binding site. These structural insights into Rv2613c may facilitate the development of novel structure-based inhibitors for treating tuberculosis.

Scoring protein relationships in functional interaction networks predicted from sequence data

The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins.

AVAILABILITY

Protein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes.

Drug repositioning for orphan diseases

The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing drug product and orphan disease. Drug Repositioning or Drug Repurposing--finding a new indication for a drug--is one way to maximize the potential of a drug. The advantages of this approach are manifold, but rational drug repositioning for orphan diseases is not trivial and poses several formidable challenges--pharmacologically and computationally. Most of the repositioned drugs currently in the market are the result of serendipity. One reason the connection between drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism 'connecting' them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.

Contribution of microarray data to the advancement of knowledge on the Mycobacterium tuberculosis interactome: use of the random partial least squares approach

Following the central dogma of molecular biology, where data flows from gene to protein through transcript, information on gene expression provides information on the functional state of an organism. Microarray technology arose to measure the expression level of thousands of genes simultaneously. These vast amounts of data generated at all levels of biological organization help to identify co-expressed genes, which may reveal proteins interacting in a complex or acting in the same pathway without direct physical contact. Discovering associations of regulatory patterns of characterized proteins with those of hypothetical proteins may identify functional relationships between them and facilitate the characterization of proteins of unknown function. Here we make use of the random partial least squares regression technique (r-PLS) to trace connections between co-expressed genes in Mycobacterium tuberculosis using data downloaded from public microarray databases. We generated the overall topology of a microbial co-expression network with the exact complexity of the model. This approach provides a general method for generating a co-expression network of an organism for the purpose of systems-level analyses.

Protein-protein interaction networks suggest different targets have different propensities for triggering drug resistance

Emergence of drug resistance is a major problem in the treatment of many diseases including tuberculosis. To tackle the problem from a wholistic perspective, it is essential to understand the molecular mechanisms by which bacteria acquire drug resistance using a systems approach. Availability of genome-scale data of expression profiles under different drug exposed conditions and protein-protein interactions, makes it feasible to reconstruct and analyze systems-level models. A number of proteins involved in different resistance mechanisms, referred to as the resistome are identified from literature. The interaction of the drug directly with the resistome is unable to explain most resistance processes adequately, including that of increased mutations in the target's binding site. We recently hypothesized that some communication might exist from the drug environment to the resistome to trigger emergence of drug resistance. We report here a network based approach to identify most plausible paths of such communication in Mycobacterium tuberculosis. Networks capturing both structural and functional linkages among various proteins were weighted based on gene expression profiles upon exposure to specific drugs and betweenness centrality of the interactions. Our analysis suggests that different drug targets and hence different drugs could trigger the resistome to different extents and through different routes. The identified paths correlate well with the mechanisms known through experiment. Some examples of the top ranked hubs in multiple drug specific networks are PolA, FadD1, CydA, a monoxygenase and GltS, which could serve as co-targets, that could be inhibited in order to retard resistance related communication in the cell.

Structural bioinformatics: deriving biological insights from protein structures

Structural bioinformatics can be described as an approach that will help decipher biological insights from protein structures. As an important component of structural biology, this area promises to provide a high resolution understanding of biology by assisting comprehension and interpretation of a large amount of structural data. Biological function of protein molecules can be inferred from their three-dimensional structures by comparing structures, classifying them and transferring function from a related protein or family. It is well known now that the structure space of protein molecules is more conserved than the sequence space, making it important to seek functional associations at the structural level. An added advantage of structural bioinformatics over simpler sequence-based methods is that the former also provides ultimate insights into the mechanisms by which various biological events take place. A bird's eye-view of the different aspects of structural bioinformatics is given here along with various recent advances in the area including how knowledge obtained from structural bioinformatics can be applied in drug discovery.

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage these data to move from a hit to a lead to a clinical candidate and potentially, a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are examined in this review. We suggest that these computational approaches should be optimally integrated within a workflow with experimental approaches to accelerate TB drug discovery.

System-level strategies for studying the metabolism of Mycobacterium tuberculosis

Despite decades of research many aspects of the biology of Mycobacterium tuberculosis remain unclear and this is reflected in the antiquated tools available to treat and prevent tuberculosis and consequently this disease remains a serious public health problem. Important discoveries linking M. tuberculosis's metabolism and pathogenesis have renewed interest in this area of research. Previous experimental studies were limited to the analysis of individual genes or enzymes whereas recent advances in computational systems biology and high throughput experimental technologies now allow metabolism to be studied on a genome scale. Here we discuss the progress being made in applying system level approaches to studying the metabolism of this important pathogen. The information from these studies will fundamentally change our approach to tuberculosis research and lead to new targets for therapeutic drugs and vaccines.

