Search

PTP-PEST (also known as PTPN12) regulates cellular signaling and transduction pathways by dephosphorylating its substrate. PTP-PEST is considered an important drug target owing to its involvement in cancer progression and myocardial injury. Till now only a few inhibitors are currently being studied in the inhibition of PTP-PEST, majorly belonging to the class of metal-based drugs. In this study, we aimed to investigate small molecules that could potentially inhibit PTP-PEST for further development of PTP-PEST inhibitors. As an approach, we used an in silico molecular docking technique to screen an in-house synthesized molecular library. Further, we validated the docking results by in vitro inhibition screening of the best molecules using the purified catalytic domain of human PTP-PEST, which was over-expressed in E.coli. We identified a myo-inositol based derivative, J1-65, which binds to PTP-PEST and results in the competitive inhibition of the protein. Further, we confirmed this protein-ligand binding using binding affinity studies based on protein thermal shift assay and in silico molecular dynamic simulations. Our efforts to discover a novel scaffold for inhibiting hPTP-PEST mark a crucial stride in laying the groundwork for the future development of selective PTP-PEST inhibitors., (© 2025 John Wiley & Sons Ltd.)

Target class-focused drug discovery has a strong track record in pharmaceutical research, yet public domain data indicate that many members of protein families remain unliganded. Here we present a systematic approach to scale up the discovery and characterization of small molecule ligands for the WD40 repeat (WDR) protein family. We developed a comprehensive suite of protocols for protein production, crystallography, and biophysical, biochemical, and cellular assays. A pilot hit-finding campaign using DNA-encoded chemical library selection followed by machine learning (DEL-ML) to predict ligands from virtual libraries yielded first-in-class, drug-like ligands for 7 of the 16 WDR domains screened, thus demonstrating the broader ligandability of WDRs. This study establishes a template for evaluation of protein family wide ligandability and provides an extensive resource of WDR protein biochemical and chemical tools, knowledge, and protocols to discover potential therapeutics for this highly disease-relevant, but underexplored target class.

Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p K a ). Despite recent architectural advances, these models often generalize poorly to novel compounds due to a scarcity of ground-truth data. Further, these models lack interpretability. To this end, with deliberate molecular embeddings, atomic-resolution information is accessible in chemical structures by observing the model response to atomic perturbations of an input molecule. Here, we present BCL-XpKa, a deep neural network (DNN)-based multitask classifier for p K a prediction that encodes local atomic environments through Mol2D descriptors. BCL-XpKa outputs a discrete distribution for each molecule, which stores the p K a prediction and the model's uncertainty for that molecule. BCL-XpKa generalizes well to novel small molecules. BCL-XpKa performs competitively with modern ML p K a predictors, outperforms several models in generalization tasks, and accurately models the effects of common molecular modifications on a molecule's ionizability. We then leverage BCL-XpKa's granular descriptor set and distribution-centered output through atomic sensitivity analysis (ASA), which decomposes a molecule's predicted p K a value into its respective atomic contributions without model retraining. ASA reveals that BCL-XpKa has implicitly learned high-resolution information about molecular substructures. We further demonstrate ASA's utility in structure preparation for protein-ligand docking by identifying ionization sites in 93.2% and 87.8% of complex small molecule acids and bases. We then applied ASA with BCL-XpKa to identify and optimize the physicochemical liabilities of a recently published KRAS-degrading PROTAC.

Targeting E3 ligases is a challenging area in drug discovery. Despite the human genome encoding for more than 600 E3 ubiquitin ligases, only a handful of E3 ligases have been pharmacologically modulated or exploited for targeted protein degradation (TPD) strategies. The main obstacle for hijacking these E3 ligases is the lack of small-molecule ligands. As research into this field advances, the identification of new small molecules capable of binding to E3 ligases has become an essential pursuit. These ligases not only expand the repertoire of druggable targets but also offer the potential for increased specificity and selectivity in protein degradation. The synergy between academia and industry is key, as it combines academic expertise in fundamental research with the industrial capabilities of translating these findings into novel therapeutics. In this review, we provide an overview of the different strategies employed in academia and industry to the discovery of new E3 ligases ligands, showing them with illustrative cases., (© 2024 The Author(s). ChemBioChem published by Wiley-VCH GmbH.)

Metabolite-protein interactions have not been systematically studied due to a lack of effective techniques. Here, we present a protocol for identifying small-molecule metabolite ligands interacting with proteins. We describe steps for mixing the sample with antibodies for immunoprecipitation and applying organic solvent to extract small-molecule metabolites. We then detail procedures for using high-resolution mass spectrometry to quantify small-molecule metabolites bound to proteins. We validate protocol feasibility using the example of an arachidonic acid-Menin protein interaction. For complete details on the use and execution of this protocol, please refer to He et al. 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Pleckstrin homology (PH) domains are structural motifs critical for cellular processes, such as signal transduction and cytoskeletal organization. Due to their involvement in various diseases, PH domains are promising therapeutic targets, yet their highly charged and hydrophobic binding sites are not ideal for traditional small drugs. In this study, we designed a DNA-encoded library (DEL) mimicking phospholipids to identify novel modulators targeting PH domains with uncharted chemical properties. Screening against several PH domains led to the discovery of 2DII, a small molecule that selectively binds to mSin1 PH . This compound can modulate mTORC2 activity by impairing mTORC2's membrane interactions, resulting in reduced AKT1 phosphorylation. A micromapping via Dexter energy transfer based on 2DII bearing an iridium catalyst (2DII-Ir), along with a biotin-diazirine small molecule was used for target identification by proteomics, which confirmed mSin1 as the primary intracellular target of 2DII, demonstrating its potential for selective mTORC2 pathway modulation. These findings introduce a novel strategy for targeting PH domains and provide a foundation for the development of therapeutic interventions that modulate PH-domain-dependent signaling pathways.

