Discovery of proqodine A derivatives with antitumor activity targeting NAD(P)H: quinone oxidoreductase 1 and nicotinamide phosphoribosyltransferase.

NAD(P)H: quinone oxidoreductase 1 (NQO1) is a flavin protease highly expressed in various cancer cells. NQO1 catalyzes a futile redox cycle in substrates, leading to substantial reactive oxygen species (ROS) production. This ROS generation results in extensive DNA damage and elevated poly (ADP-ribose) polymerase 1 (PARP1)-mediated consumption of nicotinamide adenine dinucleotide (NAD+), ultimately causing cell death. Nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in the NAD+ salvage synthesis pathway, emerges as a critical target in cancer therapy. The concurrent inhibition of NQO1 and NAMPT triggers hyperactivation of PARP1 and intensive NAD+ depletion. In this study, we designed, synthesized, and assessed a novel series of proqodine A derivatives targeting both NQO1 and NAMPT. Among these, compound T8 demonstrated potent antitumor properties. Specifically, T8 selectively inhibited the proliferation of MCF-7 cells and induced apoptosis through mechanisms dependent on both NQO1 and NAMPT. This discovery offers a promising new molecular entity for advancing anticancer research.

Exhaustively Exploring the Prevalent Interaction Pathways of Ligands Targeting the Ligand-Binding Pocket of Farnesoid X Receptor via Combined Enhanced Sampling.

It is well-known that the potency of a drug is heavily associated with its kinetic and thermodynamic properties with the target. Nuclear receptors (NRs), as an important target family, play important roles in regulating a variety of physiological processes in vivo. However, it is hard to understand the drug-NR interaction process because of the closed structure of the ligand-binding domain (LBD) of the NR proteins, which apparently hinders the rational design of drugs with controllable kinetic properties. Therefore, understanding the underlying mechanism of the ligand-NR interaction process seems necessary to help NR drug design. However, it is usually difficult for experimental approaches to interpret the kinetic process of drug-target interactions. Therefore, in silico methods were utilized to explore the optimal binding/dissociation pathways of the NR ligands. Specifically, farnesoid X receptor (FXR) is considered here as the target system since it has been an important target for the treatment of bile acid metabolism-associated diseases, and a series of structures cocrystallized with diverse scaffold ligands were resolved. By using random acceleration molecular dynamics (RAMD) simulation and umbrella sampling (US), 5 main dissociation pathways (pathways I-V) were identified in 11 representative FXR ligands, with most of them (9/11) preferring to go through Pathway III and the remaining two favoring escaping from Pathway I and IV. Furthermore, key residues functioning in the three main dissociation pathways were revealed by the kinetic residue energy analysis (KREA) based on the US trajectories, which may serve as road-marker residues for rapid identification of the (un)binding pathways of FXR ligands. Moreover, the preferred pathways explored by RAMD simulations are in good agreement with the minimum free energy path identified by the US simulations with the Pearson R = 0.76 between the predicted binding affinity and the experimental data, suggesting that RAMD is suitable for applying in large-scale (un)binding-pathway exploration in the case of ligands with obscure binding tunnels to the target.

Uncovering the Kinetic Characteristics and Degradation Preference of PROTAC Systems with Advanced Theoretical Analyses.

