The scientific impact of the Structural Genomics Consortium: a protein family and ligand-centered approach to medically-relevant human proteins

Fragment- and structure-based drug discovery for developing therapeutic agents targeting the DNA Damage Response

Cancer will directly affect the lives of over one-third of the population. The DNA Damage Response (DDR) is an intricate system involving damage recognition, cell cycle regulation, DNA repair, and ultimately cell fate determination, playing a central role in cancer etiology and therapy. Two primary therapeutic approaches involving DDR targeting include: combinatorial treatments employing anticancer genotoxic agents; and synthetic lethality, exploiting a sporadic DDR defect as a mechanism for cancer-specific therapy. Whereas, many DDR proteins have proven "undruggable", Fragment- and Structure-Based Drug Discovery (FBDD, SBDD) have advanced therapeutic agent identification and development. FBDD has led to 4 (with ∼50 more drugs under preclinical and clinical development), while SBDD is estimated to have contributed to the development of >200, FDA-approved medicines. Protein X-ray crystallography-based fragment library screening, especially for elusive or "undruggable" targets, allows for simultaneous generation of hits plus details of protein-ligand interactions and binding sites (orthosteric or allosteric) that inform chemical tractability, downstream biology, and intellectual property. Using a novel high-throughput crystallography-based fragment library screening platform, we screened five diverse proteins, yielding hit rates of ∼2-8% and crystal structures from ∼1.8 to 3.2 Å. We consider current FBDD/SBDD methods and some exemplary results of efforts to design inhibitors against the DDR nucleases meiotic recombination 11 (MRE11, a.k.a., MRE11A), apurinic/apyrimidinic endonuclease 1 (APE1, a.k.a., APEX1), and flap endonuclease 1 (FEN1).

Beyond history and "on a roll": The list of the most well-studied human protein structures and overall trends in the protein data bank

Of the roughly 20,000 canonical human protein sequences, as of January 20, 2021, 7,077 proteins have had their full or partial, medium- to high-resolution structures determined by x-ray crystallography or other methods. Which of these proteins dominate the protein data bank (the PDB) and why? In this paper, we list the 273 top human protein structures based on the number of their PDB entries. This set of proteins accounts for more than 40% of all available human PDB entries and represent past trends as well as current status for protein structural biology. We briefly discuss the relationship which some of the prominent protein structures have with protein research as a whole and mention their relevance to human diseases. The top-10 soluble and membrane proteins are all well-known (most of their first structures being deposited more than 30 years ago). Overall, there is no dramatic change in recent trends in the PDB. Remarkably, the number of structure depositions has grown nearly exponentially over the last 10 or more years (with a doubling time of 7 years for proteins, obtained from any organism). Growth in human protein structures is slightly faster (at 5.9 years). The information in this paper may be informative to senior scientists but also inspire researchers who are new to protein science, providing the year 2021 snap-shot for the state of protein structural biology.

Improving target assessment in biomedical research: the GOT-IT recommendations

Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.

Rapid Biophysical Characterization and NMR Spectroscopy Structural Analysis of Small Proteins from Bacteria and Archaea

Proteins encoded by small open reading frames (sORFs) have a widespread occurrence in diverse microorganisms and can be of high functional importance. However, due to annotation biases and their technically challenging direct detection, these small proteins have been overlooked for a long time and were only recently rediscovered. The currently rapidly growing number of such proteins requires efficient methods to investigate their structure-function relationship. Herein, a method is presented for fast determination of the conformational properties of small proteins. Their small size makes them perfectly amenable for solution-state NMR spectroscopy. NMR spectroscopy can provide detailed information about their conformational states (folded, partially folded, and unstructured). In the context of the priority program on small proteins funded by the German research foundation (SPP2002), 27 small proteins from 9 different bacterial and archaeal organisms have been investigated. It is found that most of these small proteins are unstructured or partially folded. Bioinformatics tools predict that some of these unstructured proteins can potentially fold upon complex formation. A protocol for fast NMR spectroscopy structure elucidation is described for the small proteins that adopt a persistently folded structure by implementation of new NMR technologies, including automated resonance assignment and nonuniform sampling in combination with targeted acquisition.

