Glutamate racemase as a target for drug discovery

The bacterial cell wall is a highly cross‐linked polymeric structure consisting of repeating peptidoglycan units, each of which contains a novel pentapeptide substitution which is cross‐linked through transpeptidation. The incorporation of d ‐glutamate as the second residue is strictly conserved across the bacterial kingdom. Glutamate racemase, a member of the cofactor‐independent, two‐thiol‐based family of amino acid racemases, has been implicated in the production and maintenance of sufficient d ‐glutamate pool levels required for growth. The subject of over four decades of research, it is now evident that the enzyme is conserved and essential for growth across the bacterial kingdom and has a conserved overall topology and active site architecture; however, several different mechanisms of regulation have been observed. These traits have recently been targeted in the discovery of both narrow and broad spectrum inhibitors. This review outlines the biological history of this enzyme, the recent biochemical and structural characterization of isozymes from a wide range of species and developments in the identification of inhibitors that target the enzyme as possible therapeutic agents.     

Machine learning for target discovery in drug development

The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug–target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.     

Genomics in target and drug discovery

Genomics-based discovery of novel therapeutic drug targets requires the design of well-controlled biological or pharmacological experiments with experimental questions and hypotheses that relate to the therapeutic area of interest. This will aid the validation level of differentially expressed genes and hence facilitate the de-selection of the genes that are identified in microarray experiments. We here provide an example of how this approach is followed in the manipulation of human macrophage foam cells towards the discovery of novel drug targets for treatment of atherosclerosis.     

The flavivirus protease as a target for drug discovery

Many flaviviruses are significant human pathogens causing considerable disease burdens, including encephalitis and hemorrhagic fever, in the regions in which they are endemic. A paucity of treatments for flaviviral infections has driven interest in drug development targeting proteins essential to flavivirus replication, such as the viral protease. During viral replication, the flavivirus genome is translated as a single polyprotein precursor, which must be cleaved into individual proteins by a complex of the viral protease, NS3, and its cofactor, NS2B. Because this cleavage is an obligate step of the viral life-cycle, the flavivirus protease is an attractive target for antiviral drug development. In this review, we will survey recent drug development studies targeting the NS3 active site, as well as studies targeting an NS2B/NS3 interaction site determined from flavivirus protease crystal structures.     

Polyphony: superposition independent methods for ensemble-based drug discovery

Background

Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates.

Results

Novel methodologies are described for the analysis of multiple structures of a protein. Statistical approaches that rely upon residue equivalence, but not superposition, are developed. Tasks that can be performed include the identification of hinge regions, allosteric conformational changes and transient binding sites. The approaches are tested on crystal structures of CDK2 and other CMGC protein kinases and a simulation of p38α. Known interaction - conformational change relationships are highlighted but also new ones are revealed. A transient but druggable allosteric pocket in CDK2 is predicted to occur under the CMGC insert. Furthermore, an evolutionarily-conserved conformational link from the location of this pocket, via the αEF-αF loop, to phosphorylation sites on the activation loop is discovered.

Conclusions

New methodologies are described and validated for the superimposition independent conformational analysis of large collections of structures or simulation snapshots of the same protein. The methodologies are encoded in a Python package called Polyphony, which is released as open source to accompany this paper [http://wrpitt.bitbucket.org/polyphony/].     

Proteomic methods for biomarker discovery and validation. Are we there yet?

Noninvasive molecular biomarkers are becoming attractive tools to monitor disease progression, aid drug development programs and use as surrogate outcome measures in clinical trials. Cutting edge proteomic methods to assay biomarkers in body fluids have been developed in the past few years, but transitioning them to clinical practice has been slow and depends on the qualification of both the method and the biomarker.     

Label-free target identification in drug discovery via phenotypic screening

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From drug to protein: using yeast genetics for high-throughput target discovery

The budding yeast Saccharomyces cerevisiae has long been an effective eukaryotic model system for understanding basic cellular processes. The genetic tractability and ease of manipulation in the laboratory make yeast well suited for large-scale chemical and genetic screens. Several recent studies describing the use of yeast genetics for high-throughput drug target identification are discussed in this review.     

Alchemical free energy methods for drug discovery: progress and challenges

Improved rational drug design methods are needed to lower the cost and increase the success rate of drug discovery and development. Alchemical binding free energy calculations, one potential tool for rational design, have progressed rapidly over the past decade, but still fall short of providing robust tools for pharmaceutical engineering. Recent studies, especially on model receptor systems, have clarified many of the challenges that must be overcome for robust predictions of binding affinity to be useful in rational design. In this review, inspired by a recent joint academic/industry meeting organized by the authors, we discuss these challenges and suggest a number of promising approaches for overcoming them.     

Progress in functional genomics approaches to antifungal drug target discovery

Antifungal drug discovery is starting to benefit from the enormous advances in the genomics field, which have occurred in the past decade. As traditional drug screening on existing targets is not delivering the long-awaited potent antifungals, efforts to use novel genetics and genomics-based strategies to aid in the discovery of novel drug targets are gaining increased importance. The current paradigm in antifungal drug target discovery focuses on basically two main classes of targets to evaluate: genes essential for viability and virulence or pathogenicity factors. Here we report on recent advances in genetics and genomics-based technologies that will allow us not only to identify and validate novel fungal drug targets, but hopefully in the longer run also to discover potent novel therapeutic agents. Fungal pathogens have typically presented significant obstacles when subjected to genetics, but the creativity of scientists in the anti-infectives field and the cross-talk with scientists in other areas is now yielding exciting new tools and technologies to tackle the problem of finding potent, specific and non-toxic antifungal therapeutics.     

Proteomic discovery of protease substrates

Elucidation of in vivo substrate degradomes of a protease is a daunting endeavor because of the large number of proteins in a proteome and often minute and transient amounts of key substrates. Proteomic substrate screens for proteases are currently experiencing impressive progress. Mass spectrometry-based global proteome analysis, interfaced with liquid-chromatography and together with stable isotope labeling strategies, has provided increased coverage and sensitivity for quantitative proteomics. ICAT and iTRAQ labeling have been used to identify a plethora of new matrix metalloproteinase substrates. Emerging techniques focus on the quantitative analysis of proteolytically generated neo amino-termini, which we call terminopes, on a system-wide basis. In vivo SILAC pulse-chase experiments have also enabled the study of individual protein turnover and global proteome dynamics in cells and whole organisms. Together with activity-based probes for the profiling of functional proteases, there is now in place an array of complementary technologies to dissect the 'protease web' and its distortion in pathology.