Protein-ligand binding affinity prediction is a crucial and challenging task in the field of drug discovery. However, traditional simulation-based computational approaches are often prohibitively time-consuming, limiting their practical utility. In this study, we introduce a novel deep learning method, CPIScore, which leverages the capabilities of Transformer and Graph Convolutional Networks (GCN) to enhance the prediction of protein-ligand binding affinity. CPIScore utilizes the Transformer architecture to capture comprehensive global contexts of protein and ligand sequences, while the GCN component effectively extracts local features from small molecular graphs. Our results demonstrate that CPIScore surpasses both traditional machine learning and other deep learning models in accuracy, achieving a Pearson's r of 0.74 on our test set. Furthermore, CPIScore has been validated across multiple targets, proving its ability to discern inhibitors from a diverse compound library with high enrichment rates. Notably, when applied to a generated focused library of compounds, CPIScore successfully identified six potent small-molecule inhibitors of ATR, which were tested experimentally and four small molecules exhibited inhibitory activity below ten nanomoles. These results highlight CPIScore's potential to significantly streamline and enhance the efficiency of drug discovery processes.

Proteins are dynamic macromolecules. Knowledge of a protein's thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein's structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COnformational LAndscape Visualization (COLAV), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens enable detailed mapping of proteins' conformational landscapes.

Rapid equilibrium dialysis (RED) is predominantly used for the characterization of drug absorption, distribution, metabolism, and excretion (ADME) properties in plasma and biological fluids. We describe herein improvements in the use of RED in conjunction with mass spectrometry (RED-MS) to enable robust binding affinity measurements of small molecules for recombinant proteins and complexes from a single dialysis data set. The affinities calculated from RED-MS correlated well with measurements by both surface plasmon resonance (SPR) and affinity selection mass spectrometry (AS-MS). The method was particularly useful for quantifying the binding of small molecules to large protein complexes that were not amendable by common biophysical characterization techniques. Compound pooling and integration with automated liquid handling increased assay throughput and enabled the analysis of hundreds of measurements per week. RED-MS offers a viable option for measuring compound binding in solution and may facilitate small molecule affinity optimization toward difficult-to-drug protein complexes.

DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery process enabled by parallel screening of vast libraries of encoded small molecules through affinity selection and deep sequencing. While DEL selections provide rich data sets for computational drug discovery, the underlying technical factors influencing DEL data remain incompletely understood. This review systematically examines the key parameters affecting the chemical information in DEL data and their impact on hit triaging and machine learning integration. The need for rigorous data handling and interpretation is emphasized, with standardized methods being critical for the success of DEL-based approaches. Major challenges include the relationship between sequence counts and binding affinities, frequent hitters, and the influence of factors such as inhomogeneous library composition, DNA damage, and linkers on binding modes. Experimental artifacts, such as those caused by protein immobilization and screening matrix effects, further complicate data interpretation. Recent advancements in using machine learning to denoise DEL data and predict drug candidates are highlighted. This review offers practical guidance on adopting best practices for integrating robust methodologies, comprehensive data analysis, and computational tools to improve the accuracy and efficacy of DEL-driven hit discovery.

RNA is a central molecule in life, involved in a plethora of biological processes and playing a key role in many diseases. Targeting RNA emerges as a significant endeavor in drug discovery, diverging from conventional protein-centric approaches to tackle various pathologies. Whilst identifying small molecules that bind to specific RNA regions is the first step, the abundance of non-functional RNA segments renders many interactions biologically inert. Consequently, small molecule binding does not necessarily meet stringent criteria for clinical translation, calling for solutions to push the field forward. Converting RNA-binders into RNA-degraders presents a promising avenue to enhance RNA-targeting. This mini-review outlines strategies and exemplars wherein simple small molecule RNA binders are reprogrammed into active degraders through the linkage of functional groups. These approaches encompass mechanisms that induce degradation via endogenous enzymes, termed RIBOTACs, as well as those with functional moieties acting autonomously to degrade RNA. Through this exploration, we aim to offer insights into advancing RNA-targeted therapeutic strategies., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Aims: Nonalcoholic steatohepatitis (NASH) is the severe subtype of nonalcoholic fatty diseases (NAFLD) with few options for treatment. Patients with NASH exhibit partial responses to the current therapeutics and adverse effects. Identification of the binding proteins for the drugs is essential to understanding the mechanism and adverse effects of the drugs and fuels the discovery of potent and safe drugs. This paper aims to critically discuss recent advances in covalent and noncovalent approaches for identifying binding proteins that mediate NASH progression, along with an in-depth analysis of the mechanisms by which these targets regulate NASH., Materials and Methods: A literature search was conducted to identify the relevant studies in the database of PubMed and the American Chemical Society. The search covered articles published from January 1990 to July 2024, using the search terms with keywords such as NASH, benzophenone, diazirine, photo-affinity labeling, thermal protein profiling, CETSA, target identification., Key Findings: The covalent approaches utilize drugs modified with diazirine and benzophenone to covalently crosslink with the target proteins, which facilitates the purification and identification of target proteins. In addition, they map the binding sites in the target proteins. By contrast, noncovalent approaches identify the binding targets of unmodified drugs in the intact cell proteome. The advantages and limitations of both approaches have been compared, along with a comprehensive analysis of recent innovations that further enhance the efficiency and specificity., Significance: The analyses of the applicability of these approaches provide novel tools to delineate NASH pathogenesis and promote drug discovery., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