Protein Crystallizability

Obtaining diffracting quality crystals remains a major challenge in protein structure research. We summarize and compare methods for selecting the best protein targets for crystallization, construct optimization and crystallization condition design. Target selection methods are divided into algorithms predicting the chance of successful progression through all stages of structural determination (from cloning to solving the structure) and those focusing only on the crystallization step. We tried to highlight pros and cons of different approaches examining the following aspects: data size, redundancy and representativeness, overfitting during model construction, and results evaluation. In summary, although in recent years progress was made and several sequence properties were reported to be relevant for crystallization, the successful prediction of protein crystallization behavior and selection of corresponding crystallization conditions continue to challenge structural researchers.

Open access target validation is a more efficient way to accelerate drug discovery

There is a scarcity of novel treatments to address many unmet medical needs. Industry and academia are finally coming to terms with the fact that the prevalent models and incentives for innovation in early stage drug discovery are failing to promote progress quickly enough. Here we will examine how an open model of precompetitive public-private research partnership is enabling efficient derisking and acceleration in the early stages of drug discovery, whilst also widening the range of communities participating in the process, such as patient and disease foundations.

Malaria medicines: a glass half full?

Despite substantial scientific progress over the past two decades, malaria remains a worldwide burden that causes hundreds of thousands of deaths every year. New, affordable and safe drugs are required to overcome increasing resistance against artemisinin-based treatments, treat vulnerable populations, interrupt the parasite life cycle by blocking transmission to the vectors, prevent infection and target malaria species that transiently remain dormant in the liver. In this Review, we discuss how the antimalarial drug discovery pipeline has changed over the past 10 years, grouped by the various target compound or product profiles, to assess progress and gaps, and to recommend priorities.

Towards a systematic analysis of human short-chain dehydrogenases/reductases (SDR): Ligand identification and structure-activity relationships

Short-chain dehydrogenases/reductases (SDRs) constitute a large, functionally diverse branch of enzymes within the class of NAD(P)(H) dependent oxidoreductases. In humans, over 80 genes have been identified with distinct metabolic roles in carbohydrate, amino acid, lipid, retinoid and steroid hormone metabolism, frequently associated with inherited genetic defects. Besides metabolic functions, a subset of atypical SDR proteins appears to play critical roles in adapting to redox status or RNA processing, and thereby controlling metabolic pathways. Here we present an update on the human SDR superfamily and a ligand identification strategy using differential scanning fluorimetry (DSF) with a focused library of oxidoreductase and metabolic ligands to identify substrate classes and inhibitor chemotypes. This method is applicable to investigate structure-activity relationships of oxidoreductases and ultimately to better understand their physiological roles.

A microfluidic, high throughput protein crystal growth method for microgravity

The attenuation of sedimentation and convection in microgravity can sometimes decrease irregularities formed during macromolecular crystal growth. Current terrestrial protein crystal growth (PCG) capabilities are very different than those used during the Shuttle era and that are currently on the International Space Station (ISS). The focus of this experiment was to demonstrate the use of a commercial off-the-shelf, high throughput, PCG method in microgravity. Using Protein BioSolutions' microfluidic Plug Maker™/CrystalCard™ system, we tested the ability to grow crystals of the regulator of glucose metabolism and adipogenesis: peroxisome proliferator-activated receptor gamma (apo-hPPAR-γ LBD), as well as several PCG standards. Overall, we sent 25 CrystalCards™ to the ISS, containing ~10,000 individual microgravity PCG experiments in a 3U NanoRacks NanoLab (1U = 10(3) cm.). After 70 days on the ISS, our samples were returned with 16 of 25 (64%) microgravity cards having crystals, compared to 12 of 25 (48%) of the ground controls. Encouragingly, there were more apo-hPPAR-γ LBD crystals in the microgravity PCG cards than the 1g controls. These positive results hope to introduce the use of the PCG standard of low sample volume and large experimental density to the microgravity environment and provide new opportunities for macromolecular samples that may crystallize poorly in standard laboratories.

