TDR Targets: a chemogenomics resource for neglected diseases

A review of machine learning-based methods for predicting drug-target interactions

The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.

From TgO/GABA-AT, GABA, and T-263 Mutant to Conception of Toxoplasma

Toxoplasma gondii causes morbidity, mortality, and disseminates widely via cat sexual stages. Here, we find T. gondii ornithine aminotransferase (OAT) is conserved across phyla. We solve TgO/GABA-AT structures with bound inactivators at 1.55 Å and identify an inactivator selective for TgO/GABA-AT over human OAT and GABA-AT. However, abrogating TgO/GABA-AT genetically does not diminish replication, virulence, cyst-formation, or eliminate cat's oocyst shedding. Increased sporozoite/merozoite TgO/GABA-AT expression led to our study of a mutagenized clone with oocyst formation blocked, arresting after forming male and female gametes, with "Rosetta stone"-like mutations in genes expressed in merozoites. Mutations are similar to those in organisms from plants to mammals, causing defects in conception and zygote formation, affecting merozoite capacitation, pH/ionicity/sodium-GABA concentrations, drawing attention to cyclic AMP/PKA, and genes enhancing energy or substrate formation in TgO/GABA-AT-related-pathways. These candidates potentially influence merozoite's capacity to make gametes that fuse to become zygotes, thereby contaminating environments and causing disease.

An updated review of chemical compounds with anti-Toxoplasma gondii activity

The opportunistic apicomplexan parasite Toxoplasma gondii is the etiologic agent for toxoplasmosis, which can infect a widespread range of hosts, particularly humans and warm-blooded animals. The present chemotherapy to treat or prevent toxoplasmosis is deficient and is based on diverse drugs such as atovaquone, trimethoprim, spiramycine, which are effective in acute toxoplasmosis. Therefore, a safe chemotherapy is required for toxoplasmosis considering that its responsible agent, T. gondii, provokes severe illness and death in pregnant women and immunodeficient patients. A certain disadvantage of the available treatments is the lack of effectiveness against the tissue cyst of the parasite. A safe chemotherapy to combat toxoplasmosis should be based on the metabolic differences between the parasite and the mammalian host. This article covers different relevant molecular targets to combat this disease including the isoprenoid pathway (farnesyl diphosphate synthase, squalene synthase), dihydrofolate reductase, calcium-dependent protein kinases, histone deacetylase, mitochondrial electron transport chain, etc.

Unrealized targets in the discovery of antibiotics for Gram-negative bacterial infections

Advances in areas that include genomics, systems biology, protein structure determination and artificial intelligence provide new opportunities for target-based antibacterial drug discovery. The selection of a 'good' new target for direct-acting antibacterial compounds is the first decision, for which multiple criteria must be explored, integrated and re-evaluated as drug discovery programmes progress. Criteria include essentiality of the target for bacterial survival, its conservation across different strains of the same species, bacterial species and growth conditions (which determines the spectrum of activity of a potential antibiotic) and the level of homology with human genes (which influences the potential for selective inhibition). Additionally, a bacterial target should have the potential to bind to drug-like molecules, and its subcellular location will govern the need for inhibitors to penetrate one or two bacterial membranes, which is a key challenge in targeting Gram-negative bacteria. The risk of the emergence of target-based drug resistance for drugs with single targets also requires consideration. This Review describes promising but as-yet-unrealized targets for antibacterial drugs against Gram-negative bacteria and examples of cognate inhibitors, and highlights lessons learned from past drug discovery programmes.

Comprehensive survey of target prediction web servers for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.

Ex vivo and In vitro antiplasmodial activities of approved drugs predicted to have antimalarial activities using chemogenomics and drug repositioning approach

High malaria mortality coupled with increased emergence of resistant multi-drug resistant strains of Plasmodium parasite, warrants the development of new and effective antimalarial drugs. However, drug design and discovery are costly and time-consuming with many active antimalarial compounds failing to get approved due to safety reasons. To address these challenges, the current study aimed at testing the antiplasmodial activities of approved drugs that were predicted using a target-similarity approach. This approach is based on the fact that if an approved drug used to treat another disease targets a protein similar to Plasmodium falciparum protein, then the drug will have a comparable effect on P. falciparum. In a previous study, in vitro antiplasmodial activities of 10 approved drugs was reported of the total 28 approved drugs. In this study, six out of 18 drugs that were previously not tested, namely epirubicin, irinotecan, venlafaxine, palbociclib, pelitinib, and PD153035 were tested for antiplasmodial activity. The drug susceptibility in vitro assays against five P. falciparum reference strains (D6, 3D7, W2, DD2, and F32 ART) and ex vivo assays against fresh clinical isolates were done using the malaria SYBR Green I assay. Standard antimalarial drugs were included as controls. Epirubicin and irinotecan showed excellent antiplasmodial ex vivo activity against field isolates with mean IC50 values of 0.044 ± 0.033 μM and 0.085 ± 0.055 μM, respectively. Similar activity was observed against W2 strain where epirubicin had an IC50 value of 0.004 ± 0.0009 μM, palbociclib 0.056 ± 0.006 μM, and pelinitib 0.057 ± 0.013 μM. For the DD2 strain, epirubicin, irinotecan and PD 153035 displayed potent antiplasmodial activity (IC50 < 1 μM). Epirubicin and irinotecan showed potent antiplasmodial activities (IC50 < 1 μM) against DD2, D6, 3D7, and F32 ART strains and field isolates. This shows the potential use of these drugs as antimalarials. All the tested drugs showed antiplasmodial activities with IC50 values below 20 μM, which suggests that our target similarity-based strategy is successful at predicting antiplasmodial activity of compounds thereby circumventing challenges in antimalarial drug discovery.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

Characterization of Calcium-Dependent Protein Kinase 2A, a Potential Drug Target Against Cryptosporidiosis

