Discovery of Novel GR Ligands toward Druggable GR Antagonist Conformations Identified by MD Simulations and Markov State Model Analysis

In-depth theoretical modeling to explore the mechanism of TPX-0131 overcoming lorlatinib resistance to ALKL1196M/G1202R mutation

A number of anaplastic lymphoma kinase (ALK) inhibitors have been clinically approved, with lorlatinib, particularly as a third-generation drug, demonstrating efficacy against various drug-resistant ALK single mutations. However, continued clinical use of lorlatinib has led to the emergence of ALK double mutations conferring resistance to lorlatinib, notably ALKL1196M/G1202R. TPX-0131 is a potential fourth-generation ALK inhibitor currently under development. TPX-0131 demonstrates a broader spectrum of activity against ALK-resistant mutations, efficiently inhibiting 26 single-point mutations and various double/triple mutations, including solvent front mutations and gatekeeper mutations. In this study, for the first time, a comprehensive elucidation of the molecular mechanisms by which TPX-0131 overcomes lorlatinib resistance to ALKL1196M/G1202R through modeling, MD simulations, free energy calculations, and US simulations. The results indicate that the interactions between lorlatinib and key residues at the hinge region are disturbed by L1196M/G1202R double mutation, leading to the disruption of important hydrogen bonding between Glu1197 and lorlatinib. For TPX-0131, the L1196M/G1202R mutation enhances electrostatic and van der Waals interactions, causing significant conformational changes primarily in the hinge region, G-loop, and β-strands. The tight binding of TPX-0131 to residues Arg1202, Met1199 and Arg1120 contribute significantly to overcoming lorlatinib resistance in ALKL1196M/G1202R mutant. These research results are expected to offer insights into the mechanism of TPX-0131 in treating ALKG1202R/L1196M-induced NSCLC resistance and optimizing of ALK inhibitors.

The sugar moiety in protopanaxadiol ginsenoside affects its ability to target glucocorticoid receptor to regulate lipid metabolism

Ginsenosides are natural products with hydrophobic rings adorned with sugar molecules. The elucidation of the impact of ginsenosides structure on their activity is crucial for facilitating precision-oriented modifications, thereby enhancing their suitability for drug development. Here, utilizing an ob/ob mouse model, we demonstrated that as the number of sugar moiety on the protopanaxadiol-type ginsenosides decreased, the hypolipidemic potency increased, while the aglycon exhibited negligible activity. Mechanistically, we demonstrated the dependency of ginsenosides on the glucocorticoid receptor (GR) for the regulation of lipid metabolism. Interestingly, ginsenoside CK was found to promote the transcription of lipid metabolism-related genes via GR contrast to the effects of glucocorticoids, suggesting a unique mode of action. Furthermore, we observed that a reduction in the number of sugar molecules strengthened the binding affinity of ginsenosides to GR, as determined by microscale thermophoresis. These findings highlight the critical role of the sugar moiety in modulating the lipid-regulating capacity of ginsenosides, providing valuable insights for the development of these compounds as potential therapeutic agents.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening

Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

Dissecting the role of ALK double mutations in drug resistance to lorlatinib with in-depth theoretical modeling and analysis

Anaplastic lymphoma kinase (ALK) is implicated in the genesis of multiple malignant tumors. Lorlatinib stands out as the most advanced and effective inhibitor currently used in the clinic for the treatment of ALK-positive non-small cell lung cancer. However, resistance to lorlatinib has inevitably manifested over time, with double/triple mutations of G1202, L1196, L1198, C1156 and I1171 frequently observed in clinical practice, and tumors regrow within a short time after treatment with lorlatinib. Therefore, elucidating the mechanism of resistance to lorlatinib is paramount in paving the way for innovative therapeutic strategies and the development of next-generation drugs. In this study, we leveraged multiple computational methodologies to delve into the resistance mechanisms of three specific double mutations of ALKG1202R/L1196M, ALKG1202R/L1198F and ALKI1171N/L1198F to lorlatinib. We analyzed these mechanisms through qualitative (PCA, DCCM) and quantitative (MM/GBSA, US) kinetic analyses. The qualitative analysis shows that these mutations exert minimal perturbations on the conformational dynamics of the structural domains of ALK. The energetic and structural assessments show that the van der Waals interactions, formed by the conserved residue Leu1256 within the ATP-binding site and the residues Glu1197 and Met1199 in the hinge domain with lorlatinib, play integral roles in the occurrence of drug resistance. Furthermore, the US simulation results elucidate that the pathways through which lorlatinib dissociates vary across mutant systems, and the distinct environments during the dissociation process culminate in diverse resistance mechanisms. Collectively, these insights provide important clues for the design of novel inhibitors to combat resistance.