Effective screening strategy using ensembled pharmacophore models combined with cascade docking: application to p53-MDM2 interaction inhibitors

Protein-protein interactions (PPIs) play a crucial role in cellular function and form the backbone of almost all biochemical processes. In recent years, protein-protein interaction inhibitors (PPIIs) have represented a treasure trove of potential new drug targets. Unfortunately, there are few successful drugs of PPIIs on the market. Structure-based pharmacophore (SBP) combined with docking has been demonstrated as a useful Virtual Screening (VS) strategy in drug development projects. However, the combination of target complexity and poor binding affinity prediction has thwarted the application of this strategy in the discovery of PPIIs. Here we report an effective VS strategy on p53-MDM2 PPI. First, we built a SBP model based on p53-MDM2 complex cocrystal structures. The model was then simplified by using a Receptor-Ligand complex-based pharmacophore model considering the critical binding features between MDM2 and its small molecular inhibitors. Cascade docking was subsequently applied to improve the hit rate. Based on this strategy, we performed VS on NCI and SPECS databases and successfully discovered 6 novel compounds from 15 hits with the best, compound 1 (NSC 5359), K(i) = 180 ± 50 nM. These compounds can serve as lead compounds for further optimization.

Objective assessment of cancer genes for drug discovery

Selecting the best targets is a key challenge for drug discovery, and achieving this effectively, efficiently and systematically is particularly important for prioritizing candidates from the sizeable lists of potential therapeutic targets that are now emerging from large-scale multi-omics initiatives, such as those in oncology. Here, we describe an objective, systematic, multifaceted computational assessment of biological and chemical space that can be applied to any human gene set to prioritize targets for therapeutic exploration. We use this approach to evaluate an exemplar set of 479 cancer-associated genes, reveal the tension between biological relevance and chemical tractability, and describe major gaps in available knowledge that could be addressed to aid objective decision-making. We also propose drug repurposing opportunities and identify potentially druggable cancer-associated proteins that have been poorly explored with regard to the discovery of small-molecule modulators, despite their biological relevance.

Squeezing the most from every crystal: the fine details of data collection

Modern synchrotron beamlines offer instrumentation of unprecedented quality, which in turn encourages increasingly marginal experiments, and for these, as much as ever, the ultimate success of data collection depends on the experience, but especially the care, of the experimenter. A representative set of difficult cases has been encountered at the Structural Genomics Consortium, a worldwide structural genomics initiative of which the Oxford site currently deposits three novel human structures per month. Achieving this target relies heavily on frequent visits to the Diamond Light Source, and the variety of crystal systems still demand customized data collection, diligent checks and careful planning of each experiment. Here, an overview is presented of the techniques and procedures that have been refined over the years and that are considered synchrotron best practice.

An evolutionary perspective on the kinome of malaria parasites

Malaria parasites belong to an ancient lineage that diverged very early from the main branch of eukaryotes. The approximately 90-member plasmodial kinome includes a majority of eukaryotic protein kinases that clearly cluster within the AGC, CMGC, TKL, CaMK and CK1 groups found in yeast, plants and mammals, testifying to the ancient ancestry of these families. However, several hundred millions years of independent evolution, and the specific pressures brought about by first a photosynthetic and then a parasitic lifestyle, led to the emergence of unique features in the plasmodial kinome. These include taxon-restricted kinase families, and unique peculiarities of individual enzymes even when they have homologues in other eukaryotes. Here, we merge essential aspects of all three malaria-related communications that were presented at the Evolution of Protein Phosphorylation meeting, and propose an integrated discussion of the specific features of the parasite's kinome and phosphoproteome.