Calcium-dependent protein kinases (CDPKs) are important in calcium influx, triggering several biological processes in Cryptosporidium spp. As they are not present in mammals, CDPKs are considered promising drug targets. Recent studies have characterized CpCDPK1, CpCDPK3, CpCDPK4, CpCDPK5, CpCDPK6, and CpCDPK9, but the role of CpCPK2A remains unclear. In this work, we expressed recombinant CpCDPK2A encoded by the cgd2_1060 gene in Escherichia coli and characterized the biologic functions of CpCDPK2A using qRT-PCR, immunofluorescence microscopy, immuno-electron microscopy, and in vitro neutralization. The results revealed that CpCDPK2A protein was highly expressed in the apical region of sporozoites and merozoites and in macrogamonts. Monoclonal or polyclonal antibodies against CpCDPK2A failed to block the invasion of host cells. Among the 44 candidate inhibitors from molecular docking of CpCDPK2A, one inhibitor was identified as having a potential effect on both Cryptosporidium parvum growth and CpCDPK2A enzyme activities. These data suggest that CpCDPK2A may play some roles during the development of C. parvum and might be a potential drug target against cryptosporidiosis.

Immunogenic profiling of Mycobacterium tuberculosis DosR protein Rv0569 reveals its ability to switch on Th1 based immunity

Mycobacterium tuberculosis (M.tb) is a multifaceted bacterial pathogen known to infect more than 2 billion people globally. However, a majority of the individuals (>90%) show no overt clinical symptoms of active Tuberculosis (TB) and, it is reported that M.tb in these individuals resides in the latent form. Therefore, huge burden of latently infected population poses serious threat to human health. Inconsistent efficacy of BCG vaccine and poor understanding of latency-associated determinants contribute to the failure of combating M.tb. The discovery of DosR as the master regulator of dormancy, opened new avenues to understand the pathophysiology of the bacterium. Though the specific functions of various DosR genes are yet to be discovered, they have been reported as potent T-cell activators and could elicit strong protective immune responses. Rv0569 is a DosR-encoded conserved hypothetical protein overexpressed during dormancy. However, it is not clearly understood how this protein modulates the host immune response. In the present study, we have demonstrated that Rv0569 has a high antigenic index and induces enhanced secretion of Th1 cytokines IL-12p40 and TNF-α as compared to Th2 cytokine IL-10 in macrophages. Mechanistically, Rv0569 induced the transcription of these pro-inflammatory signatures through the activation of NF-κB pathway. Further, immunization of mice with DosR protein Rv0569 switched the immune response towards Th1-biased cytokine pattern, characterized by the enhanced production of IFN-γ, IL-12p40, and TNF-α. Rv0569 augmented the expansion of antigen-specific IFN-γ and IL-2 producing effector CD4⁺ and CD8⁺ T-cells which are hallmarks of Th1 biased protective immunity. Additionally, IgG2a/IgG1 and IgG2b/IgG1 ratio in the serum of immunized mice further confirmed the ability of Rv0569 to skew Th1 biased immune response. In conclusion, we emphasize that Rv0569 has the ability to generate signals to switch on Th1-dominated responses and further suggest that it could be a potential vaccine candidate against latent M.tb infection.

Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review

Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.

From Genome to Drugs: New Approaches in Antimicrobial Discovery

Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a "big-data era" that improves target selection and lead compound identification in a cost-effective and shortened timeline.

Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project

Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.

The emerging paradigm of calcium homeostasis as a new therapeutic target for protozoan parasites

Calcium channels (CCs), a group of ubiquitously expressed membrane proteins, are involved in many pathophysiological processes of protozoan parasites. Our understanding of CCs in cell signaling, organelle function, cellular homeostasis, and cell cycle control has led to improved insights into their structure and functions. In this article, we discuss CCs characteristics of five major protozoan parasites Plasmodium, Leishmania, Toxoplasma, Trypanosoma, and Cryptosporidium. We provide a comprehensive review of current antiparasitic drugs and the potential of using CCs as new therapeutic targets. Interestingly, previous studies have demonstrated that human CC modulators can kill or sensitize parasites to antiparasitic drugs. Still, none of the parasite CCs, pumps, or transporters has been validated as drug targets. Information for this review draws from extensive data mining of genome sequences, chemical library screenings, and drug design studies. Parasitic resistance to currently approved therapeutics is a serious and emerging threat to both disease control and management efforts. In this article, we suggest that the disruption of calcium homeostasis may be an effective approach to develop new anti-parasite drug candidates and reduce parasite resistance.

Violacein-Induced Chaperone System Collapse Underlies Multistage Antiplasmodial Activity

Antimalarial drugs with novel modes of action and wide therapeutic potential are needed to pave the way for malaria eradication. Violacein is a natural compound known for its biological activity against cancer cells and several pathogens, including the malaria parasite, Plasmodium falciparum (Pf). Herein, using chemical genomic profiling (CGP), we found that violacein affects protein homeostasis. Mechanistically, violacein binds Pf chaperones, PfHsp90 and PfHsp70-1, compromising the latter's ATPase and chaperone activities. Additionally, violacein-treated parasites exhibited increased protein unfolding and proteasomal degradation. The uncoupling of the parasite stress response reflects the multistage growth inhibitory effect promoted by violacein. Despite evidence of proteotoxic stress, violacein did not inhibit global protein synthesis via UPR activation-a process that is highly dependent on chaperones, in agreement with the notion of a violacein-induced proteostasis collapse. Our data highlight the importance of a functioning chaperone-proteasome system for parasite development and differentiation. Thus, a violacein-like small molecule might provide a good scaffold for development of a novel probe for examining the molecular chaperone network and/or antiplasmodial drug design.

