The role of gaming for information, education and communication of AMR: full review of online education resources.

Background: The first objective of the Global Action Plan on antimicrobial resistance (AMR) is to improve awareness and understanding of AMR through effective communication, education and training. Towards this several efforts have been made to create AMR awareness resources. The aim of these resources is to inform the public about responsible antibiotic use and drive positive behavioural change. Digital media and specifically games can serve as unique innovative platforms in public communication programmes. Objectives: This study focuses on compiling and evaluating game-based AMR resources. Recognizing the engaging and creative potential of games as learning tools, the primary objective of this study was to identify games that can be used, individually or in combination depending on their unique focus and gameplay experience, for AMR awareness. Furthermore, games are evaluated on five objective criteria and recommendations are made towards further development of gaming resources towards AMR awareness. Methods: Meticulous curation was performed to mine information, education and communication resources, with a primary focus on games for AMR awareness and evaluating them based on game design and gameplay, AMR content and learning, engagement and replay appeal, learning outcomes, and level of difficulty and challenges. Results: In this study, we selected 12 AMR games. Our evaluations, spanning various gamification elements and interactive parameters, informed recommendations for future AMR resource development, including multilevel game design, varied graphics, simple-to-understand rules, sustained challenge and a sense of reward, among others. Conclusions: This study generated the first-ever comprehensive catalogue of AMR games that may assist public communication programmes for AMR awareness. Evaluation of these games led to actionable design recommendations for future resources towards effective communication of AMR complexity, enhanced learning and awareness.

Comparative genomics and characterization of a multidrug-resistant Acinetobacter baumannii VRL-M19 isolated from a crowded setting in India.

A crowded vegetable market serves as a mass gathering, posing a potential risk for infection transmission. In this study, we isolated a multidrug-resistant Acinetobacter baumannii strain, VRL-M19, from the air of such a market and conducted comparative genomics and phenotypic characterization. Antimicrobial susceptibility testing, genome sequencing using Illumina HiSeq X10, and pan-genome analysis with 788 clinical isolates identified core, accessory, and unique drug-resistant determinants. Mutational analysis of drug-resistance genes, virulence factor annotation, in vitro pathogenicity assessment, subsystem analysis, Multilocus sequence typing, and whole genome phylogenetic analysis were performed. VRL-M19 exhibited multidrug resistance with 69 determinants, and analysis across 788 clinical isolates and 350 Indian isolates revealed more accessory genes (52 out of 69) in the Indian isolates. Multiple mutations were observed in drug target modification genes, and the strain was identified as a moderate biofilm-former with 55 virulence factors. Whole genome phylogenetics indicated a close relationship between VRL-M19 and clinical A. baumannii strains. In conclusion, our comprehensive study suggests that VRL-M19 is a multidrug-resistant, potential pathogen with biofilm-forming capabilities, closely associated with clinical A. baumannii strains.

Host factors subverted by Mycobacterium tuberculosis: Potential targets for host directed therapy.

INTRODUCTION: Despite new approaches in the diagnosis and treatment of tuberculosis (TB), it continues to be a major health burden. Several immunotherapies that potentiate the immune response have come up as adjuncts to drug therapies against drug resistant TB strains; however, there needs to be an urgent appraisal of host specific drug targets for improving their clinical management and to curtail disease progression. Presently, various host directed therapies (HDTs) exist (repurposed drugs, nutraceuticals, monoclonal antibodies and immunomodulatory agents), but these mostly address molecules that combat disease progression. AREAS COVERED: The current review discusses major Mycobacterium tuberculosis (M. tuberculosis) survival paradigms inside the host and presents a plethora of host targets subverted by M. tuberculosis which can be further explored for future HDTs. The host factors unique to M. tuberculosis infection (in humans) have also been identified through an in-silico interaction mapping. EXPERT OPINION: HDTs could become the next-generation adjunct therapies in order to counter antimicrobial resistance and virulence, as well as to reduce the duration of existing TB treatments. However, current scientific efforts are largely directed toward combatants rather than host molecules co-opted by M. tuberculosis for its survival. This might drive the immune system to a hyper-inflammatory condition; therefore, we emphasize that host factors subverted by M. tuberculosis, and their subsequent neutralization, must be considered for development of better HDTs.

