Inhibition of leukotriene-B4 signalling-mediated host response to tuberculosis is a potential mode of adjunctive host-directed therapy.

Treatment of tuberculosis (TB) is faced with several challenges including the long treatment duration, drug toxicity and tissue pathology. Host-directed therapy provides promising avenues to find compounds for adjunctively assisting antimycobacterials in the TB treatment regimen, by promoting pathogen eradication or limiting tissue destruction. Eicosanoids are a class of lipid molecules that are potent mediators of inflammation and have been implicated in aspects of the host response against TB. Here, we have explored the blood transcriptome of pulmonary TB patients to understand the activity of leukotriene B4, a pro-inflammatory eicosanoid. Our study shows a significant upregulation in the leukotriene B4 signalling pathway in active TB patients, which is reversed with TB treatment. We have further utilized our in-house network analysis algorithm, ResponseNet, to identify potential downstream signal effectors of leukotriene B4 in TB patients including STAT1/2 and NADPH oxidase at a systemic as well as local level, followed by experimental validation of the same. Finally, we show the potential of inhibiting leukotriene B4 signalling as a mode of adjunctive host-directed therapy against TB. This study provides a new mode of TB treatment along with mechanistic insights which can be further explored in pre-clinical trials.

Mycobacterium tuberculosis requires SufT for Fe-S cluster maturation, metabolism, and survival in vivo.

Iron-sulfur (Fe-S) cluster proteins carry out essential cellular functions in diverse organisms, including the human pathogen Mycobacterium tuberculosis (Mtb). The mechanisms underlying Fe-S cluster biogenesis are poorly defined in Mtb. Here, we show that Mtb SufT (Rv1466), a DUF59 domain-containing essential protein, is required for the Fe-S cluster maturation. Mtb SufT homodimerizes and interacts with Fe-S cluster biogenesis proteins; SufS and SufU. SufT also interacts with the 4Fe-4S cluster containing proteins; aconitase and SufR. Importantly, a hyperactive cysteine in the DUF59 domain mediates interaction of SufT with SufS, SufU, aconitase, and SufR. We efficiently repressed the expression of SufT to generate a SufT knock-down strain in Mtb (SufT-KD) using CRISPR interference. Depleting SufT reduces aconitase's enzymatic activity under standard growth conditions and in response to oxidative stress and iron limitation. The SufT-KD strain exhibited defective growth and an altered pool of tricarboxylic acid cycle intermediates, amino acids, and sulfur metabolites. Using Seahorse Extracellular Flux analyzer, we demonstrated that SufT depletion diminishes glycolytic rate and oxidative phosphorylation in Mtb. The SufT-KD strain showed defective survival upon exposure to oxidative stress and nitric oxide. Lastly, SufT depletion reduced the survival of Mtb in macrophages and attenuated the ability of Mtb to persist in mice. Altogether, SufT assists in Fe-S cluster maturation and couples this process to bioenergetics of Mtb for survival under low and high demand for Fe-S clusters.

Systems-level profiling of early peripheral host-response landscape variations across COVID-19 severity states in an Indian cohort.

Host immune response to COVID-19 plays a significant role in regulating disease severity. Although big data analysis has provided significant insights into the host biology of COVID-19 across the world, very few such studies have been performed in the Indian population. This study utilizes a transcriptome-integrated network analysis approach to compare the immune responses between asymptomatic or mild and moderate-severe COVID-19 patients in an Indian cohort. An immune suppression phenotype is observed in the early stages of moderate-severe COVID-19 manifestation. A number of pathways are identified that play crucial roles in the host control of the disease such as the type I interferon response and classical complement pathway which show different activity levels across the severity spectrum. This study also identifies two transcription factors, IRF7 and ESR1, to be important in regulating the severity of COVID-19. Overall this study provides a deep understanding of the peripheral immune landscape in the COVID-19 severity spectrum in the Indian genetic background and opens up future research avenues to compare immune responses across global populations.

SiteMotif: A graph-based algorithm for deriving structural motifs in Protein Ligand binding sites.