In 2020, cancer-related deaths reached 9.96 million globally, of which China accounted for 3 million, ranking first in the world. Phosphoglycerate mutase 1 (PGAM1) is a key metabolic enzyme in glycolysis, catalysing the conversion of 3-phosphoglycerate to 2-phosphoglycerate. Based on the excellent anticancer activity of PGMI-004A and HKB99, new small molecules with an anthraquinone core were synthesised to inhibit tumour growth. Developing small molecules with an anthraquinone core targeting PGAM1 may be an effective strategy for treating cancer. In this study, accelerated molecular dynamics (aMD) simulation, dynamic cross-correlation map (DCCM) calculation, principal component analysis (PCA) and free energy landscape (FEL) analysis were used to analyse conformational changes of PGAM1 caused by binding of inhibitors 8KX, 9HU and HKB. DCCM calculations and PCA showed that inhibitor binding significantly affected the kinetic behaviour of PGAM1 and conformational rearrangement of PGAM1. The binding ability and mechanism of 8KX, 9HU and HKB to PGAM1 were studied using the molecular mechanics generalised Born surface area (MM-GBSA) method. The results showed that compared with 8KX, the binding ability of 9HU and HKB to PGAM1 was enhanced by sulphonamide reversal and aminocarboxyl trifluoromethyl substitution. There were several hydrophobic interactions between inhibitors and PGAM1, providing significant contributions for inhibitor binding. Calculation of residue-based free energy decomposition revealed that F22, R90, Y92, L95, V112, W115, R116, V121, P123, P124, R191 and M206 were key residues of the PGAM1-inhibitor interaction and could be used as effective targets for designing drugs that inhibit the activity of PGAM1.

Thyroid hormone (TH) carrier proteins play an important role in distributing TH to target tissue as well as maintaining the balance of free versus bound TH in the blood. Interference with the TH carrier proteins has been identified as a potential mechanism of thyroid system disruption. To address the lack of data regarding chemicals binding to these carrier proteins and displacing TH, a fluorescence-based in vitro screening assay was utilized to screen over 1,400 chemicals from the U.S. EPA's ToxCast phase1&#95;v2, phase 2, and e1k libraries for competitive binding to one of the carrier proteins, thyroxine-binding globulin. Initial screening at a single high concentration of 100 μM identified 714 chemicals that decreased signal of the bound fluorescent ligand by 20% or higher. Of these, 297 produced 50% or greater reduction in fluorescence and were further tested in concentration-response (0.004 to 150 μM) to determine relative potency. Ten chemicals were found to have EC50 values <1 μM, 63 < 10 μM, and 141 chemicals between 10 and 100 μM. Utilization of this assay contributes to expanding the number of in vitro assays available for identifying chemicals with the potential to disrupt TH homeostasis. These results support ranking and prioritization of chemicals to be tested in vivo to aid in the development of a framework for predicting in vivo effects from in vitro high-throughput data.

Transthyretin (TTR) is one of the serum binding proteins responsible for transport of thyroid hormones (TH) to target tissue and for maintaining the balance of available TH. Chemical binding to TTR and subsequent displacement of TH has been identified as an end point in screening chemicals for potential disruption of the thyroid system. To address the lack of data regarding chemicals binding to TTR, we optimized an in vitro assay utilizing the fluorescent probe 8-anilino-1-napthalenesulfonic acid (ANSA) and the human protein TTR to screen over 1500 chemicals from the U.S. EPA's ToxCast ph1&#95;v2, ph2, and e1k libraries utilizing a tiered approach. Testing of a single high concentration (target 100 μM) resulted in 888 chemicals with 20% or greater activity based on displacement of ANSA from TTR. Of these, 282 chemicals had activity of 85% or greater and were further tested in 12-point concentration-response with target concentrations ranging from 0.015 to 100 μM. An EC50 was obtained for 276 of these 301 chemicals. To date, this is the largest set of chemicals screened for binding to TTR. Utilization of this assay is a significant contribution toward expanding the suite of in vitro assays used to identify chemicals with the potential to disrupt thyroid hormone homeostasis.

Make-on-demand chemical libraries have drastically increased the reach of molecular docking, with the enumerated ready-to-dock ZINC-22 library approaching 6.4 billion molecules (July 2024). While ever-growing libraries result in better-scoring molecules, the computational resources required to dock all of ZINC-22 make this endeavor infeasible for most. Here, we organize and traverse chemical space with hierarchical navigable small-world graphs, a method we term retrieval augmented docking (RAD). RAD recovers most virtual actives, despite docking only a fraction of the library. Furthermore, RAD is protein-agnostic, supporting additional docking campaigns without additional computational overhead. In depth, we assess RAD on published large-scale docking campaigns against D4 and AmpC spanning 99.5 million and 138 million molecules, respectively. RAD recovers 95% of DOCK virtual actives for both targets after evaluating only 10% of the libraries. In breadth, RAD shows widespread applicability against 43 DUDE-Z proteins, evaluating 50.3 million associations. On average, RAD recovers 87% of virtual actives while docking 10% of the library without sacrificing chemical diversity.

The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence and secondary structure features and neglect small molecule characteristics, and resulting in poor performance on unknown small molecule testing. To overcome this limitation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule binding preferences by learning RNA and small molecule features to capture their interaction information. ZHMol-RLinter also combines sequence and secondary structural features with structural geometric and physicochemical environment information to capture the specificity of RNA spatial conformations in recognizing small molecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on the published RL98 testing set, representing a significant improvement over existing methods. Additionally, ZHMol-RLinter achieved a success rate of 77.1% on the unknown small molecule UNK96 testing set, showing substantial improvement over the existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule binding preferences, even for challenging unknown small molecule testing. Predicting RNA-small molecule binding preferences can help in the understanding of RNA-small molecule interactions and promote the design of RNA-related drugs for biological and medical applications.

Quantifying small molecule uptake across a biological membrane of a target cell is crucial for the development of efficacious and selective drugs. However, current methods to obtaining such data are not trivial. Herein, we present an accessible, higher-throughput (20 minutes), 1 H NMR spectroscopy assay, which enables the quantification of small molecule phospholipid passive membrane permeation and membrane adhesion parameters.

While large library docking has discovered potent ligands for multiple targets, as the libraries have grown the hit lists can become dominated by rare artifacts that cheat our scoring functions. Here, we investigate rescoring top-ranked docked molecules with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation as cross-filters. In retrospective studies, this approach deprioritized high-ranking nonbinders for nine targets while leaving true ligands relatively unaffected. We tested the method prospectively against hits from docking against AmpC β-lactamase. We prioritized 128 high-ranking molecules for synthesis and testing, a mixture of 39 molecules flagged as likely cheaters and 89 that were plausible inhibitors. None of the predicted cheating compounds inhibited AmpC detectably, while 57% of the 89 plausible compounds did so. As our libraries continue to grow, deprioritizing docking artifacts by rescoring with orthogonal methods may find wide use.