Proteolysis-targeting chimeras (PROTACs), which can selectively induce the degradation of target proteins, represent an attractive technology in drug discovery. A large number of PROTACs have been reported, but due to the complicated structural and kinetic characteristics of the target-PROTAC-E3 ligase ternary interaction process, the rational design of PROTACs is still quite challenging. Here, we characterized and analyzed the kinetic mechanism of MZ1, a PROTAC that targets the bromodomain (BD) of the bromodomain and extra terminal (BET) protein (Brd2, Brd3, or Brd4) and von Hippel-Lindau E3 ligase (VHL), from the kinetic and thermodynamic perspectives of view by using enhanced sampling simulations and free energy calculations. The simulations yielded satisfactory predictions on the relative residence time and standard binding free energy (rp > 0.9) for MZ1 in different BrdBD-MZ1-VHL ternary complexes. Interestingly, the simulation of the PROTAC ternary complex disintegration illustrates that MZ1 tends to remain on the surface of VHL with the BD proteins dissociating alone without a specific dissociation direction, indicating that the PROTAC prefers more to bind with E3 ligase at the first step in the formation of the target-PROTAC-E3 ligase ternary complex. Further exploration of the degradation difference of MZ1 in different Brd systems shows that the PROTAC with higher degradation efficiency tends to leave more lysine exposed on the target protein, which is guaranteed by the stability (binding affinity) and durability (residence time) of the target-PROTAC-E3 ligase ternary complex. It is quite possible that the underlying binding characteristics of the BrdBD-MZ1-VHL systems revealed by this study may be shared by different PROTAC systems as a general rule, which may accelerate rational PROTAC design with higher degradation efficiency.

Chemoproteomic mapping of the glycolytic targetome in cancer cells.

Hyperactivated glycolysis is a metabolic hallmark of most cancer cells. Although sporadic information has revealed that glycolytic metabolites possess nonmetabolic functions as signaling molecules, how these metabolites interact with and functionally regulate their binding targets remains largely elusive. Here, we introduce a target-responsive accessibility profiling (TRAP) approach that measures changes in ligand binding-induced accessibility for target identification by globally labeling reactive proteinaceous lysines. With TRAP, we mapped 913 responsive target candidates and 2,487 interactions for 10 major glycolytic metabolites in a model cancer cell line. The wide targetome depicted by TRAP unveils diverse regulatory modalities of glycolytic metabolites, and these modalities involve direct perturbation of enzymes in carbohydrate metabolism, intervention of an orphan transcriptional protein's activity and modulation of targetome-level acetylation. These results further our knowledge of how glycolysis orchestrates signaling pathways in cancer cells to support their survival, and inspire exploitation of the glycolytic targetome for cancer therapy.

Deciphering the Shared and Specific Drug Resistance Mechanisms of Anaplastic Lymphoma Kinase via Binding Free Energy Computation.

Anaplastic lymphoma kinase (ALK), a tyrosine receptor kinase, has been proven to be associated with the occurrence of numerous malignancies. Although there have been already at least 3 generations of ALK inhibitors approved by FDA or in clinical trials, the occurrence of various mutations seriously attenuates the effectiveness of the drugs. Unfortunately, most of the drug resistance mechanisms still remain obscure. Therefore, it is necessary to reveal the bottom reasons of the drug resistance mechanisms caused by the mutations. In this work, on the basis of verifying the accuracy of 2 main kinds of binding free energy calculation methodologies [end-point method of Molecular Mechanics with Poisson-Boltzmann/Generalized Born and Surface Area (MM/PB(GB)SA) and alchemical method of Thermodynamic Integration (TI)], we performed a systematic analysis on the ALK systems to explore the underlying shared and specific drug resistance mechanisms, covering the one-drug-multiple-mutation and multiple-drug-one-mutation cases. Through conventional molecular dynamics (cMD) simulation in conjunction with MM/PB(GB)SA and umbrella sampling (US) in conjunction with contact network analysis (CNA), the resistance mechanisms of the in-pocket, out-pocket, and multiple-site mutations were revealed. Especially for the out-pocket mutation, a possible transfer chain of the mutation effect was revealed, and the reason why different drugs exhibited various sensitivities to the same mutation was also uncovered. The proposed mechanisms may be prevalent in various drug resistance cases.

Molecular Generation with Reduced Labeling through Constraint Architecture.

