Structural genomics-impact on biomedicine and drug discovery

An Overview of Computer-aided Drug Design Tools and Recent Applications in Designing of Anti-diabetic Agents

BACKGROUND

In this fast-growing era, high throughput data is now being easily accessed by getting transformed into datasets which store the information. Such information is valuable to optimize the hypothesis and drug design via computer-aided drug design (CADD). Nowadays, we can explore the role of CADD in various disciplines like Nanotechnology, Biochemistry, Medical Sciences, Molecular Biology, etc. Methods: We searched the valuable literature using a pertinent database with given keywords like computer-aided drug design, anti-diabetic, drug design, etc. We retrieved all valuable articles which are recent and discussing the role of computation in the designing of anti-diabetic agents.

RESULTS

To facilitate the drug discovery process, the computational approach has set landmarks in the whole pipeline for drug discovery from target identification and mechanism of action to the identification of leads and drug candidates. Along with this, there is a determined endeavor to describe the significance of in-silico studies in predicting the absorption, distribution, metabolism, excretion, and toxicity profile. Thus, globally, CADD is accepted with a variety of tools for studying QSAR, virtual screening, protein structure prediction, quantum chemistry, material design, physical and biological property prediction.

CONCLUSION

Computer-assisted tools are used as the drug discovery tool in the area of different diseases, and here we reviewed the collaborative aspects of information technologies and chemoinformatic tools in the discovery of anti-diabetic agents, keeping in view the growing importance for treating diabetes.

Insights Into the Explication of Potent Tyrosinase Inhibitors with Reference to Computational Studies

Background:

Pigment melanin has primarily a photo defensive role in human skin, its unnecessary production and irregular distribution can cause uneven skin tone ultimately results in hyper pigmentation. Melanin biosynthesis is initiated by tyrosine oxidation through tyrosinase, the key enzyme for melanogenesis. Not only in humans, tyrosinase is also widely distributed in plants and liable for browning of vegetables and fruits. Search for the inhibitors of tyrosinase have been an important target to facilitate development of therapies for the prevention of hyperpigmentary disorders and an undesired browning of vegetables and fruits.

Methods:

Different natural and synthetic chemical compounds have been tested as potential tyrosinase inhibitors, but the mechanism of inhibition is not known, and the quest for information regarding interaction between tyrosinase and its inhibitors is one of the recent areas of research. Computer based methods hence are useful to overcome such issues. Successful utilization of in silico tools like molecular docking simulations make it possible to interpret the tyrosinase and its inhibitor’s intermolecular interactions and helps in identification and development of new and potent tyrosinase inhibitors.

Results:

The present review has pointed out the prominent role of computer aided approaches for the explication of promising tyrosinase inhibitors with a focus on molecular docking approach. Highlighting certain examples of natural compounds whose antityrosinase effects has been evaluated using computational simulations.

Conclusion:

The investigation of new and potent inhibitors of tyrosinase using computational chemistry and bioinformatics will ultimately help millions of peoples to get rid of hyperpigmentary disorders as well as browning of fruits and vegetables.

Integrating Molecular Docking and Molecular Dynamics Simulations

Computational methods, applied at the early stages of the drug design process, use current technology to provide valuable insights into the understanding of chemical systems in a virtual manner, complementing experimental analysis. Molecular docking is an in silico method employed to foresee binding modes of small compounds or macromolecules in contact with a receptor and to predict their molecular interactions. Moreover, the methodology opens up the possibility of ranking these compounds according to a hierarchy determined using particular scoring functions. Docking protocols assign many approximations, and most of them lack receptor flexibility. Therefore, the reliability of the resulting protein-ligand complexes is uncertain. The association with the costly but more accurate MD techniques provides significant complementary with docking. MD simulations can be used before docking since a series of "new" and broader protein conformations can be extracted from the processing of the resulting trajectory and employed as targets for docking. They also can be utilized a posteriori to optimize the structures of the final complexes from docking, calculate more detailed interaction energies, and provide information about the ligand binding mechanism. Here, we focus on protocols that offer the docking-MD combination as a logical approach to improving the drug discovery process.

