Fragment Linking and Optimization of Inhibitors of the Aspartic Protease Endothiapepsin: Fragment-Based Drug Design Facilitated by Dynamic Combinatorial Chemistry

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

Development of Receptor for Advanced Glycation End Products (RAGE) ligands through target directed dynamic combinatorial chemistry: a novel class of possible antagonists

RAGE is a transmembrane receptor of immunoglobulin family that can bind various endogenous and exogenous ligands, initiating the inflammatory downstream signaling pathways, including inflammaging. Therefore, RAGE represents an attractive drug target for age-related diseases. For the development of small-molecule RAGE antagonists, we employed protein-templated dynamic combinatorial chemistry (ptDCC) using RAGE's VC1 domain as a template, the first application of this approach in the context of RAGE. The affinities of DCC hits were validated using microscale thermophoresis. Subsequent screening against AGE2 (glyceraldehyde-modified AGE)-sRAGE (solubleRAGE) (AGE2-BSA/sRAGE) interaction using ELISA tests led to the identification of antagonists with micromolar potency. Our findings not only demonstrate the successful application of ptDCC on RAGE but also highlight its potential to address the pressing need for alternative strategies for the development of small-molecule RAGE antagonists, an area of research that has experienced a slowdown in recent years.

Application scenario-oriented molecule generation platform developed for drug discovery

Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN, etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, active learning, etc.) and input representations (1D SMILES, 2D graph, 3D shape, binding site, pharmacophore, etc.), to enable customized solutions for a given molecular design scenario. Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization. We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations. In addition, remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics.

Designing of fragment based inhibitors with improved activity against E. coli AmpC β-lactamase compared to the conventional antibiotics

One of the most common primary resistance mechanism of multi-drug resistant (MDR) Gram negative pathogenic bacteria to combat β-lactam antibiotics, such as penicillins, cephalosporins and carbapenems is the generation of β- lactamases. The uropathogenic E. coli is mostly getting multi-drug resistance due to the synthesis of AmpC β-lactamases and therefore new antibiotics and inhibitors are needed to treat the evolving infections. The current study was designed for targetting AmpC β-lactamase of E. coli using molecular docking based virtual screening, linking fragments for designing novel compounds and binding mode analysis using molecular dynamic simulation with target protein. The FCH group all-purpose fragment library consisting of 9388 fragments has been screened against AmpC β-lactamase protein of E. coli and the antibiotics and anti-infectives used in treatment of Urinary tract Infections (UTIs) were also screened with AmpC β-lactamase protein. Among the 9388 fragments, 339 fragment candidates were selected and linked with cefepime antibiotic having maximum binding affinity for AmpC target protein. Computational analysis of interactions as well as molecular dynamics (MD) simulations were also conducted for identifying the most promising ligand-pocket complexes from docking investigations to comprehend their thermodynamic properties and verify the docking outcomes as well. Overall, the linked complexes (LCs) showed good binding interactions with AmpC β-lactamase. Interestingly, our fragment-based LCs remained relatively stable in comparison with cefepime antibiotic. Moreover, S12 fragment linked complex remained the most stable during 50 ns with remarkable number of interactions indicating it as promising candidate in novel lead discovery against MDR E. coli infections.

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design

Fragment-based drug discovery is a widely used strategy for drug design in both academic and pharmaceutical industries. Although fragments can be linked to generate candidate compounds by the latest deep generative models, generating linkers with specified attributes remains underdeveloped. In this study, we presented a novel framework, DRlinker, to control fragment linking toward compounds with given attributes through reinforcement learning. The method has been shown to be effective for many tasks from controlling the linker length and log P, optimizing predicted bioactivity of compounds, to various multiobjective tasks. Specifically, our model successfully generated 91.0% and 93.9% of compounds complying with the desired linker length and log P and improved the 7.5 pChEMBL value in bioactivity optimization. Finally, a quasi-scaffold-hopping study revealed that DRlinker could generate nearly 30% molecules with high 3D similarity but low 2D similarity to the lead inhibitor, demonstrating the benefits and applicability of DRlinker in actual fragment-based drug design.

