Protein-Directed Dynamic Combinatorial Chemistry: A Guide to Protein Ligand and Inhibitor Discovery

Decoding the Structural Diversity: A New Horizon in Antimicrobial Prospecting and Mechanistic Investigation

The escalating crisis of antimicrobial resistance (AMR) underscores the urgent need for novel antimicrobials. One promising strategy is the exploration of structural diversity, as diverse structures can lead to diverse biological activities and mechanisms of action. This review delves into the role of structural diversity in antimicrobial discovery, highlighting its influence on factors such as target selectivity, binding affinity, pharmacokinetic properties, and the ability to overcome resistance mechanisms. We discuss various approaches for exploring structural diversity, including combinatorial chemistry, diversity-oriented synthesis, and natural product screening, and provide an overview of the common mechanisms of action of antimicrobials. We also describe techniques for investigating these mechanisms, such as genomics, proteomics, and structural biology. Despite significant progress, several challenges remain, including the synthesis of diverse compound libraries, the identification of active compounds, the elucidation of complex mechanisms of action, the emergence of AMR, and the translation of laboratory discoveries to clinical applications. However, emerging trends and technologies, such as artificial intelligence, high-throughput screening, next-generation sequencing, and open-source drug discovery, offer new avenues to overcome these challenges. Looking ahead, we envisage an exciting future for structural diversity-oriented antimicrobial discovery, with opportunities for expanding the chemical space, harnessing the power of nature, deepening our understanding of mechanisms of action, and moving toward personalized medicine and collaborative drug discovery. As we face the continued challenge of AMR, the exploration of structural diversity will be crucial in our search for new and effective antimicrobials.

Teaching an Old Compound New Tricks: Reversible Transamidation in Maleamic Acids

Dynamic combinatorial chemistry is a method widely used for generating responsive libraries of compounds, with applications ranging from chemical biology to materials science. It relies on dynamic covalent bonds that are able to form in a reversible manner in mild conditions, and therefore requires the discovery of new types of these bonds in order to progress. Amides, due to their high stability, have been scarcely used in this field and typically require an external catalyst or harsh conditions for exchange. Compounds able to undergo uncatalysed transamidation at room temperature are still rare exceptions. In this work, we describe reversible amide formation and transamidation in a class of compounds known as maleamic acids. Due to the presence of a carboxylic acid in β-position, these compounds are in equilibrium with their anhydride and amine precursors in organic solvents at room temperature. First, we show that this equilibrium is responsive to external stimuli: by alternating the additions of a Brønsted acid and a base, we can switch between amide and anhydride several times without side-reactions. Next, we prove that this equilibrium provides a pathway for reversible transamidation without any added catalyst, leading to thermodynamic distributions of amides at room temperature. Lastly, we use different preparation conditions and concentrations of Brønsted acid to access different library distributions, easily controlling the transition between kinetic and thermodynamic regimes. Our results show that maleamic acids can undergo transamidation in mild conditions in a reversible and tunable way, establishing them as a new addition to the toolbox of dynamic combinatorial chemistry.

Recent advances in DNA-encoded dynamic libraries

The DNA-encoded chemical library (DEL) has emerged as a powerful technology platform in drug discovery and is also gaining momentum in academic research. The rapid development of DNA-/DEL-compatible chemistries has greatly expanded the chemical space accessible to DELs. DEL technology has been widely adopted in the pharmaceutical industry and a number of clinical drug candidates have been identified from DEL selections. Recent innovations have combined DELs with other legacy and emerging techniques. Among them, the DNA-encoded dynamic library (DEDL) introduces DNA encoding into the classic dynamic combinatorial libraries (DCLs) and also integrates the principle of fragment-based drug discovery (FBDD), making DEDL a novel approach with distinct features from static DELs. In this Review, we provide a summary of the recently developed DEDL methods and their applications. Future developments in DEDLs are expected to extend the application scope of DELs to complex biological systems with unique ligand-discovery capabilities.

Identification of potent α-amylase inhibitors via dynamic combinatorial chemistry

In this study, we report for the first time the discovery of potent α-amylase inhibitors using principle of dynamic combinatorial chemistry. The best compound identified exhibited not only high inhibitory efficiency but also low cytotoxicity. The binding mode and possible mechanism are determined in the subsequent kinetic and molecular docking studies.

