Bacterial genomics: potential for antimicrobial drug discovery

Virtual Screening of compounds to 1-deoxy-Dxylulose 5-phosphate reductoisomerase (DXR) from Plasmodium falciparum

The 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) protein (Gen Bank ID AAN37254.1) from Plasmodium falciparum is a potential drug target. Therefore, it is of interest to screen DXR against a virtual library of compounds (at the ZINC database) for potential binders as possible inhibitors. This exercise helped to choose 10 top ranking molecules with ZINC00200163 [N-(2,2di methoxy ethyl)-6-methyl-2, 3, 4, 9-tetrahydro-1H-carbazol-1-amine] a having good fit (-6.43 KJ/mol binding energy) with the target protein. Thus, ZINC00200163 is identified as a potential molecule for further comprehensive characterization and in-depth analysis.

The evolving role of infectomics in drug discovery

Signatures of infectomes, which are encoded by both host and microbial genomes, and mirror the interplay between pathogens and their hosts, provide invaluable knowledge in the search for novel antimicrobial drugs. Infectomics is the study of infectomes by using systems biology and high-throughput omic approaches. There are three types of infectomic approaches that can be used for drug discovery: ecological infectomics, immunoinfectomics and chemical infectomics. Ecological infectomics, which is the ecological study of infectomes, explores symbiotic solutions to microbial infections. Research on drug discovery using infectomic signatures and immunomic approaches falls within the field of immunoinfectomics. Advances in chemical infectomics will lead to the development of a new generation of chemical drugs for therapeutics for microbial infections.

Infectomics Screening for Novel Antiviral Drug Targets

Preclinical Research

Copyright 2012 Wiley-Liss, Inc., A Wiley Company

Infectomics, a novel way to globally and comprehensively understand the interactions between microbial pathogens and their hosts, has significantly expanded understanding of the microbial infections. The infectomics view of viral–host interactions on the viral perspective principally focuses on gene acquisition, deletion, and point mutation, while traditional antiviral drug discovery concentrates on viral encoding proteins. Recently, high‐throughput technologies, such as mass spectrometry‐based proteomics, activity‐based protein profiling, microarray analysis, yeast two‐hybrid assay, small interfering RNA screening, and micro RNA profiling, have been gradually employed in the research of virus–host interactions. Besides, signaling pathways and cellular processes involved in viral–host interactions provide new insights of infectomics in antiviral drug discovery. In this review, we summarize related infectomics approaches in the studies of virus–host interactions, which shed light on the development of novel antiviral drug targets screening.

Current trends in antimicrobial agent research: chemo- and bioinformatics approaches

Databases and chemo- and bioinformatics tools that contain genomic, proteomic and functional information have become indispensable for antimicrobial drug research. The combination of chemoinformatics tools, bioinformatics tools and relational databases provides means of analyzing, linking and comparing online search results. The development of computational tools feeds on a diversity of disciplines, including mathematics, statistics, computer science, information technology and molecular biology. The computational approach to antimicrobial agent discovery and design encompasses genomics, molecular simulation and dynamics, molecular docking, structural and/or functional class prediction, and quantitative structure-activity relationships. This article reviews progress in the development of computational methods, tools and databases used for organizing and extracting biological meaning from antimicrobial research.

Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification

Leprosy is an infectious disease caused by Mycobacterium leprae. M. leprae has undergone a major reductive evolution leaving a minimal set of functional genes for survival. It remains non-cultivable. As M. leprae develops resistance against most of the drugs, novel drug targets are required in order to design new drugs. As most of the essential genes mediate several biosynthetic and metabolic pathways, the pathway predictions can predict essential genes. We used comparative genome analysis of metabolic enzymes in M. leprae and H. sapiens using KEGG pathway database and identified 179 non-homologues enzymes. On further comparison of these 179 non-homologous enzymes to the list of minimal set of 48 essential genes required for cell-wall biosynthesis of M. leprae reveals eight common enzymes. Interestingly, six of these eight common enzymes map to that of peptidoglycan biosynthesis and they all belong to Mur enzymes. The machinery for peptidoglycan biosynthesis is a rich source of crucial targets for antibacterial chemotherapy and thus targeting these enzymes is a step towards facilitating the search for new antibiotics.

Aminoacyl-tRNA synthetases: essential and still promising targets for new anti-infective agents

The emergence of resistance to existing antibiotics demands the development of novel antimicrobial agents directed against novel targets. Historically, bacterial cell wall synthesis, protein, and DNA and RNA synthesis have been major targets of very successful classes of antibiotics such as beta-lactams, glycopeptides, macrolides, aminoglycosides, tetracyclines, rifampicins and quinolones. Recently, efforts have been made to develop novel agents against validated targets in these pathways but also against new, previously unexploited targets. The era of genomics has provided insights into novel targets in microbial pathogens. Among the less exploited--but still promising--targets is the family of 20 aminoacyl-tRNA synthetases (aaRSs), which are essential for protein synthesis. These targets have been validated in nature as aaRS inhibition has been shown as the specific mode of action for many natural antimicrobial agents synthesized by bacteria and fungi. Therefore, aaRSs have the potential to be targeted by novel agents either from synthetic or natural sources to yield specific and selective anti-infectives. Numerous high-throughput screening programs aimed at identifying aaRS inhibitors have been performed over the last 20 years. A large number of promising lead compounds have been identified but only a few agents have moved forward into clinical development. This review provides an update on the present strategies to develop novel aaRS inhibitors as anti-infective drugs.

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

Background

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees.

Results

To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes.

Conclusion

By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

Large-scale survey for potentially targetable indels in bacterial and protozoan proteins

Our previous results demonstrated that some essential, housekeeping proteins from pathogenic microorganisms may contain sizable insertions-deletions in their sequences (compared to close human homologs) that can be responsible for unexpected virulence properties. For example, we found that indel-bearing elongation factor-1alpha from several pathogenic protozoa can activate a human tyrosine phosphatase SHP-1 leading to deactivation of macrophages. On the one hand, these findings allowed development of a strategy for targeting some indel-containing pathogen proteins that have similar human counterparts. On the other hand, the results raised numerous questions regarding the nature and implications of sequence indels in pathogen proteins. In the present study, we conducted a large-scale survey of indels in proteins from 136 bacterial and protozoan genomes. It has been established that sizable insertions and deletions occur in approximately 5-10% of bacterial proteins with close human homologs, while proteins from the protozoan pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Leishmania donovani exhibit elevated indel content that can reach up to 25%. The finding suggested that the occurrence of sequence indels may be involved in the evolution of pathogenic mechanisms in these protozoa.