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The CHK2 protein regulates the cell division cycle and responds to DNA damage. Additionally, it facilitates the repair of DNA damage and maintains the integrity of its biological processes. Dysregulation of the CHK2 protein is associated with a predisposition to harmful diseases. The current research protocol was designed to identify novel hit molecules as CHK2 inhibitors and disrupt the normal biological function of the CHK2 protein via a fragment‐based drug discovery approach. The protocol involved generating fragments using the MacFrag tool, followed by a chemical similarity search utilizing RDKit to identify fragment molecules analogous to previously established CHK2 inhibitor scaffolds. The bioactive molecules were constructed using the Fragmenstein tool, followed by molecular docking simulations to investigate their binding affinity. In addition, pharmacokinetic properties were analyzed, and a molecular dynamics simulation study was conducted to assess the stability of selected compounds with CHK2 protein. Finally, five novel compounds were identified as excellent CHK2 inhibitors through the FBDD and show good binding interactions at active sites of CHK2 with beneficial ADMET properties. This research work presents novel CHK2 inhibitor molecules that have the potential to be utilized in drug discovery, serving as key leads for future advancements in healthcare industries and sectors. [ABSTRACT FROM AUTHOR]

Fragment-based drug discovery (FBDD) focuses on small compounds, known as fragments, typically with a molecular weight of less than 300 Da. This study highlights the benefits of employing a pure natural product library for FBDD, contrasting with the predominant use of synthetic libraries. Practical methods for rapidly constructing such libraries from crude extracts were demonstrated across various plant and microbial samples. Twenty-nine (29) natural product fragments, including a new compound (20), were identified. Antimicrobial activities were assessed for a subset of the isolated compounds, revealing potent fragments (MICs 4–8 μg/mL) against Mycobacterium bovis bacille Calmette-Guérin (BCG), Staphylococcus aureus (SA), and methicillin-resistant S. aureus (MRSA). Furthermore, a native mass spectrometry technique was introduced to rapidly identify non-competitive fragments against malarial proteins. As a result, two pairs of non-competitive fragments, lepiotin C (31) and 7-amino deacetoxy cephalosporanic acid (32) binding to dynein light chain 1, methyl gallate (33) and β-santanin (34) binding to dUTPase, were identified, serving as promising starting points for developing potent malarial protein inhibitors. [ABSTRACT FROM AUTHOR]

Influenza is a seasonal respiratory illness that kills hundreds of thousands of people every year. Currently, neuraminidase inhibitors and endonuclease inhibitors are used in antiviral therapy. However, both drug types have encountered drug-resistant influenza strains in the human body. Fortunately, there is currently no resistance to endonuclease inhibitors in wild strains of influenza. We obtained the molecules with endonuclease inhibitor activity independent of the existing drug-resistant strains through computer-aided drug design, and we hope the obtained results can lay a theoretical foundation for the development of high-activity endonucleases. Combining a traditional fragment-based drug discovery approach with AI-directed fragment growth, we selected and designed a compound that achieved antiviral activity on drug-resistant strains by avoiding mutable residues and drug-resistant residues. We predicted the related properties using an ADMET model. Finally, we obtained a compound similar to baloxavir in terms of binding free energy but not affected by baloxavir resistance. [ABSTRACT FROM AUTHOR]

NMR spectroscopy has played a pivotal role in fragment-based drug discovery by coupling detection of weak ligand-target binding with structural mapping of the binding site. Fragment-based screening by NMR has been successfully applied to many soluble protein targets, but only to a limited number of membrane proteins, despite the fact that many drug targets are membrane proteins. This is partly because of difficulties preparing membrane proteins for NMR—especially human membrane proteins—and because of the inherent complexity associated with solution NMR spectroscopy on membrane protein samples, which require the inclusion of membrane-mimetic agents such as micelles, nanodiscs, or bicelles. Here, we developed a generalizable protocol for fragment-based screening of membrane proteins using NMR. We employed two human membrane protein targets, both in fully protonated detergent micelles: the single-pass C-terminal domain of the amyloid precursor protein, C99, and the tetraspan peripheral myelin protein 22 (PMP22). For both we determined the optimal NMR acquisition parameters, protein concentration, protein-to-micelle ratio, and upper limit to the concentration of D6-DMSO in screening samples. Furthermore, we conducted preliminary screens of a plate-format molecular fragment mixture library using our optimized conditions and were able to identify hit compounds that selectively bound to the respective target proteins. It is hoped that the approaches presented here will be useful in complementing existing methods for discovering lead compounds that target membrane proteins.