Development of potent inhibitors by fragment-linking strategies

Fragment-based drug discovery (FBDD) is a method of identifying small molecule hits that can be elaborated rationally through fragment growing, merging, and linking, to afford high affinity ligands for biological targets. Despite the promised theoretical potential of fragment linking, examples are still surprisingly sparse and remain overshadowed by the successes of fragment growing. The aim of this review is to outline a number of key examples of fragment linking strategies and discuss their strengths and limitations. Structure-based approaches including X-ray crystallography and in silico methods fragment optimisation are discussed, as well as fragment linking guided by NMR experiments. Target-guided approaches, exploiting the biological target to assemble its own inhibitors through dynamic combinatorial chemistry (DCC) and kinetic target-guided synthesis (KTGS), are identified as alternative efficient methods for fragment linking.

Proteomic tools for the quantitative analysis of artificial peptide libraries: detection and characterization of target-amplified PD-1 inhibitors

We report a quantitative proteomics data analysis pipeline which, coupled to protein-directed dynamic combinatorial chemistry (DDC) experiments, enables the rapid discovery and direct characterization of protein-protein interaction (PPI) modulators. A low-affinity PD-1 binder was incubated with a library of >100 D-peptides under thiol-exchange favoring conditions, in the presence of the target protein PD-1, and we determined the S-linked dimeric species that resulted amplified in the protein samples versus the controls. We chemically synthesized the target dimer candidates and validated them by thermophoresis binding and protein-protein interaction assays. The results provide a proof-of-concept for using this strategy in the high-throughput search of improved drug-like peptide binders that block therapeutically relevant protein-protein interactions.

Identification and synthesis of selective cholesterol esterase inhibitor using dynamic combinatorial chemistry

In this study, the concept of dynamic combinatorial chemistry (DCC) was applied to explore novel cholesterol esterase (CEase) inhibitors. In the presence of enzyme, two substrates (A1H3 and A2H3) were amplified from the dynamic combinatorial library (DCL), which was generated through reversible acylhydrazone formation reaction. In the in vitro biological evaluation, compound A1H3 exhibited not only potent (IC₅₀ in nanomolar range) but also selective inhibition (>120 folds of selectivity for CEase over AChE). Furthermore, the binding pattern and possible binding mechanism were investigated in the kinetic experiment and molecular docking study, respectively.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

Applications of Solution NMR in Drug Discovery

During the past decades, solution nuclear magnetic resonance (NMR) spectroscopy has demonstrated itself as a promising tool in drug discovery. Especially, fragment-based drug discovery (FBDD) has benefited a lot from the NMR development. Multiple candidate compounds and FDA-approved drugs derived from FBDD have been developed with the assistance of NMR techniques. NMR has broad applications in different stages of the FBDD process, which includes fragment library construction, hit generation and validation, hit-to-lead optimization and working mechanism elucidation, etc. In this manuscript, we reviewed the current progresses of NMR applications in fragment-based drug discovery, which were illustrated by multiple reported cases. Moreover, the NMR applications in protein-protein interaction (PPI) modulators development and the progress of in-cell NMR for drug discovery were also briefly summarized.