Studying similarities in protein molecules has become a fundamental activity in much of biology and biomedical research, for which methods such as multiple sequence alignments are widely used. Most methods available for such comparisons cater to studying proteins which have clearly recognizable evolutionary relationships but not to proteins that recognize the same or similar ligands but do not share similarities in their sequence or structural folds. In many cases, proteins in the latter class share structural similarities only in their binding sites. While several algorithms are available for comparing binding sites, there are none for deriving structural motifs of the binding sites, independent of the whole proteins. We report the development of SiteMotif, a new algorithm that compares binding sites from multiple proteins and derives sequence-order independent structural site motifs. We have tested the algorithm at multiple levels of complexity and demonstrate its performance in different scenarios. We have benchmarked against 3 current methods available for binding site comparison and demonstrate superior performance of our algorithm. We show that SiteMotif identifies new structural motifs of spatially conserved residues in proteins, even when there is no sequence or fold-level similarity. We expect SiteMotif to be useful for deriving key mechanistic insights into the mode of ligand interaction, predict the ligand type that a protein can bind and improve the sensitivity of functional annotation.

PathTracer Comprehensively Identifies Hypoxia-Induced Dormancy Adaptations in Mycobacterium tuberculosis.

Mining large-scale data to discover biologically relevant information remains a challenge despite the rapid development of bioinformatics tools. Here, we have developed a new tool, PathTracer, to identify biologically relevant information flows by mining genome-wide protein-protein interaction networks following integration of gene expression data. PathTracer successfully mines interactions between genes and traces the most perturbed paths of perceived activities under the conditions of the study. We further demonstrated the utility of this tool by identifying adaptation mechanisms of hypoxia-induced dormancy in Mycobacterium tuberculosis (Mtb).

Functional and Biochemical Characterization of the MazEF6 Toxin-Antitoxin System of Mycobacterium tuberculosis.

The Mycobacterium tuberculosis genome harbors nine toxin-antitoxin (TA) systems that are members of the mazEF family, unlike other prokaryotes, which have only one or two. Although the overall tertiary folds of MazF toxins are predicted to be similar, it is unclear how they recognize structurally different RNAs and antitoxins with divergent sequence specificity. Here, we have expressed and purified the individual components and complex of the MazEF6 TA system from M. tuberculosis. Size exclusion chromatography-multiangle light scattering (SEC-MALS) was performed to determine the oligomerization status of the toxin, antitoxin, and the complex in different stoichiometric ratios. The relative stabilities of the proteins were determined by nano-differential scanning fluorimetry (nano-DSF). Microscale thermophoresis (MST) and yeast surface display (YSD) were performed to measure the relative affinities between the cognate toxin-antitoxin partners. The interaction between MazEF6 complexes and cognate promoter DNA was also studied using MST. Analysis of paired-end RNA sequencing data revealed that the overexpression of MazF6 resulted in differential expression of 323 transcripts in M. tuberculosis. Network analysis was performed to identify the nodes from the top-response network. The analysis of mRNA protection ratios resulted in identification of putative MazF6 cleavage site in its native host, M. tuberculosis. IMPORTANCE M. tuberculosis harbors a large number of type II toxin-antitoxin (TA) systems, the exact roles for most of which are unclear. Prior studies have reported that overexpression of several of these type II toxins inhibits bacterial growth and contributes to the formation of drug-tolerant populations in vitro. To obtain insights into M. tuberculosis MazEF6 type II TA system function, we determined stability, oligomeric states, and binding affinities of cognate partners with each other and with their promoter operator DNA. Using RNA-seq data obtained from M. tuberculosis overexpression strains, we have identified putative MazF6 cleavage sites and targets in its native, cellular context.

Moxifloxacin-Mediated Killing of Mycobacterium tuberculosis Involves Respiratory Downshift, Reductive Stress, and Accumulation of Reactive Oxygen Species.