Hit-optimization using target-directed dynamic combinatorial chemistry: development of inhibitors of the anti-infective target 1-deoxy-d-xylulose-5-phosphate synthase

Target-directed dynamic combinatorial chemistry (tdDCC) enables identification, as well as optimization of ligands for un(der)explored targets such as the anti-infective target 1-deoxy-d-xylulose-5-phosphate synthase (DXPS). We report the use of tdDCC to first identify and subsequently optimize binders/inhibitors of the anti-infective target DXPS. The initial hits were also optimized for their antibacterial activity against E. coli and M. tuberculosis during subsequent tdDCC runs. Using tdDCC, we were able to generate acylhydrazone-based inhibitors of DXPS. The tailored tdDCC runs also provided insights into the structure-activity relationship of this novel class of DXPS inhibitors. The competition tdDCC runs provided important information about the mode of inhibition of acylhydrazone-based inhibitors. This approach holds the potential to expedite the drug-discovery process and should be applicable to a range of biological targets.

Multivalent butyrylcholinesterase inhibitor discovered by exploiting dynamic combinatorial chemistry

In this study, we report the generation of a polymer-based dynamic combinatorial library (DCL) incorporating exchangeable side chains using acylhydrazone formation reaction. In combination with tetrameric butyrylcholinesterase (BChE), the most potent binding side chain was identified, and the information obtained was further used for the synthesis of a multivalent BChE inhibitor. In the in vitro biological evaluation, this multivalent inhibitor exhibited not only better inhibitory effect than the commercial reference but also high selectivity on BChE over acetylcholinesterase (AChE).

Biomimetic selenocystine based dynamic combinatorial chemistry for thiol-disulfide exchange

Dynamic combinatorial chemistry applied to biological environments requires the exchange chemistry of choice to take place under physiological conditions. Thiol-disulfide exchange, one of the most popular dynamic combinatorial chemistries, usually needs long equilibration times to reach the required equilibrium composition. Here we report selenocystine as a catalyst mimicking Nature's strategy to accelerate thiol-disulfide exchange at physiological pH and low temperatures. Selenocystine is able to accelerate slow thiol-disulfide systems and to promote the correct folding of an scrambled RNase A enzyme, thus broadening the practical range of pH conditions for oxidative folding. Additionally, dynamic combinatorial chemistry target-driven self-assembly processes are tested using spermine, spermidine and NADPH (casting) and glucose oxidase (molding). A non-competitive inhibitor is identified in the glucose oxidase directed dynamic combinatorial library.

Generative Network Complex for the Automated Generation of Drug-like Molecules

Current drug discovery is expensive and time-consuming. It remains a challenging task to create a wide variety of novel compounds that not only have desirable pharmacological properties but also are cheaply available to low-income people. In this work, we develop a generative network complex (GNC) to generate new drug-like molecules based on the multiproperty optimization via the gradient descent in the latent space of an autoencoder. In our GNC, both multiple chemical properties and similarity scores are optimized to generate drug-like molecules with desired chemical properties. To further validate the reliability of the predictions, these molecules are reevaluated and screened by independent 2D fingerprint-based predictors to come up with a few hundreds of new drug candidates. As a demonstration, we apply our GNC to generate a large number of new BACE1 inhibitors, as well as thousands of novel alternative drug candidates for eight existing market drugs, including Ceritinib, Ribociclib, Acalabrutinib, Idelalisib, Dabrafenib, Macimorelin, Enzalutamide, and Panobinostat.

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.

Tailored therapeutics based on 1,2,3-1H-triazoles: a mini review

Contemporary drug discovery approaches rely on library synthesis coupled with combinatorial methods and high-throughput screening to identify leads. However, due to the multitude of components involved, a majority of optimization techniques face persistent challenges related to the efficiency of synthetic processes and the purity of compound libraries. These methods have recently found an upgradation as fragment-based approaches for target-guided synthesis of lead molecules with active involvement of their biological target. The click chemistry approach serves as a promising tool for tailoring the therapeutically relevant biomolecules of interest, improving their bioavailability and bioactivity and redirecting them as efficacious drugs. 1,2,3-1H-Triazole nucleus, being a planar and biologically acceptable scaffold, plays a crucial role in the design of biomolecular mimetics and tailor-made molecules with therapeutic relevance. This versatile scaffold also forms an integral part of the current fragment-based approaches for drug design, kinetic target guided synthesis and bioorthogonal methodologies.