Fragment-based drug discovery (FBDD) focuses on small compounds, known as fragments, typically with a molecular weight of less than 300 Da. This study highlights the benefits of employing a pure natural product library for FBDD, contrasting with the predominant use of synthetic libraries. Practical methods for rapidly constructing such libraries from crude extracts were demonstrated across various plant and microbial samples. Twenty-nine (29) natural product fragments, including a new compound (20), were identified. Antimicrobial activities were assessed for a subset of the isolated compounds, revealing potent fragments (MICs 4–8 μg/mL) against Mycobacterium bovis bacille Calmette-Guérin (BCG), Staphylococcus aureus (SA), and methicillin-resistant S. aureus (MRSA). Furthermore, a native mass spectrometry technique was introduced to rapidly identify non-competitive fragments against malarial proteins. As a result, two pairs of non-competitive fragments, lepiotin C (31) and 7-amino deacetoxy cephalosporanic acid (32) binding to dynein light chain 1, methyl gallate (33) and β-santanin (34) binding to dUTPase, were identified, serving as promising starting points for developing potent malarial protein inhibitors.

Protein–fragment complex structures are particularly sought after in medicinal chemistry to rationally design lead molecules. These structures are usually derived using X-ray crystallography, but the failure rate is non-neglectable. NMR is a possible alternative for the calculation of weakly interacting complexes. Nevertheless, the time-consuming protein signal assignment step remains a barrier to its routine application. NMR Molecular Replacement (NMR2) is a versatile and rapid method that enables the elucidation of a protein–ligand complex structure. It has been successfully applied to peptides, drug-like molecules, and more recently to fragments. Due to the small size of the fragments, ca < 300 Da, solving the structures of the protein–fragment complexes is particularly challenging. Here, we present the expected performances of NMR2 when applied to protein–fragment complexes. The NMR2 approach has been benchmarked with the SERAPhic fragment library to identify the technical challenges in protein–fragment NMR structure calculation. A straightforward strategy is proposed to increase the method's success rate further. The presented work confirms that NMR2 is an alternative method to X-ray crystallography for solving protein–fragment complex structures. [ABSTRACT FROM AUTHOR]

Fragment-based drug discovery (FBDD) and validation of small molecule binders using NMR spectroscopy is an established and widely used method in the early stages of drug discovery. Starting from a library of small compounds, ligand- or protein-observed NMR methods are employed to detect binders, typically weak, that become the starting points for structure–activity relationships (SAR) by NMR. Unlike the more frequently used ligand-observed 1D NMR techniques, protein-observed 2D 1H-15N or 1H-13C heteronuclear correlation (HSQC or HMQC) methods offer insights that include the mechanism of ligand engagement on the target and direct binding affinity measurements in addition to routine screening. We hereby present the development of a set of software tools within the MestReNova (Mnova) package for analyzing 2D NMR for FBDD and hit validation purposes. The package covers three main tasks: (1) unsupervised profiling of raw data to identify outlier data points to exclude in subsequent analyses; (2) batch processing of single-point spectra to identify and rank binders based on chemical shift perturbations or spectral peak intensity changes; and (3) batch processing of multiple titration series to derive binding affinities (KD) by tracing the changes in peak locations or measuring global spectral changes. Toward this end, we implemented and evaluated a set of algorithms for automated peak tracing, spectral binning, and variance analysis by PCA, and a new tool for spectral data intensity comparison using ECHOS. The accuracy and speed of the tools are demonstrated on 2D NMR binding data collected on ligands used in the development of potential inhibitors of the anti-apoptotic MCL-1 protein. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fragment-based discovery of triazole-containing KAT6 inhibitors. • Key substituents likely form interactions with Ile319 to drive potency. • Hit-to-lead campaign improved potency >70-fold over original hit. KAT6, a histone acetyltransferase from the MYST family, has emerged as an attractive oncology target due to its role in regulating genes that control cell cycle progression and cellular senescence. Amplification of the KAT6A gene has been seen among patients with worse clinical outcome in ER+ breast cancers. Although multiple inhibitors have been reported, no KAT6 inhibitors have been approved to date. Here, we report the fragment-based discovery of a series of N -(1-phenyl-1 H -1,2,3-triazol-4-yl)benzenesulfonamide KAT6 inhibitors and early hit-to-lead efforts to improve the KAT6 potency. [ABSTRACT FROM AUTHOR]