Investigating the molecular mechanism of staphylococcal DNA gyrase inhibitors: A combined ligand-based and structure-based resources pipeline

Appropriate therapeutic solutions against Staphylococcal infections are currently limited. To work out the complex task of challenging drug resistance in Staphylococcus aureus, new compounds with novel modes of action are required. In this study, we performed target-driven virtual screening to filter exhaustive phytochemical libraries that can inhibit the activity of S. aureus DNA Gyrase B (Gyr B). Three top-ranked hit molecules (Mangostenone E, Candenatenin A and 2,4,4'-trihydroxydihydrochalcone) were identified from comprehensive molecular docking studies based on their strong spatial affinity with key catalytic residues of the binding pocket of DNA GyrB, especially with the well-known crucial residue Asp81. Molecular dynamics (MD) simulations were performed for these identified hit molecules for better understanding of their dynamical and structural profiles throughout the MD simulations. These compounds can be explored as future lead optimization molecules to discover a new class of antibiotics against resistant Staphylococcus aureus strains.

A new landscape of host-protozoa interactions involving the extracellular vesicles world

Extracellular vesicles (EVs) are released by a wide number of cells including blood cells, immune system cells, tumour cells, adult and embryonic stem cells. EVs are a heterogeneous group of vesicles (~30-1000 nm) including microvesicles and exosomes. The physiological release of EVs represents a normal state of the cell, raising a metabolic equilibrium between catabolic and anabolic processes. Moreover, when the cells are submitted to stress with different inducers or in pathological situations (malignancies, chronic diseases, infectious diseases.), they respond with an intense and dynamic release of EVs. The EVs released from stimulated cells vs those that are released constitutively may themselves differ, both physically and in their cargo. EVs contain protein, lipids, nucleic acids and biomolecules that can alter cell phenotypes or modulate neighbouring cells. In this review, we have summarized findings involving EVs in certain protozoan diseases. We have commented on strategies to study the communicative roles of EVs during host-pathogen interaction and hypothesized on the use of EVs for diagnostic, preventative and therapeutic purposes in infectious diseases. This kind of communication could modulate the innate immune system and reformulate concepts in parasitism. Moreover, the information provided within EVs could produce alternatives in translational medicine.

Computer-Aided Drug Discovery in Plant Pathology

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.

Novel dihydropyrimidine derivatives as potential HDAC inhibitors: in silico study

Dihydropyrimidine derivatives possess many biological activities due to presence of pyrimidine ring structure in various nucleic acids, vitamins, coenzymes, uric acid and their derivatives. They have possessed broad spectrum actions like antibacterial, antifungal, antiviral, anticancer and antihypertensive etc. Before synthesis of compounds, it is good to predict biological activity using in silico methods. Here, we have selected some of N (3a-f) and O (4a-f) mannich bases of dihydro pyrimidine derivatives emphasized on histone deacetylase 4 (HDAC-4) inhibitions activity. We have used the different software tools like Lipinski's rule of five; pass online; osiris property explorer and docking studies to predict anti cancer activity. All the selected compounds exhibited potential drug like molecule with anti cancer activity. Among all compound the substitution with methoxy group (3c) exhibited more drugs like property and substation with hydrogens (4a) showed high anti neoplastic activity; whereas substitution with dichloro groups (4e) showed more drug docking scores. These were compared with standard drugs tamoxifen and 5-flourouracil. The approach of predicting anticancer activity using in silico method may be more useful to select and synthesis novel compounds in research as well as in industry.

Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures

Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accurate propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. We conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.

NMR in structural genomics to increase structural coverage of the protein universe: Delivered by Prof. Kurt Wüthrich on 7 July 2013 at the 38th FEBS Congress in St. Petersburg, Russia

