A decade of fragment-based drug design: strategic advances and lessons learned

Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications

Fragment-based drug design (FBDD) has become an effective methodology for drug development for decades. Successful applications of this strategy brought both opportunities and challenges to the field of Pharmaceutical Science. Recent progress in the computational fragment-based drug design provide an additional approach for future research in a time- and labor-efficient manner. Combining multiple in silico methodologies, computational FBDD possesses flexibilities on fragment library selection, protein model generation, and fragments/compounds docking mode prediction. These characteristics provide computational FBDD superiority in designing novel and potential compounds for a certain target. The purpose of this review is to discuss the latest advances, ranging from commonly used strategies to novel concepts and technologies in computational fragment-based drug design. Particularly, in this review, specifications and advantages are compared between experimental and computational FBDD, and additionally, limitations and future prospective are discussed and emphasized.

A Fragment Library Screening Approach to Identify Selective Inhibitors against an Essential Fungal Enzyme

Pathogenic fungi represent a growing threat to human health, with an increase in the frequency of drug-resistant fungal infections. Identifying targets from among the selected metabolic pathways that are unique to microbial species presents an opportunity to develop new antifungal agents against new and untested targets to combat this growth threat. Aspartate semialdehyde dehydrogenase (ASADH) catalyzes a key step in a uniquely microbial amino acid biosynthetic pathway and is essential for microbial viability. This enzyme, purified from four pathogenic fungal organisms ( Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, and Blastomyces dermatitidis), has been screened against fragment libraries to identify initial enzyme inhibitors. The binding of structural analogs of the most promising lead compounds was measured against these fungal ASADHs to establish important structure-activity relationships among these different inhibitor classes. The most potent of these inhibitors have been docked into structures of this fungal enzyme target to identify important structural elements that serve as critical binding determinants. Several inhibitors with low micromolar inhibition constants have been identified that showed selectivity against these related enzymes from different fungal species. Subsequent screening against a library of drugs and drug candidates identified some additional inhibitors containing a consistent set of functional groups required for fungal ASADH inhibition. Additional elaboration of these core structures will likely lead to more potent and selective inhibitors.

Success stories of natural product-based hybrid molecules for multi-factorial diseases

Complex diseases comprises of highly complicated etiology resulting in limited applicability of conventional targeted therapies. Consequently, conventional medicinal compounds suffer major failure when used for such disease conditions. Additionally, development of multidrug resistance (MDR), adverse drug reactions and clinical specificity of single targeted drug therapy has increased thrust for novel drug therapy. In this rapidly evolving era, natural product-based discovery of hybrid molecules or multi-targeted drug therapies have shown promising results and are trending now a days. Historically, nature has blessed human with different sources viz. plant, animal, microbial, marine and ethnopharmaceutical sources which has given a wide variety of medicinally active compounds. These compounds from natural origin are always choice of interest of medicinal chemists because of their minimum side effects. Hybrid molecules synthesized by fusing or conjugating different active molecules obtained from these sources are reported to synergistically block different pathways which contribute in the pathogenesis of complex diseases. This review strives to encompass all natural product-derived hybrid molecules which act as multi-targeting agents striking various targets involved in different pathways of complex diseased conditions reported in literature.

Identification of small-molecule inhibitors of USP2a

USP2a is a deubiquitinating protease that rescues its target proteins from destruction by the proteasome by reversing the process of protein ubiquitination. USP2a shows oncogenic properties in vivo and has been found to be a specific activator of cyclin D1. Many types of cancers are addicted to cyclin D1 expression. Targeting USP2a is a promising strategy for cancer therapy but little progress has been made in the field of inhibition of USP2a. Using NMR-based fragment screening and biophysical binding assays, we have discovered small molecules that bind to USP2a. Iterations of fragment combination and structure-driven design identified two 5-(2-thienyl)-3-isoxazoles as the inhibitors of the USP2a-ubiquitin protein-protein interaction. The affinity of these molecules for the catalytic domain of USP2a parallels their ability to interfere with USP2a binding to ubiquitin in vitro. Altogether, our results establish the 5-(2-thienyl)-3-isoxazole pharmacophore as an attractive starting point for lead optimization.

In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs

The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.