Shape-based reprofiling of FDA-approved drugs for the H₁ histamine receptor

Reprofiling of existing drugs to treat conditions not originally targeted is an attractive means of addressing the problem of a decreasing stream of approved drugs. To determine if 3D shape similarity can be used to rationalize an otherwise serendipitous process, we employed 3D shape-based virtual screening to reprofile existing FDA-approved drugs. The study was conducted in two phases. First, multiple histamine H(1) receptor antagonists were identified to be used as query molecules, and these were compared to a database of approved drugs. Second, the hits were ranked according to 3D similarity and the top drugs evaluated in a cell-based assay. The virtual screening methodology proved highly successful, as 13 of 23 top drugs tested selectively inhibited histamine-induced calcium release with the best being chlorprothixene (IC(50) 1 nM). Finally, we confirmed that the drugs identified using the cell-based assay were all acting at the receptor level by conducting a radioligand-binding assay using rat membrane.

Structural modelling pipelines in next generation sequencing projects

Our capacity to reliably predict protein structure from sequence is steadily improving due to the increased numbers and better targeting of protein structures being experimentally determined by structural genomics projects, along with the development of better modeling methodologies. Template-based (homology) modeling and de novo modeling methods are being combined to fill in remaining gaps in template coverage, and powerful automated structural modeling pipelines are being applied to large data sets of protein sequences. The improved quality of 3D models of proteins has led to their routine use in assessing the functional impact of nonsynonymous single nucleotide polymorphisms (nsSNPs) in specific protein systems, with the development of approaches that may be applied in a predictive fashion to nsSNPs emerging from next-generation sequencing projects. The challenges encountered in deriving functionally meaningful deductions from structural modeling can be quite different for proteins of different protein functional classes. The specific challenges to the assessment of the structural and functional impact of nsSNPs in globular proteins such as binding and regulatory proteins, structural proteins, and enzymes are discussed, as well as membrane transport proteins and ion channels. The mapping of reliable predictions of the structural and functional impact of SNPs, generated from automated modeling pipelines, on to protein-protein interaction networks will facilitate new approaches to understanding complex polygenic disorders and predisposition to disease.

Epigenetics—an emerging and highly promising source of new drug targets

Epigenetic modifications to DNA and its associated histone proteins are major influences on gene expression. This regulatory process is disrupted in cancer and a range of chronic human diseases, and provides attractive new intervention points and targets for drug discovery.

Structural and evolutionary divergence of eukaryotic protein kinases in Apicomplexa

Background

The Apicomplexa constitute an evolutionarily divergent phylum of protozoan pathogens responsible for widespread parasitic diseases such as malaria and toxoplasmosis. Many cellular functions in these medically important organisms are controlled by protein kinases, which have emerged as promising drug targets for parasitic diseases. However, an incomplete understanding of how apicomplexan kinases structurally and mechanistically differ from their host counterparts has hindered drug development efforts to target parasite kinases.

Results

We used the wealth of sequence data recently made available for 15 apicomplexan species to identify the kinome of each species and quantify the evolutionary constraints imposed on each family of apicomplexan kinases. Our analysis revealed lineage-specific adaptations in selected families, namely cyclin-dependent kinase (CDK), calcium-dependent protein kinase (CDPK) and CLK/LAMMER, which have been identified as important in the pathogenesis of these organisms. Bayesian analysis of selective constraints imposed on these families identified the sequence and structural features that most distinguish apicomplexan protein kinases from their homologs in model organisms and other eukaryotes. In particular, in a subfamily of CDKs orthologous to Plasmodium falciparum crk-5, the activation loop contains a novel PTxC motif which is absent from all CDKs outside Apicomplexa. Our analysis also suggests a convergent mode of regulation in a subset of apicomplexan CDPKs and mammalian MAPKs involving a commonly conserved arginine in the αC helix. In all recognized apicomplexan CLKs, we find a set of co-conserved residues involved in substrate recognition and docking that are distinct from metazoan CLKs.