RNA molecules, similar to proteins, fold into complex structures to confer diverse functions in cells. The intertwining of functions with RNA structures offers a new therapeutic opportunity for small molecules to bind and manipulate disease-relevant RNA pathways, thus creating a therapeutic realm of RNA-binding small molecules. The ongoing interest in RNA targeting and subsequent screening campaigns have led to the identification of numerous compounds that can regulate RNAs from splicing, degradation to malfunctions, with therapeutic benefits for a variety of diseases. Moreover, along with the rise of RNA-based therapeutics, RNA-binding small molecules have expanded their application to the modification, regulation, and delivery of RNA drugs, leading to the burgeoning interest in this field. This Perspective overviews the emerging roles of RNA-binding small molecules in drug discovery and delivery, covering aspects from their action fundamentals to therapeutic applications, which may inspire researchers to advance the field.

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions. To address this limitation, we developed a new deep learning model based on spatial correlation, ZHmolReSTasite, which can accurately predict binding nucleotides of small and large RNA with junctions. We utilize RNA surface topography to consider the spatial correlation, characterizing nucleotides from sequence and tertiary structures to learn a high-level representation. Our method outperforms existing methods for benchmark test sets composed of simple RNA structures, achieving precision values of 72.9% on TE18 and 76.7% on RB9 test sets. For a challenging test set composed of RNA structures with junctions, our method outperforms the second best method by 11.6% in precision. Moreover, ZHmolReSTasite demonstrates robustness regarding the predicted RNA structures. In summary, ZHmolReSTasite successfully incorporates spatial correlation, outperforms previous methods on small and large RNA structures using RNA surface topography, and can provide valuable insights into RNA small molecule prediction and accelerate RNA inhibitor design.

Accurate in silico predictions of how strongly small molecules bind to proteins, such as those afforded by relative binding free energy (RBFE) calculations, can greatly increase the efficiency of the hit-to-lead and lead optimization stages of the drug discovery process. The success of such calculations, however, relies heavily on their precision. Here, we show that a recently developed alchemical enhanced sampling (ACES) approach can consistently improve the precision of RBFE calculations on a large and diverse set of proteins and small molecule ligands. The addition of ACES to conventional RBFE calculations lowered the average hysteresis by over 35% (0.3-0.4 kcal/mol) and the average replicate spread by over 25% (0.2-0.3 kcal/mol) across a set of 10 protein targets and 213 small molecules while maintaining similar or improved accuracy. We show in atomic detail how ACES improved convergence of several representative RBFE calculations through enhancing the sampling of important slowly transitioning ligand degrees of freedom.

Genomic technologies have led to massive gains in our understanding of human gene function and disease relevance. Chemical biologists are a primary beneficiary of this information, which can guide the prioritization of proteins for chemical probe and drug development. The vast functional and structural diversity of disease-relevant proteins, however, presents challenges for conventional small molecule screening libraries and assay development that in turn raise questions about the broader "druggability" of the human proteome. Here, we posit that activity-based protein profiling (ABPP), by generating global maps of small molecule-protein interactions in native biological systems, is well positioned to address major obstacles in human biology-guided chemical probe and drug discovery. We will support this viewpoint with case studies highlighting a range of small molecule mechanisms illuminated by ABPP that include the disruption and stabilization of biomolecular (protein-protein/nucleic acid) interactions and underscore allostery as a rich source of chemical tools for historically "undruggable" protein classes., Competing Interests: Declaration of interests Dr. Cravatt is a member of the Board of Directors of Vividion Therapeutics., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Targeted protein degradation through the lysosomal pathway has attracted increasing attention and expanded the scope of degradable proteins. However, the endogenous lysosomal degradation strategies are mainly based on antibodies or nanobodies. Effective small molecule lysosomal degraders are still rather rare. Herein, a new lysosomal degradation approach, termed peptide-mediated small molecule lysosome-targeting chimeras (PSMLTACs), was developed by the incorporation of small molecule ligands with a lysosome-sorting NPGY motif containing the cell-penetrating peptide. PSMLTACs were successfully applied to degrade both membrane and intracellular targets. In particular, the PSMLTAC strategy demonstrated higher degradation efficiency on membrane target PD-L1 and intracellular target PDEδ than corresponding PROTAC degraders. Taken together, this proof-of-concept provides a convenient and effective strategy for targeted protein degradation.

Type I melanoma antigen (MAGE) family members are detected in numerous tumor types, and expression is correlated with poor prognosis, high tumor grade, and increased metastasis. Type I MAGE proteins are typically restricted to reproductive tissues, but expression can recur during tumorigenesis. Several biochemical functions have been elucidated for them, and notably, MAGEs regulate proteostasis by serving as substrate recognition modules for E3 ligase complexes. The repertoire of E3 ligase complexes that can be hijacked for targeted protein degradation continues to expand, and MAGE-E3 complexes are an especially attractive platform given their cancer-selective expression. Additionally, type I MAGE-derived peptides are presented on cancer cell surfaces, so targeted MAGE degradation may increase antigen presentation and improve immunotherapy outcomes. Motivated by these applications, we developed novel, small-molecule ligands for MAGE-A3, a type I MAGE that is widely expressed in tumors and associates with TRIM28, a RING E3 ligase. Chemical matter was identified through DNA-encoded library (DEL) screening, and hit compounds were validated for in vitro binding to MAGE-A3. We obtained a cocrystal structure with a DEL analog and hypothesize that the small molecule binds at a dimer interface. We utilized this ligand to develop PROTAC molecules that induce MAGE-A3 degradation through VHL recruitment and inhibit the proliferation of MAGE-A3 positive cell lines. These ligands and degraders may serve as valuable probes for investigating MAGE-A3 biology and as foundations for the ongoing development of tumor-specific PROTACs.