Identification of differential protein expression and putative drug target in metacyclic stage of Leishmania major and Leishmania tropica: A quantitative proteomics and computational view

Cutaneous leishmaniasis (CL) is an infectious disease that commonly caused by Leishmania (L.) major and L.tropica. Recently there has been a growing interest in proteomics analysis on Leishmania for drug target discovery. Therefore, we aimed to distinguish proteins which might be characteristic for each of the species from those shared by both to the detection of drug targets, which may become helpful for designing new drugs for CL. To identify differences in protein profiles of L. major and L. tropica, we conducted a Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) analysis. Totally 67 differentially expressed proteins (DEPs) (fold change> 2 and p < 0.05) were identified between species. Of these, 42 and 25 proteins were up-regulated in L. major and L. tropica, respectively. Several enriched GO terms were identified via biological process of up-regulated proteins. Furthermore, the small molecule metabolic process and translation were detected as significant biological processes for up-regulated proteins in L. major, while translation was identified for L. tropica. Also, KEGG analysis has revealed glycolysis/gluconeogenesis and translation as the top pathways in the proteins up-regulated in L. major and L. tropica, respectively. Finally glycosomal malate dehydrogenase was identified as putative drug target using network and homology analyses. The DEPs between the species are essential in host-pathogen interactions and parasite survival in the macrophage. Furthermore, L. major and L. tropica possibly uses different pathogenicity mechanisms that leads to anthroponotic or zoonotic CL. Our results may help in the drug discovery and chemotherapeutic interventions.

COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics

The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists ∼130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), ∼40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.

Bioinformatics: new tools and applications in life science and personalized medicine

While we have a basic understanding of the functioning of the gene when coding sequences of specific proteins, we feel the lack of information on the role that DNA has on specific diseases or functions of thousands of proteins that are produced. Bioinformatics combines the methods used in the collection, storage, identification, analysis, and correlation of this huge and complex information. All this work produces an "ocean" of information that can only be "sailed" with the help of computerized methods. The goal is to provide scientists with the right means to explain normal biological processes, dysfunctions of these processes which give rise to disease and approaches that allow the discovery of new medical cures. Recently, sequencing platforms, a large scale of genomes and transcriptomes, have created new challenges not only to the genomics but especially for bioinformatics. The intent of this article is to compile a list of tools and information resources used by scientists to treat information from the massive sequencing of recent platforms to new generations and the applications of this information in different areas of life sciences including medicine. KEY POINTS: • Biological data mining • Omic approaches • From genotype to phenotype.

Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery

Chagas disease still has no effective treatment option for all of its phases despite being discovered more than 100 years ago. The development of commercial drugs has been stagnating since the 1960s, a fact that sheds light on the question of how drug discovery research has progressed and taken advantage of technological advances. Could it be that technological advances have not yet been sufficient to resolve this issue or is there a lack of protocol, validation and standardization of the data generated by different research teams? This work presents an overview of commercial drugs and those that have been evaluated in studies and clinical trials so far. A brief review is made of recent target-based and phenotypic studies based on the search for molecules with anti-Trypanosoma cruzi action. It also discusses how proteochemometric (PCM) modeling and microcrystal electron diffraction (MicroED) can help in the case of the lack of a 3D protein structure; more specifically, Trypanosoma cruzi carbonic anhydrase.

Structural features and development of an assay platform of the parasite target deoxyhypusine synthase of Brugia malayi and Leishmania major

Deoxyhypusine synthase (DHS) catalyzes the first step of the post-translational modification of eukaryotic translation factor 5A (eIF5A), which is the only known protein containing the amino acid hypusine. Both proteins are essential for eukaryotic cell viability, and DHS has been suggested as a good candidate target for small molecule-based therapies against eukaryotic pathogens. In this work, we focused on the DHS enzymes from Brugia malayi and Leishmania major, the causative agents of lymphatic filariasis and cutaneous leishmaniasis, respectively. To enable B. malayi (Bm)DHS for future target-based drug discovery programs, we determined its crystal structure bound to cofactor NAD⁺. We also reported an in vitro biochemical assay for this enzyme that is amenable to a high-throughput screening format. The L. major genome encodes two DHS paralogs, and attempts to produce them recombinantly in bacterial cells were not successful. Nevertheless, we showed that ectopic expression of both LmDHS paralogs can rescue yeast cells lacking the endogenous DHS-encoding gene (dys1). Thus, functionally complemented dys1Δ yeast mutants can be used to screen for new inhibitors of the L. major enzyme. We used the known human DHS inhibitor GC7 to validate both in vitro and yeast-based DHS assays. Our results show that BmDHS is a homotetrameric enzyme that shares many features with its human homologue, whereas LmDHS paralogs are likely to form a heterotetrameric complex and have a distinct regulatory mechanism. We expect our work to facilitate the identification and development of new DHS inhibitors that can be used to validate these enzymes as vulnerable targets for therapeutic interventions against B. malayi and L. major infections.

Target-based drug discovery strategies hold the promise to discover safer and more effective treatments for Neglected Tropical Diseases (NTDs). Genetic manipulation techniques have been used to successfully identify essential genes in eukaryotic parasites. Unfortunately, the fact that a gene is essential under controlled laboratory conditions does not automatically make the corresponding gene-product vulnerable to pharmacological intervention in a clinical setting within the human host. To allow the discovery and development of small molecule tool compounds that can be used to validate pharmacologically vulnerable targets, one must first establish compound screening assays and obtain structural information for the candidate target. Eukaryotic cells lacking deoxyhypusine synthase (DHS) function are not viable. DHS catalyzes the first step in a post-translational modification that is critical for the function of eIF5A. Presence of mature eIF5A is also essential for eukaryotic cell viability. Here we reported compound screening assays (yeast-based for Brugia malayi and Leishmania major; in vitro for B. malayi only) and provided further regulatory and structural insights we hope will aid in the identification and development of inhibitors for the DHS enzymes from two NTD-causing organisms—B. malayi, the causative agent of lymphatic filariasis and L. major, the causative agent of cutaneous leishmaniasis.