Markov State Models: To Optimize or Not to Optimize

Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or hyperparameters, are often evaluated by expert judgment or, in the case of MSMs, by maximizing variational scores such as the VAMP-2 score. Modern ML and statistical pipelines often use automatic hyperparameter selection techniques ranging from the simple, choosing the best score from a random selection of hyperparameters, to the complex, optimization via, e.g., Bayesian optimization. In this work, we ask whether it is possible to automatically select MSM models this way by estimating and analyzing over 16,000,000 observations from over 280,000 estimated MSMs. We find that differences in hyperparameters can change the physical interpretation of the optimization objective, making automatic selection difficult. In addition, we find that enforcing conditions of equilibrium in the VAMP scores can result in inconsistent model selection. However, other parameters that specify the VAMP-2 score (lag time and number of relaxation processes scored) have only a negligible influence on model selection. We suggest that model observables and variational scores should be only a guide to model selection and that a full investigation of the MSM properties should be undertaken when selecting hyperparameters.

Discovery of Nonretinoid Inhibitors of CRBP1: Structural and Dynamic Insights for Ligand-Binding Mechanisms

The dysregulation of retinoid metabolism has been linked to prevalent ocular diseases including age-related macular degeneration and Stargardt disease. Modulating retinoid metabolism through pharmacological approaches holds promise for the treatment of these eye diseases. Cellular retinol-binding protein 1 (CRBP1) is the primary transporter of all-trans-retinol (atROL) in the eye, and its inhibition has recently been shown to protect mouse retinas from light-induced retinal damage. In this report, we employed high-throughput screening to identify new chemical scaffolds for competitive, nonretinoid inhibitors of CRBP1. To understand the mechanisms of interaction between CRBP1 and these inhibitors, we solved high-resolution X-ray crystal structures of the protein in complex with six selected compounds. By combining protein crystallography with hydrogen/deuterium exchange mass spectrometry, we quantified the conformational changes in CRBP1 caused by different inhibitors and correlated their magnitude with apparent binding affinities. Furthermore, using molecular dynamic simulations, we provided evidence for the functional significance of the "closed" conformation of CRBP1 in retaining ligands within the binding pocket. Collectively, our study outlines the molecular foundations for understanding the mechanism of high-affinity interactions between small molecules and CRBPs, offering a framework for the rational design of improved inhibitors for this class of lipid-binding proteins.

Biased regulation of glucocorticoid receptors signaling

Glucocorticoids (GCs), steroid hormones that depend on glucocorticoid receptor (GR) binding for their action, are essential for regulating numerous homeostatic functions in the body.GR signals are biased, that is, GR signals are various in different tissue cells, disease states and ligands. This biased regulation of GR signaling appears to depend on ligand-induced metameric regulation, protein post-translational modifications, assembly at response elements, context-specific assembly (recruitment of co-regulators) and intercellular differences. Based on the bias regulation of GR, selective GR agonists and modulators (SEGRAMs) were developed to bias therapeutic outcomes toward expected outcomes (e.g., anti-inflammation and immunoregulation) by influencing GR-mediated gene expression. This paper provides a review of the bias regulation and mechanism of GR and the research progress of drugs.