Moxifloxacin is central to treatment of multidrug-resistant tuberculosis. Effects of moxifloxacin on the Mycobacterium tuberculosis redox state were explored to identify strategies for increasing lethality and reducing the prevalence of extensively resistant tuberculosis. A noninvasive redox biosensor and a reactive oxygen species (ROS)-sensitive dye revealed that moxifloxacin induces oxidative stress correlated with M. tuberculosis death. Moxifloxacin lethality was mitigated by supplementing bacterial cultures with an ROS scavenger (thiourea), an iron chelator (bipyridyl), and, after drug removal, an antioxidant enzyme (catalase). Lethality was also reduced by hypoxia and nutrient starvation. Moxifloxacin increased the expression of genes involved in the oxidative stress response, iron-sulfur cluster biogenesis, and DNA repair. Surprisingly, and in contrast with Escherichia coli studies, moxifloxacin decreased expression of genes involved in respiration, suppressed oxygen consumption, increased the NADH/NAD+ ratio, and increased the labile iron pool in M. tuberculosis. Lowering the NADH/NAD+ ratio in M. tuberculosis revealed that NADH-reductive stress facilitates an iron-mediated ROS surge and moxifloxacin lethality. Treatment with N-acetyl cysteine (NAC) accelerated respiration and ROS production, increased moxifloxacin lethality, and lowered the mutant prevention concentration. Moxifloxacin induced redox stress in M. tuberculosis inside macrophages, and cotreatment with NAC potentiated the antimycobacterial efficacy of moxifloxacin during nutrient starvation, inside macrophages, and in mice, where NAC restricted the emergence of resistance. Thus, NADH-reductive stress contributes to moxifloxacin-mediated killing of M. tuberculosis, and the respiration stimulator (NAC) enhances lethality and suppresses the emergence of drug resistance.

PathExt: a general framework for path-based mining of omics-integrated biological networks.

MOTIVATION: Transcriptomes are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs. RESULTS: We provide a computational tool-PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (i) Mycobacterium tuberculosis response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (ii) tissue-relevant genes and processes using transcriptomic data for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls. AVAILABILITYAND IMPLEMENTATION: The source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cyclic di-GMP sensing histidine kinase PdtaS controls mycobacterial adaptation to carbon sources.

Cell signaling relies on second messengers to transduce signals from the sensory apparatus to downstream signaling pathway components. In bacteria, one of the most important and ubiquitous second messenger is the small molecule cyclic diguanosine monophosphate (c-di-GMP). While the biosynthesis, degradation, and regulatory pathways controlled by c-di-GMP are well characterized, the mechanisms through which c-di-GMP controls these processes are not entirely understood. Herein we present the report of a c-di-GMP sensing sensor histidine kinase PdtaS (Rv3220c), which binds to c-di-GMP at submicromolar concentrations, subsequently perturbing signaling of the PdtaS-PdtaR (Rv1626) two-component system. Aided by biochemical analysis, genetics, molecular docking, FRET microscopy, and structural modelling, we have characterized the binding of c-di-GMP in the GAF domain of PdtaS. We show that a pdtaS knockout in Mycobacterium smegmatis is severely compromised in growth on amino acid deficient media and exhibits global transcriptional dysregulation. The perturbation of the c-di-GMP-PdtaS-PdtaR axis results in a cascade of cellular changes recorded by a multiparametric systems' approach of transcriptomics, unbiased metabolomics, and lipid analyses.

Fast Local Alignment of Protein Pockets (FLAPP): A System-Compiled Program for Large-Scale Binding Site Alignment.

Protein function is a direct consequence of its sequence, structure, and the arrangement at the binding site. Bioinformatics using sequence analysis is typically used to gain a first insight into protein function. Protein structures, on the other hand, provide a higher resolution platform into understanding functions. As the protein structural information is increasingly becoming available through experimental structure determination and through advances in computational methods for structure prediction, the opportunity to utilize these data is also increasing. Structural analysis of small molecule ligand binding sites in particular provides a direct and more accurate window to infer protein function. However, it remains a poorly utilized resource due to the huge computational cost of existing methods that make large-scale structural comparisons of binding sites prohibitive. Here, we present an algorithm called FLAPP that produces very rapid atomic level alignments. By combining clique matching in graphs and the power of modern CPU architectures, FLAPP aligns a typical pair of binding sites at ∼12.5 ms using a single CPU core, ∼1 ms using 12 cores on a standard desktop machine, and performs a PDB-wide scan in 1-2 min. We perform rigorous validation of the algorithm at multiple levels of complexity and show that FLAPP provides accurate alignments. We also present a case study involving vitamin B12 binding sites to showcase the usefulness of FLAPP for performing an exhaustive alignment-based PDB-wide scan. We expect that this tool will be invaluable to the scientific community to quickly align millions of site pairs on a normal desktop machine to gain insights into protein function and drug discovery for drug target and off-target identification and polypharmacology.

Design of a heme-binding peptide motif adopting a ß-hairpin conformation.