From the reference human genome to human pangenome: Premise, promise and challenge.

The Reference Human Genome remains the single most important resource for mapping genetic variations and assessing their impact. However, it is monophasic, incomplete and not representative of the variation that exists in the population. Given the extent of ethno-geographic diversity and the consequent diversity in clinical manifestations of these variations, population specific references were developed overtime. The dramatically plummeting cost of sequencing whole genomes and the advent of third generation long range sequencers allowing accurate, error free, telomere-to-telomere assemblies of human genomes present us with a unique and unprecedented opportunity to develop a more composite standard reference consisting of a collection of multiple genomes that capture the maximal variation existing in the population, with the deepest annotation possible, enabling a realistic, reliable and actionable estimation of clinical significance of specific variations. The Human Pangenome Project thus is a logical next step promising a more accurate and global representation of genomic variations. The pangenome effort must be reciprocally complemented with precise variant discovery tools and exhaustive annotation to ensure unambiguous clinical assessment of the variant in ethno-geographical context. Here we discuss a broad roadmap, the challenges and way forward in developing a universal pangenome reference including data visualization techniques and integration of prior knowledge base in the new graph based architecture and tools to submit, compare, query, annotate and retrieve relevant information from the pangenomes. The biggest challenge, however, will be the ethical, legal and social implications and the training of human resource to the new reference paradigm.

Galaxy ASIST: A web-based platform for mapping and assessment of global standards of antimicrobial susceptibility: A case study in Acinetobacter baumannii genomes.

Introduction: Antimicrobial susceptibility testing (AST) is used to determine the susceptibility of an organism to antibiotics. The determination of susceptibility is based on MIC breakpoints and is provided by EUCAST and CLSI. Likewise, phenotypic classification criteria developed by CDC/ECDC are used for the classification of pathogens into susceptible, multidrug-resistant, extremely drug-resistant, or totally drug-resistant categories. Whole-genome sequencing (WGS)-based diagnosis is now supplementing existing gold-standard microbiology methods for rapid and more precise AST, and therefore, EUCAST recommended quality criteria to assess whole-genome sequence for reporting the same. In this study, these three global standards, MIC breakpoints, phenotypic classification, and genome quality, are applied to the largest publicly available data for Acinetobacter baumannii (AB), the most critical priority pathogen identified by WHO. Materials and Methods: The drug sensitivity profile and genomes for isolates of AB were obtained from PATRIC and evaluated with respect to AST standards (CLSI and EUCAST). Whole genome quality assessment and antimicrobial resistance mapping is performed with QUAST and ABRicate, respectively. Four in-house methods are developed for mapping standards and are integrated into a Galaxy workflow based system, Galaxy-ASIST. Analysis of the extent of agreement between CLSI 2022 and EUCAST 2022 for antibiotics was carried out using Cohen's kappa statistics. Results and Discussion: An automated pipeline, Galaxy-ASIST, is designed and developed for the characterization of clinical isolates based on these standards. Evaluation of over 6,500 AB strains using Galaxy-ASIST indicated that only 10% of the publicly available datasets have metadata to implement these standards. Furthermore, given that CLSI and EUCAST have different MIC breakpoints, discrepancies are observed in the classification of resistant and susceptible isolates following these standards. It is, therefore, imperative that platforms are developed that allow the evaluation of ever increasing phenotypic and genome sequence datasets for AST. Galaxy-ASIST offers a centralized repository and a structured metadata architecture to provide a single globally acceptable framework for AST profiling of clinical isolates based on global standards. The platform also offers subsequent fine mapping of antimicrobial-resistant determinants. Galaxy-ASIST is freely available at https://ab-openlab.csir.res.in/asist.