Insights into real-time chemical processes in a calcium sensor protein-directed dynamic library

Dynamic combinatorial chemistry (DCC) has proven its potential in drug discovery speeding the identification of modulators of biological targets. However, the exchange chemistries typically take place under specific reaction conditions, with limited tools capable of operating under physiological parameters. Here we report a catalyzed protein-directed DCC working at low temperatures that allows the calcium sensor NCS-1 to find the best ligands in situ. Ultrafast NMR identifies the reaction intermediates of the acylhydrazone exchange, tracing the molecular assemblies and getting a real-time insight into the essence of DCC processes at physiological pH. Additionally, NMR, X-ray crystallography and computational methods are employed to elucidate structural and mechanistic aspects of the molecular recognition event. The DCC approach leads us to the identification of a compound stabilizing the NCS-1/Ric8a complex and whose therapeutic potential is proven in a Drosophila model of disease with synaptic alterations.

Dynamic combinatorial chemistry (DCC) is instrumental in the discovery of ligands for pharmaceutical targets. Here, the authors adapted DCC to work at 4 degrees Celsius and used it to identify a ligand for Neuronal Calcium Sensor-1 that promotes NCS-1/Ric8a protein-protein interaction.

Microflow-based dynamic combinatorial chemistry: a microscale synthesis and screening platform for the rapid and accurate identification of bioactive molecules

The first flow-based synthesis and screening platform that integrates both microflow chemistry and protein-directed dynamic combinatorial chemistry into a single modular unit was disclosed and validated by a case study.

Generative network complex (GNC) for drug discovery

It remains a challenging task to generate a vast variety of novel compounds with desirable pharmacological properties. In this work, a generative network complex (GNC) is proposed as a new platform for designing novel compounds, predicting their physical and chemical properties, and selecting potential drug candidates that fulfill various druggable criteria such as binding affinity, solubility, partition coefficient, etc. We combine a SMILES string generator, which consists of an encoder, a drug-property controlled or regulated latent space, and a decoder, with verification deep neural networks, a target-specific three-dimensional (3D) pose generator, and mathematical deep learning networks to generate new compounds, predict their drug properties, construct 3D poses associated with target proteins, and reevaluate druggability, respectively. New compounds were generated in the latent space by either randomized output, controlled output, or optimized output. In our demonstration, 2.08 million and 2.8 million novel compounds are generated respectively for Cathepsin S and BACE targets. These new compounds are very different from the seeds and cover a larger chemical space. For potentially active compounds, their 3D poses are generated using a state-of-the-art method. The resulting 3D complexes are further evaluated for druggability by a championing deep learning algorithm based on algebraic topology, differential geometry, and algebraic graph theories. Performed on supercomputers, the whole process took less than one week. Therefore, our GNC is an efficient new paradigm for discovering new drug candidates.

Multiprotein Dynamic Combinatorial Chemistry: A Strategy for the Simultaneous Discovery of Subfamily-Selective Inhibitors for Nucleic Acid Demethylases FTO and ALKBH3

Dynamic combinatorial chemistry (DCC) is a powerful supramolecular approach for discovering ligands for biomolecules. To date, most, if not all, biologically templated DCC systems employ only a single biomolecule to direct the self-assembly process. To expand the scope of DCC, herein, a novel multiprotein DCC strategy has been developed that combines the discriminatory power of a zwitterionic "thermal tag" with the sensitivity of differential scanning fluorimetry. This strategy is highly sensitive and could differentiate the binding of ligands to structurally similar subfamily members. Through this strategy, it was possible to simultaneously identify subfamily-selective probes against two clinically important epigenetic enzymes: FTO (7; IC₅₀ =2.6 μm) and ALKBH3 (8; IC₅₀ =3.7 μm). To date, this is the first report of a subfamily-selective ALKBH3 inhibitor. The developed strategy could, in principle, be adapted to a broad range of proteins; thus it is of broad scientific interest.

Fluorescent "keep-on" type pharmacophore obtained from dynamic combinatorial library of Schiff bases

We established a novel principle for fluorescence detection of a target protein. A low-molecular-weight fluorescent pharmacophore, as a targeted probe, was selected from a dynamic combinatorial library of Schiff bases. The pharmacophore retains its fluorescence when bound to the hydrophobic site of the target, whereas it loses it because of hydrolysis when unbound. Graphical abstract We describe a novel concept for detection of a target protein (i.e., HSA) by using a keep-on-type fluorescent pharmacophore which is discovered from a dynamic combinatorial library of Schiff bases. When the target is absent, the keep-on-pharmacophore is degraded by hydrolysis, with the result that we can see no fluorescence.