Dynamics of protein synthesis in the initial steps of strobilation in the model cestode parasite Mesocestoides corti (syn. vogae)

Mesocestoides corti (syn. vogae) is a useful model for developmental studies of platyhelminth parasites of the Cestoda class, such as Taenia spp. or Echinococcus spp. It has been used in studies to characterize cestode strobilation, i.e. the development of larvae into adult worms. So far, little is known about the initial molecular events involved in cestode strobilation and, therefore, we carried out a study to characterize newly synthesized (NS) proteins upon strobilation induction. An approach based on bioorthogonal noncanonical amino acid tagging and mass spectrometry was used to label, isolate, identify, and quantify NS proteins in the initial steps of M. corti strobilation. Overall, 121 NS proteins were detected exclusively after induction of strobilation, including proteins related to development pathways, such as insulin and notch signaling. Metabolic changes that take place in the transition from the larval stage to adult worm were noted in special NS protein subsets related to developmental processes, such as focal adhesion, cell leading edge, and maintenance of location. The data shed light on mechanisms underlying early steps of cestode strobilation and enabled identification of possible developmental markers. We also consider the use of developmental responsive proteins as potential drug targets for developing novel anthelmintics. BIOLOGICAL SIGNIFICANCE: Larval cestodiases are life-threatening parasitic diseases that affect both man and domestic animals worldwide. Cestode parasites present complex life cycles, in which they undergo major morphological and physiological changes in the transition from one life-stage to the next. One of these transitions occurs during cestode strobilation, when the mostly undifferentiated and non-segmented larval or pre-adult form develops into a fully segmented and sexually differentiated (strobilated) adult worm. Although the proteomes of bona fide larvae and strobialted adults have been previously characterized for a few cestode species, little is still known about the dynamic of protein synthesis during the early steps of cestode strobilation. Now, the assessment of newly synthesized (NS) proteins within the first 48 h of strobilation the model cestode M. corti allowed to shed light on molecular mechanisms that are triggered by strobilation induction. The functional analyses of this repertoire of over a hundred NS proteins pointed out to changes in metabolism and activation of classical developmental signaling pathways in early strobilation. Many of the identified NS proteins may become valuable cestode developmental markers and their involvement in vital processes make them also good candidate targets for novel anthelmintic drugs.

Computational/in silico methods in drug target and lead prediction

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance

Tuberculosis (TB) and leprosy are mycobacterial infections caused by Mycobacterium tuberculosis and Mycobacterium leprae respectively. These diseases continue to be endemic in developing countries where the cost of new medicines presents major challenges. The situation is further exacerbated by the emergence of resistance to many front-line antibiotics. A priority now is to design new antimycobacterials that are not only effective in combatting the diseases but are also less likely to give rise to resistance. In both these respects understanding the structure of drug targets in M. tuberculosis and M. leprae is crucial. In this review we describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

Computational Chemogenomics Drug Repositioning Strategy Enables the Discovery of Epirubicin as a New Repurposed Hit for Plasmodium falciparum and P. vivax

Widespread resistance against antimalarial drugs thwarts current efforts for controlling the disease and urges the discovery of new effective treatments. Drug repositioning is increasingly becoming an attractive strategy since it can reduce costs, risks, and time-to-market. Herein, we have used this strategy to identify novel antimalarial hits. We used a comparative in silico chemogenomics approach to select Plasmodium falciparum and Plasmodium vivax proteins as potential drug targets and analyzed them using a computer-assisted drug repositioning pipeline to identify approved drugs with potential antimalarial activity.

Widespread resistance against antimalarial drugs thwarts current efforts for controlling the disease and urges the discovery of new effective treatments. Drug repositioning is increasingly becoming an attractive strategy since it can reduce costs, risks, and time-to-market. Herein, we have used this strategy to identify novel antimalarial hits. We used a comparative in silico chemogenomics approach to select Plasmodium falciparum and Plasmodium vivax proteins as potential drug targets and analyzed them using a computer-assisted drug repositioning pipeline to identify approved drugs with potential antimalarial activity. Among the seven drugs identified as promising antimalarial candidates, the anthracycline epirubicin was selected for further experimental validation. Epirubicin was shown to be potent in vitro against sensitive and multidrug-resistant P. falciparum strains and P. vivax field isolates in the nanomolar range, as well as being effective against an in vivo murine model of Plasmodium yoelii. Transmission-blocking activity was observed for epirubicin in vitro and in vivo. Finally, using yeast-based haploinsufficiency chemical genomic profiling, we aimed to get insights into the mechanism of action of epirubicin. Beyond the target predicted in silico (a DNA gyrase in the apicoplast), functional assays suggested a GlcNac-1-P-transferase (GPT) enzyme as a potential target. Docking calculations predicted the binding mode of epirubicin with DNA gyrase and GPT proteins. Epirubicin is originally an antitumoral agent and presents associated toxicity. However, its antiplasmodial activity against not only P. falciparum but also P. vivax in different stages of the parasite life cycle supports the use of this drug as a scaffold for hit-to-lead optimization in malaria drug discovery.

Computational approaches for drug discovery against trypanosomatid-caused diseases

During three decades, only about 20 new drugs have been developed for malaria, tuberculosis and all neglected tropical diseases (NTDs). This critical situation was reached because NTDs represent only 10% of health research investments; however, they comprise about 90% of the global disease burden. Computational simulations applied in virtual screening (VS) strategies are very efficient tools to identify pharmacologically active compounds or new indications for drugs already administered for other diseases. One of the advantages of this approach is the low time-consuming and low-budget first stage, which filters for testing experimentally a group of candidate compounds with high chances of binding to the target and present trypanocidal activity. In this work, we review the most common VS strategies that have been used for the identification of new drugs with special emphasis on those applied to trypanosomiasis and leishmaniasis. Computational simulations based on the selected protein targets or their ligands are explained, including the method selection criteria, examples of successful VS campaigns applied to NTDs, a list of validated molecular targets for drug development and repositioned drugs for trypanosomatid-caused diseases. Thereby, here we present the state-of-the-art of VS and drug repurposing to conclude pointing out the future perspectives in the field.