The fragment approach has emerged as a method of choice for drug design, as it allows difficult therapeutic targets to be addressed. Success lies in the choice of the screened chemical library and the biophysical screening method, and also in the quality of the selected fragment and structural information used to develop a drug-like ligand. It has recently been proposed that promiscuous compounds, i.e., those that bind to several proteins, present an advantage for the fragment approach because they are likely to give frequent hits in screening. In this study, we searched the Protein Data Bank for fragments with multiple binding modes and targeting different sites. We identified 203 fragments represented by 90 scaffolds, some of which are not or hardly present in commercial fragment libraries. By contrast to other available fragment libraries, the studied set is enriched in fragments with a marked three-dimensional character (download at 10.5281/zenodo.7554649).

We have recently proposed a novel drug discovery approach based on biophysical screening of focused positional scanning libraries in which each element of the library contained a common binding moiety for the given target or class of targets. In this Letter, we report on the implementation of this approach to target metal containing proteins. In our implementation, we first derived a focused positional scanning combinatorial library of peptide mimetics (of approximately 100,000 compounds) in which each element of the library contained the metal-chelating moiety hydroxamic acid at the C-terminal. Screening of this library by nuclear magnetic resonance spectroscopy in solution allowed the identification of a novel and selective compound series targeting MMP-12. The data supported that our general approach, perhaps applied using other metal chelating agents or other initial binding fragments, may result very effective in deriving novel and selective agents against metalloenzyme.

The emergence of drug-resistant mycobacteria, including Mycobacterium tuberculosis (Mtb) and non-tuberculous mycobacteria (NTM), poses an increasing global threat that urgently demands the development of new potent anti-mycobacterial drugs. One of the approaches toward the identification of new drugs is fragment-based drug discovery (FBDD), which is the most ingenious among other drug discovery models, such as structure-based drug design (SBDD) and high-throughput screening. Specialized techniques, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and many others, are part of the drug discovery approach to combat the Mtb and NTM global menaces. Moreover, the primary drawbacks of traditional methods, such as the limited measurement of biomolecular toxicity and uncertain bioavailability evaluation, are successfully overcome by the FBDD approach. The current review focuses on the recognition of fragment-based drug discovery as a popular approach using virtual, computational, and biophysical methods to identify potent fragment molecules. FBDD focuses on designing optimal inhibitors against potential therapeutic targets of NTM and Mtb (PurC, ArgB, MmpL3, and TrmD). Additionally, we have elaborated on the challenges associated with the FBDD approach in the identification and development of novel compounds. Insights into the applications and overcoming the challenges of FBDD approaches will aid in the identification of potential therapeutic compounds to treat drug-sensitive and drug-resistant NTMs and Mtb infections. [ABSTRACT FROM AUTHOR]

Glycogen synthase kinase 3 beta (GSK-3β) is an evolutionarily conserved serine-threonine kinase dysregulated in numerous pathologies, such as Alzheimer's disease and cancer. Even though GSK-3β is a validated pharmacological target most of its inhibitors have two main limitations: the lack of selectivity due to the high homology that characterizes the ATP binding site of most kinases, and the toxicity that emerges from GSK-3β complete inhibition which translates into the impairment of the plethora of pathways GSK-3β is involved in. Starting from a 1D 19F NMR fragment screening, we set up several biophysical assays for the identification of GSK-3β inhibitors capable of binding protein hotspots other than the ATP binding pocket or to the ATP binding pocket, but with an affinity able of competing with a reference binder. A phosphorylation activity assay on a panel of several kinases provided selectivity data that were further rationalized and corroborated by structural information on GSK-3β in complex with the hit compounds. In this study, we identified promising fragments, inhibitors of GSK-3β, while proposing an alternative screening workflow that allows facing the flaws that characterize the most common GSK-3β inhibitors through the identification of selective inhibitors and/or inhibitors able to modulate GSK-3β activity without leading to its complete inhibition. [ABSTRACT FROM AUTHOR]