For more than a decade, the Joint Center for Structural Genomics (JCSG; www.jcsg.org) worked toward increased three-dimensional structure coverage of the protein universe. This coordinated quest was one of the main goals of the four high-throughput (HT) structure determination centers of the Protein Structure Initiative (PSI; www.nigms.nih.gov/Research/specificareas/PSI). To achieve the goals of the PSI, the JCSG made use of the complementarity of structure determination by X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy to increase and diversify the range of targets entering the HT structure determination pipeline. The overall strategy, for both techniques, was to determine atomic resolution structures for representatives of large protein families, as defined by the Pfam database, which had no structural coverage and could make significant contributions to biological and biomedical research. Furthermore, the experimental structures could be leveraged by homology modeling to further expand the structural coverage of the protein universe and increase biological insights. Here, we describe what could be achieved by this structural genomics approach, using as an illustration the contributions from 20 NMR structure determinations out of a total of 98 JCSG NMR structures, which were selected because they are the first three-dimensional structure representations of the respective Pfam protein families. The information from this small sample is representative for the overall results from crystal and NMR structure determination in the JCSG. There are five new folds, which were classified as domains of unknown functions (DUF), three of the proteins could be functionally annotated based on three-dimensional structure similarity with previously characterized proteins, and 12 proteins showed only limited similarity with previous deposits in the Protein Data Bank (PDB) and were classified as DUFs.

Molecular docking of Glycine max and Medicago truncatula ureases with urea; bioinformatics approaches

Urease (EC 3.5.1.5) is a nickel-dependent metalloenzyme catalyzing the hydrolysis of urea into ammonia and carbon dioxide. It is present in many bacteria, fungi, yeasts and plants. Most species, with few exceptions, use nickel metalloenzyme urease to hydrolyze urea, which is one of the commonly used nitrogen fertilizer in plant growth thus its enzymatic hydrolysis possesses vital importance in agricultural practices. Considering the essentiality and importance of urea and urease activity in most plants, this study aimed to comparatively investigate the ureases of two important legume species such as Glycine max (soybean) and Medicago truncatula (barrel medic) from Fabaceae family. With additional plant species, primary and secondary structures of 37 plant ureases were comparatively analyzed using various bioinformatics tools. A structure based phylogeny was constructed using predicted 3D models of G. max and M. truncatula, whose crystallographic structures are not available, along with three additional solved urease structures from Canavalia ensiformis (PDB: 4GY7), Bacillus pasteurii (PDB: 4UBP) and Klebsiella aerogenes (PDB: 1FWJ). In addition, urease structures of these species were docked with urea to analyze the binding affinities, interacting amino acids and atom distances in urease-urea complexes. Furthermore, mutable amino acids which could potentially affect the protein active site, stability and flexibility as well as overall protein stability were analyzed in urease structures of G. max and M. truncatula. Plant ureases demonstrated similar physico-chemical properties with 833-878 amino acid residues and 89.39-90.91 kDa molecular weight with mainly acidic (5.15-6.10 pI) nature. Four protein domain structures such as urease gamma, urease beta, urease alpha and amidohydro 1 characterized the plant ureases. Secondary structure of plant ureases also demonstrated conserved protein architecture, with predominantly α-helix and random coil structures. In structure-based phylogeny, plant ureases from G. max, M. truncatula and C. ensiformis were clearly diverged from bacterial ureases of B. pasteurii and K. aerogenes. Glu, Thr, His and Gly were commonly found as interacting residues in most urease-urea docking complexes while Glu was available in all docked structures. Besides, Ala and Arg residues, which are reported in active-site architecture of plant and bacterial ureases were present in G. max urea-urease complex but not present in others. Moreover, Arg435 and Arg437 in M. truncatula and G. max, respectively were identified as highly mutable hotspot residues residing in amidohydro 1 domain of enzyme. In addition, a number of stabilizing residues were predicted upon mutation of these hotspot residues however Cys and Thr made strong implications since they were also found in codon-aligned sequences as substitutions of hotspot residues. Comparative analyses of primary sequence and secondary structure in 37 different plants demonstrated quite conserved natures of ureases in plant kingdom. Structure-based phylogeny indicated the presence of a possible prokaryote-eukaryote split and implicated the subjection of bacterial ureases to heavy selection in prokaryotic evolution compared to plants. Urea-urease docking complexes suggested that different species could share common interacting residues as well as may have some other uncommon residues at species-dependent way. In silico mutation analyses identified mutable amino acids, which were predicted to reside in catalytic site of enzyme therefore mutagenesis at these sites seemed to have adverse effects on enzyme efficiency or function. This study findings will become valuable preliminary resource for future studies to further understand the primary, secondary and tertiary structures of urease sequences in plants as well as it will provide insights about various binding features of urea-urease complexes.