Identification of β-strand mediated protein-protein interaction inhibitors using ligand-directed fragment ligation

β-Strand mediated protein-protein interactions (PPIs) represent underexploited targets for chemical probe development despite representing a significant proportion of known and therapeutically relevant PPI targets. β-Strand mimicry is challenging given that both amino acid side-chains and backbone hydrogen-bonds are typically required for molecular recognition, yet these are oriented along perpendicular vectors. This paper describes an alternative approach, using GKAP/SHANK1 PDZ as a model and dynamic ligation screening to identify small-molecule replacements for tranches of peptide sequence. A peptide truncation of GKAP functionalized at the N- and C-termini with acylhydrazone groups was used as an anchor. Reversible acylhydrazone bond exchange with a library of aldehyde fragments in the presence of the protein as template and in situ screening using a fluorescence anisotropy (FA) assay identified peptide hybrid hits with comparable affinity to the GKAP peptide binding sequence. Identified hits were validated using FA, ITC, NMR and X-ray crystallography to confirm selective inhibition of the target PDZ-mediated PPI and mode of binding. These analyses together with molecular dynamics simulations demonstrated the ligands make transient interactions with an unoccupied basic patch through electrostatic interactions, establishing proof-of-concept that this unbiased approach to ligand discovery represents a powerful addition to the armory of tools that can be used to identify PPI modulators.

Self-Synthesizing Nanorods from Dynamic Combinatorial Libraries against Drug Resistant Cancer

Molecular self-assembly has been widely used to develop nanocarriers for drug delivery. However, most of them have unsatisfactory drug loading capacity (DLC) and the dilemma between stimuli-responsiveness and stability, stagnating their translational process. Herein, we overcame these drawbacks using dynamic combinatorial chemistry. A carrier molecule was spontaneously and quantitatively synthesized, aided by co-self-assembly with a template molecule and an anti-cancer drug doxorubicin (DOX) from a dynamic combinatorial library that was operated by disulfide exchange under thermodynamic control. The highly selective synthesis guaranteed a stable yet pH- and redox- responsive nanocarrier with a maximized DLC of 40.1 % and an enhanced drug potency to fight DOX resistance in vitro and in vivo. Our findings suggested that harnessing the interplay between synthesis and self-assembly in complex chemical systems could yield functional nanomaterials for advanced applications.

Hydrogen-Bond Catalysis of Imine Exchange in Dynamic Covalent Systems

The reversibility of imine bonds has been exploited to great effect in the field of dynamic covalent chemistry, with applications such as preparation of functional systems, dynamic materials, molecular machines, and covalent organic frameworks. However, acid catalysis is commonly needed for efficient equilibration of imine mixtures. Herein, it is demonstrated that hydrogen bond donors such as thioureas and squaramides can catalyze the equilibration of dynamic imine systems under unprecedentedly mild conditions. Catalysis occurs in a range of solvents and in the presence of many sensitive additives, showing moderate to good rate accelerations for both imine metathesis and transimination with amines, hydrazines, and hydroxylamines. Furthermore, the catalyst proved simple to immobilize, introducing both reusability and extended control of the equilibration process.

Discovery of Pyrazine-Carboxamide-Diphenyl-Ethers as Novel Succinate Dehydrogenase Inhibitors via Fragment Recombination

The discovery of novel succinate dehydrogenase inhibitors (SDHIs) has attracted great attention worldwide. Herein, a fragment recombination strategy was proposed to design new SDHIs by understanding the ligand-receptor interaction mechanism of SDHIs. Three fragments, pyrazine from pyraziflumid, diphenyl-ether from flubeneteram, and a prolonged amide linker from pydiflumetofen and fluopyram, were identified and recombined to produce a pyrazine-carboxamide-diphenyl-ether scaffold as a new SDHI. After substituent optimization, compound 6y was successfully identified with good inhibitory activity against porcine SDH, which was about 2-fold more potent than pyraziflumid. Furthermore, compound 6y exhibited 95% and 80% inhibitory rates against soybean gray mold and wheat powdery mildew at a dosage of 100 mg/L in vivo assay, respectively. The results of the present work showed that the pyrazine-carboxamide-diphenyl-ether scaffold could be used as a new starting point for the discovery of new SDHIs.