Heme-binding proteins constitute a large family of catalytic and transport proteins. Their widespread presence as globins and as essential oxygen and electron transporters, along with their diverse enzymatic functions, have made them targets for protein design. Most previously reported designs involved the use of α-helical scaffolds, and natural peptides also exhibit a strong preference for these scaffolds. However, the reason for this preference is not well-understood, in part because alternative protein designs, such as those with ß-sheets or hairpins, are challenging to perform. Here, we report the computational design and experimental validation of a water-soluble heme-binding peptide, Pincer-1, composed of predominantly ß-scaffold secondary structures. Such heme-binding proteins are rarely observed in nature, and by designing such a scaffold, we simultaneously increase the known fold space of heme-binding proteins and expand the limits of computational design methods. For a ß-scaffold, two tryptophan zipper ß-hairpins sandwiching a heme molecule were linked through an N-terminal cysteine disulfide bond. ß-Hairpin orientations and residue selection were performed computationally. Heme binding was confirmed through absorbance experiments and surface plasmon resonance experiments (KD = 730 ± 160 nm). CD and NMR experiments validated the ß-hairpin topology of the designed peptide. Our results indicate that a helical scaffold is not essential for heme binding and reveal the first designed water-soluble, heme-binding ß-hairpin peptide. This peptide could help expand the search for and design space to cytoplasmic heme-binding proteins.

Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria.

There is a pressing need for new therapeutics to combat multidrug- and carbapenem-resistant bacterial pathogens. This challenge prompted us to use a long short-term memory (LSTM) language model to understand the underlying grammar, i.e. the arrangement and frequencies of amino acid residues, in known antimicrobial peptide sequences. According to the output of our LSTM network, we synthesized 10 peptides and tested them against known bacterial pathogens. All of these peptides displayed broad-spectrum antimicrobial activity, validating our LSTM-based peptide design approach. Our two most effective antimicrobial peptides displayed activity against multidrug-resistant clinical isolates of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and coagulase-negative staphylococci strains. High activity against extended-spectrum ß-lactamase, methicillin-resistant S. aureus, and carbapenem-resistant strains was also observed. Our peptides selectively interacted with and disrupted bacterial cell membranes and caused secondary gene-regulatory effects. Initial structural characterization revealed that our most effective peptide appeared to be well folded. We conclude that our LSTM-based peptide design approach appears to have correctly deciphered the underlying grammar of antimicrobial peptide sequences, as demonstrated by the experimentally observed efficacy of our designed peptides.

Role of genetic heterogeneity in determining the epidemiological severity of H1N1 influenza.

Genetic differences contribute to variations in the immune response mounted by different individuals to a pathogen. Such differential response can influence the spread of infectious disease, indicating why such diseases impact some populations more than others. Here, we study the impact of population-level genetic heterogeneity on the epidemic spread of different strains of H1N1 influenza. For a population with known HLA class-I allele frequency and for a given H1N1 viral strain, we classify individuals into sub-populations according to their level of susceptibility to infection. Our core hypothesis is that the susceptibility of a given individual to a disease such as H1N1 influenza is inversely proportional to the number of high affinity viral epitopes the individual can present. This number can be extracted from the HLA genetic profile of the individual. We use ethnicity-specific HLA class-I allele frequency data, together with genome sequences of various H1N1 viral strains, to obtain susceptibility sub-populations for 61 ethnicities and 81 viral strains isolated in 2009, as well as 85 strains isolated in other years. We incorporate these data into a multi-compartment SIR model to analyse the epidemic dynamics for these (ethnicity, viral strain) epidemic pairs. Our results show that HLA allele profiles which lead to a large spread in individual susceptibility values can act as a protective barrier against the spread of influenza. We predict that populations skewed such that a small number of highly susceptible individuals coexist with a large number of less susceptible ones, should exhibit smaller outbreaks than populations with the same average susceptibility but distributed more uniformly across individuals. Our model tracks some well-known qualitative trends of influenza spread worldwide, suggesting that HLA genetic diversity plays a crucial role in determining the spreading potential of different influenza viral strains across populations.

Methionine synthase is localized to the nucleus in Pichia pastoris and Candida albicans and to the cytoplasm in Saccharomyces cerevisiae.