The L-arginine biosynthesis pathway consists of eight enzymes that catalyse the conversion of L-glutamate to L-arginine. Arginine auxotrophs (argB/argF deletion mutants) of Mycobacterium tuberculosis are rapidly sterilised in mice, while inhibition of ArgJ with Pranlukast was found to clear chronic M. tuberculosis infection in a mouse model. Enzymes in the arginine biosynthetic pathway have therefore emerged as promising targets for anti-tuberculosis drug discovery. In this work, the ligandability of four enzymes of the pathway ArgB, ArgC, ArgD and ArgF is assessed using a fragment-based approach. We identify several hits against these enzymes validated with biochemical and biophysical assays, as well as X-ray crystallographic data, which in the case of ArgB were further confirmed to have on-target activity against M. tuberculosis. These results demonstrate the potential for more enzymes in this pathway to be targeted with dedicated drug discovery programmes.

The pandemic COVID-19, caused by SARS-COV-2, has been a global concern and burden since April 2020 due to high contagiousness and pathogenesis. A great effort is being devoted to identify and investigate different druggable targets for SARS-COV-2 drug discovery. At least three targets have been identified among them is the spike glycoprotein which facilitates viral entry by binding to angiotensin converting enzyme (ACE-2 receptor) in host cell. In the current study, different computational tools were used to design potential cell entry inhibitors targeting spike glycoprotein. The essential pharmacophoric features were identified by e-pharmacophore mapping and fragments virtual screening was run using three different libraries. Docking scores were used to select the best fragments which were linked to afford novel molecules. The designed molecules were filtered via molecular docking, MM-GBSA free energy calculation, ADMET, drug-like properties and DFT calculations. Moreover, synthetic feasibility of the best ligands was studied. These ligands would be envisioned as potential leads for SARS-COV-2 cell entry inhibition and could be explored further towards COVID-19 drug discovery and development. [ABSTRACT FROM AUTHOR]

Cystic fibrosis (CF) is progressive genetic disease that predisposes lungs and other organs to multiple long-lasting microbial infections. Pseudomonas aeruginosa is the most prevalent and deadly pathogen among these microbes. Lung function of CF patients worsens following chronic infections with P. aeruginosa and is associated with increased mortality and morbidity. Emergence of multidrug-resistant, extensively drug-resistant and pandrug-resistant strains of P. aeruginosa due to intrinsic and adaptive antibiotic resistance mechanisms has failed the current anti-pseudomonal antibiotics. Hence new antibacterials are urgently needed to treat P. aeruginosa infections. Structure-guided fragment-based drug discovery (FBDD) is a powerful approach in the field of drug development that has succeeded in delivering six FDA approved drugs over the past 20 years targeting a variety of biological molecules. However, FBDD has not been widely used in the development of anti-pseudomonal molecules. In this review, we first give a brief overview of our structure-guided FBDD pipeline and then give a detailed account of FBDD campaigns to combat P. aeruginosa infections by developing small molecules having either bactericidal or anti-virulence properties. We conclude with a brief overview of the FBDD efforts in our lab at the University of Cambridge towards targeting P. aeruginosa infections.

Transforming Growth Factor- β Receptor type 1 (TGF-βR1) is an important anticancer target involved in promoting cell proliferation, progression, and metastasis through the induction of angiogenesis and suppression of immunological responses during the late stage of malignancy. Tranilast was initially approved for the treatment of bronchial asthma and allergic conditions in 1982. Later, it was revealed that Tranilast had numerous effects on cancer hallmarks, including immune evasion and sustained proliferation via the inhibition of TGF-βR1. This research describes the design of a novel series of anthranilate derivatives having various modes of interactions with TGF-βR1 compared with Tranilast. A database of novel Tranilast analogues was generated using Molecular Operating Environment Software (MOE 2020.09, Chemical Computing Group CCG, Montréal, Canada) using fragment-based drug design. Representative compounds were selected from the database and docked in the identified binding site of TGF-βR1. Several compounds showed higher binding affinity for TGF-βR1 compared with the lead compound in this work, Tranilast. Compounds with high docking scores contained a positively charged amine group that interacted with Asp290 or a negatively charged carboxylate group with Lys 335 in the TGF-βR1 ATP binding site. Additionally, compounds containing an aromatic group showed high docking scores through interacting with Ser287, Lys337, or Ile 211. Compounds A11, A14, A16, and B5 which had the best poses in terms of binding interactions and docking scores to the binding site will be considered for further synthesis and biological evaluation.