We present a computational scheme for predicting the ligands that bind to a pocket of a known structure. It is based on the generation of a general abstract representation of the molecules, which is invariant to rotations, translations, and permutations of atoms, and has some degree of isometry with the space of conformations. We use these representations to train a nondeep machine learning algorithm to classify the binding between pockets and molecule pairs and show that this approach has a better generalization capability than existing methods.

Ligand binding hotspots are regions of protein surfaces that form particularly favourable interactions with small molecule pharmacophores. Targeting interactions with these hotspots maximises the efficiency of ligand binding. Existing methods are capable of identifying hotspots but often lack assays to quantify ligand binding and direct elaboration at these sites. Herein, we describe a fragment-based competitive 19 F Ligand Based NMR (LB-NMR) screening platform that enables routine, quantitative ligand profiling focused at ligand-binding hotspots. As a proof of concept, the method was applied to 4'-phosphopantetheine adenylyltransferase (PPAT) from Mycobacterium abscessus (Mabs). X-ray crystallographic characterisation of the hits from a 960-member fragment screen identified three ligand-binding hotspots across the PPAT active site. From the fragment hits a collection of 19 F reporter candidates were designed and synthesised. By rigorous prioritisation and use of optimisation workflows, a single 19 F reporter molecule was generated for each hotspot. Profiling the binding of a set of structurally characterised ligands by competitive 19 F LB-NMR with this suite of 19 F reporters recapitulated the binding affinity and site ID assignments made by ITC and X-ray crystallography. This quantitative mapping of ligand binding events at hotspot level resolution establishes the utility of the fragment-based competitive 19 F LB-NMR screening platform for hotspot-directed ligand profiling., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Recent advances in chemical proteomics have focused on developing chemical probes that react with nucleophilic amino acid residues. Although histidine is an attractive candidate due to its importance in enzymatic catalysis, metal binding and protein-protein interaction, its moderate nucleophilicity poses challenges. Its modification is frequently influenced by cysteine and lysine, which results in poor selectivity and narrow proteome coverage. Here we report a singlet oxygen and chemical probe relay labelling method that achieves high selectivity towards histidine. Libraries of small-molecule photosensitizers and chemical probes were screened to optimize histidine labelling, enabling histidine profiling in live cells with around 7,200 unique sites. Using NMR spectroscopy and X-ray crystallography, we characterized the reaction mechanism and the structures of the resulting products. We then applied this method to discover unannotated histidine sites key to enzymatic activity and metal binding in select metalloproteins. This method also revealed the accessibility change of histidine mediated by protein-protein interaction that influences select protein subcellular localization, underscoring its capability in discovering functional histidines., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Hydrogen-deuterium eXchange mass spectrometry (HDX-MS) is increasingly used in drug development to locate binding sites and to identify allosteric effects in drug/target interactions. However, the potential of this technique to quantitatively analyze drug candidate libraries remains largely unexplored. Here, a collection of 13 WDR5-targeting small molecules with surface plasmon resonance (SPR) dissociation coefficients ( K D ) ranging from 20 nM to ∼116 μM were characterized using differential HDX-MS (ΔHDX-MS). Conventional qualitative analysis of the ΔHDX-MS data set revealed the binding interfaces for all compounds and allosteric effects where present. We then demonstrated that ΔHDX-MS signal-to-noise (S/N) not only can rank library-relative affinity but also can accurately predict K D from a calibration curve constructed from high-quality SPR data. Three methods for S/N calculation are explored, each suitable for libraries with different characteristics. Our results demonstrate the potential for ΔHDX-MS use in drug candidate library affinity validation and/or determination while simultaneously characterizing structure.

Interactions between proteins and small organic compounds play a crucial role in regulating protein functions. These interactions can modulate various aspects of protein behavior, including enzymatic activity, signaling cascades, and structural stability. By binding to specific sites on proteins, small organic compounds can induce conformational changes, alter protein-protein interactions, or directly affect catalytic activity. Therefore, many drugs available on the market today are small molecules (72% of all approved drugs in the last 5 years). Proteins are composed of one or more domains: evolutionary units that convey function or fitness either singly or in concert with others. Understanding which domain(s) of the target protein binds to a drug can lead to additional opportunities for discovering novel targets. The evolutionary classification of protein domains (ECOD) classifies domains into an evolutionary hierarchy that focuses on distant homology. Previously, no structure-based protein domain classification existed that included information about both the interaction between small molecules or drugs and the structural domains of a target protein. This data is especially important for multidomain proteins and large complexes. Here, we present the DrugDomain database that reports the interaction between ECOD of human target proteins and DrugBank molecules and drugs. The pilot version of DrugDomain describes the interaction of 5160 DrugBank molecules associated with 2573 human proteins. It describes domains for all experimentally determined structures of these proteins and incorporates AlphaFold models when such structures are unavailable. The DrugDomain database is available online: http://prodata.swmed.edu/DrugDomain/., (© 2024 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

The determination of k on and k off values through kinetic analysis is crucial for understanding the intricacies of aptamer-target binding interactions. By employing kinetic ITC, we systematically analyzed a range of ITC data of various aptamers. Upon plotting their k on and k off values as a function of their K d values, a notable trend emerged. Across a range of K d values spanning from 28 nM to 864 μM, the k on value decreased from 2×10 5  M -1  s -1 to 96 M -1  s -1 , whereas the k off value increased from 1.03×10 -3  s -1 to 0.012 s -1 . Thus, both k on and k off contributed to the change of K d in the same direction, although the range of k on change was larger. Since experiments are often run at close to the K d value, this concentration effect also played an important role in the observed binding kinetics. The effect of these kinetic parameters on two common sensing mechanisms, including aptamer beacons and strand-displacement assays, are discussed. This work has provided the kinetic values of small molecule binding aptamers and offered insights into aptamer-based biosensors., (© 2024 The Author(s). ChemBioChem published by Wiley-VCH GmbH.)