Efficient and accurate large library ligand docking with KarmaDock

Ligand docking is one of the core technologies in structure-based virtual screening for drug discovery. However, conventional docking tools and existing deep learning tools may suffer from limited performance in terms of speed, pose quality and binding affinity accuracy. Here we propose KarmaDock, a deep learning approach for ligand docking that integrates the functions of docking acceleration, binding pose generation and correction, and binding strength estimation. The three-stage model consists of the following components: (1) encoders for the protein and ligand to learn the representations of intramolecular interactions; (2) E(n) equivariant graph neural networks with self-attention to update the ligand pose based on both protein-ligand and intramolecular interactions, followed by post-processing to ensure chemically plausible structures; (3) a mixture density network for scoring the binding strength. KarmaDock was validated on four benchmark datasets and tested in a real-world virtual screening project that successfully identified experiment-validated active inhibitors of leukocyte tyrosine kinase (LTK).

Structure-based virtual screening, molecular docking, molecular dynamics simulation and MM/PBSA calculations towards identification of steroidal and non-steroidal selective glucocorticoid receptor modulators

Glucocorticoids have been used in the treatment of many diseases including inflammatory and autoimmune diseases. Despite the wide therapeutic effects of synthetic glucocorticoids, the use of these compounds has been limited due to side effects such as osteoporosis, immunodeficiency, and hyperglycaemia. To this end, extensive studies have been performed to discover new glucocorticoid modulators with the aim of increasing affinity for the receptor and thus less side effects. In the present work, structure-based virtual screening was used for the identification of novel potent compounds with glucocorticoid effects. The molecules derived from ZINC database were screened on account of structural similarity with some glucocorticoid agonists as the template. Subsequently, molecular docking was performed on 200 selected compounds to obtain the best steroidal and non-steroidal conformations. Three compounds, namely ZINC_000002083318, ZINC_000253697499 and ZINC_000003845653, were selected with the binding energies of -11.5, -10.5, and -9.5 kcal/mol, respectively. Molecular dynamic simulations on superior structures were accomplished with the glucocorticoid receptor. Additionally, root mean square deviations, root mean square fluctuation, radius of gyration, hydrogen bonds, and binding-free energy analysis showed the binding stability of the proposed compounds compared to budesonide as an approved drug. The results demonstrated that all the compounds had suitable binding stability compared to budesonide, while ZINC_000002083318 showed a tighter binding energy compared to the other compounds.Communicated by Ramaswamy H. Sarma.

Stress and the risk of Alzheimer dementia: Can deconstructed engrams be rebuilt?

The exact neuropathological mechanism by which the dementia process unfolds is under intense scrutiny. The disease affects about 38 million people worldwide, 70% of which are clinically diagnosed with Alzheimer's disease (AD). If the destruction of synapses essential for learning, planning and decision-making is part of the problem, must the restoration of previously lost synapses be part of the solution? It is plausible that neuronal capacity to restitute information corresponds with the adaptive capacity of its connectivity reserve. A challenge will be to promote the functional connectivity that can compensate for the lost one. This will require better clarification of the remodeling of functional connectivity during the progression of AD dementia and its reversal upon experimental treatment. A major difficulty is to promote the neural pathways that are atrophied in AD dementia while suppressing others that are bolstered. Therapeutic strategies should aim at scaling functional connectivity to a just balance between the atrophic and hypertrophic systems. However, the exact factors that can help reach this objective are still unclear. Similarities between the effects of chronic stress and some neuropathological mechanisms underlying AD dementia support the idea that common components deserve prime attention as therapeutic targets.