MitoLink: A generic integrated web-based workflow system to evaluate genotype-phenotype correlations in human mitochondrial diseases: Observations from the GenomeAsia Pilot project.

MitoLink is a generic, scalable and modular web-based workflow system developed to study genotype-phenotype correlations in human mitochondrial diseases. MitoLink integrates applications for assessment of genomic variation and currently houses genome-wide datasets from GenomeAsia Pilot project, gnomAD, ClinVar and DisGenNet. In this study, a reference list of nearly 3975 proteins (both nuclear and mitochondrial encoded) with mitochondrial function is reported. This protein set is mapped to disease associated variants in the GenomeAsia Pilot Project and DisGenNet and evaluated for pathogenicity as defined by ClinVar. Observations of shared genetic components in potential comorbidities are discussed from gene-disease network in Asian population, however, the platform is generic and can be applied to any population dataset. MitoLink is a unique customized workflow system that allows for systematic storage, extraction, analysis and visualization of genomic variation to understand genotype-phenotype correlations for mitochondrial diseases. Given the modularity of tool and data integration, MitoLink is a scalable system that can accommodate a diverse set of applications linked via standard data structure within the framework of Galaxy. MitoLink is built on FAIR principles and supports creation of reproducible workflows towards understanding genotype-phenotype correlations across several disease phenotypes globally.

Specifications of the ACMG/AMP standards and guidelines for mitochondrial DNA variant interpretation.

Mitochondrial DNA (mtDNA) variant pathogenicity interpretation has special considerations given unique features of the mtDNA genome, including maternal inheritance, variant heteroplasmy, threshold effect, absence of splicing, and contextual effects of haplogroups. Currently, there are insufficient standardized criteria for mtDNA variant assessment, which leads to inconsistencies in clinical variant pathogenicity reporting. An international working group of mtDNA experts was assembled within the Mitochondrial Disease Sequence Data Resource Consortium and obtained Expert Panel status from ClinGen. This group reviewed the 2015 American College of Medical Genetics and Association of Molecular Pathology standards and guidelines that are widely used for clinical interpretation of DNA sequence variants and provided further specifications for additional and specific guidance related to mtDNA variant classification. These Expert Panel consensus specifications allow for consistent consideration of the unique aspects of the mtDNA genome that directly influence variant assessment, including addressing mtDNA genome composition and structure, haplogroups and phylogeny, maternal inheritance, heteroplasmy, and functional analyses unique to mtDNA, as well as specifications for utilization of mtDNA genomic databases and computational algorithms.

Correction: MitoLSDB: A Comprehensive Resource to Study Genotype to Phenotype Correlations in Human Mitochondrial DNA Variations.

[This corrects the article DOI: 10.1371/journal.pone.0060066.].

Hybrid Dynamic Pharmacophore Models as Effective Tools to Identify Novel Chemotypes for Anti-TB Inhibitor Design: A Case Study With Mtb-DapB.