Conclusions

We pinpoint key conserved residues that can be predicted to mediate functional differences from eukaryotic homologs in three identified kinase families. We discuss the structural, functional and evolutionary implications of these lineage-specific variations and propose specific hypotheses for experimental investigation. The apicomplexan-specific kinase features reported in this study can be used in the design of selective kinase inhibitors.

Structural genomics of infectious disease drug targets: the SSGCID

The Seattle Structural Genomics Center for Infectious Disease (SSGCID) is a consortium of researchers at Seattle BioMed, Emerald BioStructures, the University of Washington and Pacific Northwest National Laboratory that was established to apply structural genomics approaches to drug targets from infectious disease organisms. The SSGCID is currently funded over a five-year period by the National Institute of Allergy and Infectious Diseases (NIAID) to determine the three-dimensional structures of 400 proteins from a variety of Category A, B and C pathogens. Target selection engages the infectious disease research and drug-therapy communities to identify drug targets, essential enzymes, virulence factors and vaccine candidates of biomedical relevance to combat infectious diseases. The protein-expression systems, purified proteins, ligand screens and three-dimensional structures produced by SSGCID constitute a valuable resource for drug-discovery research, all of which is made freely available to the greater scientific community. This issue of Acta Crystallographica Section F, entirely devoted to the work of the SSGCID, covers the details of the high-throughput pipeline and presents a series of structures from a broad array of pathogenic organisms. Here, a background is provided on the structural genomics of infectious disease, the essential components of the SSGCID pipeline are discussed and a survey of progress to date is presented.

Targets in epigenetics: inhibiting the methyl writers of the histone code

Growing evidence suggests that protein lysine methyltransferases (PKMTs) and protein arginine methyltransferases (PRMTs) are associated with the development of various human diseases, including cancer, inflammation, and psychiatric disorders. Given the significant role of these proteins in human disease, efforts to discover selective small-molecule inhibitors of these enzymes are quickly gaining momentum. In this review, we focus on the recent progress in the discovery of selective PKMT and PRMT inhibitors. A future perspective on developing methyltransferase inhibitors is also offered.

Assessment of radiation damage behaviour in a large collection of empirically optimized datasets highlights the importance of unmeasured complicating effects

The radiation damage behaviour in 43 datasets of 34 different proteins collected over a year was examined, in order to gauge the reliability of decay metrics in practical situations, and to assess how these datasets, optimized only empirically for decay, would have benefited from the precise and automatic prediction of decay now possible with the programs RADDOSE [Murray, Garman & Ravelli (2004). J. Appl. Cryst. 37, 513-522] and BEST [Bourenkov & Popov (2010). Acta Cryst. D66, 409-419]. The results indicate that in routine practice the diffraction experiment is not yet characterized well enough to support such precise predictions, as these depend fundamentally on three interrelated variables which cannot yet be determined robustly and practically: the flux density distribution of the beam; the exact crystal volume; the sensitivity of the crystal to dose. The former two are not satisfactorily approximated from typical beamline information such as nominal beam size and transmission, or two-dimensional images of the beam and crystal; the discrepancies are particularly marked when using microfocus beams (<20 µm). Empirically monitoring decay with the dataset scaling B factor (Bourenkov & Popov, 2010) appears more robust but is complicated by anisotropic and/or low-resolution diffraction. These observations serve to delineate the challenges, scientific and logistic, that remain to be addressed if tools for managing radiation damage in practical data collection are to be conveniently robust enough to be useful in real time.