TB-IECS: an accurate machine learning-based scoring function for virtual screening

Machine learning-based scoring functions (MLSFs) have shown potential for improving virtual screening capabilities over classical scoring functions (SFs). Due to the high computational cost in the process of feature generation, the numbers of descriptors used in MLSFs and the characterization of protein-ligand interactions are always limited, which may affect the overall accuracy and efficiency. Here, we propose a new SF called TB-IECS (theory-based interaction energy component score), which combines energy terms from Smina and NNScore version 2, and utilizes the eXtreme Gradient Boosting (XGBoost) algorithm for model training. In this study, the energy terms decomposed from 15 traditional SFs were firstly categorized based on their formulas and physicochemical principles, and 324 feature combinations were generated accordingly. Five best feature combinations were selected for further evaluation of the model performance in regard to the selection of feature vectors with various length, interaction types and ML algorithms. The virtual screening power of TB-IECS was assessed on the datasets of DUD-E and LIT-PCBA, as well as seven target-specific datasets from the ChemDiv database. The results showed that TB-IECS outperformed classical SFs including Glide SP and Dock, and effectively balanced the efficiency and accuracy for practical virtual screening.

Computationally guided discovery of novel non-steroidal AR-GR dual antagonists demonstrating potency against antiandrogen resistance

As a major class of medicine for treating the lethal type of castration-resistant prostate cancer (PCa), long-term use of androgen receptor (AR) antagonists commonly leads to antiandrogen resistance. When AR signaling pathway is blocked by AR-targeted therapy, glucocorticoid receptor (GR) could compensate for AR function especially at the late stage of PCa. AR-GR dual antagonist is expected to be a good solution for this situation. Nevertheless, no effective non-steroidal AR-GR dual antagonist has been reported so far. In this study, an AR-GR dual binder H18 was first discovered by combining structure-based virtual screening and biological evaluation. Then with the aid of computationally guided design, the AR-GR dual antagonist HD57 was finally identified with antagonistic activity towards both AR (IC50 = 0.394 μM) and GR (IC50 = 17.81 μM). Moreover, HD57 could effectively antagonize various clinically relevant AR mutants. Further molecular dynamics simulation provided more atomic insights into the mode of action of HD57. Our research presents an efficient and rational strategy for discovering novel AR-GR dual antagonists, and the new scaffold provides important clues for the development of novel therapeutics for castration-resistant PCa.

Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?

Binding affinity prediction largely determines the discovery efficiency of lead compounds in drug discovery. Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.

MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions

Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and docking powers of three competitive docking tools (i.e., PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.

Structural basis of glucocorticoid receptor signaling bias

Dissociation between the healthy and toxic effects of cortisol, a major stress-responding hormone has been a widely used strategy to develop anti-inflammatory glucocorticoids with fewer side effects. Such strategy falls short when treating brain disorders as timing and activity state within large-scale neuronal networks determine the physiological and behavioral specificity of cortisol response. Advances in structural molecular dynamics posit the bases for engineering glucocorticoids with precision bias for select downstream signaling pathways. Design of allosteric and/or cooperative control for the glucocorticoid receptor could help promote the beneficial and reduce the deleterious effects of cortisol on brain and behavior in disease conditions.

Markov State Models Underlying the N-Terminal Premodel of TOPK/PBK

Lymphokine-activated killer T-cell-originated protein kinase (TOPK) is a potential target for cancer therapy. To explore the micromechanism, we proposed the N-terminal premodel (NTPM) of the TOPK monomer via homology modeling and molecular dynamic simulations and analyzed the conformational dynamics by Markov state model analysis. The electronegative insert (ENI) motif of the NTPM can be opened with a small probability under wild type, regulated by the so-called "N-C" interaction zone consisting of the N-terminal head, the coil between β₃-strand and αC-helix, and the ENI motif. Glutamate substitution at threonine residue 9 or tyrosine residue 74 promotes the closed-open transition, revealing the details of phosphorylation. Allosteric effects induce functionally relevant structural changes, such as increased structural flexibility and active sites, which are thought to be necessary for further activation or binding. These findings provide rational structural templates for designing state-dependent inhibitors and give insight into the molecular regulatory mechanisms of TOPK monomers.