Exhaustive sampling of the fragment space associated to a molecule leading to the generation of conserved fragments

The first step in hit optimization is the identification of the pharmacophore, which is normally achieved by deconstruction of the hit molecule to generate "deletion analogues." In silico fragmentation approaches often focus on the generation of small fragments that do not describe properly the fragment space associated to the deletion analogues. We present significant modifications to the molecular fragmentation programme molBLOCKS, which allows the exhaustive sampling of the fragment space associated with a molecule to generate all possible molecular fragments. This generates larger fragments, by combining the smallest fragments. Additionally, it has been modified to deal with the problem of changing pharmacophoric properties through fragmentation, by highlighting bond cuts. The modified molBLOCKS programme was used on a set of drug compounds, where it generated more unique fragments than standard fragmentation approaches by increasing the number of fragments derived per compound. This fragment set was found to be more diverse than those generated by standard fragmentation programmes and was relevant to drug discovery as it contains the key fragments representing the pharmacophoric elements associated with ligand recognition. The use of dummy atoms to highlight bond cuts further increases the information content of fragments by visualizing their previous bonding pattern.

Negishi cross-couplings in the synthesis of amino acids

The Negishi cross-coupling is a powerful C-C bond-forming reaction widely utilised in many areas of organic synthesis. This review details the use of Negishi cross-couplings in the synthesis of unnatural amino acids. The application of this reaction in the preparation of aromatic, heteroaromatic, and, complex amino acid derivatives are reviewed and presented herein.

Binding-Site Compatible Fragment Growing Applied to the Design of β2-Adrenergic Receptor Ligands

Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the β₂-adrenergic receptor (β₂AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.

Imaginative Order from Reasonable Chaos: Conformation-Driven Activity and Reactivity in Exploring Protein–Ligand Interactions

Sir Derek Barton’s seminal work on steroid conformational analysis opened up a new era of enquiry into how the preferred conformation of any molecule could have profound effects on its physical–chemical properties and activities. Conformation-based effects on molecular activity and reactivity continue to manifest, with one key area of investigation currently focussed on conformational entropy in driving protein–ligand interactions. Carrying on from Barton’s initial insight on natural product conformational properties, new questions now address how conformational flexibility within a bioactive natural product structural framework (reasonable chaos), can be directed to confer dynamically new protein–ligand interactions beyond the basic lock–key model (imaginative order). Here we summarise our work on exploring conformational diversity from fluorinated natural product fragments, and how this approach of conformation-coupled diversity-oriented synthesis can be used to iteratively derive ligands with enhanced specificity against highly homologous protein domains. Our results demonstrate that the conformation entropic states of highly conserved protein domains differ significantly, and this conformational diversity, beyond primary sequence analysis, can be duly captured and exploited by natural-product derived ligands with complementary conformational dynamics for enhancing recognition specificity in drug lead discovery.

19F multiple-quantum coherence NMR spectroscopy for probing protein–ligand interactions

A new ¹⁹F NMR method is presented which can be used to detect weak protein binding of small molecules with up to mM affinity. The method capitalizes on the synthetic availability of unique SF₅ containing compounds and the generation of five-quantum coherences (5QC). Given the high sensitivity of 5QC relaxation to exchange events (i.e. reversible protein binding) fragments which bind to the target with weak affinity can be identified. The utility of the method in early stage drug discovery programs is demonstrated with applications to two model proteins, the neurotoxic NGAL and the prominent tumor target β-catenin.

An NMR experiment is presented that allows identification of weak binders typically found in early stages of drug discovery programs.

Impact of Molecular Descriptors on Computational Models

Molecular descriptors encode a wide variety of molecular information and have become the support of many contemporary chemoinformatic and bioinformatic applications. They grasp specific molecular features (e.g., geometry, shape, pharmacophores, or atomic properties) and directly affect computational models, in terms of outcome, performance, and applicability. This chapter aims to illustrate the impact of different molecular descriptors on the structural information captured and on the perceived chemical similarity among molecules. After introducing the fundamental concepts of molecular descriptor theory and application, a step-by-step retrospective virtual screening procedure guides users through the fundamental processing steps and discusses the impact of different types of molecular descriptors.