Protein-Templated Hit Identification through an Ugi Four-Component Reaction*

Kinetic target-guided synthesis represents an efficient hit-identification strategy, in which the protein assembles its own inhibitors from a pool of complementary building blocks via an irreversible reaction. Herein, we pioneered an in situ Ugi reaction for the identification of novel inhibitors of a model enzyme and binders for an important drug target, namely, the aspartic protease endothiapepsin and the bacterial β-sliding clamp DnaN, respectively. Highly sensitive mass-spectrometry methods enabled monitoring of the protein-templated reaction of four complementary reaction partners, which occurred in a background-free manner for endothiapepsin or with a clear amplification of two binders in the presence of DnaN. The Ugi products we identified show low micromolar activity on endothiapepsin or moderate affinity for the β-sliding clamp. We succeeded in expanding the portfolio of chemical reactions and biological targets and demonstrated the efficiency and sensitivity of this approach, which can find application on any drug target.

Protein-Directed Dynamic Combinatorial Chemistry: An Efficient Strategy in Drug Design

Protein-directed dynamic combinatorial chemistry (P-D DCC) is considered a powerful strategy to identify ligands to pharmacologically relevant protein targets. The protein selects its affinity ligands in situ through a thermodynamic templated effect in which the library composition shifts to the formation of specific library members at the expense of other (nonbinding) species. The increase in concentration of the selected species is known as amplification and leads to the discovery of new hit compounds for protein targets. This Mini-Review contains an updated overview of the protein-directed DCC applications and the fundamental aspects to take into account when designing a P-D DCC experiment such as the most biocompatible reversible reactions and the methodology used to analyze the experiments.

Application of Fragment-Based Drug Discovery to Versatile Targets

Fragment-based drug discovery (FBDD) is a powerful method to develop potent small-molecule compounds starting from fragments binding weakly to targets. As FBDD exhibits several advantages over high-throughput screening campaigns, it becomes an attractive strategy in target-based drug discovery. Many potent compounds/inhibitors of diverse targets have been developed using this approach. Methods used in fragment screening and understanding fragment-binding modes are critical in FBDD. This review elucidates fragment libraries, methods utilized in fragment identification/confirmation, strategies applied in growing the identified fragments into drug-like lead compounds, and applications of FBDD to different targets. As FBDD can be readily carried out through different biophysical and computer-based methods, it will play more important roles in drug discovery.

SyntaLinker: automatic fragment linking with deep conditional transformer neural networks

Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD). Hereby, we propose a new program named SyntaLinker, which is based on a syntactic pattern recognition approach using deep conditional transformer neural networks. This state-of-the-art transformer can link molecular fragments automatically by learning from the knowledge of structures in medicinal chemistry databases (e.g. ChEMBL database). Conventionally, linking molecular fragments was viewed as connecting substructures that were predefined by empirical rules. In SyntaLinker, however, the rules of linking fragments can be learned implicitly from known chemical structures by recognizing syntactic patterns embedded in SMILES notations. With deep conditional transformer neural networks, SyntaLinker can generate molecular structures based on a given pair of fragments and additional restrictions. Case studies have demonstrated the advantages and usefulness of SyntaLinker in FBDD.

Emerging functional materials based on chemically designed molecular recognition

The specific interactions responsible for molecular recognition play a crucial role in the fundamental functions of biological systems. Mimicking these interactions remains one of the overriding challenges for advances in both fundamental research in biochemistry and applications in material science. However, current molecular recognition systems based on host–guest supramolecular chemistry rely on familiar platforms (e.g., cyclodextrins, crown ethers, cucurbiturils, calixarenes, etc.) for orienting functionality. These platforms limit the opportunity for diversification of function, especially considering the vast demands in modern material science. Rational design of novel receptor-like systems for both biological and chemical recognition is important for the development of diverse functional materials. In this review, we focus on recent progress in chemically designed molecular recognition and their applications in material science. After a brief introduction to representative strategies, we describe selected advances in these emerging fields. The developed functional materials with dynamic properties including molecular assembly, enzyme-like and bio-recognition abilities are highlighted. We have also selected materials with dynamic properties in contract to traditional supramolecular host–guest systems. Finally, the current limitations and some future trends of these systems are discussed.