In the past few years, a number of machine learning (ML)-based molecular generative models have been proposed for generating molecules with desirable properties, but they all require a large amount of label data of pharmacological and physicochemical properties. However, experimental determination of these labels, especially bioactivity labels, is very expensive. In this study, we analyze the dependence of various multi-property molecule generation models on biological activity label data and propose Frag-G/M, a fragment-based multi-constraint molecular generation framework based on conditional transformer, recurrent neural networks (RNNs), and reinforcement learning (RL). The experimental results illustrate that, using the same number of labels, Frag-G/M can generate more desired molecules than the baselines (several times more than the baselines). Moreover, compared with the known active compounds, the molecules generated by Frag-G/M exhibit higher scaffold diversity than those generated by the baselines, thus making it more promising to be used in real-world drug discovery scenarios.

Cooperation of structural motifs controls drug selectivity in cyclin-dependent kinases: an advanced theoretical analysis.

Understanding drug selectivity mechanism is a long-standing issue for helping design drugs with high specificity. Designing drugs targeting cyclin-dependent kinases (CDKs) with high selectivity is challenging because of their highly conserved binding pockets. To reveal the underlying general selectivity mechanism, we carried out comprehensive analyses from both the thermodynamics and kinetics points of view on a representative CDK12 inhibitor. To fully capture the binding features of the drug-target recognition process, we proposed to use kinetic residue energy analysis (KREA) in conjunction with the community network analysis (CNA) to reveal the underlying cooperation effect between individual residues/protein motifs to the binding/dissociating process of the ligand. The general mechanism of drug selectivity in CDKs can be summarized as that the difference of structural cooperation between the ligand and the protein motifs leads to the difference of the energetic contribution of the key residues to the ligand. The proposed mechanisms may be prevalent in drug selectivity issues, and the insights may help design new strategies to overcome/attenuate the drug selectivity associated problems.

ChemistGA: A Chemical Synthesizable Accessible Molecular Generation Algorithm for Real-World Drug Discovery.

Many deep learning (DL)-based molecular generative models have been proposed to design novel molecules. These models may perform well on benchmarks, but they usually do not take real-world constraints into account, such as available training data set, synthetic accessibility, and scaffold diversity in drug discovery. In this study, a new algorithm, ChemistGA, was proposed by combining the traditional heuristic algorithm with DL, in which the crossover of the traditional genetic algorithm (GA) was redefined by DL in conjunction with GA, and an innovative backcrossing operation was implemented to generate desired molecules. Our results clearly show that ChemistGA not only retains the strength of the traditional GA but also greatly enhances the synthetic accessibility and success rate of the generated molecules with desired properties. Calculations on the two benchmarks illustrate that ChemistGA achieves impressive performance among the state-of-the-art baselines, and it opens a new avenue for the application of generative models to real-world drug discovery scenarios.

Predicting the mutation effects of protein-ligand interactions via end-point binding free energy calculations: strategies and analyses.

Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.

Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning.

The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to the ligand-binding pocket (LBP) of NRs can regulate the downstream signaling pathways with different physiological effects. However, it is still hard to determine the molecular type of a LBP-bound ligand because both the agonists and antagonists bind to the same position of the protein. Therefore, it is necessary to develop precise and efficient methods to facilitate the discrimination of agonists and antagonists targeting the LBP of NRs. Here, combining structural and energetic analyses with machine-learning (ML) algorithms, we constructed a series of structure-based ML models to determine the molecular category of the LBP-bound ligands. We show that the proposed models work robustly and with high accuracy (ACC > 0.9) for determining the category of molecules derived from docking-based and crystallized poses. Furthermore, the models are also capable of determining the molecular category of ligands with dual opposite functions on different NRs (i.e., working as an agonist in one NR target, whereas functioning as an antagonist in another) with reasonable accuracy. The proposed method is expected to facilitate the determination of the molecular properties of ligands targeting the LBP of NRs with structural interpretation.

fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation.

Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex structures. In fastDRH server, AutoDock Vina and AutoDock-GPU docking engines, structure-truncated MM/PB(GB)SA free energy calculation procedures and multiple poses based per-residue energy decomposition analysis were well integrated into a user-friendly and multifunctional online platform. Benefit from the modular architecture, users can flexibly use one or more of three features, including molecular docking, docking pose rescoring and hotspot residue prediction, to obtain the key information clearly based on a result analysis panel supported by 3Dmol.js and Apache ECharts. In terms of protein-ligand binding mode prediction, the integrated structure-truncated MM/PB(GB)SA rescoring procedures exhibit a success rate of >80% in benchmark, which is much better than the AutoDock Vina (~70%). For hotspot residue identification, our multiple poses based per-residue energy decomposition analysis strategy is a more reliable solution than the one using only a single pose, and the performance of our solution has been experimentally validated in several drug discovery projects. To summarize, the fastDRH server is a useful tool for predicting the ligand binding mode and the hotspot residue of protein for ligand binding. The fastDRH server is accessible free of charge at http://cadd.zju.edu.cn/fastdrh/.

Exploring PI3Kγ binding preference with Eganelisib, Duvelisib, and Idelalisib via energetic, pharmacophore and dissociation pathway analyses.

Phosphatidylinositol 3-kinase (PI3K) is the central regulator of cellular functions and is suggested as a target for various diseases; thus, effective PI3K inhibitors provide a promising opportunity for the pharmaceutical intervention of many diseases. Among them, PI3Kγ has received more attention because of its essential role in immune signaling. However, the development of novel selective PI3Kγ inhibitors is a major challenge due to the high sequence homology across the class I PI3K isoforms. Therefore, understanding the substrate specificity and receptor-ligand interaction of PI3Kγ would be an appropriate strategy for the rational design of potent γ-selective inhibitors. In this study, by combining various molecular modeling approaches (including classic and enhanced sampling molecular dynamics (MD) simulations, end-point binding free energy calculations, and pharmacophore models), three quinolinone core-containing inhibitors, Idelalisib/CAL-101, Duvelisib/IPI-145, and Eganelisib/IPI-549, were employed to reveal the selective binding mechanisms targeting PI3Kγ. The classic MD and free energy calculations highlight the significant interaction and some key residues for the selective binding against PI3Kγ. Furthermore, the dissociation pathway analysis based on umbrella sampling simulations reveals that hydrophobic interactions are dominant for binding of the three ligands during the dissociation processes, and cooperation between the P-loop and the ligands always exists in the binding/dissociation process. Finally, the pharmacophore model revealed that IPI-549 contains a unique hydrophobic feature, and PI3Kγ exhibits an important hydrogen bond donor feature of hydrogen amide. These findings may provide some important information for the rational design and optimization of PI3Kγ-selective inhibitors.

Characterizing the stabilization effects of stabilizers in protein-protein systems with end-point binding free energy calculations.

Drug design targeting protein-protein interactions (PPIs) associated with the development of diseases has been one of the most important therapeutic strategies. Besides interrupting the PPIs with PPI inhibitors/blockers, increasing evidence shows that stabilizing the interaction between two interacting proteins may also benefit the therapy, such as the development of various types of molecular glues/stabilizers that mostly work by stabilizing the two interacting proteins to regulate the downstream biological effects. However, characterizing the stabilization effect of a stabilizer is usually hard or too complicated for traditional experiments since it involves ternary interactions [protein-protein-stabilizer (PPS) interaction]. Thus, developing reliable computational strategies will facilitate the discovery/design of molecular glues or PPI stabilizers. Here, by fully analyzing the energetic features of the binary interactions in the PPS ternary complex, we systematically investigated the performance of molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) methods on characterizing the stabilization effects of stabilizers in 14-3-3 systems. The results show that both MM/PBSA and MM/GBSA are powerful tools in distinguishing the stabilizers from the decoys (with area under the curves of 0.90-0.93 for all tested cases) and are reasonable for ranking protein-peptide interactions in the presence or absence of stabilizers as well (with the average Pearson correlation coefficient of ~0.6 at a relatively high dielectric constant for both methods). Moreover, to give a detailed picture of the stabilization effects, the stabilization mechanism is also analyzed from the structural and energetic points of view for individual systems containing strong or weak stabilizers. This study demonstrates a potential strategy to accelerate the discovery of PPI stabilizers.