Label-free NMR-based dissociation kinetics determination

Understanding the dissociation of molecules is the basis to modulate interactions of biomedical interest. Optimizing drugs for dissociation rates is found to be important for their efficacy, selectivity, and safety. Here, we show an application of the high-power relaxation dispersion (RD) method to the determination of the dissociation rates of weak binding ligands from receptors. The experiment probes proton RD on the ligand and, therefore, avoids the need for any isotopic labeling. The large ligand excess eases the detection significantly. Importantly, the use of large spin-lock fields allows the detection of faster dissociation rates than other relaxation approaches. Moreover, this experimental approach allows to access directly the off-rate of the binding process without the need for analyzing a series of samples with increasing ligand saturation. The validity of the method is shown with small molecule interactions using two macromolecules, bovine serum albumin and tubulin heterodimers.

Ligand-detected NMR Methods in Drug Discovery

This book chapter describes the basic principles of NMR-based techniques for detecting ligand binding and uses examples of the application of these techniques in drug discovery programs for screening, hit validation and optimization to illustrate their utility in characterizing ligand–protein interactions. The binding of small molecules to biological receptors can be observed directly by detecting changes in a particular NMR parameter when the protein is added to a sample containing the ligand, or indirectly, using a “spy” molecule in competitive NMR experiments. Combinations of different NMR experiments can be used to confirm binding and also to obtain structural information that can be used to guide medicinal chemistry decisions. Ligand-observed NMR methods are able to identify weak affinity ligands that cannot be detected by other biophysical techniques, which means that NMR-based methods are extremely valuable tools for fragment-based drug discovery approaches.

Current perspectives in fragment-based lead discovery (FBLD)

It is over 20 years since the first fragment-based discovery projects were disclosed. The methods are now mature for most ‘conventional’ targets in drug discovery such as enzymes (kinases and proteases) but there has also been growing success on more challenging targets, such as disruption of protein–protein interactions. The main application is to identify tractable chemical startpoints that non-covalently modulate the activity of a biological molecule. In this essay, we overview current practice in the methods and discuss how they have had an impact in lead discovery – generating a large number of fragment-derived compounds that are in clinical trials and two medicines treating patients. In addition, we discuss some of the more recent applications of the methods in chemical biology – providing chemical tools to investigate biological molecules, mechanisms and systems.

CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy

Developing tools to accurately predict the clinical prevalence of drug-resistant mutations is a key step toward generating more effective therapeutics. Here we describe a high-throughput CRISPR-Cas9-based saturated mutagenesis approach to generate comprehensive libraries of point mutations at a defined genomic location and systematically study their effect on cell growth. As proof of concept, we mutagenized a selected region within the leukemic oncogene BCR-ABL1 Using bulk competitions with a deep-sequencing readout, we analyzed hundreds of mutations under multiple drug conditions and found that the effects of mutations on growth in the presence or absence of drug were critical for predicting clinically relevant resistant mutations, many of which were cancer adaptive in the absence of drug pressure. Using this approach, we identified all clinically isolated BCR-ABL1 mutations and achieved a prediction score that correlated highly with their clinical prevalence. The strategy described here can be broadly applied to a variety of oncogenes to predict patient mutations and evaluate resistance susceptibility in the development of new therapeutics.

Crystal structure and functional characterization of SF216 from Shigella flexneri

Shigella flexneri is a Gram-negative anaerobic bacterium that causes highly infectious bacterial dysentery in humans. Here, we solved the crystal structure of SF216, a hypothetical protein from the S. flexneri 5a strain M90T, at 1.7 Å resolution. The crystal structure of SF216 represents a homotrimer stabilized by intersubunit interactions and ion-mediated electrostatic interactions. Each subunit consists of three β-strands and five α-helices with the β-β-β-α-α-α-α-α topology. Based on the structural information, we also demonstrate that SF216 shows weak ribonuclease activity by a fluorescence quenching assay. Furthermore, we identify potential druggable pockets (putative hot spots) on the surface of the SF216 structure by computational mapping.