The 12th AFMC International Medicinal Chemistry Symposium (AIMECS 2019) in Istanbul, Turkey

AFMC-AIMECS meetings are internationally organized biannually by the Asian Federation for Medicinal Chemistry (AFMC) and are focused on recent studies in drug discovery and development both in academia and industry. Member organizations of the AFMC are the Pharmaceutical Society of Japan, the Chinese Pharmaceutical Association, the Royal Australian Chemical Institute, the Pharmaceutical Society of Korea, the Korean Chemical Society, the Chemical Society Located in Taipei, the Indonesian Society of Medicinal Chemistry, the Medicinal Chemistry Section of the Israel Chemical Society, and the Computer-Aided Drug Design & Development Society in Turkey. Each time, the symposium is organized within these member countries. The AIMECS 2019 symposium was held in Turkey this year, as Prof. Dr. Esin Aki-Yalcin is the current president of the AFMC (2018-2020); the next AIMECS meeting will be organized in 2021 in Tokyo, Japan. In this report, we discuss key topics at the 12^th AFMC International Medicinal Chemistry Symposium - New Avenues for Design and Development of Translational Medicine (AIMECS 2019) held in Istanbul, September 8-11, 2019.

Concepts and Core Principles of Fragment-Based Drug Design

In this review, a general introduction to fragment-based drug design and the underlying concepts is given. General considerations and methodologies ranging from library selection/construction over biophysical screening and evaluation methods to in-depth hit qualification and subsequent optimization strategies are discussed. These principles can be generally applied to most classes of drug targets. The examples given for fragment growing, merging, and linking strategies at the end of the review are set in the fields of enzyme-inhibitor design and macromolecule–macromolecule interaction inhibition. Building upon the foundation of fragment-based drug discovery (FBDD) and its methodologies, we also highlight a few new trends in FBDD.

Development of a multivalent acetylcholinesterase inhibitor via dynamic combinatorial chemistry

In this study, we report the generation of a polymer based dynamic combinatorial library (DCL) using aldehyde-functionalized linear poly(glycidol) and hydrazide derivatives as initial building blocks. In combination with tetrameric acetylcholinesterase (AChE), a certain type of amplified acylhydrazone side chain is identified and further used for the synthesis of a multivalent AChE inhibitor. The cytotoxicity and inhibition properties of the multivalent inhibitor are evaluated, and the results indicate superior bioactivity compared to the commercial reference Edrophonium chloride.

Characterization of the endothiapepsin-like protein in the entomopathogenic fungus Beauveria bassiana and its virulence effect on the silkworm, Bombyx mori

Endothiapepsin is an aspartic proteinase that was first isolated from the plant pathogenic fungus Endothia parasitica. In previous studies, we reported on three endothiapepsin-like proteins in the entomopathogenic fungus Beauveria bassiana; the genes were up-regulated in B. bassiana hyper-virulent strain GXsk1011 at early stage infection in the silkworm. However, whether these proteins play a role in pathogenicity or not remains unknown. In this study, we cloned one protein, BbepnL-1 gene (BBA-07766), that has 98% homology with B. bassiana strain Bb2860, and expressed it in the yeast Pichia pastoris to investigate its function. The endothiapepsin-like protein is a secreted proteinase of molecular weight approximately 40 kDa. It has an N-glycosylation site and a mutation in the C-terminal conserved domain- a Thr was mutated to Gly in B. bassiana GXsk1011 and is different than the endothiapepsin of Endothia parasitica. The recombinant endothiapepsin-like protein showed enzyme activity and degraded the protein components of the silkworm cuticle. To further investigate the activity of the endothiapepsin-like protein, we knocked out the gene BbepnL-1 and showed that the loss of BbepnL-1 reduced the virulence in the silkworm. These results demonstrated that the endothiapepsin-like protein of B. bassiana is a virulence factor.