Kinetic mechanism determination and analysis of metal requirement of dehydroquinate synthase from Mycobacterium tuberculosis H37Rv: an essential step in the function-based rational design of anti-TB drugs

The number of new cases of tuberculosis (TB) arising each year is increasing globally. Migration, socio-economic deprivation, HIV co-infection and the emergence of drug-resistant strains of Mycobacterium tuberculosis, the main causative agent of TB in humans, have all contributed to the increasing number of TB cases worldwide. Proteins that are essential to the pathogen survival and absent in the host, such as enzymes of the shikimate pathway, are attractive targets to the development of new anti-TB drugs. Here we describe the metal requirement and kinetic mechanism determination of M. tuberculosis dehydroquinate synthase (MtDHQS). True steady-state kinetic parameters determination and ligand binding data suggested that the MtDHQS-catalyzed chemical reaction follows a rapid-equilibrium random mechanism. Treatment with EDTA abolished completely the activity of MtDHQS, and addition of Co(2+) and Zn(2+) led to, respectively, full and partial recovery of the enzyme activity. Excess Zn(2+) inhibited the MtDHQS activity, and isotitration microcalorimetry data revealed two sequential binding sites, which is consistent with the existence of a secondary inhibitory site. We also report measurements of metal concentrations by inductively coupled plasma atomic emission spectrometry. The constants of the cyclic reduction and oxidation of NAD(+) and NADH, respectively, during the reaction of MtDHQS was monitored by a stopped-flow instrument, under single-turnover experimental conditions. These results provide a better understanding of the mode of action of MtDHQS that should be useful to guide the rational (function-based) design of inhibitors of this enzyme that can be further evaluated as anti-TB drugs.

Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection

Background

Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease.

Results

We have predicted the protein interaction network of Leishmania major by using three validated methods: PSIMAP, PEIMAP, and iPfam. Combining the results from these methods, we calculated a high confidence network (confidence score > 0.70) with 1,366 nodes and 33,861 interactions. We were able to predict the biological process for 263 interacting proteins by doing enrichment analysis of the clusters detected. Analyzing the topology of the network with metrics such as connectivity and betweenness centrality, we detected 142 potential drug targets after homology filtering with the human proteome. Further experiments can be done to validate these targets.

Conclusion

We have constructed the first protein interaction network of the Leishmania major parasite by using a computational approach. The topological analysis of the protein network enabled us to identify a set of candidate proteins that may be both (1) essential for parasite survival and (2) without human orthologs. These potential targets are promising for further experimental validation. This strategy, if validated, may augment established drug discovery methodologies, for this and possibly other tropical diseases, with a relatively low additional investment of time and resources.

Systems biology of the metabolism of Mycobacterium tuberculosis

Despite decades of research, many aspects of the biology of Mycobacterium tuberculosis remain unclear, and this is reflected in the antiquated tools available to treat and prevent tuberculosis and consequently this disease remains a serious public health problem. Important discoveries linking the metabolism of M. tuberculosis and pathogenesis has renewed interest in this area of research. Previous experimental studies were limited to the analysis of individual genes or enzymes, whereas recent advances in computational systems biology and high-throughput experimental technologies now allows metabolism to be studied on a genome scale. In the present article, we discuss the progress being made in applying system-level approaches to study the metabolism of this important pathogen.

Identification of attractive drug targets in neglected-disease pathogens using an in silico approach

Background

The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni.

Methodology/Principal Findings

Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.

Conclusions/Significance

Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

In cell-based drug development, researchers attempt to create drugs that kill a pathogen without necessarily understanding the details of how the drugs work. In contrast, target-based drug development entails the search for compounds that act on a specific intracellular target—often a protein known or suspected to be required for survival of the pathogen. The latter approach to drug development has been facilitated greatly by the sequencing of many pathogen genomes and the incorporation of genome data into user-friendly databases. The present paper shows how the database TDRtargets.org can identify proteins that might be considered good drug targets for diseases such as African sleeping sickness, Chagas disease, parasitic worm infections, tuberculosis, and malaria. These proteins may score highly in searches of the database because they are dissimilar to human proteins, are structurally similar to other “druggable” proteins, have functions that are easy to measure, and/or fulfill other criteria. Researchers can use the lists of high-scoring proteins as a basis for deciding which potential drug targets to pursue experimentally.