Antimicrobial resistance (AMR) is one of the most serious global public health threats as it compromises the successful treatment of deadly infectious diseases like tuberculosis. New therapeutics are constantly needed but it takes a long time and is expensive to explore new biochemical space. One way to address this issue is to repurpose the validated targets and identify novel chemotypes that can simultaneously bind to multiple binding pockets of these targets as a new lead generation strategy. This study reports such a strategy, dynamic hybrid pharmacophore model (DHPM), which represents the combined interaction features of different binding pockets contrary to the conventional approaches, where pharmacophore models are generated from single binding sites. We have considered Mtb-DapB, a validated mycobacterial drug target, as our model system to explore the effectiveness of DHPMs to screen novel unexplored compounds. Mtb-DapB has a cofactor binding site (CBS) and an adjacent substrate binding site (SBS). Four different model systems of Mtb-DapB were designed where, either NADPH/NADH occupies CBS in presence/absence of an inhibitor 2, 6-PDC in the adjacent SBS. Two more model systems were designed, where 2, 6-PDC was linked to NADPH and NADH to form hybrid molecules. The six model systems were subjected to 200 ns molecular dynamics simulations and trajectories were analyzed to identify stable ligand-receptor interaction features. Based on these interactions, conventional pharmacophore models (CPM) were generated from the individual binding sites while DHPMs were created from hybrid-molecules occupying both binding sites. A huge library of 1,563,764 publicly available molecules were screened by CPMs and DHPMs. The screened hits obtained from both types of models were compared based on their Hashed binary molecular fingerprints and 4-point pharmacophore fingerprints using Tanimoto, Cosine, Dice and Tversky similarity matrices. Molecules screened by DHPM exhibited significant structural diversity, better binding strength and drug like properties as compared to the compounds screened by CPMs indicating the efficiency of DHPM to explore new chemical space for anti-TB drug discovery. The idea of DHPM can be applied for a wide range of mycobacterial or other pathogen targets to venture into unexplored chemical space.

MtBrowse: An integrative genomics browser for human mitochondrial DNA.

The human mitochondrion is a unique semi-autonomous organelle with a genome of its own and also requires nuclear encoded components to carry out its functions. In addition to being the powerhouse of the cell, mitochondria plays a central role in several metabolic pathways. It is therefore challenging to delineate the cause-effect relationship in context of mitochondrial dysfunction. Several studies implicate mutations in mitochondrial DNA (mtDNA) in various complex diseases. The human mitochondrial DNA (mtDNA) encodes a set of 37 genes, 13 protein coding, 22 tRNAs and two ribosomal RNAs, which are essential structural and functional components of the electron transport chain. As mentioned above, variations in these genes have been implicated in a broad spectrum of diseases and are extensively reported in literature and various databases. A large number of databases and prediction methods have been published to elucidate the role of human mitochondrial DNA in various disease phenotypes. However, there is no centralized resource to visualize this genotype-phenotype data. Towards this, we have developed MtBrowse: an integrative genomics browser for human mtDNA. As of now, MtBrowse has four categories - Gene, Disease, Reported variation and Variation prediction. These categories have 105 tracks and house data on mitochondrial reference genes, around 600 variants reported in literature with respect to various disease phenotypes and predictions for potential pathogenic variations in protein-coding genes. MtBrowse also hosts genomic variation data from over 5000 individuals on 22 disease phenotypes. MtBrowse may be accessed at http://ab-openlab.csir.res.in/cgi-bin/gb2/gbrowse.

RepTB: a gene ontology based drug repurposing approach for tuberculosis.

Tuberculosis (TB) is the world's leading infectious killer with 1.8 million deaths in 2015 as reported by WHO. It is therefore imperative that alternate routes of identification of novel anti-TB compounds are explored given the time and costs involved in new drug discovery process. Towards this, we have developed RepTB. This is a unique drug repurposing approach for TB that uses molecular function correlations among known drug-target pairs to predict novel drug-target interactions. In this study, we have created a Gene Ontology based network containing 26,404 edges, 6630 drug and 4083 target nodes. The network, enriched with molecular function ontology, was analyzed using Network Based Inference (NBI). The association scores computed from NBI are used to identify novel drug-target interactions. These interactions are further evaluated based on a combined evidence approach for identification of potential drug repurposing candidates. In this approach, targets which have no known variation in clinical isolates, no human homologs, and are essential for Mtb's survival and or virulence are prioritized. We analyzed predicted DTIs to identify target pairs whose predicted drugs may have synergistic bactericidal effect. From the list of predicted DTIs from RepTB, four TB targets, namely, FolP1 (Dihydropteroate synthase), Tmk (Thymidylate kinase), Dut (Deoxyuridine 5'-triphosphate nucleotidohydrolase) and MenB (1,4-dihydroxy-2-naphthoyl-CoA synthase) may be selected for further validation. In addition, we observed that in some cases there is significant chemical structure similarity between predicted and reported drugs of prioritized targets, lending credence to our approach. We also report new chemical space for prioritized targets that may be tested further. We believe that with increasing drug-target interaction dataset RepTB will be able to offer better predictive value and is amenable for identification of drug-repurposing candidates for other disease indications too.