Converting SMILES to Stacking Interaction Energies

Predicting the strength of stacking interactions involving heterocycles is vital for several fields, including structure-based drug design. While quantum chemical computations can provide accurate stacking interaction energies, these come at a steep computational cost. To address this challenge, we recently developed quantitative predictive models of stacking interactions between druglike heterocycles and the aromatic amino acids Phe, Tyr, and Trp (DOI: 10.1021/jacs.9b00936 ). These models depend on heterocycle descriptors derived from electrostatic potentials (ESPs) computed using density functional theory and provide accurate stacking interactions without the need for expensive computations on stacked dimers. Herein, we show that these ESP-based descriptors can be reliably evaluated directly from the atom connectivity of the heterocycle, providing a means of predicting both the descriptors and the potential for a given heterocycle to engage in stacking interactions without resorting to any quantum chemical computations. This enables the rapid conversion of simple molecular representations (e.g., SMILES) directly into accurate stacking interaction energies using a freely available online tool, thereby providing a way to rank the stacking abilities of large sets of heterocycles.

Protein-Templated Dynamic Combinatorial Chemistry: Brief Overview and Experimental Protocol

Dynamic combinatorial chemistry (DCC) is a powerful tool to identify bioactive compounds. This efficient technique allows the target to select its own binders and circumvents the need for synthesis and biochemical evaluation of all individual derivatives. An ever-increasing number of publications report the use of DCC on biologically relevant target proteins. This minireview complements previous reviews by focusing on the experimental protocol and giving detailed examples of essential steps and factors that need to be considered, such as protein stability, buffer composition and cosolvents.

A Candidate for Multitopic Probes for Ligand Discovery in Dynamic Combinatorial Chemistry

Multifunctionalized materials are expected to be versatile probes to find specific interactions between a ligand and a target biomaterial. Thus, efficient methods to prepare possible combinations of the functionalities is desired. The concept of dynamic combinatorial chemistry (DCC) is ideal for the generation of any possible combination, as well as screening for target biomaterials. Here, we propose a new molecular design of multitopic probes for ligand discovery in DCC. We synthesized a new Gable Porphyrin, GP1, having prop-2-yne groups as a scaffold to introduce various functional groups. GP1 is a bis(imidazolylporphyrinatozinc) compound connected through a 1,3-phenylene moiety, and it gives macrocycles spontaneously and quantitatively by strong imidazole-to-zinc complementary coordination. Some different types of functional groups were introduced into GP1 in high yields. Formation of heterogeneous macrocycles composed of GP1 derivatives having different types of substituents was accomplished under equilibrium conditions. These results promise that enormous numbers of macrocycles having various functional groups can be provided when the kinds of GP components increase. These features are desirable for DCC, and the present system using GP1 is a potential candidate to provide a dynamic combinatorial library of multitopic probes to discover specific interactions between a ligand and a biomaterial.

Using reversible non-covalent and covalent bonds to create assemblies and equilibrating molecular networks that survive 5 molar urea

The limits of self-assembly and host-guest chemistry in water solutions containing competitive solutes are largely unexplored. We report here a new family of self-assembling systems that are stitched together at two levels by reversible hydrazone bonds and by non-covalent self-assembly in strongly denaturing conditions. Three different hydrazides of various charge and hydrophobicity are combined with an aldehyde-containing calixarene, and each system spontaneously forms AB hydrazones that subsequently self-assemble into four-component (AB)2 structures in water. The assemblies display varying responses to added NaCl and/or urea. The most robust assembly survives completely intact in solution up to 5 M urea. We also combine the aldehyde calixarene with two different hydrazides in the same tube to create complex, competitive dynamic libraries. We report experiments in which the composition of the dynamic equilibrating library is under the control of self-assembly, allowing the systems to choose the components that form the most stable assemblies under a variety of competitive solutions conditions. These dynamic networks of equilibrating molecules maintain remarkably similar equilibrium positions under widely varying concentrations of urea and NaCl.