As organoids and organ-on-chip (OoC) systems move toward preclinical and clinical applications, there is an increased need for method validation. Using a liquid chromatography-mass spectrometry (LC-MS)-based approach, we developed a method for measuring small-molecule drugs and metabolites in the cell medium directly sampled from liver organoids/OoC systems. The LC-MS setup was coupled to an automatic filtration and filter flush system with online solid-phase extraction (SPE), allowing for robust and automated sample cleanup/analysis. For the matrix, rich in, e.g., protein, salts, and amino acids, no preinjection sample preparation steps (protein precipitation, SPE, etc.) were necessary. The approach was demonstrated with tolbutamide and its liver metabolite, 4-hydroxytolbutamide (4HT). The method was validated for analysis of cell media of human stem cell-derived liver organoids cultured in static conditions and on a microfluidic platform according to Food and Drug Administration (FDA) guidelines with regards to selectivity, matrix effects, accuracy, precision, etc. The system allows for hundreds of injections without replacing chromatography hardware. In summary, drug/metabolite analysis of organoids/OoCs can be performed robustly with minimal sample preparation.

Protein-protein interactions (PPIs) are of utmost importance for maintenance of cellular homeostasis. Herein, a central role can be found for 14-3-3 proteins. These hub-proteins are known to bind hundreds of interaction partners, thereby regulating their activity, localization, and/or stabilization. Due to their ability to bind a large variety of client proteins, studies of 14-3-3 protein complexes flourished over the last decades, aiming to gain greater molecular understanding of these complexes and their role in health and disease. Because of their crucial role within the cell, 14-3-3 protein complexes are recognized as highly interesting therapeutic targets, encouraging the discovery of small molecule modulators of these PPIs. We discuss various examples of 14-3-3-mediated regulation of its binding partners on a mechanistic level, highlighting the versatile and multi-functional role of 14-3-3 within the cell. Furthermore, an overview is given on the development of stabilizers of 14-3-3 protein complexes, from initially used natural products to fragment-based approaches. These studies show the potential of 14-3-3 PPI stabilizers as novel agents in drug discovery and as tool compounds to gain greater molecular understanding of the role of 14-3-3-based protein regulation., (© 2024 The Authors. ChemBioChem published by Wiley-VCH GmbH.)

β-Glucocerebrosidase (GBA/GCase) mutations leading to misfolded protein cause Gaucher's disease and are a major genetic risk factor for Parkinson's disease and dementia with Lewy bodies. The identification of small molecule pharmacological chaperones that can stabilize the misfolded protein and increase delivery of degradation-prone mutant GCase to the lysosome is a strategy under active investigation. Here, we describe the first use of fragment-based drug discovery (FBDD) to identify pharmacological chaperones of GCase. The fragment hits were identified by using X-ray crystallography and biophysical techniques. This work led to the discovery of a series of compounds that bind GCase with nM potency and positively modulate GCase activity in cells.

Synthetic modulators of plant 14-3-3s are promising chemical tools both for understanding the 14-3-3-related signaling pathways and controlling plant physiology. Herein, we describe a novel small-molecule inhibitor for 14-3-3 proteins of Arabidopsis thaliana. The inhibitor was identified from unexpected products in a stock solution in dimethyl sulfoxide (DMSO) of an in-house chemical library. Mass spectroscopy, mutant-based analyses, fluorescence polarization assays, and thermal shift assays revealed that the inhibitor covalently binds to an allosteric site of 14-3-3 with isoform selectivity. Moreover, infiltration of the inhibitor to Arabidopsis leaves suppressed the stomatal aperture. The inhibitor should provide new insight into the design of potent and isoform-selective 14-3-3 modulators., (© 2024 Wiley-VCH GmbH.)

We present a novel and interpretable approach for assessing small-molecule binding using context explanation networks. Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to predict the contributions of precalculated terms to the overall binding affinity. We show that CENsible can effectively distinguish active vs inactive compounds for many systems. Its primary benefit over related machine-learning scoring functions, however, is that it retains interpretability, allowing researchers to identify the contribution of each precalculated term to the final affinity prediction, with implications for subsequent lead optimization.

Comprehensive characterization of small-molecule degraders, including binary and ternary complex formation and degradation efficiency, is critical for bifunctional ligand development and understanding structure-activity relationships. Here, we present a protocol for the biochemical and cellular profiling of small-molecule degraders based on CoraFluor time-resolved fluorescence resonance energy transfer (TR-FRET) technology. We describe steps for labeling antibodies and proteins, tracer saturation binding, binary target engagement, ternary complex profiling, and off-rate determination. We then detail procedures for the quantification of endogenous and GFP fusion proteins in cell lysates. For complete details on the use and execution of this protocol, please refer to Ichikawa et al. 1 ., Competing Interests: Declaration of interests C.M.W. and S.I. are inventors of a patent application filed by Harvard University covering the chemical structures of cyclimids and their use. R.M. and N.C.P. are inventors on patent applications related to the CoraFluor TR-FRET technology used in this work., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Macrocycles offer an attractive format for drug development due to their good binding properties and potential to cross cell membranes. To efficiently identify macrocyclic ligands for new targets, methods for the synthesis and screening of large combinatorial libraries of small cyclic peptides were developed, many of them using thiol groups for efficient peptide macrocyclization. However, a weakness of these libraries is that invariant thiol-containing building blocks such as cysteine are used, resulting in a region that does not contribute to library diversity but increases molecule size. Herein, we synthesized a series of structurally diverse thiol-containing elements and used them for the combinatorial synthesis of a 2,688-member library of small, structurally diverse peptidic macrocycles with unprecedented skeletal complexity. We then used this library to discover potent thrombin and plasma kallikrein inhibitors, some also demonstrating favorable membrane permeability. X-ray structure analysis of macrocycle-target complexes showed that the size and shape of the newly developed thiol elements are key for binding. The strategy and library format presented in this work significantly enhance structural diversity by allowing combinatorial modifications to a previously invariant region of peptide macrocycles, which may be broadly applied in the development of membrane permeable therapeutics., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