Adversarial Threshold Neural Computer for Molecular de Novo Design

In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp³-rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed in vitro validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds.

A user's guide to the thiol-thioester exchange in organic media: scope, limitations, and applications in material science

The dynamic exchange of thiols and thioesters in organic media was explored, leading to room temperature plasticity in crosslinked polymers.

Dynamic Combinatorial Chemistry to Identify Binders of ThiT, an S-Component of the Energy-Coupling Factor Transporter for Thiamine

We applied dynamic combinatorial chemistry (DCC) to identify ligands of ThiT, the S-component of the energy-coupling factor (ECF) transporter for thiamine in Lactococcus lactis. We used a pre-equilibrated dynamic combinatorial library (DCL) and saturation-transfer difference (STD) NMR spectroscopy to identify ligands of ThiT. This is the first report in which DCC is used for fragment growing to an ill-defined pocket, and one of the first reports for its application with an integral membrane protein as target.

Directing evolution: the next revolution in drug discovery?

The strong biological rationale to pursue challenging drug targets such as protein-protein interactions has stimulated the development of novel screening strategies, such as DNA-encoded libraries, to allow broader areas of chemical space to be searched. There has also been renewed interest in screening natural products, which are the result of evolutionary selection for a function, such as interference with a key signalling pathway of a competing organism. However, recent advances in several areas, such as understanding of the biosynthetic pathways for natural products, synthetic biology and the development of biosensors to detect target molecules, are now providing new opportunities to directly harness evolutionary pressure to identify and optimize compounds with desired bioactivities. Here, we describe innovations in the key components of such strategies and highlight pioneering examples that indicate the potential of the directed-evolution concept. We also discuss the scientific gaps and challenges that remain to be addressed to realize this potential more broadly in drug discovery.

Investigating isoquinoline derivatives for inhibition of inhibitor of apoptosis proteins for ovarian cancer treatment

Objective

To discover novel isoquinoline derivatives for inhibition of inhibitor of apoptosis proteins (IAP) for the treatment of ovarian cancer.

Methods

We first synthesized 533 isoquinoline derivatives, and screened them using CCK-8 to measure their antiproliferative activity. These compounds were further tested by Hoechst staining and flow cytometric analysis to assess proapoptotic activity. The in vivo antitumor efficacy and safety of the screened compounds were evaluated on the xenograft mouse model. Ki-67 staining and TUNEL assay were used to evaluate proliferation and apoptosis in the resected tumors, respectively. Western blot and polymerase chain reaction (PCR) were conducted to evaluate the levels of proliferating cell nuclear antigen (PCNA), caspase-3, PARP, and IAP in resected tumors.

Results

Compound B01002 and C26001 displayed antiproliferative and proapoptotic activity on SKOV3 ovarian cancer with an IC50 of 7.65 and 11.68 µg/mL, respectively. Both compounds inhibited tumor growth in a xenografted mouse model with good safety profiles, and tumor growth inhibition (TGI) of B01002 and C26001 was 99.53% and 84.23%, respectively. Resected tumors showed that both compounds inhibited tumor cell proliferation and induced apoptosis in vivo. Caspase-3 and PARP were activated, whereas IAP proteins were downregulated at the protein level.

Conclusion

Compound B01002 and C26001 could inhibit ovarian tumor growth and promote tumor apoptosis, partly by downregulating the IAPs, and, thus, might be promising candidates for treatment of ovarian cancer.

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.

De Novo Modular Development of a Foldameric Protein-Protein Interaction Inhibitor for Separate Hot Spots: A Dynamic Covalent Assembly Approach

Protein-protein interactions stabilized by multiple separate hot spots are highly challenging targets for synthetic scaffolds. Surface-mimetic foldamers bearing multiple recognition segments are promising candidate inhibitors. In this work, a modular bottom-up approach is implemented by identifying short foldameric recognition segments that interact with the independent hot spots, and connecting them through dynamic covalent library (DCL) optimization. The independent hot spots of a model target (calmodulin) are mapped with hexameric β-peptide helices using a pull-down assay. Recognition segment hits are subjected to a target-templated DCL ligation through thiol-disulfide exchange. The most potent derivative displays low nanomolar affinity towards calmodulin and effectively inhibits the calmodulin-TRPV1 interaction. The DCL assembly of the folded segments offers an efficient approach towards the de novo development of a high-affinity inhibitor of protein-protein interactions.