Molecular docking and structure-based drug design strategies

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.

Target-based molecular modeling strategies for schistosomiasis drug discovery

Schistosomiasis, a neglected tropical disease caused by worms from the class Trematoda (genus Schistosoma), is a serious chronic condition that has been reported in approximately 80 countries. Nearly 250 million people are affected worldwide, mostly in the sub-Saharan Africa. Praziquantel, the mainstay of treatment, has been used for 30 years, and cases of resistance have been reported. The purpose of this perspective is to discuss current target-based molecular modeling strategies in schistosomiasis drug discovery. Advances in the field and the role played by the integration between computational modeling and experimental validation are also discussed. Finally, recent cases of the contribution of modern approaches in computational medicinal chemistry to the field are explored.

Integrating in silico prediction methods, molecular docking, and molecular dynamics simulation to predict the impact of ALK missense mutations in structural perspective

Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual's susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application of in silico prediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient's drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians and in silico resources in tailoring treatments to the patients' specific genotype.

The role of protein structural analysis in the next generation sequencing era

Proteins are macromolecules that serve a cell’s myriad processes and functions in all living organisms via dynamic interactions with other proteins, small molecules and cellular components. Genetic variations in the protein-encoding regions of the human genome account for >85% of all known Mendelian diseases, and play an influential role in shaping complex polygenic diseases. Proteins also serve as the predominant target class for the design of small molecule drugs to modulate their activity. Knowledge of the shape and form of proteins, by means of their three-dimensional structures, is therefore instrumental to understanding their roles in disease and their potentials for drug development. In this chapter we outline, with the wide readership of non-structural biologists in mind, the various experimental and computational methods available for protein structure determination. We summarize how the wealth of structure information, contributed to a large extent by the technological advances in structure determination to date, serves as a useful tool to decipher the molecular basis of genetic variations for disease characterization and diagnosis, particularly in the emerging era of genomic medicine, and becomes an integral component in the modern day approach towards rational drug development.

High-throughput cloning and expression of integral membrane proteins in Escherichia coli

Recently, several structural genomics centers have been established and a remarkable number of three-dimensional structures of soluble proteins have been solved. For membrane proteins, the number of structures solved has been significantly trailing those for their soluble counterparts, not least because over-expression and purification of membrane proteins is a much more arduous process. By using high-throughput technologies, a large number of membrane protein targets can be screened simultaneously and a greater number of expression and purification conditions can be employed, leading to a higher probability of successfully determining the structure of membrane proteins. This unit describes the cloning, expression, and screening of membrane proteins using high-throughput methodologies developed in the laboratory. Basic Protocol 1 describes cloning of inserts into expression vectors by ligation-independent cloning. Basic Protocol 2 describes the expression and purification of the target proteins on a miniscale. Lastly, for the targets that do express on the miniscale, Basic Protocols 3 and 4 outline the methods employed for the expression and purification of targets on a midi-scale, as well as a procedure for detergent screening and identification of detergent(s) in which the target protein is stable.

Identification of Similar Binding Sites to Detect Distant Polypharmacology

The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.

E. coli sabotages the in vivo production of O-linked β-N-acetylglucosamine-modified proteins

The O-linked β-N-acetylglucosamine (O-GlcNAc) post-translational modification is an important, regulatory modification of cytosolic and nuclear enzymes. To date, no 3-dimensional structures of O-GlcNAc-modified proteins exist due to difficulties in producing sufficient quantities with either in vitro or in vivo techniques. Recombinant co-expression of substrate protein and O-GlcNAc transferase in Escherichia coli was used to produce O-GlcNAc-modified domains of human cAMP responsive element-binding protein (CREB1) and Abelson tyrosine-kinase 2 (ABL2). Recombinant expression in E. coli is an advantageous approach, but only small quantities of insoluble O-GlcNAc-modified protein were produced. Adding β-N-acetylglucosaminidase inhibitor, O-(2-acetamido-2-dexoy-D-glucopyranosylidene)amino-N-phenylcarbamate (PUGNAc), to the culture media provided the first evidence that an E. coli enzyme cleaves O-GlcNAc from proteins in vivo. With the inhibitor present, the yields of O-GlcNAc-modified protein increased. The E. coli β-N-acetylglucosaminidase was isolated and shown to cleave O-GlcNAc from a synthetic O-GlcNAc-peptide in vitro. The identity of the interfering β-N-acetylglucosaminidase was confirmed by testing a nagZ knockout strain. In E. coli, NagZ natively cleaves the GlcNAc-β1,4-N-acetylmuramic acid linkage to recycle peptidoglycan in the cytoplasm and cleaves the GlcNAc-β-O-linkage of foreign O-GlcNAc-modified proteins in vivo, sabotaging the recombinant co-expression system.