Docking methods for structure-based library design

The drug discovery process mainly relies on the experimental high-throughput screening of huge compound libraries in their pursuit of new active compounds. However, spiraling research and development costs and unimpressive success rates have driven the development of more rational, efficient, and cost-effective methods. With the increasing availability of protein structural information, advancement in computational algorithms, and faster computing resources, in silico docking-based methods are increasingly used to design smaller and focused compound libraries in order to reduce screening efforts and costs and at the same time identify active compounds with a better chance of progressing through the optimization stages. This chapter is a primer on the various docking-based methods developed for the purpose of structure-based library design. Our aim is to elucidate some basic terms related to the docking technique and explain the methodology behind several docking-based library design methods. This chapter also aims to guide the novice computational practitioner by laying out the general steps involved for such an exercise. Selected successful case studies conclude this chapter.

Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches.

Structure-based drug discovery and protein targets in the CNS

Structure-based methods are having an increasing role and impact in drug discovery. The crystal structures of an increasing number of therapeutic targets are becoming available. These structures can transform our understanding of how these proteins perform their biological function and often provide insights into the molecular basis of disease. In addition, the structures can help the discovery process. Methods such as virtual screening and experimental fragment screening can provide starting hit compounds for a discovery project. Crystal structures of compounds bound to the protein can direct or guide the medicinal chemistry optimisation to improve drug-like properties - not only providing ideas on how to improve binding affinity or selectivity, but also showing where the compound can be modified in attempting to modulate physico-chemical properties and biological efficacy. The majority of drug discovery projects against globular protein targets now use these methods at some stage. This review provides a summary of the range of structure-based drug discovery methods that are in use and surveys the suitability of the methods for targets currently identified for CNS drugs. Until recently, structure-based discovery was difficult or unknown for these targets. The recent determination of the structures of a number of GPCR proteins, together with the steady increase in structures for other membrane proteins, is opening up the possibility for these structure-based methods to find increased use in drug discovery for CNS diseases and conditions.

Protein crystallizability

Obtaining well-diffracting crystals remains a major challenge in protein structure research. In this chapter, we review currently available computational methods to estimate the crystallization potential of a protein, to optimize amino acid sequences toward improved crystallization likelihood, and to design optimal crystal screen conditions.

From protein sequences to 3D-structures and beyond: the example of the UniProt knowledgebase

With the dramatic increase in the volume of experimental results in every domain of life sciences, assembling pertinent data and combining information from different fields has become a challenge. Information is dispersed over numerous specialized databases and is presented in many different formats. Rapid access to experiment-based information about well-characterized proteins helps predict the function of uncharacterized proteins identified by large-scale sequencing. In this context, universal knowledgebases play essential roles in providing access to data from complementary types of experiments and serving as hubs with cross-references to many specialized databases. This review outlines how the value of experimental data is optimized by combining high-quality protein sequences with complementary experimental results, including information derived from protein 3D-structures, using as an example the UniProt knowledgebase (UniProtKB) and the tools and links provided on its website ( http://www.uniprot.org/ ). It also evokes precautions that are necessary for successful predictions and extrapolations.

Benefits of structural genomics for drug discovery research

While three dimensional structures have long been used to search for new drug targets, only a fraction of new drugs coming to the market has been developed with the use of a structure-based drug discovery approach. However, the recent years have brought not only an avalanche of new macromolecular structures, but also significant advances in the protein structure determination methodology only now making their way into structure-based drug discovery. In this paper, we review recent developments resulting from the Structural Genomics (SG) programs, focusing on the methods and results most likely to improve our understanding of the molecular foundation of human diseases. SG programs have been around for almost a decade, and in that time, have contributed a significant part of the structural coverage of both the genomes of pathogens causing infectious diseases and structurally uncharacterized biological processes in general. Perhaps most importantly, SG programs have developed new methodology at all steps of the structure determination process, not only to determine new structures highly efficiently, but also to screen protein/ligand interactions. We describe the methodologies, experience and technologies developed by SG, which range from improvements to cloning protocols to improved procedures for crystallographic structure solution that may be applied in "traditional" structural biology laboratories particularly those performing drug discovery. We also discuss the conditions that must be met to convert the present high-throughput structure determination pipeline into a high-output structure-based drug discovery system.