STD-NMR experiments identify a structural motif with novel second-site activity against West Nile virus NS2B-NS3 protease

West Nile virus (WNV) belongs to the genus Flavivirus of the family Flaviviridae. This mosquito-borne virus that is highly pathogenic to humans has been evolving into a global threat during the past two decades. Despite many efforts, neither antiviral drugs nor vaccines are available. The viral protease NS2B-NS3^pro is essential for viral replication, and therefore it is considered a prime drug target. However, success in the development of specific NS2B-NS3^pro inhibitors had been moderate so far. In the search for new structural motifs with binding affinity for NS2B-NS3^pro, we have screened a fragment library, the Maybridge Ro5 library, employing saturation transfer difference (STD) NMR experiments as readout. About 30% of 429 fragments showed binding to NS2B-NS3^pro. Subsequent STD-NMR competition experiments using the known active site fragment A as reporter ligand yielded 14 competitively binding fragments, and 22 fragments not competing with A. In a fluorophore-based protease assay, all of these fragments showed inhibition in the micromolar range. Interestingly, 10 of these 22 fragments showed a notable increase of STD intensities in the presence of compound A suggesting cooperative binding. The most promising non-competitive inhibitors 1 and 2 (IC₅₀ ∼ 500 μM) share a structural motif that may guide the development of novel second-site (potentially allosteric) inhibitors of NS2B-NS3^pro. To identify the matching protein binding site, chemical shift perturbation studies employing ¹H,¹⁵N-TROSY-HSQC experiments with uniformly ²H,¹⁵N-labeled protease were performed in the presence of 1, and in the concomitant absence or presence of A. The data suggest that 1 interacts with Met 52* of NS2B, identifying a secondary site adjacent to the binding site of A. Therefore, our study paves the way for the synthesis of novel bidentate NS2B-NS3^pro inhibitors.

A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein-Protein Interaction Interfaces

The binding affinity of a protein-protein interaction is concentrated at amino acids known as hot spots. It has been suggested that small molecules disrupt protein-protein interactions by either (i) engaging receptor protein hot spots or (ii) mimicking hot spots of the protein ligand. Yet, no systematic studies have been done to explore how effectively existing small-molecule protein-protein interaction inhibitors mimic or engage hot spots at protein interfaces. Here, we employ explicit-solvent molecular dynamics simulations and end-point MM-GBSA free energy calculations to explore this question. We select 36 compounds for which high-quality binding affinity and cocrystal structures are available. Five complexes that belong to three classes of protein-protein interactions (primary, secondary, and tertiary) were considered, namely, BRD4•H4, XIAP•Smac, MDM2•p53, Bcl-xL•Bak, and IL-2•IL-2Rα. Computational alanine scanning using MM-GBSA identified hot-spot residues at the interface of these protein interactions. Decomposition energies compared the interaction of small molecules with individual receptor hot spots to those of the native protein ligand. Pharmacophore analysis was used to investigate how effectively small molecules mimic the position of hot spots of the protein ligand. Finally, we study whether small molecules mimic the effects of the native protein ligand on the receptor dynamics. Our results show that, in general, existing small-molecule inhibitors of protein-protein interactions do not optimally mimic protein-ligand hot spots, nor do they effectively engage protein receptor hot spots. The more effective use of hot spots in future drug design efforts may result in smaller compounds with higher ligand efficiencies that may lead to greater success in clinical trials.

Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus

Molecular topology was used to develop a mathematical model capable of classifying compounds according to antimicrobial activity against methicillin resistant Staphylococcus aureus (MRSA). Topological indices were used as structural descriptors and their relation to antimicrobial activity was determined by using linear discriminant analysis. This topological model establishes new structure activity relationships which show that the presence of cyclopropyl, chlorine and ramification pairs at a distance of two bonds favor this activity, while the presence of tertiary amines decreases it. This model was applied to a combinatorial library of a thousand and one 6-fluoroquinolones, from which 117 theoretical active molecules were obtained. The compound 10 and five new quinolones were tested against MRSA. They all showed some activity against MRSA, although compounds 6, 8 and 9 showed anti-MRSA activity similar to ciprofloxacin. This model was also applied to 263 theoretical antibacterial agents described by us in a previous work, from which 34 were predicted as theoretically active. Anti-MRSA activity was found bibliographically in 9 of them (ensuring at least 26% of success), and from the rest, 3 compounds were randomly chosen and tested, finding mitomycin C to be more active than ciprofloxacin. The results demonstrate the utility of the molecular topology approaches for identifying new drugs active against MRSA.

Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases

Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs.

Baicalin positively regulates osteoclast function by activating MAPK/Mitf signalling

Activation of osteoblasts in bone formation and osteoclasts in bone resorption is important during the bone fracture healing process. There has been a long interest in identifying and developing a natural therapy for bone fracture healing. In this study, we investigated the regulation of osteoclast differentiation by baicalin, which is a natural molecule extracted from Eucommiaulmoides (small tree native to China). It was determined that baicalin enhanced osteoclast maturation and bone resorption activity in a dose-dependent manner. Moreover, this involves the activation of MAPK, increased Mitf nuclear translocation and up-regulation of downstream osteoclast-related target genes expression. The baicalin-induced effect on osteoclast differentiation can be mimicked by specific inhibitors of p-ERK (U0126) and the Mitf-specific siRNA, respectively. Protein-ligand docking prediction identified that baicalin might bind to RANK, which is the upstream receptor of p-ERK/Mitf signalling in osteoclasts. This indicated that RANK might be the binding target of baicalin. In sum, our findings revealed baicalin increased osteoclast maturation and function via p-ERK/Mitf signalling. In addition, the results suggest that baicalin can potentially be used as a natural product for the treatment of bone fracture.

Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry

The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein-protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called 'linear model', where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world.

Quantitative Characterization of the Binding and Unbinding of Millimolar Drug Fragments with Molecular Dynamics Simulations

A quantitative characterization of the binding properties of drug fragments to a target protein is an important component of a fragment-based drug discovery program. Fragments typically have a weak binding affinity, however, making it challenging to experimentally characterize key binding properties, including binding sites, poses, and affinities. Direct simulation of the binding equilibrium by molecular dynamics (MD) simulations can provide a computational route to characterize fragment binding, but this approach is so computationally intensive that it has thus far remained relatively unexplored. Here, we perform MD simulations of sufficient length to observe several different fragments spontaneously and repeatedly bind to and unbind from the protein FKBP, allowing the binding affinities, on- and off-rates, and relative occupancies of alternative binding sites and alternative poses within each binding site to be estimated, thereby illustrating the potential of long time scale MD as a quantitative tool for fragment-based drug discovery. The data from the long time scale fragment binding simulations reported here also provide a useful benchmark for testing alternative computational methods aimed at characterizing fragment binding properties. As an example, we calculated binding affinities for the same fragments using a standard free energy perturbation approach and found that the values agreed with those obtained from the fragment binding simulations within statistical error.

Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5

GPCR allosteric modulators target at the allosteric binding pockets of G protein-coupled receptors (GPCRs) with indirect influence on the effects of an orthosteric ligand. Such modulators exhibit significant advantages compared to the corresponding orthosteric ligands, including better chemical tractability or physicochemical properties, improved selectivity, and reduced risk of oversensitization towards their receptors. Metabotropic glutamate receptor 5 (mGlu₅), a member of class C GPCRs, is a promising therapeutic target for treating many central nervous system diseases. The crystal structure of mGlu₅ in the complex with the negative allosteric modulator mavoglurant was recently reported, providing a fundamental model for designing new allosteric modulators. Computational fragment-based drug discovery represents a powerful scaffold-hopping and lead structure-optimization tool for drug design. In the present work, a set of integrated computational methodologies was first used, such as fragment library generation and retrosynthetic combinatorial analysis procedure (RECAP) for novel compound generation. Then, the compounds generated were assessed by benchmark dataset verification, docking studies, and QSAR model simulation. Subsequently, structurally diverse compounds, with reported or unreported scaffolds, can be observed from top 20 in silico synthesized compounds, which were predicted to be potential mGlu₅ modulators. In silico compounds with reported scaffolds may fill SAR holes in known, patented series of mGlu₅ modulators. And the generation of compounds without reported tests on mGluR indicates that our approach is doable for exploring and designing novel compounds. Our case study of designing allosteric modulators on mGlu₅ demonstrated that the established computational fragment-based approach is a useful methodology for facilitating new compound design in the future.