The modulation of protein-protein interactions with small molecules is one of the most rapidly developing areas in drug discovery. In this review, we discuss advances over the past decade (2014-2023) focusing on molecular glues (MGs)-monovalent small molecules that induce proximity, either by stabilizing native interactions or by inducing neomorphic interactions. We include both serendipitous and rational discoveries and describe the different approaches that were used to identify them. We classify the compounds in three main categories: degradative MGs, non-degradative MGs or PPI stabilizers, and MGs that induce self-association. Diverse, illustrative examples with structural data are described in detail, emphasizing the elements of molecular recognition and cooperative binding at the interface that are fundamental for a MG mechanism of action., Competing Interests: Declaration of interest M.R.A. is a co-founder and consultant of Ambagon Therapeutics., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Molecular glue (MG) degraders are monovalent small molecule compounds that co-opt E3 ubiquitin ligases to target neo-substrates for proteasomal degradation. Here, we provide a concise review of recent advances in rational MG discovery, which are categorized into two major strategies, ligand modification and de novo discovery. We also highlight the structural mechanisms underlying the formation of MG-enabled ternary complexes and their thermodynamic properties. Finally, we summarize the broader category of proximity inducers including MGs, proteolysis-targeting chimeras (PROTACs), peptides, and viral proteins. MGs are specified as a unique class of proximity inducers with chemical simplicity and a requirement of pre-existing weak protein-protein interactions. We propose that leveraging the weak basal interaction provides a starting point to prospectively develop MGs to degrade high-value therapeutic targets., Competing Interests: Declaration of competing interest N.Z. is a scientific cofounder of and has financial interests in SEED Therapeutics. N.Z. serves as a member of the scientific advisory board of SyntheX with financial interests. The authors declare no other competing interests., (Published by Elsevier Ltd.)

Hybrid small-molecule/protein fluorescent probes are powerful tools for visualizing protein localization and function in living cells. These hybrid probes are constructed by diverse site-specific chemical protein labeling approaches through chemical reactions to exogenous peptide/small protein tags, enzymatic post-translational modifications, bioorthogonal reactions for genetically incorporated unnatural amino acids, and ligand-directed chemical reactions. The hybrid small-molecule/protein fluorescent probes are employed for imaging protein trafficking, conformational changes, and bioanalytes surrounding proteins. In addition, fluorescent hybrid probes facilitate visualization of protein dynamics at the single-molecule level and the defined structure with super-resolution imaging. In this review, we discuss development and the bioimaging applications of fluorescent probes based on small-molecule/protein hybrids.

Although transition metals constitute less than 0.1% of the total mass within a human body, they have a substantial impact on fundamental biological processes across all kingdoms of life. Indeed, these nutrients play crucial roles in the physiological functions of enzymes, with the redox properties of many of these metals being essential to their activity. At the same time, imbalances in transition metal pools can be detrimental to health. Modern analytical techniques are helping to illuminate the workings of metal homeostasis at a molecular and atomic level, their spatial localization in real time, and the implications of metal dysregulation in disease pathogenesis. Fluorescence microscopy has proven to be one of the most promising non-invasive methods for studying metal pools in biological samples. The accuracy and sensitivity of bioimaging experiments are predominantly determined by the fluorescent metal-responsive sensor, highlighting the importance of rational probe design for such measurements. This review covers activity- and binding-based fluorescent metal sensors that have been applied to cellular studies. We focus on the essential redox-active metals: iron, copper, manganese, cobalt, chromium, and nickel. We aim to encourage further targeted efforts in developing innovative approaches to understanding the biological chemistry of redox-active metals.

Targeted protein degradation (TPD) has recently emerged as an exciting new drug modality. However, the strategy of developing small molecule-based protein degraders has evolved over the past two decades and has now established molecular tags that are already in clinical use, as well as chimeric molecules, PROteolysis TArgeting Chimeras (PROTACs), based mainly on ligand systems developed for the two E3 ligases CRBN and VHL. The large size of the human E3 ligase family suggests that PROTACs can be developed by targeting a large diversity of E3 ligases, some of which have restricted expression patterns with the potential to design disease- or tissue-specific degraders. Indeed, many new E3 ligands have been published recently, confirming the druggability of E3 ligases. This review summarises recent data on E3 ligases and highlights the challenges in developing these molecules into efficient PROTACs rivalling the established degrader systems., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Stefan Knapp reports financial support was provided by Innovative Health Initiative. Martin Schwalm reports financial support was provided by German Research Foundation. Stefan Knapp reports a relationship with German Cancer Foundation that includes: board membership and travel reimbursement. Advisory board member of Recludix Pharmaceuticals If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.]., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Cytochrome P450 enzymes (CYPs) play a crucial role in Phase I drug metabolism in the human body, and CYP activity toward compounds can significantly affect druggability, making early prediction of CYP activity and substrate identification essential for therapeutic development. Here, we established a deep learning model for assessing potential CYP substrates, DeepP450, by fine-tuning protein and molecule pretrained models through feature integration with cross-attention and self-attention layers. This model exhibited high prediction accuracy (0.92) on the test set, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.89 to 0.98 in substrate/nonsubstrate predictions across the nine major human CYPs, surpassing current benchmarks for CYP activity prediction. Notably, DeepP450 uses only one model to predict substrates/nonsubstrates for any of the nine CYPs and exhibits certain generalizability on novel compounds and different categories of human CYPs, which could greatly facilitate early stage drug design by avoiding CYP-reactive compounds.