Deep phosphoproteome analysis of Schistosoma mansoni leads development of a kinomic array that highlights sex-biased differences in adult worm protein phosphorylation

Although helminth parasites cause enormous suffering worldwide we know little of how protein phosphorylation, one of the most important post-translational modifications used for molecular signalling, regulates their homeostasis and function. This is particularly the case for schistosomes. Herein, we report a deep phosphoproteome exploration of adult Schistosoma mansoni, providing one of the richest phosphoprotein resources for any parasite so far, and employ the data to build the first parasite-specific kinomic array. Complementary phosphopeptide enrichment strategies were used to detect 15,844 unique phosphopeptides mapping to 3,176 proteins. The phosphoproteins were predicted to be involved in a wide range of biological processes and phosphoprotein interactome analysis revealed 55 highly interconnected clusters including those enriched with ribosome, proteasome, phagosome, spliceosome, glycolysis, and signalling proteins. 93 distinct phosphorylation motifs were identified, with 67 providing a ‘footprint’ of protein kinase activity; CaMKII, PKA and CK1/2 were highly represented supporting their central importance to schistosome function. Within the kinome, 808 phosphorylation sites were matched to 136 protein kinases, and 68 sites within 37 activation loops were discovered. Analysis of putative protein kinase-phosphoprotein interactions revealed canonical networks but also novel interactions between signalling partners. Kinomic array analysis of male and female adult worm extracts revealed high phosphorylation of transformation:transcription domain associated protein by both sexes, and CDK and AMPK peptides by females. Moreover, eight peptides including protein phosphatase 2C gamma, Akt, Rho2 GTPase, SmTK4, and the insulin receptor were more highly phosphorylated by female extracts, highlighting their possible importance to female worm function. We envision that these findings, tools and methodology will help drive new research into the functional biology of schistosomes and other helminth parasites, and support efforts to develop new therapeutics for their control.

Schistosomes are formidable parasites that cause the debilitating and life-threatening disease human schistosomiasis. We need to better understand the cellular biology of these parasites to develop novel strategies for their control. Within cells, a process called protein phosphorylation controls many aspects of molecular communication or ‘signalling’ and is central to cellular function and homeostasis. Here, using complementary strategies, we have performed the first in-depth characterisation and functional annotation of protein phosphorylation events in schistosomes, providing one of the richest phosphoprotein resources for any parasite to date. Using this knowledge, we have developed a novel tool to simultaneously evaluate signalling processes in these worms and highlight sex-biased differences in adult worm protein phosphorylation. Several proteins were found to be more greatly phosphorylated by female worm extracts, suggesting their possible importance to female worm function. This work will help drive new research into the fundamental biology of schistosomes, as well as related parasites, and will support efforts to develop new drug or vaccine-based therapeutics for their control.

Outwitting an Old Neglected Nemesis: A Review on Leveraging Integrated Data-Driven Approaches to Aid in Unraveling of Leishmanicides of Therapeutic Potential

The global prevalence of leishmaniasis has increased with skyrocketed mortality in the past decade. The causative agent of leishmaniasis is Leishmania species, which infects populations in almost all the continents. Prevailing treatment regimens are consistently inefficient with reported side effects, toxicity and drug resistance. This review complements existing ones by discussing the current state of treatment options, therapeutic bottlenecks including chemoresistance and toxicity, as well as drug targets. It further highlights innovative applications of nanotherapeutics-based formulations, inhibitory potential of leishmanicides, anti-microbial peptides and organometallic compounds on leishmanial species. Moreover, it provides essential insights into recent machine learning-based models that have been used to predict novel leishmanicides and also discusses other new models that could be adopted to develop fast, efficient, robust and novel algorithms to aid in unraveling the next generation of anti-leishmanial drugs. A plethora of enriched functional genomic, proteomic, structural biology, high throughput bioassay and drug-related datasets are currently warehoused in both general and leishmania-specific databases. The warehoused datasets are essential inputs for training and testing algorithms to augment the prediction of biotherapeutic entities. In addition, we demonstrate how pharmacoinformatics techniques including ligand-, structure- and pharmacophore-based virtual screening approaches have been utilized to screen ligand libraries against both modeled and experimentally solved 3D structures of essential drug targets. In the era of data-driven decision-making, we believe that highlighting intricately linked topical issues relevant to leishmanial drug discovery offers a one-stop-shop opportunity to decipher critical literature with the potential to unlock implicit breakthroughs.

MycoResistance: a curated resource of drug resistance molecules in Mycobacteria

The emergence and spread of drug-resistant Mycobacterium tuberculosis is of global concern. To improve the understanding of drug resistance in Mycobacteria, numerous studies have been performed to discover diagnostic markers and genetic determinants associated with resistance to anti-tuberculosis drug. However, the related information is scattered in a massive body of literature, which is inconvenient for researchers to investigate the molecular mechanism of drug resistance. Therefore, we manually collected 1707 curated associations between 73 compounds and 132 molecules (including coding genes and non-coding RNAs) in 6 mycobacterial species from 465 studies. The experimental details of molecular epidemiology and mechanism exploration research were also summarized and recorded in our work. In addition, multidrug resistance and extensively drug resistance molecules were also extracted to interpret the molecular mechanisms that are responsible for cross resistance among anti-tuberculosis drugs. Finally, we constructed an omnibus repository named MycoResistance, a user friendly interface to conveniently browse, search and download all related entries. We hope that this elaborate database will serve as a beneficial resource for mechanism explanations, precise diagnosis and effective treatment of drug-resistant mycobacterial strains.

TDR Targets 6: driving drug discovery for human pathogens through intensive chemogenomic data integration

The volume of biological, chemical and functional data deposited in the public domain is growing rapidly, thanks to next generation sequencing and highly-automated screening technologies. These datasets represent invaluable resources for drug discovery, particularly for less studied neglected disease pathogens. To leverage these datasets, smart and intensive data integration is required to guide computational inferences across diverse organisms. The TDR Targets chemogenomics resource integrates genomic data from human pathogens and model organisms along with information on bioactive compounds and their annotated activities. This report highlights the latest updates on the available data and functionality in TDR Targets 6. Based on chemogenomic network models providing links between inhibitors and targets, the database now incorporates network-driven target prioritizations, and novel visualizations of network subgraphs displaying chemical- and target-similarity neighborhoods along with associated target-compound bioactivity links. Available data can be browsed and queried through a new user interface, that allow users to perform prioritizations of protein targets and chemical inhibitors. As such, TDR Targets now facilitates the investigation of drug repurposing against pathogen targets, which can potentially help in identifying candidate targets for bioactive compounds with previously unknown targets. TDR Targets is available at https://tdrtargets.org.

Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens

Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the most relevant microorganisms for human health (Mycobacterium tuberculosis, Mycobacterium leprae, Klebsiella pneumoniae, Plasmodium vivax, Toxoplasma gondii, Leishmania major, Wolbachia bancrofti, Trypanosoma brucei, Shigella dysenteriae and Schistosoma Smanosoni) and show its applicability. New genomes can be uploaded upon request.

A comprehensive review of feature based methods for drug target interaction prediction

Drug target interaction is a prominent research area in the field of drug discovery. It refers to the recognition of interactions between chemical compounds and the protein targets in the human body. Wet lab experiments to identify these interactions are expensive as well as time consuming. The computational methods of interaction prediction help limit the search space for these experiments. These computational methods can be divided into ligand based approaches, docking approaches and chemogenomic approaches. In this review, we aim to describe the various feature based chemogenomic methods for drug target interaction prediction. It provides a comprehensive overview of the various techniques, datasets, tools and metrics. The feature based methods have been categorized, explained and compared. A novel framework for drug target interaction prediction has also been proposed that aims to improve the performance of existing methods. To the best of our knowledge, this is the first comprehensive review focusing only on feature based methods of drug target interaction.

Drug repurposing for antimicrobial discovery

Antimicrobial resistance continues to be a public threat on a global scale. The ongoing need to develop new antimicrobial drugs that are effective against multi-drug-resistant pathogens has spurred the research community to invest in various drug discovery strategies, one of which is drug repurposing-the process of finding new uses for existing drugs. While still nascent in the antimicrobial field, the approach is gaining traction in both the public and private sector. While the approach has particular promise in fast-tracking compounds into clinical studies, it nevertheless has substantial obstacles to success. This Review covers the art of repurposing existing drugs for antimicrobial purposes. We discuss enabling screening platforms for antimicrobial discovery and present encouraging findings of novel antimicrobial therapeutic strategies. Also covered are general advantages of repurposing over de novo drug development and challenges of the strategy, including scientific, intellectual property and regulatory issues.

Brazilian malaria molecular targets (BraMMT): selected receptors for virtual high-throughput screening experiments

BACKGROUND

Owing to increased spending on pharmaceuticals since 2010, discussions about rising costs for the development of new medical technologies have been focused on the pharmaceutical industry. Computational techniques have been developed to reduce costs associated with new drug development. Among these techniques, virtual high-throughput screening (vHTS) can contribute to the drug discovery process by providing tools to search for new drugs with the ability to bind a specific molecular target.

OBJECTIVES

In this context, Brazilian malaria molecular targets (BraMMT) was generated to execute vHTS experiments on selected molecular targets of Plasmodium falciparum.

METHODS

In this study, 35 molecular targets of P. falciparum were built and evaluated against known antimalarial compounds.

FINDINGS

As a result, it could predict the correct molecular target of market drugs, such as artemisinin. In addition, our findings suggested a new pharmacological mechanism for quinine, which includes inhibition of falcipain-II and a potential new antimalarial candidate, clioquinol.

MAIN CONCLUSIONS

The BraMMT is available to perform vHTS experiments using OCTOPUS or Raccoon software to improve the search for new antimalarial compounds. It can be retrieved from www.drugdiscovery.com.br or download of Supplementary data.

Discovery and Genetic Validation of Chemotherapeutic Targets for Chagas' Disease

There is an urgent need to develop new treatments for Chagas' disease. To identify drug targets, it is important to understand the basic biology of Trypanosoma cruzi, in particular with respect to the biological pathways or proteins that are essential for its survival within the host. This review provides a streamlined approach for identifying drug targets using freely available chemogenetic databases and outlines the relevant characteristics of an ideal chemotherapeutic target. Among those are their essentiality, druggability, availability of structural information, and selectivity. At the moment only 16 genes have been found as essential by gene disruption in T. cruzi. At the TDR Targets database, a chemogenomics resource for neglected diseases, information about published structures for these genes was only found for three of these genes, and annotation of validated inhibitors was found in two. These inhibitors have activity against the parasitic stages present in the host. We then analyzed three of the pathways that are considered promising in the search for new targets: (1) Ergosterol biosynthesis, (2) Resistance to oxidative stress, (3) Synthesis of surface glycoconjugates. We have annotated all the genes that participate in them, identified those that are considered as druggable, and incorporated evidence from either Trypanosoma brucei, and Leishmania spp. that supports the hypothesis that these pathways are essential for T. cruzi survival.

MDAD: A Special Resource for Microbe-Drug Associations

The human-associated microbiota is diverse and complex. It takes an essential role in human health and behavior and is closely related to the occurrence and development of disease. Although the diversity and distribution of microbial communities have been widely studied, little is known about the function and dynamics of microbes in the human body or the complex mechanisms of interaction between them and drugs, which are important for drug discovery and design. A high-quality comprehensive microbe and drug association database will be extremely beneficial to explore the relationship between them. In this article, we developed the Microbe-Drug Association Database (MDAD), a collection of clinically or experimentally supported associations between microbes and drugs, collecting 5,055 entries that include 1,388 drugs and 180 microbes from multiple drug databases and related publications. Moreover, we provided detailed annotations for each record, including the molecular form of drugs or hyperlinks from DrugBank, microbe target information from Uniprot and the original reference links. We hope MDAD will be a useful resource for deeper understanding of microbe and drug interactions and will also be beneficial to drug design, disease therapy and human health.

Interactive online application for the prediction, ranking and prioritisation of drug targets in Schistosoma haematobium

BACKGROUND

Human schistosomiasis is a neglected tropical disease caused by parasitic worms of the genus Schistosoma that still affects some 200 million people. The mainstay of schistosomiasis control is a single drug, praziquantel. The reliance on this drug carries a risk of resistance emerging to this anthelmintic, such that research towards alternative anti-schistosomal drugs is warranted. In this context, a number of studies have employed computational approaches to prioritise proteins for investigation as drug targets, based on extensive genomic, transcriptomic and small-molecule data now available.