Discovery and Optimization of N-Acyl-6-sulfonamide-tetrahydroquinoline Derivatives as Novel Non-Steroidal Selective Glucocorticoid Receptor Modulators

Selective glucocorticoid receptor modulators (SGRMs), which can dissociate the transactivation from the transrepression of the glucocorticoid receptor (GR), are regarded as very promising therapeutics for inflammatory and autoimmune diseases. We previously discovered a SGRM HP-19 based on the passive antagonistic conformation of GR and bioassays. In this study, we further analyzed the dynamic changes of the passive antagonistic state upon the binding of HP-19 and designed and synthesized 62 N-acyl-6-sulfonamide-tetrahydroquinoline derivatives by structural optimization of HP-19. Therein, compound B53 exhibits the best transrepression activity (IC_{50 NF-κB} = 0.009 ± 0.001 μM) comparable with dexamethasone (IC_{50 NF-κB} = 0.005 ± 0.001 μM) and no transactivation activity. B53 can efficiently reduce the expression of inflammatory factors IL-6, IL-1β, TNF-α, and so on and makes a milder adverse effect and is highly specific to GR. Furthermore, B53 is able to significantly relieve dermatitis on a mouse model via oral drug intervention.

Discovery of 2-(1-(3-Chloro-4-cyanophenyl)-1H-pyrazol-3-yl)acetamides as Potent, Selective, and Orally Available Antagonists Targeting the Androgen Receptor

The androgen receptor (AR) antagonists are efficient therapeutics for the treatment of prostate cancer (PCa). All the approved AR antagonists to date are targeted to the ligand-binding pocket (LBP) of AR and have suffered from various drug resistances, whereas AR antagonist targeting non-LBP site of AR is conceived as a promising strategy. Through the scaffold hopping of AR LBP antagonists, the 2-chloro-4-(1H-pyrazol-1-yl)benzonitrile was designed as a new core structure for AR antagonists. A total of 46 compounds were synthesized and biologically evaluated to disclose compounds 2f, 2k, and 4c, exhibiting potent AR antagonistic activities (IC₅₀ up to 69 nM), force against antiandrogen resistance, and untraditional targeting site of probably AR binding function 3. Therein, 4c exhibited effective tumor growth inhibition in LNCaP xenograft study upon oral administration. This work provides a novel chemical scaffold for AR antagonists and offers new perspective for the development of PCa therapy.

Discovery of a novel nonsteroidal selective glucocorticoid receptor modulator by virtual screening and bioassays

Synthetic glucocorticoids (GCs) have been widely used in the treatment of a broad range of inflammatory diseases, but their clinic use is limited by undesired side effects such as metabolic disorders, osteoporosis, skin and muscle atrophies, mood disorders and hypothalamic-pituitary-adrenal (HPA) axis suppression. Selective glucocorticoid receptor modulators (SGRMs) are expected to have promising anti-inflammatory efficacy but with fewer side effects caused by GCs. Here, we reported HT-15, a prospective SGRM discovered by structure-based virtual screening (VS) and bioassays. HT-15 can selectively act on the NF-κB/AP1-mediated transrepression function of glucocorticoid receptor (GR) and repress the expression of pro-inflammation cytokines (i.e., IL-1β, IL-6, COX-2, and CCL-2) as effectively as dexamethasone (Dex). Compared with Dex, HT-15 shows less transactivation potency that is associated with the main adverse effects of synthetic GCs, and no cross activities with other nuclear receptors. Furthermore, HT-15 exhibits very weak inhibition on the ratio of OPG/RANKL. Therefore, it may reduce the side effects induced by normal GCs. The bioactive compound HT-15 can serve as a starting point for the development of novel therapeutics for high dose or long-term anti-inflammatory treatment.

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.