Allosteric Targeting of the Fanconi Anemia Ubiquitin-Conjugating Enzyme Ube2T by Fragment Screening

Ube2T is the E2 ubiquitin-conjugating enzyme of the Fanconi anemia DNA repair pathway and it is overexpressed in several cancers, representing an attractive target for the development of inhibitors. Despite the extensive efforts in targeting the ubiquitin system, very few E2 binders have currently been discovered. Herein we report the identification of a new allosteric pocket on Ube2T through a fragment screening using biophysical methods. Several fragments binding to this site inhibit ubiquitin conjugation in vitro.

Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis

We report the construction of a novel Systems Biology based virtual drug discovery model for the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a data-intensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identified through a systems approach in Mtb were evaluated for non-synonymous mutations. The 33 genes showed none or one variation in the entire 1623 strains, including 1084 Russian MDR strains. These invariant targets were further evaluated for their experimental and in silico essentiality as well as availability of their crystal structure in Protein Data Bank (PDB). Along with this, targets for the common existing antibiotics and the new Tb drug candidates were also screened for their variation across 1623 strains of Mtb for understanding the drug resistance. We propose that the reduced set of these reported targets could be a more effective starting point for medicinal chemists in generating new chemical leads. This approach has the potential of fueling the dried up Tuberculosis (Tb) drug discovery pipeline.

Exchange proteins directly activated by cAMP (EPACs): Emerging therapeutic targets

Exchange proteins directly activated by cAMP (EPACs) are critical cAMP-dependent signaling pathway mediators. The discovery of EPAC proteins has significantly facilitated understanding on cAMP-dependent signaling pathway and efforts along this line open new avenues for developing novel therapeutics for cancer, diabetes, heart failure, inflammation, infections, neurological disorders and other human diseases. Over the past decade, important progress has been made in the identification of EPAC agonists, antagonists and their biological and pharmacological applications. In this review, we briefly summarize recently reported novel functions of EPACs and the discovery of their small molecule modulators. The challenges and future perspectives are also discussed.

Ligand and Target Discovery by Fragment-Based Screening in Human Cells

Advances in the synthesis and screening of small-molecule libraries have accelerated the discovery of chemical probes for studying biological processes. Still, only a small fraction of the human proteome has chemical ligands. Here, we describe a platform that marries fragment-based ligand discovery with quantitative chemical proteomics to map thousands of reversible small molecule-protein interactions directly in human cells, many of which can be site-specifically determined. We show that fragment hits can be advanced to furnish selective ligands that affect the activity of proteins heretofore lacking chemical probes. We further combine fragment-based chemical proteomics with phenotypic screening to identify small molecules that promote adipocyte differentiation by engaging the poorly characterized membrane protein PGRMC2. Fragment-based screening in human cells thus provides an extensive proteome-wide map of protein ligandability and facilitates the coordinated discovery of bioactive small molecules and their molecular targets.

Molecular Modelling, Dynamics, and Docking of Membrane Proteins

Computational tools and techniques are now most popular and promising to progress the research at rapid rate. Molecular modelling studies contribute their maximum role in wide variety of disciplines especially in proteomics and drug discovery strategies. Molecular dynamics and molecular docking algorithms are now became an essential part in daily research activities of every laboratory throughout the world. These strategies are now well established and standardised to study any specific protein of interest and drug molecule. But still there exist considerable drawbacks in a special concern with membrane proteins as the presently available tools and methods cannot be applied directly to them. Modelling, dynamics and docking studies of membrane proteins need a special care and attention as several challenges are to be crossed with an intensive care to produce a reliable result. This chapter is aimed to discuss such challenges and solutions to handle membrane proteins.

Ribavirin suppresses bacterial virulence by targeting LysR-type transcriptional regulators