Analyses of the general rule on residue pair frequencies in local amino acid sequences of soluble, ordered proteins

The amino acid sequences of soluble, ordered proteins with stable structures have evolved due to biological and physical requirements, thus distinguishing them from random sequences. Previous analyses have focused on extracting the features that frequently appear in protein substructures, such as α-helix and β-sheet, but the universal features of protein sequences have not been addressed. To clarify the differences between native protein sequences and random sequences, we analyzed 7368 soluble, ordered protein sequences, by inspecting the observed and expected occurrences of 400 amino acid pairs in local proximity, up to 10 residues along the sequence in comparison with their expected occurrence in random sequence. We found the trend that the hydrophobic residue pairs and the polar residue pairs are significantly decreased, whereas the pairs between a hydrophobic residue and a polar residue are increased. This trend was universally observed regardless of the secondary structure content but was not observed in protein sequences that include intrinsically disordered regions, indicating that it can be a general rule of protein foldability. The possible benefits of this rule are discussed from the viewpoints of protein aggregation and disorder, which are both caused by low-complexity regions of hydrophobic or polar residues.

Chemoisosterism in the proteome

The concept of chemoisosterism of protein environments is introduced as the complementary property to bioisosterism of chemical fragments. In the same way that two chemical fragments are considered bioisosteric if they can bind to the same protein environment, two protein environments will be considered chemoisosteric if they can interact with the same chemical fragment. The basis for the identification of chemoisosteric relationships among protein environments was the increasing amount of crystal structures available currently for protein-ligand complexes. It is shown that one can recover the right location and orientation of chemical fragments constituting the native ligand in a nuclear receptor structure by using only chemoisosteric environments present in enzyme structures. Examples of the potential applicability of chemoisosterism in fragment-based drug discovery are provided.

Comprehensive classification and diversity assessment of atomic contacts in protein-small ligand interactions

Elucidating the molecular mechanisms of selective ligand recognition by proteins is a long-standing problem in drug discovery. Rapid increase in the availability of three-dimensional protein structural data indicates that a data-driven approach for finding the rules that govern protein-ligand interactions is increasingly attractive. However, this approach is not straightforward because of the complexity of molecular interactions and our inadequate understanding of the diversity of molecular interactions that occur during ligand recognition. Thus, we aimed to provide a comprehensive classification of the spatial arrangements of ligand atoms based on the local coordinates of each interacting "protein fragment" consisting of three atoms with covalent bonds in each amino acid. We used a pattern recognition technique based on the Gaussian mixture model and found 13,519 patterns in the spatial arrangements of interacting ligand atoms, each of which was described as a Gaussian function of the local coordinates. Some typical well-known interaction patterns such as hydrogen bonds were ubiquitous in several hundred protein families, whereas others were only observed in a few specific protein families. After removing protein sequence redundancy from the data set, we found that 63.4% of ligand atoms interacted via one or more interaction patterns and that 25.7% of ligand atoms interacted without patterns, whereas the remainder had no direct interactions. The top 3115 major patterns included 90% of the interacting pairs of residues and ligand atoms with patterns, while the top 6229 included all of them.

Architecture and conservation of the bacterial DNA replication machinery, an underexploited drug target

New antibiotics with novel modes of action are required to combat the growing threat posed by multi-drug resistant bacteria. Over the last decade, genome sequencing and other high-throughput techniques have provided tremendous insight into the molecular processes underlying cellular functions in a wide range of bacterial species. We can now use these data to assess the degree of conservation of certain aspects of bacterial physiology, to help choose the best cellular targets for development of new broad-spectrum antibacterials.