Methionine synthase (MS) catalyzes methylation of homocysteine, the last step in the biosynthesis of methionine, which is essential for the regeneration of tetrahydrofolate and biosynthesis of S-adenosylmethionine. Here, we report that MS is localized to the nucleus of Pichia pastoris and Candida albicans but is cytoplasmic in Saccharomyces cerevisiae The P. pastoris strain carrying a deletion of the MET6 gene encoding MS (Ppmet6) exhibits methionine as well as adenine auxotrophy indicating that MS is required for methionine as well as adenine biosynthesis. Nuclear localization of P. pastoris MS (PpMS) was abrogated by the deletion of 107 C-terminal amino acids or the R742A mutation. In silico analysis of the PpMS structure indicated that PpMS may exist in a dimer-like configuration in which Arg-742 of a monomer forms a salt bridge with Asp-113 of another monomer. Biochemical studies indicate that R742A as well as D113R mutations abrogate nuclear localization of PpMS and its ability to reverse methionine auxotrophy of Ppmet6 Thus, association of two PpMS monomers through the interaction of Arg-742 and Asp-113 is essential for catalytic activity and nuclear localization. When PpMS is targeted to the cytoplasm employing a heterologous nuclear export signal, it is expressed at very low levels and is unable to reverse methionine and adenine auxotrophy of Ppmet6 Thus, nuclear localization is essential for the stability and function of MS in P. pastoris. We conclude that nuclear localization of MS is a unique feature of respiratory yeasts such as P. pastoris and C. albicans, and it may have novel moonlighting functions in the nucleus.

Biophysical and biochemical characterization of Rv3405c, a tetracycline repressor protein from Mycobacterium tuberculosis.

Mycobacterium tuberculosis, the causative agent of tuberculosis disease, is one among the deadliest pathogens in the world. Due to long treatment regimen, HIV co-infection, persistence of bacilli in latent form and development of XDR and TDR strains of Mtb, tuberculosis has posed serious concerns for managing the disease, and calls for discovery of new drugs and drug targets. Using a computational pipeline involving analysis of the structural models of the Mtb proteome and an analysis of the ATPome, followed by a series of filters to identify druggable proteins, solubility and length of the protein, several candidate proteins were shortlisted. From this, Rv3405c, a tetR family of DNA binding protein involved in antibiotic resistance, was identified as one of the good drug targets. Rv3405c binds to the upstream non-coding region of Rv3406 and causes repression of Rv3406 activity there by affecting the downstream processes involved in antibiotic resistance was further characterized. The Rv3405c gene was cloned; the gene product was over-expressed in E. coli and purified by Ni NTA chromatography. DNA binding studies by EMSA showed that the recombinant Rv3405c protein binds to the DNA sequence corresponding to the promoter region of Rv3406 and upon addition of tetracycline, the DNA binding activity was lost. ß-galactosidase reporter assay in E. coli using both wild type and a DNA binding defective mutant protein indeed proved that Rv3405c acts as a repressor.

Deciphering common recognition principles of nucleoside mono/di and tri-phosphates binding in diverse proteins via structural matching of their binding sites.

Nucleoside triphosphate (NTP) ligands are of high biological importance and are essential for all life forms. A pre-requisite for them to participate in diverse biochemical processes is their recognition by diverse proteins. It is thus of great interest to understand the basis for such recognition in different proteins. Towards this, we have used a structural bioinformatics approach and analyze structures of 4677 NTP complexes available in Protein Data Bank (PDB). Binding sites were extracted and compared exhaustively using PocketMatch, a sensitive in-house site comparison algorithm, which resulted in grouping the entire dataset into 27 site-types. Each of these site-types represent a structural motif comprised of two or more residue conservations, derived using another in-house tool for superposing binding sites, PocketAlign. The 27 site-types could be grouped further into 9 super-types by considering partial similarities in the sites, which indicated that the individual site-types comprise different combinations of one or more site features. A scan across PDB using the 27 structural motifs determined the motifs to be specific to NTP binding sites, and a computational alanine mutagenesis indicated that residues identified to be highly conserved in the motifs are also most contributing to binding. Alternate orientations of the ligand in several site-types were observed and rationalized, indicating the possibility of some residues serving as anchors for NTP recognition. The presence of multiple site-types and the grouping of multiple folds into each site-type is strongly suggestive of convergent evolution. Knowledge of determinants obtained from this study will be useful for detecting function in unknown proteins. Proteins 2017; 85:1699-1712. © 2017 Wiley Periodicals, Inc.

Resolving protein structure-function-binding site relationships from a binding site similarity network perspective.