The Role of Conformational Dynamics and Allostery in the Control of Distinct Efficacies of Agonists to the Glucocorticoid Receptor

Glucocorticoid receptor (GR) regulates various cellular functions. Given its broad influence on metabolic activities, it has been the target of drug discovery for decades. However, how drugs induce conformational changes in GR has remained elusive. Herein, we used five GR agonists (dex, AZ938, pred, cor, and dibC) with different efficacies to investigate which aspect of the ligand induced the differences in efficacy. We performed molecular dynamics simulations on the five systems (dex-, AZ938-, pred-, cor-, and dibC-bound systems) and observed a distinct discrepancy in the conformation of the cofactor TIF2. Moreover, we discovered ligand-induced differences regarding the level of conformational changes posed by the binding of cofactor TIF2 and identified a pair of essential residues D590 and T39. We further found a positive correlation between the efficacies of ligands and the interaction of the two binding pockets' domains, where D590 and T739 were involved, implying their significance in the participation of allosteric communication. Using community network analysis, two essential communities containing D590 and T739 were identified with their connectivity correlating to the efficacy of ligands. The potential communication pathways between these two residues were revealed. These results revealed the underlying mechanism of allosteric communication between the ligand-binding and cofactor-binding pockets and identified a pair of important residues in the allosteric communication pathway, which can serve as a guide for future drug discovery.

Insights into the Allosteric Effect of SENP1 Q597A Mutation on the Hydrolytic Reaction of SUMO1 via an Integrated Computational Study

Small ubiquitin-related modifier (SUMO)-specific protease 1 (SENP1) is a cysteine protease that catalyzes the cleavage of the C-terminus of SUMO1 for the processing of SUMO precursors and deSUMOylation of target proteins. SENP1 is considered to be a promising target for the treatment of hepatocellular carcinoma (HCC) and prostate cancer. SENP1 Gln597 is located at the unstructured loop connecting the helices α4 to α5. The Q597A mutation of SENP1 allosterically disrupts the hydrolytic reaction of SUMO1 through an unknown mechanism. Here, extensive multiple replicates of microsecond molecular dynamics (MD) simulations, coupled with principal component analysis, dynamic cross-correlation analysis, community network analysis, and binding free energy calculations, were performed to elucidate the detailed mechanism. Our MD simulations showed that the Q597A mutation induced marked dynamic conformational changes in SENP1, especially in the unstructured loop connecting the helices α4 to α5 which the mutation site occupies. Moreover, the Q597A mutation caused conformational changes to catalytic Cys603 and His533 at the active site, which might impair the catalytic activity of SENP1 in processing SUMO1. Moreover, binding free energy calculations revealed that the Q597A mutation had a minor effect on the binding affinity of SUMO1 to SENP1. Together, these results may broaden our understanding of the allosteric modulation of the SENP1-SUMO1 complex.

Organic Compound Synthetic Accessibility Prediction Based on the Graph Attention Mechanism

Accurate estimation of the synthetic accessibility of small molecules is needed in many phases of drug discovery. Several expert-crafted scoring methods and descriptor-based quantitative structure-activity relationship (QSAR) models have been developed for synthetic accessibility assessment, but their practical applications in drug discovery are still quite limited because of relatively low prediction accuracy and poor model interpretability. In this study, we proposed a data-driven interpretable prediction framework called GASA (Graph Attention-based assessment of Synthetic Accessibility) to evaluate the synthetic accessibility of small molecules by distinguishing compounds to be easy- (ES) or hard-to-synthesize (HS). GASA is a graph neural network (GNN) architecture that makes self-feature deduction by applying an attention mechanism to automatically capture the most important structural features related to synthetic accessibility. The sampling around the hypothetical classification boundary was used to improve the ability of GASA to distinguish structurally similar molecules. GASA was extensively evaluated and compared with two descriptor-based machine learning methods (random forest, RF; eXtreme gradient boosting, XGBoost) and four existing scores (SYBA: SYnthetic Bayesian Accessibility; SCScore: Synthetic Complexity score; RAscore: Retrosynthetic Accessibility score; SAscore: Synthetic Accessibility score). Our analysis demonstrates that GASA achieved remarkable performance in distinguishing similar molecules compared with other methods and had a broader applicability domain. In addition, we show how GASA learns the important features that affect molecular synthetic accessibility by assigning attention weights to different atoms. An online prediction service for GASA was offered at http://cadd.zju.edu.cn/gasa/.