Discovery of a Biologically Active Bromodomain Inhibitor by Target-Directed Dynamic Combinatorial Chemistry

Target-directed dynamic combinatorial chemistry (DCC) has emerged as a strategy for the identification of inhibitors of relevant therapeutic targets. In this contribution, we use this strategy for the identification of a high-affinity binder of a parasite target, the Trypanosoma cruzi bromodomain-containing protein TcBDF3. This protein is essential for viability of T. cruzi, the protozoan parasite that causes Chagas disease. A small dynamic library of acylhydrazones was prepared from aldehydes and acylhydrazides at neutral pH in the presence of aniline. The most amplified library member shows (a) high affinity for the template, (b) interesting antiparasitic activity against different parasite forms, and (c) low toxicity against Vero cells. In addition, parasites are rescued from the compound toxicity by TcBDF3 overexpression, suggesting that the toxicity of this compound is due to the TcBDF3 inhibition, i.e., the binding event that initially drives the molecular amplification is reproduced in the parasite, leading to selective toxicity.

Stacking Interactions of Heterocyclic Drug Fragments with Protein Amide Backbones

Stacking interactions can be important enthalpic contributors to drug binding. Among the less well-studied stacking interactions are those occurring between an arene and the π-face of an amide group. Given the ubiquity of heterocycles in drugs, combined with the abundance of amides in the protein backbone, optimizing these noncovalent interactions can provide a potential route to enhanced drug binding. Previously, Diederich et al. (ChemMedChem 2013, 8, 397-404) studied stacked dimers of a model amide with a set of 18 heterocycles, showing that computed interaction energies correlate with the dipole moments of the heterocycles and providing guidelines for the optimization of these interactions. We considered stacked dimers of the same model amide with a larger set of 28 heterocycles common in pharmaceuticals, by using more robust ab initio methods. While the overall trends in these new data corroborate many of the results of Diederich et al., these data provide a more refined view of the nature of amide stacking interactions. We present a robust scoring function for amide stacking interaction energies based on the molecular dipole moment and strength of the electric field above the arene.

Insights into the Impact of Linker Flexibility and Fragment Ionization on the Design of CK2 Allosteric Inhibitors: Comparative Molecular Dynamics Simulation Studies

Protein kinase is a novel therapeutic target for human diseases. The off-target and side effects of ATP-competitive inhibitors preclude them from the clinically relevant drugs. The compounds targeting the druggable allosteric sites outside the highly conversed ATP binding pocket have been identified as promising alternatives to overcome current barriers of ATP-competitive inhibitors. By simultaneously interacting with the αD region (new allosteric site) and sub-ATP binding pocket, the attractive compound CAM4066 was named as allosteric inhibitor of CK2α. It has been demonstrated that the rigid linker and non-ionizable substituted fragment resulted in significant decreased inhibitory activities of compounds. The molecular dynamics simulations and energy analysis revealed that the appropriate coupling between the linker and pharmacophore fragments were essential for binding of CAM4066 with CK2α. The lower flexible linker of compound 21 lost the capability of coupling fragments A and B to αD region and positive area, respectively, whereas the methyl benzoate of fragment B induced the re-orientated Pre-CAM4066 with the inappropriate polar interactions. Most importantly, the match between the optimized linker and pharmacophore fragments is the challenging work of fragment-linking based drug design. These results provide rational clues to further structural modification and development of highly potent allosteric inhibitors of CK2.