Structures of sea anemone toxins

Sea anemones produce a variety of toxic peptides and proteins, including many ion channel blockers and modulators, as well as potent cytolysins. This review describes the structures that have been determined to date for the major classes of peptide and protein toxins. In addition, established and emerging methods for structure determination are summarized and the prospects for modelling newly described toxins are evaluated. In common with most other classes of proteins, toxins display conformational flexibility which may play a role in receptor binding and function. The prospects for obtaining atomic resolution structures of toxins bound to their receptors are also discussed.

Recent advances in 17beta-hydroxysteroid dehydrogenases

The metabolism of steroids at position 17 is catalysed by a growing number of 17beta-hydroxysteroid dehydrogenases (17beta-HSDs). Several human diseases like breast or prostate cancer, endometriosis,metabolic syndrome and mental diseases were associated with dysfunctions of 17beta-HSDs, which consequently became drug targets. This review will focus on identities of 17beta-HSDs and recent advances in analyses of their physiological roles in steroid and lipid metabolism. It will also address the potential of metabolomics in drug development.

targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Background

Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation.

Results

We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed.

Conclusion

The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Crystal structure and functional assignment of YfaU, a metal ion dependent class II aldolase from Escherichia coli K12

One of the major challenges in the postgenomic era is the functional assignment of proteins using sequence- and structure-based predictive methods coupled with experimental validation. We have used these approaches to investigate the structure and function of the Escherichia coli K-12 protein YfaU, annotated as a putative 4-hydroxy-2-ketoheptane-1,7-dioate aldolase (HpcH) in the sequence databases. HpcH is the final enzyme in the degradation pathway of the aromatic compound homoprotocatechuate. We have determined the crystal structure of apo-YfaU and the Mg (2+)-pyruvate product complex. Despite greater sequence and structural similarity to HpcH, genomic context suggests YfaU is instead a 2-keto-3-deoxy sugar aldolase like the homologous 2-dehydro-3-deoxygalactarate aldolase (DDGA). Enzyme kinetic measurements show activity with the probable physiological substrate 2-keto-3-deoxy- l-rhamnonate, supporting the functional assignment, as well as the structurally similar 2-keto-3-deoxy- l-mannonate and 2-keto-3-deoxy- l-lyxonate (see accompanying paper: Rakus, J. F., Fedorov, A. A., Fedorov, E. V., Glasner, M. E., Hubbard, B. K., Delli, J. D., Babbitt, P. C., Almo, S. C., and Gerlt, J. A. (2008) Biochemistry 47, 9944-9954). YfaU has similar activity toward the HpcH substrate 4-hydroxy-2-ketoheptane-1,7-dioate and synthetic substrates 4-hydroxy-2-ketopentanoic acid and 4-hydroxy-2-ketohexanoic acid. This indicates a relaxed substrate specificity that complicates the functional assignment of members of this enzyme superfamily. Crystal structures suggest these enzymes use an Asp-His intersubunit dyad to activate a metal-bound water or hydroxide for proton transfer during catalysis.

Genome-informed contributions to malaria therapies: feeding somewhere down the (pipe)line

Whole-genome sequences of Plasmodium spp. have helped redefine malaria research, including initiatives that seek to develop drugs, vaccines, and other antimalarial therapies. The problems caused by malaria were brought to the public's attention once more on World Malaria Day (April 25). Unfortunately, the current impact of genome-informed research is not as great as might have been hoped, and the reasons for this and continuing challenges are discussed.

PCR-based gene synthesis to produce recombinant proteins for crystallization

BACKGROUND

Gene synthesis technologies are an important tool for structural biology projects, allowing increased protein expression through codon optimization and facilitating sequence alterations. Existing methods, however, can be complex and not always reproducible, prompting researchers to use commercial suppliers rather than synthesize genes themselves.