Targeting bacterial virulence mechanisms without compromising bacterial growth is a promising strategy to prevent drug resistance. LysR-type transcriptional regulators (LTTRs) possess structural conservation across bacterial species and regulate virulence in numerous pathogens, making them attractive targets for antimicrobial agents. We targeted AphB, a Vibrio cholerae LTTR, which regulates the expression of genes encoding cholera toxin and toxin-co-regulated pilus for inhibitor designing. Since AphB ligand is unknown, we followed a molecular fragment-based approach for ligand designing using FDA-approved drugs and subsequent screen to identify molecules that exhibited high-affinity binding to AphB ligand-binding pocket. Among the identified compounds, ribavirin, an anti-viral drug, antagonized AphB functions. Ribavirin perturbed Vibrio cholerae pathogenesis in animal models. The inhibitory effects of the drug was limited to the bacteria expressing wild type AphB, but not its constitutively active mutant (AphB_N100E), which represents the ligand-bound state, suggesting that ribavirin binds to the active site of AphB to exert its inhibitory role and there exists no AphB-independent mechanism of its action. Similarly, ribavirin suppressed the functions of Salmonella Typhi LTTR Hrg, indicating its broad spectrum efficacy. Moreover, ribavirin did not affect the bacterial viability in culture. This study cites an example of drug repurposing for anti-infective therapy.

Recent advances on the encoding and selection methods of DNA-encoded chemical library

DNA-encoded chemical library (DEL) has emerged as a powerful and versatile tool for ligand discovery in chemical biology research and in drug discovery. Encoding and selection methods are two of the most important technological aspects of DEL that can dictate the performance and utilities of DELs. In this digest, we have summarized recent advances on the encoding and selection strategies of DEL and also discussed the latest developments on DNA-encoded dynamic library, a new frontier in DEL research.

Measurement of Ligand-Target Residence Times by 1H Relaxation Dispersion NMR Spectroscopy

A ligand-observed ¹H NMR relaxation experiment is introduced for measuring the binding kinetics of low-molecular-weight compounds to their biomolecular targets. We show that this approach, which does not require any isotope labeling, is applicable to ligand–target systems involving proteins and nucleic acids of variable molecular size. The experiment is particularly useful for the systematic investigation of low affinity molecules with residence times in the micro- to millisecond time regime.

Protein-Observed Fluorine NMR Is a Complementary Ligand Discovery Method to 1H CPMG Ligand-Observed NMR

To evaluate its potential as a ligand discovery tool, we compare a newly developed 1D protein-observed fluorine NMR (PrOF NMR) screening method with the well-characterized ligand-observed 1H CPMG NMR screen. We selected the first bromodomain of Brd4 as a model system to benchmark PrOF NMR because of the high ligandability of Brd4 and the need for small molecule inhibitors of related epigenetic regulatory proteins. We compare the two methods' hit sensitivity, triaging ability, experiment speed, material consumption, and the potential for false positives and negatives. To this end, we screened 930 fragment molecules against Brd4 in mixtures of five and followed up these studies with mixture deconvolution and affinity characterization of the top hits. In selected examples, we also compare the environmental responsiveness of the 19F chemical shift to 1H in 1D-protein observed 1H NMR experiments. To address concerns of perturbations from fluorine incorporation, ligand binding trends and affinities were verified via thermal shift assays and isothermal titration calorimetry. We conclude that for the protein understudy here, PrOF NMR and 1H CPMG have similar sensitivity, with both being effective tools for ligand discovery. In cases where an unlabeled protein can be used, 1D protein-observed 1H NMR may also be effective; however, the 19F chemical shift remains significantly more responsive.

Determination of ligand binding modes in weak protein-ligand complexes using sparse NMR data

We describe a general approach to determine the binding pose of small molecules in weakly bound protein-ligand complexes by deriving distance constraints between the ligand and methyl groups from all methyl-containing residues of the protein. We demonstrate that using a single sample, which can be prepared without the use of expensive precursors, it is possible to generate high-resolution data rapidly and obtain the resonance assignments of Ile, Leu, Val, Ala and Thr methyl groups using triple resonance scalar correlation data. The same sample may be used to obtain Met ^εCH₃ assignments using NOESY-based methods, although the superior sensitivity of NOESY using [U-¹³C,¹⁵N]-labeled protein makes the use of this second sample more efficient. We describe a structural model for a weakly binding ligand bound to its target protein, DsbA, derived from intermolecular methyl-to-ligand nuclear Overhauser enhancements, and demonstrate that the ability to assign all methyl resonances in the spectrum is essential to derive an accurate model of the structure. Once the methyl assignments have been obtained, this approach provides a rapid means to generate structural models for weakly bound protein-ligand complexes. Such weak complexes are often found at the beginning of programs of fragment based drug design and can be challenging to characterize using X-ray crystallography.