DNA replication is a conserved and essential process, and the large number of proteins that interact to replicate DNA in bacteria are distinct from those in eukaryotes and archaea; yet none of the antibiotics in current clinical use acts directly on the replication machinery. Bacterial DNA synthesis thus appears to be an underexploited drug target. However, before this system can be targeted for drug design, it is important to understand which parts are conserved and which are not, as this will have implications for the spectrum of activity of any new inhibitors against bacterial species, as well as the potential for development of drug resistance. In this review we assess similarities and differences in replication components and mechanisms across the bacteria, highlight current progress towards the discovery of novel replication inhibitors, and suggest those aspects of the replication machinery that have the greatest potential as drug targets.

A global comparison of the human and T. brucei degradomes gives insights about possible parasite drug targets

We performed a genome-level computational study of sequence and structure similarity, the latter using crystal structures and models, of the proteases of Homo sapiens and the human parasite Trypanosoma brucei. Using sequence and structure similarity networks to summarize the results, we constructed global views that show visually the relative abundance and variety of proteases in the degradome landscapes of these two species, and provide insights into evolutionary relationships between proteases. The results also indicate how broadly these sequence sets are covered by three-dimensional structures. These views facilitate cross-species comparisons and offer clues for drug design from knowledge about the sequences and structures of potential drug targets and their homologs. Two protease groups (“M32” and “C51”) that are very different in sequence from human proteases are examined in structural detail, illustrating the application of this global approach in mining new pathogen genomes for potential drug targets. Based on our analyses, a human ACE2 inhibitor was selected for experimental testing on one of these parasite proteases, TbM32, and was shown to inhibit it. These sequence and structure data, along with interactive versions of the protein similarity networks generated in this study, are available at http://babbittlab.ucsf.edu/resources.html.

Human African trypanosomiasis (HAT) is caused by the protozoan parasite Trypanosoma brucei. HAT is fatal unless treated, yet the current treatment itself can cause death. New treatments are urgently needed. Our study focuses on proteases, which are enzymes that break down proteins. Because of their roles in many centrally important biological processes, proteases are targets for drugs to treat a variety of diseases including parasite infection. The recent explosion of protein sequence and structure information in public databases has made surveys of proteins on a genomic scale possible. However, collecting specific data of interest from diverse databases and synthesizing them in a way that is easy to interpret can be difficult. We used T. brucei and human protease sequences, crystal structures, and models to create network views that show how proteases cluster by similarity. Such views are valuable not only for understanding the evolution of the protein repertoire in each species, but also can give important clues for drug design. Two T. brucei protease groups (“M32” and “C51”) that are very different in sequence from human proteases were examined in structural detail. Based on our analyses, a human ACE2 inhibitor was selected for experimental testing on one of these parasite proteases, TbM32, and was shown to inhibit it.

Investigating protein variants using structural calculation techniques

Structure calculation techniques can be very useful to bridge the gap between available sequence information and structural knowledge. In order to understand the molecular mechanisms behind diseases caused by residue exchanges, knowledge about the modified structure is needed. In this chapter, we describe how energy minimizations and molecular dynamics can be useful tools in order to study the structural effects of sequence variation. With these techniques, together with investigation of other properties, it is often possible to obtain a complete picture of the effect and mechanism behind disease-causing mutations. To take this information one step further, we also describe prediction methods that can be used to judge the effects of mutations and how to evaluate these and the interplay between the protein properties.

Bioinformatics and variability in drug response: a protein structural perspective

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

Histone demethylation and steroid receptor function in cancer

Steroid receptors recruit various cofactors to form multi-protein complexes which locally alter chromatin structure and control DNA accessibility in order to regulate gene transcription. Some of these factors are enzymes that add or remove histone marks in the vicinity of regulatory regions of target genes. Numerous histone modifications added by specific writer enzymes and removed by eraser enzymes have been identified. Histone methylation is a modification with a complex outcome, as it can lead to gene activation or repression, depending on the modified residue and the context. Methylation marks are added by different enzyme families displaying exquisite substrate specificity. Lysine methylation is reversible and two different demethylase families have been identified in humans, the Jumonji C and the lysine-specific demethylase families. A regulatory role of histone demethylases in fine-tuning the function of steroid receptors, especially the androgen receptor and estrogen receptor, has emerged in recent years. This is mostly inferred from in vitro studies, but more recently first in vivo data have further supported this concept. This and the deregulated expression observed for several histone demethylases suggest a role in tumours such as prostate and breast cancer.