NMR spectroscopy has played a pivotal role in fragment-based drug discovery by coupling detection of weak ligand-target binding with structural mapping of the binding site. Fragment-based screening by NMR has been successfully applied to many soluble protein targets, but only to a limited number of membrane proteins, despite the fact that many drug targets are membrane proteins. This is partly because of difficulties preparing membrane proteins for NMR-especially human membrane proteins-and because of the inherent complexity associated with solution NMR spectroscopy on membrane protein samples, which require the inclusion of membrane-mimetic agents such as micelles, nanodiscs, or bicelles. Here, we developed a generalizable protocol for fragment-based screening of membrane proteins using NMR. We employed two human membrane protein targets, both in fully protonated detergent micelles: the single-pass C-terminal domain of the amyloid precursor protein, C99, and the tetraspan peripheral myelin protein 22 (PMP22). For both we determined the optimal NMR acquisition parameters, protein concentration, protein-to-micelle ratio, and upper limit to the concentration of D 6 -DMSO in screening samples. Furthermore, we conducted preliminary screens of a plate-format molecular fragment mixture library using our optimized conditions and were able to identify hit compounds that selectively bound to the respective target proteins. It is hoped that the approaches presented here will be useful in complementing existing methods for discovering lead compounds that target membrane proteins., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Fragment-based screening has evolved as a remarkable approach within the drug discovery process both in the industry and academia. Fragment screening has become a more structure-based approach to inhibitor development, but also towards development of pathway-specific clinical probes. However, it is often witnessed that the availability, immediate and long-term, of a high quality fragment-screening library is still beyond the reach of most academic laboratories. Within iNEXT (Infrastructure for NMR, EM and X-rays for Translational research), a EU-funded Horizon 2020 program, a collection of 782 fragments were assembled utilizing the concept of "poised fragments" with the aim to facilitate downstream synthesis of ligands with high affinity by fragment ligation. Herein, we describe the analytical procedure to assess the quality of this purchased and assembled fragment library by NMR spectroscopy. This quality assessment requires buffer solubility screening, comparison with LC/MS quality control and is supported by state-of-the-art software for high throughput data acquisition and on-the-fly data analysis. Results from the analysis of the library are presented as a prototype of fragment progression through the quality control process. [ABSTRACT FROM AUTHOR]

Fragment-based drug discovery or FBDD is one of the main methods used by industry and academia for identifying drug-like candidates in early stages of drug discovery. NMR has a significant impact at any stage of the drug discovery process, from primary identification of small molecules to the elucidation of binding modes for guiding optimisations. The essence of NMR as an analytical tool, however, requires the processing and analysis of relatively large amounts of single data items, e.g. spectra, which can be daunting when managed manually. One bottleneck in FBDD by NMR is a lack of adequate and well-integrated resources for NMR data analysis that are freely available to the community. Thus, scientists typically resort to manually inspecting large datasets and relying predominantly on subjective interpretations. In this manuscript, we present CcpNmr AnalysisScreen, a software package that provides computational tools for automated analysis of FBDD data by NMR. We outline how the quality of collected spectra can be evaluated quickly, and how robust workflows can be optimised for reliable and rapid hit identification. With an intuitive graphical user interface and powerful algorithms, AnalysisScreen enables easy analysis of the large datasets needed in the early process of drug discovery by NMR. [ABSTRACT FROM AUTHOR]

Recently, there has been increasing interest in new modalities such as therapeutic antibodies and gene therapy at a number of pharmaceutical companies. Moreover, in small-molecule drug discovery at such companies, efforts have focused on hard-to-drug targets such as inhibiting protein–protein interactions. Biomolecular NMR spectroscopy has been used in drug discovery in a variety of ways, such as for the reliable detection of binding and providing three-dimensional structural information for structure-based drug design. The advantages of using NMR spectroscopy have been known for decades (Jahnke in J Biomol NMR 39:87–90, (2007); Gossert and Jahnke in Prog Nucl Magn Reson Spectrosc 97:82–125, (2016)). For tackling hard-to-drug targets and increasing the success in discovering drug molecules, in-depth analysis of drug–target protein interactions performed by biophysical methods will be more and more essential. Here, we review the advantages of NMR spectroscopy as a key technology of biophysical methods and also discuss issues such as using cutting-edge NMR spectrometers and increasing the demand of utilizing conformational dynamics information for promoting small-molecule drug discovery. [ABSTRACT FROM AUTHOR]