Redox Control over Acyl Hydrazone Photoswitches

Photoisomerization provides a clean and efficient way of reversibly altering physical properties of chemical systems and injecting energy into them. These effects have been applied in development of systems such as photoresponsive materials, molecular motors, and photoactivated drugs. Typically, switching from more to less stable isomer(s) is performed by irradiation with UV or visible light, while the reverse process proceeds thermally or by irradiation using another wavelength. In this work we developed a method of rapid and tunable Z→E isomerization of C═N bond in acyl hydrazones, using aromatic thiols as nucleophilic catalysts. As thiols can be oxidized into catalytically inactive disulfides, the isomerization rates can be controlled via the oxidation state of the catalyst, which, together with the UV irradiation, provides orthogonal means to control the E/Z state of the system. As a proof of this concept, we have applied this method to control the diversity of acyl hydrazone based dynamic combinatorial libraries.

Target-directed Dynamic Combinatorial Chemistry: A Study on Potentials and Pitfalls as Exemplified on a Bacterial Target

Target-directed dynamic combinatorial chemistry (DCC) is an emerging technique for the efficient identification of inhibitors of pharmacologically relevant targets. In this contribution, we present an application for a bacterial target, the lectin FimH, a crucial virulence factor of uropathogenic E. coli being the main cause of urinary tract infections. A small dynamic library of acylhydrazones was formed from aldehydes and hydrazides and equilibrated at neutral pH in presence of aniline as nucleophilic catalyst. The major success factors turned out to be an accordingly adjusted ratio of scaffolds and fragments, an adequate sample preparation prior to HPLC analysis, and the data processing. Only then did the ranking of the dynamic library constituents correlate well with affinity data. Furthermore, as a support of DCC applications especially to larger libraries, a new protocol for improved hit identification was established.

Proteintemplat-gesteuerte Fragmentligationen - von der molekularen Erkennung zur Wirkstofffindung

Proteintemplat‐gesteuerte Fragmentligationen sind ein neuartiges Konzept zur Unterstützung der Wirkstofffindung und können dazu beitragen, die Wirksamkeit von Proteinliganden zu verbessern. Es handelt sich dabei um chemische Reaktionen zwischen niedermolekularen Verbindungen (“Fragmenten”), die die Oberfläche eines Proteins als Reaktionsgefäß verwenden, um die Bildung eines Proteinliganden mit erhöhter Bindungsaffinität zu katalysieren. Die Methode nutzt die molekulare Erkennung kleiner reaktiver Fragmente durch die Proteine sowohl zur Assemblierung der Liganden als auch zur Identifizierung bioaktiver Fragmentkombinationen. Chemische Synthese und Bioassay werden dabei in einem Schritt vereint. Dieser Aufsatz diskutiert die biophysikalischen Grundlagen der reversiblen und irreversiblen Fragmentligationen und gibt einen Überblick über die Methoden, mit denen die durch das Proteintemplat gebildeten Ligationsprodukte detektiert werden können. Der chemische Reaktionsraum und aktuelle Anwendungen wie auch die Bedeutung dieses Konzeptes für die Wirkstofffindung werden erörtert.

Protein-Templated Fragment Ligations-From Molecular Recognition to Drug Discovery

Protein-templated fragment ligation is a novel concept to support drug discovery and can help to improve the efficacy of protein ligands. Protein-templated fragment ligations are chemical reactions between small molecules ("fragments") utilizing a protein's surface as a reaction vessel to catalyze the formation of a protein ligand with increased binding affinity. The approach exploits the molecular recognition of reactive small-molecule fragments by proteins both for ligand assembly and for the identification of bioactive fragment combinations. In this way, chemical synthesis and bioassay are integrated in one single step. This Review discusses the biophysical basis of reversible and irreversible fragment ligations and gives an overview of the available methods to detect protein-templated ligation products. The chemical scope and recent applications as well as future potential of the concept in drug discovery are reviewed.

Identification of inhibitors targeting Mycobacterium tuberculosis cell wall biosynthesis via dynamic combinatorial chemistry

In this study, we report a dynamic combinatorial approach along with highly efficient in situ screening to identify inhibitors of UDP-galactopyranose mutase (UGM), an essential enzyme involved in mycobacterial cell wall biosynthesis.