Tuberculosis: global approaches to a global disease

Mycobacterium tuberculosis is a remarkably successful human pathogen. The interaction with the human host is complex and much remains unknown. Recent advances in systems biology have allowed the integration of data from humans and animal models into computational approaches. For example, mathematical models provide a platform for in silico manipulation of host-pathogen interactions to gain insight into this infection across temporal and biologic scales. Here, we review recent studies on global approaches toward identifying comprehensive responses of both host and bacillus during infection, and the potential for incorporation of these data into many types of useful computational systems. Systems biology approaches provide a unique opportunity to study interventions that may improve therapy and vaccines against this major killer.

Structural and functional analysis of Rv0554 from Mycobacterium tuberculosis: testing a putative role in menaquinone biosynthesis

Mycobacterium tuberculosis, the cause of tuberculosis, is a devastating human pathogen against which new drugs are urgently needed. Enzymes from the biosynthetic pathway for menaquinone are considered to be valid drug targets. The protein encoded by the open reading frame Rv0554 has been expressed, purified and subjected to structural and functional analysis to test for a putative role in menaquinone biosynthesis. The crystal structure of Rv0554 has been solved and refined in two different space groups at 2.35 and 1.9 A resolution. The protein is dimeric, with an alpha/beta-hydrolase monomer fold. In each monomer, a large cavity adjacent to the catalytic triad is enclosed by a helical lid. Dimerization is mediated by the lid regions. Small-molecule additives used in crystallization bind in the active site, but no binding of ligands related to menaquinone biosynthesis could be detected and functional assays failed to support possible roles in menaquinone biosynthesis.

Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis

Tuberculosis continues to be a major health challenge, warranting the need for newer strategies for therapeutic intervention and newer approaches to discover them. Here, we report the identification of efficient metabolism disruption strategies by analysis of a reactome network. Protein-protein dependencies at a genome scale are derived from the curated metabolic network, from which insights into the nature and extent of inter-protein and inter-pathway dependencies have been obtained. A functional distance matrix and a subsequent nearness index derived from this information, helps in understanding how the influence of a given protein can pervade to the metabolic network. Thus, the nearness index can be viewed as a metabolic disruptability index, which suggests possible strategies for achieving maximal metabolic disruption by inhibition of the least number of proteins. A greedy approach has been used to identify the most influential singleton, and its combination with the other most pervasive proteins to obtain highly influential pairs, triplets and quadruplets. The effect of deletion of these combinations on cellular metabolism has been studied by flux balance analysis. An obvious outcome of this study is a rational identification of drug targets, to efficiently bring down mycobacterial metabolism.

Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a trans-formative tool in biotechnology.

Systems biology approaches to understanding mycobacterial survival mechanisms

The advent of high-throughput platforms for the interrogation of biological systems at the cellular and molecular level have allowed living cells to be observed and understood at a hitherto unprecedented level of detail and have enabled the construction of comprehensive, predictive in silico models. Here, we review the application of such high-throughput, systems-biological techniques to mycobacteria-specifically to the pernicious human pathogen Mycobacterium tuberculosis (MTb) and its ability to survive in human hosts. We discuss the development and application of transcriptomic, proteomic, regulomic, and metabolomic techniques for MTb as well as the development and application of genome-scale in silico models. Thus far, systems-biological approaches have largely focused on in vitro models of MTb growth; reliably extending these approaches to in vivo conditions relevant to infection is a significant challenge for the future that holds the ultimate promise of novel chemotherapeutic interventions.

Construction and analysis of protein-protein interaction networks

Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.

Informatics for Infectious Disease Research and Control

The goal of infectious disease informatics is to optimize the clinical and public health management of infectious diseases through improvements in the development and use of antimicrobials, the design of more effective vaccines, the identification of biomarkers for life-threatening infections, a better understanding of host-pathogen interactions, and biosurveillance and clinical decision support. Infectious disease informatics can lead to more targeted and effective approaches for the prevention, diagnosis and treatment of infections through a comprehensive review of the genetic repertoire and metabolic profiles of a pathogen. The developments in informatics have been critical in boosting the translational science and in supporting both reductionist and integrative research paradigms.

A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis

The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.