METHODS

Here, we established a customisable, online application for the prioritisation of drug targets and applied it, for the first time, to the entire inferred proteome of S. haematobium. This application enables selection of weighted and ranked proteins representing potential drug targets, and integrates transcriptional data, orthology and gene essentiality information as well as drug-drug target associations and chemical properties of predicted ligands.

RESULTS

Using this application, we defined 25 potential drug targets in S. haematobium that associated with approved drugs, and 3402 targets that (although they could not be linked to any compounds) are conserved among a range of socioeconomically important flatworm species and might represent targets for new trematocides.

CONCLUSIONS

The online application developed here represents an interactive, customisable, expandable and reproducible drug target ranking and prioritisation approach that should be useful for the prediction of drug targets in schistosomes and other species of parasitic worms in the future. We have demonstrated the utility of this online application by predicting potential drug targets in S. haematobium that can now be evaluated using functional genomics tools and/or small molecules, to establish whether they are indeed essential for parasite survival, and to assist in the discovery of novel anti-schistosomal compounds.

Chemoinformatics Strategies for Leishmaniasis Drug Discovery

Leishmaniasis is a fatal neglected tropical disease (NTD) that is caused by more than 20 species of Leishmania parasites. The disease kills approximately 20,000 people each year and more than 1 billion are susceptible to infection. Although counting on a few compounds, the therapeutic arsenal faces some drawbacks such as drug resistance, toxicity issues, high treatment costs, and accessibility problems, which highlight the need for novel treatment options. Worldwide efforts have been made to that aim and, as well as in other therapeutic areas, chemoinformatics have contributed significantly to leishmaniasis drug discovery. Breakthrough advances in the comprehension of the parasites’ molecular biology have enabled the design of high-affinity ligands for a number of macromolecular targets. In addition, the use of chemoinformatics has allowed highly accurate predictions of biological activity and physicochemical and pharmacokinetics properties of novel antileishmanial compounds. This review puts into perspective the current context of leishmaniasis drug discovery and focuses on the use of chemoinformatics to develop better therapies for this life-threatening condition.

Network-Based Methods for Prediction of Drug-Target Interactions

Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.

CSGID Solves Structures and Identifies Phenotypes for Five Enzymes in Toxoplasma gondii

Toxoplasma gondii, an Apicomplexan parasite, causes significant morbidity and mortality, including severe disease in immunocompromised hosts and devastating congenital disease, with no effective treatment for the bradyzoite stage. To address this, we used the Tropical Disease Research database, crystallography, molecular modeling, and antisense to identify and characterize a range of potential therapeutic targets for toxoplasmosis. Phosphoglycerate mutase II (PGMII), nucleoside diphosphate kinase (NDK), ribulose phosphate 3-epimerase (RPE), ribose-5-phosphate isomerase (RPI), and ornithine aminotransferase (OAT) were structurally characterized. Crystallography revealed insights into the overall structure, protein oligomeric states and molecular details of active sites important for ligand recognition. Literature and molecular modeling suggested potential inhibitors and druggability. The targets were further studied with vivoPMO to interrupt enzyme synthesis, identifying the targets as potentially important to parasitic replication and, therefore, of therapeutic interest. Targeted vivoPMO resulted in statistically significant perturbation of parasite replication without concomitant host cell toxicity, consistent with a previous CRISPR/Cas9 screen showing PGM, RPE, and RPI contribute to parasite fitness. PGM, RPE, and RPI have the greatest promise for affecting replication in tachyzoites. These targets are shared between other medically important parasites and may have wider therapeutic potential.

Machine Learning for Drug-Target Interaction Prediction

Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers.

Identification of Missing Carbon Fixation Enzymes as Potential Drug Targets in Mycobacterium Tuberculosis

Metabolic adaptation to the host environment has been recognized as an essential mechanism of pathogenicity and the growth of Mycobacterium tuberculosis (Mtb) in the lungs for decades. The Mtb uses CO₂ as a source of carbon during the dormant or non-replicative state. However, there is a lack of biochemical knowledge of its metabolic networks. In this study, we investigated the CO₂ fixation pathways (such as ko00710 and ko00720) most likely involved in the energy production and conversion of CO₂ in Mtb. Extensive pathway evaluation of 23 completely sequenced strains of Mtb confirmed the existence of a complete list of genes encoding the relevant enzymes of the reductive tricarboxylic acid (rTCA) cycle. This provides the evidence that an rTCA cycle may function to fix CO₂ in this bacterium. We also proposed that as CO₂ is plentiful in the lungs, inhibition of CO₂ fixation pathways (by targeting the relevant CO₂ fixation enzymes) could be used in the expansion of new drugs against the dormant Mtb. In support of the suggested hypothesis, the CO₂ fixation enzymes were confirmed as a potential drug target by analyzing a number of attributes necessary to be a good bacterial target.

Data mart construction based on semantic annotation of scientific articles: A case study for the prioritization of drug targets

BACKGROUND AND OBJECTIVES

Semantic text annotation enables the association of semantic information (ontology concepts) to text expressions (terms), which are readable by software agents. In the scientific scenario, this is particularly useful because it reveals a lot of scientific discoveries that are hidden within academic articles. The Biomedical area has more than 300 ontologies, most of them composed of over 500 concepts. These ontologies can be used to annotate scientific papers and thus, facilitate data extraction. However, in the context of a scientific research, a simple keyword-based query using the interface of a digital scientific texts library can return more than a thousand hits. The analysis of such a large set of texts, annotated with such numerous and large ontologies, is not an easy task. Therefore, the main objective of this work is to provide a method that could facilitate this task.

METHODS

This work describes a method called Text and Ontology ETL (TOETL), to build an analytical view over such texts. First, a corpus of selected papers is semantically annotated using distinct ontologies. Then, the annotation data is extracted, organized and aggregated into the dimensional schema of a data mart.

RESULTS

Besides the TOETL method, this work illustrates its application through the development of the TaP DM (Target Prioritization data mart). This data mart has focus on the research of gene essentiality, a key concept to be considered when searching for genes showing potential as anti-infective drug targets.