Background: A safe, effective, and reversible nonhormonal male contraceptive drug is greatly needed for male contraception as well as for circumventing the side effects of female hormonal contraceptives. Phosducin-like 2 (PDCL2) is a testis-specific phosphoprotein in mice and humans. We recently found that male PDCL2 knockout mice are sterile due to globozoospermia caused by impaired sperm head formation, indicating that PDCL2 is a potential target for male contraception. Herein, our study for the first time developed a biophysical assay for PDCL2 allowing us to screen a series of small molecules, to study structure-activity relationships, and to discover two PDCL2 binders with novel chemical structure., Objective: To identify a PDCL2 ligand for therapeutic male contraception, we performed DNA-encoded chemical library (DECL) screening and off-DNA hit validation using a unique affinity selection mass spectrometry (ASMS) biophysical profiling strategy., Materials and Methods: We employed the screening process of DECL, which contains billions of chemically unique DNA-barcoded compounds generated through individual sequences of reactions and different combinations of functionalized building blocks. The structures of the PDCL2 binders are proposed based on the sequencing analysis of the DNA barcode attached to each individual DECL compound. The proposed structure is synthesized through multistep reactions. To confirm and determine binding affinity between the DECL identified molecules and PDCL2, we developed an ASMS assay that incorporates liquid chromatography with tandem mass spectrometry (LC-MS/MS)., Results: After a screening process of PDCL2 with DECLs containing >440 billion compounds, we identified a series of hits. The selected compounds were synthesized as off-DNA small molecules, characterized by spectroscopy data, and subjected to our ASMS/LC-MS/MS binding assay. By this assay, we discovered two novel compounds, which showed good binding affinity for PDCL2 in comparison to other molecules generated in our laboratory and which were further confirmed by a thermal shift assay., Discussion and Conclusion and Relevance: With the ASMS/LC-MS/MS assay developed in this paper, we successfully discovered a PDCL2 ligand that warrants further development as a male contraceptive., (© 2022 The Authors. Andrology published by Wiley Periodicals LLC on behalf of American Society of Andrology and European Academy of Andrology.)

Duchenne muscular dystrophy is a lethal muscle disease, caused by mutations in the gene encoding dystrophin, an actin-binding cytoskeletal protein. Absence of functional dystrophin results in muscle weakness and degeneration, eventually leading to cardiac and respiratory failure. Strategies to replace the missing dystrophin via gene therapy have been intensively pursued. However, the dystrophin gene is too large for current gene therapy approaches. Currently available micro-dystrophin constructs lack the actin-binding domain 2 and show decreased actin-binding affinity in vitro compared to full-length dystrophin. Thus, increasing the actin-binding affinity of micro-dystrophin, using small molecules, could be a beneficial therapeutic approach. Here, we have developed and validated a novel high-throughput screening (HTS) assay to discover small molecules that increase the binding affinity of dystrophin's actin-binding domain 1 (ABD1). We engineered a novel FRET biosensor, consisting of the mClover3, fluorescent protein (donor) attached to the C-terminus of dystrophin ABD1, and Alexa Fluor 568 (acceptor) attached to the C-terminal cysteine of actin. We used this biosensor in small-molecule screening, using a unique high-precision, HTS fluorescence lifetime assay, identifying several compounds from an FDA-approved library that significantly increase the binding between actin and ABD1. This HTS assay establishes feasibility for the discovery of small-molecule modulators of the actin-dystrophin interaction, with the ultimate goal of developing therapies for muscular dystrophy., Competing Interests: Conflict of interest D. D. T. holds equity in and serves as President of Photonic Pharma LLC. This relationship has been reviewed and managed by the University of Minnesota. Photonic Pharma had no role in this study, except to provide instrumentation, as stated in Experimental procedures. J. M. M., J. M. E., and D. D. T. are entitled to royalties from Sarepta Therapeutics, the company sponsoring this research project. This royalty interest has been reviewed and managed according to the University of Minnesota's conflict of interest policies. J. M. E. has received compensation for consulting for Sarepta. This relationship has been reviewed and managed by the University of Minnesota in accordance with its conflict of interest polices. All other authors declare no conflicts of interest with the contents of the article., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Natural Killer Group 2D (NKG2D) is a homo-dimeric transmembrane protein which is typically expressed on the surface of natural killer (NK) cells, natural killer T (NKT) cells, gamma delta T (γδT) cells, activated CD8 positive T-cells and activated macrophages. Bispecific molecules, capable of bridging NKG2D with a target protein expressed on the surface of tumor cells, may be used to redirect the cytotoxic activity of NK-cells towards antigen-positive malignant T-cells. In this work, we report the discovery of a novel NKG2D small molecule binder [K D =(410±60) nM], isolated from a DNA-Encoded Chemical Library (DEL). The discovery of small organic NKG2D ligands may facilitate the generation of fully synthetic bispecific adaptors, which may serve as an alternative to bispecific antibody products and which may benefit from better tumor targeting properties., (© 2022 Wiley-VCH GmbH.)

ATP synthases are unique rotatory molecular machines that supply biochemical reactions with adenosine triphosphate (ATP)-the universal "currency", which cells use for synthesis of vital molecules and sustaining life. ATP synthases of F-type (F O F 1 ) are found embedded in bacterial cellular membrane, in thylakoid membranes of chloroplasts, and in mitochondrial inner membranes in eukaryotes. The main functions of ATP synthases are control of the ATP synthesis and transmembrane potential. Although the key subunits of the enzyme remain highly conserved, subunit composition and structural organization of ATP synthases and their assemblies are significantly different. In addition, there are hypotheses that the enzyme might be involved in the formation of the mitochondrial permeability transition pore and play a role in regulation of the cell death processes. Dysfunctions of this enzyme lead to numerous severe disorders with high fatality levels. In our review, we focus on F O F 1 -structure-based approach towards development of new therapies by using F O F 1 structural features inherited by the representatives of this enzyme family from different taxonomy groups. We analyzed and systematized the most relevant information about the structural organization of F O F 1 to discuss how this approach might help in the development of new therapies targeting ATP synthases and design tools for cellular bioenergetics control., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The fluorescence properties of an emissive guanine surrogate, thienoguanine (thGN, 2-aminothieno[3,4- d ]pyrimidin-4(3 H )-one), were exploited to design two real-time chemosensors of O 6 -methylguanine-DNA-methyltransferase (MGMT), a key DNA repair enzyme involved in the resistance to DNA-alkylating anti-cancer drugs though direct reversal of O 6 -alkylated guanine adducts.