Fragment-based screening by ligand-observed 1D NMR and binding interface mapping by protein-observed 2D NMR are popular methods used in drug discovery. These methods allow researchers to detect compound binding over a wide range of affinities and offer a simultaneous assessment of solubility, purity, and chemical formula accuracy of the target compounds and the 15 N-labeled protein when examined by 1D and 2D NMR, respectively. These methods can be applied for screening fragment binding to the active (GMPPNP-bound) and inactive (GDP-bound) states of oncogenic KRAS mutants., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Protein-based NMR spectroscopy has proven to be a very robust method for finding fragment leads to protein targets. However, one limitation of protein-based NMR is that the data acquisition and analysis can be time consuming. In order to streamline the scoring of protein-based NMR fragment screening data and the determination of ligand affinities using 2D NMR experiments we have developed a data analysis workflow based on principal component analysis (PCA) within the TREND NMR Pro software package. We illustrate this using four different proteins and sets of ligands which interact with these proteins over a range of affinities. Also, these PCA-based methods can be successfully applied even to systems where ligand binding to target proteins is in intermediate or even slow exchange on the NMR time scale. Finally, these methods will work for scoring of fragment binding data using protein spectra that are either highly overlapped or lower in resolution. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract Netherton syndrome (NS) is a rare and debilitating severe autosomal recessive genetic skin disease with high mortality rates particularly in neonates. NS is caused by loss-of-function SPINK5 mutations leading to unregulated kallikrein 5 (KLK5) and kallikrein 7 (KLK7) activity. Furthermore, KLK5 inhibition has been proposed as a potential therapeutic treatment for NS. Identification of potent and selective KLK5 inhibitors would enable further exploration of the disease biology and could ultimately lead to a treatment for NS. This publication describes how fragmentation of known trypsin-like serine protease (TLSP) inhibitors resulted in the identification of a series of phenolic amidine-based KLK5 inhibitors 1. X-ray crystallography was used to find alternatives to the phenol interaction leading to identification of carbonyl analogues such as lactam 13 and benzimidazole 15. These reversible inhibitors, with selectivity over KLK1 (10–100 fold), provided novel starting points for the guided growth towards suitable tool molecules for the exploration of KLK5 biology. [ABSTRACT FROM AUTHOR]

We introduce a new computer-aided design approach based on free binary decision diagrams (FBDDs) for implementing Boolean functions on crossbars using flow-based computing. Our crossbar synthesis procedure uses generalized FBDDs to design crossbars for a Boolean formula such that there is a flow of current from an input nanowire to an output nanowire through the sneak paths in the crossbar if and only if the Boolean formula evaluates to true. Generalized FBDDs are more succinct representations of Boolean formulas than traditional reduced ordered binary decision diagrams (ROBDDs) because they do not require the same variable ordering along all paths of the decision diagram. Our experimental results with the middle bit of a multiplier show that our designs are 69.9% more succinct than flow-based crossbar computing approaches designed using ROBDDs. [ABSTRACT FROM PUBLISHER]

NAMPT expression is elevated in many cancers, making this protein a potential target for anticancer therapy. We have carried out both NMR based and TR-FRET based fragment screens against human NAMPT and identified six novel binders with a range of potencies. Co-crystal structures were obtained for two of the fragments bound to NAMPT while for the other four fragments force-field driven docking was employed to generate a bound pose. Based on structural insights arising from comparison of the bound fragment poses to that of bound FK866 we were able to synthetically elaborate one of the fragments into a potent NAMPT inhibitor. [ABSTRACT FROM AUTHOR]

Illustrated here is the development of a new class of antibiotic lead molecules targeted at Pseudomonas aeruginosa glutaredoxin (PaGRX). This lead was produced to (a) circumvent efflux-mediated resistance mechanisms via covalent inhibition while (b) taking advantage of species selectivity to target a fundamental metabolic pathway. This work involved four components: a novel workflow for generating protein specific fragment hits via independent nuclear magnetic resonance (NMR) measurements, NMR-based modeling of the target protein structure, NMR guided docking of hits, and synthetic modification of the fragment hit with a vinyl cysteine trap moiety, i.e., acrylamide warhead, to generate the chimeric lead. Reactivity of the top warhead-fragment lead suggests that the ortholog selectivity observed for a fragment hit can translate into a substantial kinetic advantage in the mature warhead lead, which bodes well for future work to identify potent, species specific drug molecules targeted against proteins heretofore deemed undruggable.