Predicting promiscuous antigenic T cell epitopes of Mycobacterium tuberculosis mymA operon proteins binding to MHC Class I and Class II molecules.

Limited efficacy of Bacillus Calmette-Guérin vaccine has raised the need to explore other immunogenic candidates to develop an effective vaccine against Mycobacterium tuberculosis (Mtb). Both CD4+ and CD8+ T cells play a critical role in host immunity to Mtb. Infection of macrophages with Mtb results in upregulation of mymA operon genes thereby suggesting their importance as immune targets. In the present study, after exclusion of self-peptides mymA operon proteins of Mtb were analyzed in silico for the presence of Human Leukocyte Antigen (HLA) Class I and Class II binding peptides using Bioinformatics and molecular analysis section, NetMHC 3.4, ProPred and Immune epitope database software. Out of 56 promiscuous epitopes obtained, 41 epitopes were predicted to be antigenic for MHC Class I. In MHC Class II, out of 336 promiscuous epitopes obtained, 142 epitopes were predicted to be antigenic. The comparative bioinformatics analysis of mymA operon proteins found Rv3083 to be the best vaccine candidate. Molecular docking was performed with the most antigenic peptides of Rv3083 (LASGAASVV with alleles HLA-B51:01, HAATSGTLI with HLA-A02, IVTATGLNI and EKIHYGLKVNTA with HLA-DRB1_01:01) to study the structural basis for recognition of peptides by various HLA molecules. The software binding prediction was validated by the obtained molecular docking score of peptide-HLA complex. These peptides can be further investigated for their immunological relevance in patients of tuberculosis using major histocompatibility complex tetramer approach.

Analysis of the DosR regulon genes to select cytotoxic T lymphocyte epitope specific vaccine candidates using a reverse vaccinology approach.

OBJECTIVE/BACKGROUND: There is an urgent need for a more effective vaccine against Mycobacterium tuberculosis (Mtb). Although CD4+ T cells play a central role in host immunity to Mtb, recent evidence suggests a critical role of CD8+ T cells in combating Mtb. In the present study, we have predicted HLA antigen class I binding peptides of DosR operon using an in-silico approach. This method is useful as an initial computational filtration of probable epitopes based on their binding ability and antigenicity. METHODS: CD8+ epitopes were predicted by software NetMHC 3.4 and BIMAS. Self-peptides were found and excluded by indigenously developed Perl script. Antigenicity of promiscuous peptides was predicted using a VaxiJen server. The top VaxiJen scoring antigenic peptides were docked to globally relevant HLA allele using CABS dock and Hex program. RESULTS: A total of 1436 overlapping nonamer peptides were generated which gave 46 promiscuous epitopes, 25 were predicted to be antigenic. Rv2627 epitope "SAFRPPLV" which gave the highest Vaxijen score of 1.9157 and showed binding to all the three HLA loci. The top VaxiJen scoring antigenic peptides were docked and had significant interactions with residues of the HLA class I molecule indicating them to be good cytotoxic T lymphocyte epitopes. CONCLUSION: Our study has generated several promiscuous antigenic peptides capable of binding to major histocompatibility complex class I with high affinity. These epitopes can become part of a postexposure multivalent subunit vaccine upon experimental validation.

dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides.