A decade of the human genome sequence--how does the medicinal chemist benefit?

Many have claimed that the sequencing of the human genome has failed to deliver the promised new era of drug discovery and development. Here, we argue that in fact, the availability of the human genome sequence and the genomics technologies that resulted from those research efforts have had a major impact on drug discovery. Medicinal chemists are actively using the data gleaned from structural genomics projects over the past decade to design more selective and more effective drug candidates. For example, large superfamilies of related enzymes, such as the kinome, proteome, proteasome, transportome, identified because of the sequencing of the human genome represent a huge number of potential drug targets. Ten years on, we're able to design multitarget drugs where the selectivity for a certain subgroup of receptors can lead to increased efficacy rather than the side effects traditionally associated with "off-targets". New trends and discoveries in biomedical research are notoriously slow to show their value, and this is also true for genomics technologies. However, the examples we've selected show that these are firmly set in the drug-discovery process, and without the human genome sequence, a number of current clinical candidates and promising drug leads would not have been possible.

xtalPiMS: a PiMS-based web application for the management and monitoring of crystallization trials

A major advance in protein structure determination has been the advent of nanolitre-scale crystallization and (in a high-throughput environment) the development of robotic systems for storing and imaging crystallization trials. Most of these trials are carried out in 96-well (or higher density) plates and managing them is a significant information management challenge. We describe xtalPiMS, a web-based application for the management and monitoring of crystallization trials. xtalPiMS has a user-interface layer based on the standards of the Protein Information Management System (PiMS) and a database layer which links the crystallization trial images to the meta-data associated with a particular crystallization trial. The user interface has been optimized for the efficient monitoring of high-throughput environments with three different automated imagers and work to support a fourth imager is in progress, but it can even be of use without robotics. The database can either be a PiMS database or a legacy database for which a suitable mapping layer has been developed.

Efficiency of hit generation and structural characterization in fragment-based ligand discovery

Fragment-based ligand discovery constitutes a useful strategy for the generation of high affinity ligands with suitable physico-chemical properties to serve as drug leads. There is an increasing number of generic biophysical screening strategies established with the potential for accelerating the generation of useful fragment hits. Crystal structures of these hits can subsequently be used as starting points for fragment evolution to high affinity ligands. Emerging understanding of the efficiency and operative aspects of hit generation and structural characterization in FBLD suggests that this method should be well suited for academic ligand development of chemical tools and experimental therapeutics.

High-throughput structural biology of metabolic enzymes and its impact on human diseases

The Structural Genomics Consortium (SGC) is a public-private partnership that aims to determine the three-dimensional structures of human proteins of medical relevance and place them into the public domain without restriction. To date, the Oxford Metabolic Enzyme Group at SGC has deposited the structures of more than 140 human metabolic enzymes from diverse protein families such as oxidoreductases, hydrolases, oxygenases and fatty acid transferases. A subset of our target proteins are involved in the inherited disorders of carbohydrate, fatty acid, amino acid and vitamin metabolism. This article will provide an overview of the structural data gathered from our high-throughput efforts and the lessons learnt in the structure-function relationship of these enzymes, small molecule development and the molecular basis of disease mutations.

Unmet challenges of structural genomics

Structural genomics (SG) programs have developed during the last decade many novel methodologies for faster and more accurate structure determination. These new tools and approaches led to the determination of thousands of protein structures. The generation of enormous amounts of experimental data resulted in significant improvements in the understanding of many biological processes at molecular levels. However, the amount of data collected so far is so large that traditional analysis methods are limiting the rate of extraction of biological and biochemical information from 3D models. This situation has prompted us to review the challenges that remain unmet by SG, as well as the areas in which the potential impact of SG could exceed what has been achieved so far.