More than a Leaving Group: N-Phenyltrifluoroacetimidate as a Remote Directing Group for Highly α-Selective 1,2-cis Glycosylation.

The anomeric configuration can greatly affect the biological functions and activities of carbohydrates. Herein, we report that N-phenyltrifluoroacetimidoyl (PTFAI), a well-known leaving group for catalytic glycosylation, can act as a stereodirecting group for the challenging 1,2-cis α-glycosylation. Utilizing rapidly accessible 1,6-di-OPTFAI glycosyl donors, TMSOTf-catalyzed glycosylation occurred with excellent α-selectivity and broad substrate scope, and the remaining 6-OPTFAI group can be cleaved chemoselectively. The remote participation of 6-OPTFAI is supported by the first characterization of the crucial 1,6-bridged bicyclic oxazepinium ion intermediates by low-temperature NMR spectroscopy. These cations were found to be relatively stable and mainly responsible for the present stereoselectivities. Further application is highlighted in glycosylation reactions toward trisaccharide heparins as well as the convergent synthesis of chacotriose derivatives using a bulky 2,4-di-O-glycosylated donor.

Discovery of Small-Molecule Inhibitors of the PD-1/PD-L1 Axis That Promote PD-L1 Internalization and Degradation.

Several monoclonal antibodies targeting the programmed cell death-1/programmed cell death-ligand 1 (PD-1/PD-L1) pathway have been used successfully in anticancer immunotherapy. Inherent limitations of antibody-based therapies remain, however, and alternative small-molecule inhibitors that can block the PD-1/PD-L1 axis are urgent needed. Herein, we report the discovery of compound 17 as a bifunctional inhibitor of PD-1/PD-L1 interactions. 17 inhibits PD-1/PD-L1 interactions and promotes dimerization, internalization, and degradation of PD-L1. 17 promotes cell-surface PD-L1 internalized into the cytosol and induces the degradation of PD-L1 in tumor cells through a lysosome-dependent pathway. Furthermore, 17 suppresses tumor growth in vivo by activating antitumor immunity. These results demonstrate that 17 targets the PD-1/PD-L1 axis and induces PD-L1 degradation.

Discovery of N-(4-(Benzyloxy)-phenyl)-sulfonamide Derivatives as Novel Antagonists of the Human Androgen Receptor Targeting the Activation Function 2.

Androgen receptor (AR) antagonists have been widely used for the treatment of prostate cancer (PCa). As a link between the AR and its transcriptional function, the activation function 2 (AF2) region has recently been revealed as a novel targeting site for developing AR antagonists. Here, we reported a series of N-(4-(benzyloxy)-phenyl)-sulfonamide derivatives as new-scaffold AR antagonists targeting the AR AF2. Therein, compound T1-12 showed excellent AR antagonistic activity (IC50 = 0.47 µM) and peptide displacement activity (IC50 = 18.05 µM). Furthermore, the in vivo LNCaP xenograft study confirmed that T1-12 offered effective inhibition on tumor growth when administered intratumorally. The study represents the first successful attempt to identify a small molecule targeting the AR AF2 with submicromolar AR antagonistic activity by structure-based virtual screening and provides important clues for the development of novel therapeutics for PCa treatment.

Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning.