Increasingly, biofilms are being recognised for their causative role in persistent infections (like cystic fibrosis, otitis media, diabetic foot ulcers) and nosocomial diseases (biofilm-infected vascular catheters, implants and prosthetics). Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. We have developed dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides. The six SVM and Weka models implemented on dPABBs were observed to identify anti-biofilm peptides on the basis of their whole amino acid composition, selected residue features and the positional preference of the residues (maximum accuracy, sensitivity, specificity and MCC of 95.24%, 92.50%, 97.73% and 0.91, respectively, on the training datasets). On the N-terminus, it was seen that either of the cationic polar residues, R and K, is present at all five positions in case of the anti-biofilm peptides, whereas in the QS peptides, the uncharged polar residue S is preponderant at the first (also anionic polar residues D, E), third and fifth positions. Positive predictions were also obtained for 29 FDA-approved peptide drugs and ten antimicrobial peptides in clinical development, indicating at their possible repurposing for anti-biofilm therapy. dPABBs is freely accessible on: http://ab-openlab.csir.res.in/abp/antibiofilm/.

Resources, challenges and way forward in rare mitochondrial diseases research.

Over 300 million people are affected by about 7000 rare diseases globally. There are tremendous resource limitations and challenges in driving research and drug development for rare diseases. Hence, innovative approaches are needed to identify potential solutions. This review focuses on the resources developed over the past years for analysis of genome data towards understanding disease biology especially in the context of mitochondrial diseases, given that mitochondria are central to major cellular pathways and their dysfunction leads to a broad spectrum of diseases. Platforms for collaboration of research groups, clinicians and patients and the advantages of community collaborative efforts in addressing rare diseases are also discussed. The review also describes crowdsourcing and crowdfunding efforts in rare diseases research and how the upcoming initiatives for understanding disease biology including analyses of large number of genomes are also applicable to rare diseases.

FROG - Fingerprinting Genomic Variation Ontology.

Genetic variations play a crucial role in differential phenotypic outcomes. Given the complexity in establishing this correlation and the enormous data available today, it is imperative to design machine-readable, efficient methods to store, label, search and analyze this data. A semantic approach, FROG: "FingeRprinting Ontology of Genomic variations" is implemented to label variation data, based on its location, function and interactions. FROG has six levels to describe the variation annotation, namely, chromosome, DNA, RNA, protein, variations and interactions. Each level is a conceptual aggregation of logically connected attributes each of which comprises of various properties for the variant. For example, in chromosome level, one of the attributes is location of variation and which has two properties, allosomes or autosomes. Another attribute is variation kind which has four properties, namely, indel, deletion, insertion, substitution. Likewise, there are 48 attributes and 278 properties to capture the variation annotation across six levels. Each property is then assigned a bit score which in turn leads to generation of a binary fingerprint based on the combination of these properties (mostly taken from existing variation ontologies). FROG is a novel and unique method designed for the purpose of labeling the entire variation data generated till date for efficient storage, search and analysis. A web-based platform is designed as a test case for users to navigate sample datasets and generate fingerprints. The platform is available at http://ab-openlab.csir.res.in/frog.

BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts.

BACKGROUND: Tuberculosis (TB) is the second leading cause of death from a single infectious organism, demanding attention towards discovery of novel anti-tubercular compounds. Natural products or their derivatives have provided more than 50% of all existing drugs, offering a chemically diverse space for discovery of novel drugs. DESCRIPTION: BioPhytMol has been designed to systematically curate and analyze the anti-mycobacterial natural product chemical space. BioPhytMol is developed as a drug-discovery community resource with anti-mycobacterial phytomolecules and plant extracts. Currently, it holds 2582 entries including 188 plant families (692 genera and 808 species) from global flora, manually curated from literature. In total, there are 633 phytomolecules (with structures) curated against 25 target mycobacteria. Multiple analysis approaches have been used to prioritize the library for drug-like compounds, for both whole cell screening and target-based approaches. In order to represent the multidimensional data on chemical diversity, physiochemical properties and biological activity data of the compound library, novel approaches such as the use of circular graphs have been employed. CONCLUSION: BioPhytMol has been designed to systematically represent and search for anti-mycobacterial phytochemical information. Extensive compound analyses can also be performed through web-application for prioritizing drug-like compounds. The resource is freely available online at http://ab-openlab.csir.res.in/biophytmol/. Graphical AbstractBioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts generated using Crowdsourcing. The platform comprises of manually curated data on antimycobacterial natural products along with tools to perform structure similarity and visualization. The platform allows for prioritization of drug like natural products for antimycobacterial drug discovery.

Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high-value drug targets.

BACKGROUND: The effectiveness of current therapeutic regimens for Mycobacterium tuberculosis (Mtb) is diminished by the need for prolonged therapy and the rise of drug resistant/tolerant strains. This global health threat, despite decades of basic research and a wealth of legacy knowledge, is due to a lack of systems level understanding that can innovate the process of fast acting and high efficacy drug discovery. METHODS: The enhanced functional annotations of the Mtb genome, which were previously obtained through a crowd sourcing approach was used to reconstruct the metabolic network of Mtb in a bottom up manner. We represent this information by developing a novel Systems Biology Spindle Map of Metabolism (SBSM) and comprehend its static and dynamic structure using various computational approaches based on simulation and design. RESULTS: The reconstructed metabolism of Mtb encompasses 961 metabolites, involved in 1152 reactions catalyzed by 890 protein coding genes, organized into 50 pathways. By accounting for static and dynamic analysis of SBSM in Mtb we identified various critical proteins required for the growth and survival of bacteria. Further, we assessed the potential of these proteins as putative drug targets that are fast acting and less toxic. Further, we formulate a novel concept of metabolic persister genes (MPGs) and compared our predictions with published in vitro and in vivo experimental evidence. Through such analyses, we report for the first time that de novo biosynthesis of NAD may give rise to bacterial persistence in Mtb under conditions of metabolic stress induced by conventional anti-tuberculosis therapy. We propose such MPG's as potential combination of drug targets for existing antibiotics that can improve their efficacy and efficiency for drug tolerant bacteria. CONCLUSION: The systems level framework formulated by us to identify potential non-toxic drug targets and strategies to circumvent the issue of bacterial persistence can substantially aid in the process of TB drug discovery and translational research.

Investigating the role of site specific synonymous variation in disease association studies.

Synonymous codon changes may not always be neutral indicating their significance in disease association studies, which is almost always overlooked. Synonymous substitutions may affect protein-folding rates leading to protein misfolding and aggregation. Genome wide analysis of 2301 mitochondrial genomes is performed to evaluate the significance of synonymous codons in disease association studies. The analysis revealed usage of rare codons at several sites in mitochondrial genes with rare codon usage higher for hydrophobic amino acids. The analysis suggests that variation data in association studies should be analyzed using site-specific codon usage values to infer the potential phenotypic impact of synonymous changes.

Open source software and web services for designing therapeutic molecules.

Despite the tremendous progress in the field of drug designing, discovering a new drug molecule is still a challenging task. Drug discovery and development is a costly, time consuming and complex process that requires millions of dollar and 10-15 years to bring new drug molecules in the market. This huge investment and long-term process are attributed to high failure rate, complexity of the problem and strict regulatory rules, in addition to other factors. Given the availability of 'big' data with ever improving computing power, it is now possible to model systems which is expected to provide time and cost effectiveness to drug discovery process. Computer Aided Drug Designing (CADD) has emerged as a fast alternative method to bring down the cost involved in discovering a new drug. In past, numerous computer programs have been developed across the globe to assist the researchers working in the field of drug discovery. Broadly, these programs can be classified in three categories, freeware, shareware and commercial software. In this review, we have described freeware or open-source software that are commonly used for designing therapeutic molecules. Major emphasis will be on software and web services in the field of chemo- or pharmaco-informatics that includes in silico tools used for computing molecular descriptors, inhibitors designing against drug targets, building QSAR models, and ADMET properties.