CONCLUSIONS

This work reveals that the proposed approach is a relevant tool to support decision making in the prioritization of new drug targets, being more efficient than the keyword-based traditional tools.

TIBLE: a web-based, freely accessible resource for small-molecule binding data for mycobacterial species

Database URL

http://www-cryst.bioc.cam.ac.uk/tible/.

In silico Tools for Target Identification and Drug Molecular Docking in Leishmania

Neglected tropical diseases represent a significant health burden in large parts of the world. Drug discovery is currently a key bottleneck in the pipeline of these diseases. In this chapter, the in silico approaches used for the processes involved in drug discovery, identification and validation of druggable Leishmania targets, and design and optimisation of new anti-leishmanial drugs are discussed. We also provide a general view of the different computational tools that can be employed in pursuit of this aim, along with the most interesting cases found in the literature.

Target-similarity search using Plasmodium falciparum proteome identifies approved drugs with anti-malarial activity and their possible targets

Malaria causes about half a million deaths annually, with Plasmodium falciparum being responsible for 90% of all the cases. Recent reports on artemisinin resistance in Southeast Asia warrant urgent discovery of novel drugs for the treatment of malaria. However, most bioactive compounds fail to progress to treatments due to safety concerns. Drug repositioning offers an alternative strategy where drugs that have already been approved as safe for other diseases could be used to treat malaria. This study screened approved drugs for antimalarial activity using an in silico chemogenomics approach prior to in vitro verification. All the P. falciparum proteins sequences available in NCBI RefSeq were mined and used to perform a similarity search against DrugBank, TTD and STITCH databases to identify similar putative drug targets. Druggability indices of the potential P. falciparum drug targets were obtained from TDR targets database. Functional amino acid residues of the drug targets were determined using ConSurf server which was used to fine tune the similarity search. This study predicted 133 approved drugs that could target 34 P. falciparum proteins. A literature search done at PubMed and Google Scholar showed 105 out of the 133 drugs to have been previously tested against malaria, with most showing activity. For further validation, drug susceptibility assays using SYBR Green I method were done on a representative group of 10 predicted drugs, eight of which did show activity against P. falciparum 3D7 clone. Seven had IC50 values ranging from 1 μM to 50 μM. This study also suggests drug-target association and hence possible mechanisms of action of drugs that did show antiplasmodial activity. The study results validate the use of proteome-wide target similarity approach in identifying approved drugs with activity against P. falciparum and could be adapted for other pathogens.

Decoding the similarities and differences among mycobacterial species

Mycobacteriaceae comprises pathogenic species such as Mycobacterium tuberculosis, M. leprae and M. abscessus, as well as non-pathogenic species, for example, M. smegmatis and M. thermoresistibile. Genome comparison and annotation studies provide insights into genome evolutionary relatedness, identify unique and pathogenicity-related genes in each species, and explore new targets that could be used for developing new diagnostics and therapeutics. Here, we present a comparative analysis of ten-mycobacterial genomes with the objective of identifying similarities and differences between pathogenic and non-pathogenic species. We identified 1080 core orthologous clusters that were enriched in proteins involved in amino acid and purine/pyrimidine biosynthetic pathways, DNA-related processes (replication, transcription, recombination and repair), RNA-methylation and modification, and cell-wall polysaccharide biosynthetic pathways. For their pathogenicity and survival in the host cell, pathogenic species have gained specific sets of genes involved in repair and protection of their genomic DNA. M. leprae is of special interest owing to its smallest genome (1600 genes and ~1300 psuedogenes), yet poor genome annotation. More than 75% of the pseudogenes were found to have a functional ortholog in the other mycobacterial genomes and belong to protein families such as transferases, oxidoreductases and hydrolases.

Members of the Mycobacteriaceae family, which are known to adapt to different environmental niches, comprise bacterial species with varied genome sizes. They are unique in their cell-wall composition, which is remarkably thick and lipid-rich as compared to other bacteria. We performed a comparative analysis at the proteome level for ten mycobacterial species that differ in their pathogenicity, genome size and environmental niches. A total of 1080 orthologous clusters with representation from all ten species were obtained, and these were further examined for their domain annotations, domain architecture similarities and enriched GO terms. These core orthologous clusters are enriched in various biosynthetic pathways. The proteins that are specific to each of the ten species were also investigated for their GO functions. The M. leprae genome has a large number of pseudogenes and we searched for their functional orthologs in other mycobacterial species in order to understand the functions that are lost from the M. leprae genome. The proteins present exclusively in M. leprae genome were studied in more detail, in order to predict putative drug targets and diagnostic markers. These findings, which have implications in understanding evolution of mycobacterial genomes, identify species-specific proteins that have potential for use in developing new diagnostic tools and therapeutics.

Chagas Disease Diagnostic Applications: Present Knowledge and Future Steps

Chagas disease, caused by the protozoan Trypanosoma cruzi, is a lifelong and debilitating illness of major significance throughout Latin America and an emergent threat to global public health. Being a neglected disease, the vast majority of Chagasic patients have limited access to proper diagnosis and treatment, and there is only a marginal investment into R&D for drug and vaccine development. In this context, identification of novel biomarkers able to transcend the current limits of diagnostic methods surfaces as a main priority in Chagas disease applied research. The expectation is that these novel biomarkers will provide reliable, reproducible and accurate results irrespective of the genetic background, infecting parasite strain, stage of disease, and clinical-associated features of Chagasic populations. In addition, they should be able to address other still unmet diagnostic needs, including early detection of congenital T. cruzi transmission, rapid assessment of treatment efficiency or failure, indication/prediction of disease progression and direct parasite typification in clinical samples. The lack of access of poor and neglected populations to essential diagnostics also stresses the necessity of developing new methods operational in point-of-care settings. In summary, emergent diagnostic tests integrating these novel and tailored tools should provide a significant impact on the effectiveness of current intervention schemes and on the clinical management of Chagasic patients. In this chapter, we discuss the present knowledge and possible future steps in Chagas disease diagnostic applications, as well as the opportunity provided by recent advances in high-throughput methods for biomarker discovery.