RESULTS

A PCR-based gene synthesis method, referred to as SeqTBIO, is described to efficiently assemble the coding regions of two novel hyperthermophilic proteins, PAZ (Piwi/Argonaute/Zwille) domain, a siRNA-binding domain of an Argonaute protein homologue and a deletion mutant of a family A DNA polymerase (PolA). The gene synthesis procedure is based on sequential assembly such that homogeneous DNA products can be obtained after each synthesis step without extensive manipulation or purification requirements. Coupling the gene synthesis procedure to in vivo homologous recombination techniques allows efficient subcloning and site-directed mutagenesis for error correction. The recombinant proteins of PAZ and PolA were subsequently overexpressed in E. coli and used for protein crystallization. Crystals of both proteins were obtained and they were suitable for X-ray analysis.

CONCLUSION

We demonstrate, by using PAZ and PolA as examples, the feasibility of integrating the gene synthesis, error correction and subcloning techniques into a non-automated gene to crystal pipeline such that genes can be designed, synthesized and implemented for recombinant expression and protein crystallization.

Ligand supplementation as a method to increase soluble heterologous protein production

Ligand interactions are central to enzyme or receptor function, constituting a cornerstone in biochemistry and pharmacology. Here we discuss a ligand application that can be exploited to significantly increase the proportion of recombinant protein expressed in soluble form, by including ligands during the culture. Provided that a sufficiently soluble, cell-permeable and avid ligand is available, one can use it to stabilize nascently synthesized proteins, and in this manner promote solubility and prevent aggregation. To our knowledge, this concept has not been explored systematically and we provide here the first data on ligand supplementation in expression experiments across a whole human protein family: the short-chain dehydrogenases/reductases (SDR). We identified glycerrhitinic acid and its hemisuccinate ester, carbenoxolone (CBX), as ligands with variable affinities ranging from low nanomolar to micromolar binding constants against several SDRs. CBX was utilized as a culture additive in Escherichia coli expression systems against a total of approximately 500 constructs derived from 65 SDR targets, and significantly higher levels of soluble protein were obtained for more than four distinct targets. One of these, the glucocorticoid-activating enzyme type 1 11 beta-hydroxysteroid dehydrogenase (11 beta-HSD1), was solubly expressed only at a very low level (<10 microg/l culture) in the absence of ligand; however, soluble expression could be enhanced to mg/l levels by inclusion of CBX or other inhibitors. Other compounds with different chemical scaffolds were used against 11 beta-HSD1 in equivalent expression experiments yielding similar results. Taken together, if suitable ligands for a given protein are available, this approach could be tested quickly and might represent an easy and effective strategy to enhance soluble protein production, suitable for structural and functional characterization studies.

Structural genomics and drug discovery: all in the family

Structural genomics is starting to have an impact on the early stages of drug discovery and target validation through the contribution of new structures of known and potential drug targets, their complexes with ligands and protocols and reagents for additional structural work within a drug discovery program. Recent progress includes structures of targets from bacterial, viral and protozoan human pathogens, and human targets from known or potential druggable protein families such as, kinases, phosphatases, dehydrogenases/oxidoreductases, sulfo-, acetyl- and methyl-transferases, and a number of other key metabolic enzymes. Importantly, many of these structures contained ligands in the active sites, including for example, the first structures of target-bound therapeutics. Structural genomics of protein families combined with ligand discovery holds particular promise for advancing early stage discovery programs.

Protein production and purification

In selecting a method to produce a recombinant protein, a researcher is faced with a bewildering array of choices as to where to start. To facilitate decision-making, we describe a consensus 'what to try first' strategy based on our collective analysis of the expression and purification of over 10,000 different proteins. This review presents methods that could be applied at the outset of any project, a prioritized list of alternate strategies and a list of pitfalls that trip many new investigators.