Marine animals and plants provide abundant secondary metabolites with antitumor activity. Itampolin A is a brominated natural tyrosine secondary metabolite that is isolated from the sponge Iotrochota purpurea. Recently, we have achieved the first total synthesis of this brominated tyrosine secondary metabolite, which was found to be a potent p38α inhibitor exhibiting anticancer effects. A fragment-based drug design (FBDD) was carried out to optimize itampolin A. Forty-five brominated tyrosine derivatives were synthesized with interesting biological activities. Then, a QSAR study was carried out to explore the structural determinants responsible for the activity of brominated tyrosine skeleton p38α inhibitors. The lead compound was optimized by a FBDD method, then three series of brominated tyrosine derivatives were synthesized and evaluated for their inhibitory activities against p38α and tumor cells. Compound 6o (IC50 = 0.66 μM) exhibited significant antitumor activity against non-small cell lung A549 cells (A549). This also demonstrated the feasibility of the FBDD method of structural optimization.

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. [ABSTRACT FROM AUTHOR]

The molecular interactions of inhibitors of bromodomains (BRDs) were investigated. BRDs are protein interaction modules that recognizing ε- N -acetyl-lysine (εAc-Lys) motifs found in histone tails and are promising protein–protein interaction (PPI) targets. First, we analyzed a peptide ligand containing εAc-Lys to evaluate native PPIs. We then analyzed tetrahydroquinazoline-6-yl-benzensulfonamide derivatives found by fragment-based drug design (FBDD) and examined their interactions with the protein compared with the peptide ligand in terms of the inter-fragment interaction energy. In addition, we analyzed benzodiazepine derivatives that are high-affinity ligands for BRDs and examined differences in the CH/π interactions of the amino acid residues. We further surveyed changes in the charges of the amino acid residues among individual ligands, performed pair interaction energy decomposition analysis and estimated the water profile within the ligand binding site. Thus, useful insights for drug design were provided. Through these analyses and considerations, we show that the FMO method is a useful drug design tool to evaluate the process of FBDD and to explore PPI inhibitors. [ABSTRACT FROM AUTHOR]

Cholesteryl Ester Transfer Protein (CETP) is an important therapeutic target for the treatment of atherosclerotic cardiovascular disease. Our molecular modeling study revealed that pentacyclic triterpenoid compounds could mimic the protein-ligand interactions of the endogenous ligand cholesteryl ester (CE) by occupying its binding site. Alignment of the docking conformations of oleanolic acid (OA), ursolic acid (UA) and the crystal conformations of known CETP inhibitor Torcetrapib in the active site proposed the applicability of fragment-based drug design (FBDD) approaches in this study. Accordingly, a series of pentacyclic triterpenoid derivatives have been designed and synthesized as novel CETP inhibitors. The most potent compound 12e (IC 50 :0.28 μM) validated our strategy for molecular design. Molecular dynamics simulations illustrated that the more stable hydrogen bond interaction of the UA derivative 12e with Ser191 and stronger hydrophobic interactions with Val198, Phe463 than those of OA derivative 12b mainly led to their significantly different CETP inhibitory activity. These novel potent CETP inhibitors based on ursane-type scaffold should deserve further investigation. [ABSTRACT FROM AUTHOR]

We have developed a computational method that predicts the positions of active compounds, making it possible to increase activity as a fragment evolution strategy. We refer to the positions of these compounds as the active position. When an active fragment compound is found, the following lead generation process is performed, primarily to increase activity. In the current method, to predict the location of the active position, hydrogen atoms are replaced by small side chains, generating virtual compounds. These virtual compounds are docked to a target protein, and the docking scores (affinities) are examined. The hydrogen atom that gives the virtual compound with good affinity should correspond to the active position and it should be replaced to generate a lead compound. This method was found to work well, with the prediction of the active position being 2 times more efficient than random synthesis. In the current study, 15 examples of lead generation were examined. The probability of finding active positions among all hydrogen atoms was 26%, and the current method accurately predicted 60% of the active positions.