Functional annotation is seldom straightforward with complexities arising due to functional divergence in protein families or functional convergence between non-homologous protein families, leading to mis-annotations. An enzyme may contain multiple domains and not all domains may be involved in a given function, adding to the complexity in function annotation. To address this, we use binding site information from bound cognate ligands and catalytic residues, since it can help in resolving fold-function relationships at a finer level and with higher confidence. A comprehensive database of 2,020 fold-function-binding site relationships has been systematically generated. A network-based approach is employed to capture the complexity in these relationships, from which different types of associations are deciphered, that identify versatile protein folds performing diverse functions, same function associated with multiple folds and one-to-one relationships. Binding site similarity networks integrated with fold, function, and ligand similarity information are generated to understand the depth of these relationships. Apart from the observed continuity in the functional site space, network properties of these revealed versatile families with topologically different or dissimilar binding sites and structural families that perform very similar functions. As a case study, subtle changes in the active site of a set of evolutionarily related superfamilies are studied using these networks. Tracing of such similarities in evolutionarily related proteins provide clues into the transition and evolution of protein functions. Insights from this study will be helpful in accurate and reliable functional annotations of uncharacterized proteins, poly-pharmacology, and designing enzymes with new functional capabilities. Proteins 2017; 85:1319-1335. © 2017 Wiley Periodicals, Inc.

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials.

MOTIVATION: T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. RESULTS: We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles. CONCLUSIONS: HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods. AVAILABILITY AND IMPLEMENTATION: The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy CONTACT: : nchandra@biochem.iisc.ernet.in SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploring anti-malarial potential of FDA approved drugs: an in silico approach.

BACKGROUND: The critically important issue on emergence of drug-resistant malarial parasites is compounded by cross resistance, where resistance to one drug confers resistance to other chemically similar drugs or those that share mode of action. This aspect requires discovery of new anti-malarial compounds or formulation of new combination therapy. The current study attempts to contribute towards accelerating anti-malarial drug development efforts, by exploring the potential of existing FDA-approved drugs to target proteins of Plasmodium falciparum. METHODS: Using comparative sequence and structure analyses, FDA-approved drugs, originally developed against other pathogens, were identified as potential repurpose-able candidates against P. falciparum. The rationale behind the undertaken approach is the likeliness of small molecules to bind to homologous targets. Such a study of evolutionary relationships between established targets and P. falciparum proteins aided in identification of approved drug candidates that can be explored for their anti-malarial potential. RESULTS: Seventy-one FDA-approved drugs were identified that could be repurposed against P. falciparum. A total of 89 potential targets were recognized, of which about 70 are known to participate in parasite housekeeping machinery, protein biosynthesis, metabolic pathways and cell growth and differentiation, which can be prioritized for chemotherapeutic interventions. An additional aspect of prioritization of predicted repurpose-able drugs has been explored on the basis of ability of the drugs to permeate cell membranes, i.e., lipophilicity, since the parasite resides within a parasitophorous vacuole, within the erythrocyte, during the blood stages of infection. Based on this consideration, 46 of 71 FDA-approved drugs have been identified as feasible repurpose-able candidates against P. falciparum, and form a first-line for laboratory investigations. At least five of the drugs identified in the current analysis correspond to existing antibacterial agents already under use as repurposed anti-malarial agents. CONCLUSIONS: The drug-target associations predicted, primarily by taking advantage of evolutionary information, provide a valuable resource of attractive and feasible candidate drugs that can be readily taken through further stages of anti-malarial drug development pipeline.

NrichD database: sequence databases enriched with computationally designed protein-like sequences aid in remote homology detection.

NrichD (http://proline.biochem.iisc.ernet.in/NRICHD/) is a database of computationally designed protein-like sequences, augmented into natural sequence databases that can perform hops in protein sequence space to assist in the detection of remote relationships. Establishing protein relationships in the absence of structural evidence or natural 'intermediately related sequences' is a challenging task. Recently, we have demonstrated that the computational design of artificial intermediary sequences/linkers is an effective approach to fill naturally occurring voids in protein sequence space. Through a large-scale assessment we have demonstrated that such sequences can be plugged into commonly employed search databases to improve the performance of routinely used sequence search methods in detecting remote relationships. Since it is anticipated that such data sets will be employed to establish protein relationships, two databases that have already captured these relationships at the structural and functional domain level, namely, the SCOP database and the Pfam database, have been 'enriched' with these artificial intermediary sequences. NrichD database currently contains 3,611,010 artificial sequences that have been generated between 27,882 pairs of families from 374 SCOP folds. The data sets are freely available for download. Additional features include the design of artificial sequences between any two protein families of interest to the user.