Accurate prediction of atomic partial charges with high-level quantum mechanics (QM) methods suffers from high computational cost. Numerous feature-engineered machine learning (ML)-based predictors with favorable computability and reliability have been developed as alternatives. However, extensive expertise effort was needed for feature engineering of atom chemical environment, which may consequently introduce domain bias. In this study, SuperAtomicCharge, a data-driven deep graph learning framework, was proposed to predict three important types of partial charges (i.e. RESP, DDEC4 and DDEC78) derived from high-level QM calculations based on the structures of molecules. SuperAtomicCharge was designed to simultaneously exploit the 2D and 3D structural information of molecules, which was proved to be an effective way to improve the prediction accuracy of the model. Moreover, a simple transfer learning strategy and a multitask learning strategy based on self-supervised descriptors were also employed to further improve the prediction accuracy of the proposed model. Compared with the latest baselines, including one GNN-based predictor and two ML-based predictors, SuperAtomicCharge showed better performance on all the three external test sets and had better usability and portability. Furthermore, the QM partial charges of new molecules predicted by SuperAtomicCharge can be efficiently used in drug design applications such as structure-based virtual screening, where the predicted RESP and DDEC4 charges of new molecules showed more robust scoring and screening power than the commonly used partial charges. Finally, two tools including an online server (http://cadd.zju.edu.cn/deepchargepredictor) and the source code command lines (https://github.com/zjujdj/SuperAtomicCharge) were developed for the easy access of the SuperAtomicCharge services.

Comprehensive assessment of deep generative architectures for de novo drug design.

Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the performances of different generative models were evaluated by analyzing the properties of the generated molecules in different scenarios, such as goal-directed (rediscovery, optimization and scaffold hopping of active compounds) and target-specific (generation of novel compounds for a given target) tasks. In overall, the DL-based models have significant advantages over the baseline models built by the traditional methods in learning the physicochemical property distributions of the training sets and may be more suitable for target-specific tasks. However, both the baselines and DL-based generative models cannot fully exploit the scaffolds of the training sets, and the molecules generated by the DL-based methods even have lower scaffold diversity than those generated by the traditional models. Moreover, our assessment illustrates that the DL-based methods do not exhibit obvious advantages over the genetic algorithm-based baselines in goal-directed tasks. We believe that our study provides valuable guidance for the effective use of generative models in de novo drug design.

Deep learning approaches for de novo drug design: An overview.

De novo drug design is the process of generating novel lead compounds with desirable pharmacological and physiochemical properties. The application of deep learning (DL) in de novo drug design has become a hot topic, and many DL-based approaches have been developed for molecular generation tasks. Generally, these approaches were developed as per four frameworks: recurrent neural networks; encoder-decoder; reinforcement learning; and generative adversarial networks. In this review, we first introduced the molecular representation and assessment metrics used in DL-based de novo drug design. Then, we summarized the features of each architecture. Finally, the potential challenges and future directions of DL-based molecular generation were prospected.

TEPP-46-Based AIE Fluorescent Probe for Detection and Bioimaging of PKM2 in Living Cells.

Pyruvate kinase (PK) M2 (PKM2), a glycolytic enzyme, is a hallmark of different types of tumors and plays a significant role in the Warburg effect. However, there is no fluorescent probe for PKM2 that has been reported yet. In this study, TEPC466, a novel TEPP-46-based aggregation-induced emission (AIE) probe for the detection of PKM2, was designed, synthesized, and fully characterized by 1H NMR, 13C NMR, and high-resolution mass spectrometry. When the fluorescent agent, coumarine, was conjugated to TEPP-46, the bioprobe TEPC466 showed a high degree of selectivity and sensitivity for the detection of PKM2 protein via the AIE effect. TEPC466 was then successfully applied in imaging the PKM2 protein in colorectal cancer cells with low toxicity. Moreover, structure-based modeling and the PK activity assay confirmed that TEPC466 has a better binding with PKM2 than TEPP-46, which suggests that TEPC466 could also be a good agonist of PKM2. Taken together, the bioprobe shows potential in selective detection of PKM2 and provides a useful tool for cancer diagnosis and therapy.