Uncovering the Mechanism of Drug Resistance Caused by the T790M Mutation in EGFR Kinase From Absolute Binding Free Energy Calculations

The emergence of drug resistance may increase the death rates in advanced non-small cell lung cancer (NSCLC) patients. The resistance of erlotinib, the effective first-line antitumor drug for NSCLC with the L858R mutation of epidermal growth factor receptor (EGFR), happens after the T790M mutation of EGFR, because this mutation causes the binding of adenosine triphosphate (ATP) to EGFR more favorable than erlotinib. However, the mechanism of the enhancement of the binding affinity of ATP to EGFR, which is of paramount importance for the development of new inhibitors, is still unclear. In this work, to explore the detailed mechanism of the drug resistance due to the T790M mutation, molecular dynamics simulations and absolute binding free energy calculations have been performed. The results show that the binding affinity of ATP with respect to the L858R/T790M mutant is higher compared with the L858R mutant, in good agreement with experiments. Further analysis demonstrates that the T790M mutation significantly changes the van der Waals interaction of ATP and the binding site. We also find that the favorable binding of ATP to the L858R/T790M mutant, compared with the L858R mutant, is due to a conformational change of the αC-helix, the A-loop and the P-loop of the latter induced by the T790M mutation. This change makes the interaction of ATP and P-loop, αC-helix in the L858R/T790M mutant higher than that in the L858R mutant, therefore increasing the binding affinity of ATP to EGFR. We believe the drug-resistance mechanism proposed in this study will provide valuable guidance for the design of drugs for NSCLC.

Overexpression of PxαE14 Contributing to Detoxification of Multiple Insecticides in Plutella xylostella (L.)

The diamondback moth, Plutella xylostella (L.), has evolved with varying degrees of resistance to almost all major classes of insecticides and has become the most resistant pest worldwide. The multiresistance to different types of insecticides has been frequently reported in P. xylostella, but little is known about the mechanism. In this study, a carboxylesterase (CarE) gene, PxαE14, was found significantly overexpressed in a field-evolved multiresistant P. xylostella population and can be dramatically induced by eight of nine tested insecticides. Results of the real-time quantitative polymerase chain reaction (RT-qPCR) showed that PxαE14 was predominantly expressed in the midgut and malpighian tubule of larvae. Knockdown of PxαE14 dramatically increased the susceptibility of the larvae to β-cypermethrin, bifenthrin, chlorpyrifos, fenvalerate, malathion, and phoxim, while overexpression of PxαE14 in Drosophila melanogaster increased the tolerance of the fruit flies to these insecticides obviously. More importantly, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay showed that the recombinant PxαE14 expressed in Escherichia coli exhibited metabolic activity against the six insecticides. The homology modeling, molecular docking, and molecular dynamics simulation analyses showed that these six insecticides could stably bind to PxαE14. Taken together, these results demonstrate that constitutive and inductive overexpression of PxαE14 contributes to detoxification of multiple insecticides involved in multiresistance in P. xylostella. Our findings provide evidence for understanding the molecular mechanisms underlying the multiresistance in insect pests.

Understanding the P-Loop Conformation in the Determination of Inhibitor Selectivity Toward the Hepatocellular Carcinoma-Associated Dark Kinase STK17B

As a member of the death-associated protein kinase family of serine/threonine kinases, the STK17B has been associated with diverse diseases such as hepatocellular carcinoma. However, the conformational dynamics of the phosphate-binding loop (P-loop) in the determination of inhibitor selectivity profile to the STK17B are less understood. Here, a multi-microsecond length molecular dynamics (MD) simulation of STK17B in the three different states (ligand-free, ADP-bound, and ligand-bound states) was carried out to uncover the conformational plasticity of the P-loop. Together with the analyses of principal component analysis, cross-correlation and generalized correlation motions, secondary structural analysis, and community network analysis, the conformational dynamics of the P-loop in the different states were revealed, in which the P-loop flipped into the ADP-binding site upon the inhibitor binding and interacted with the inhibitor and the C-lobe, strengthened the communication between the N- and C-lobes. These resulting interactions contributed to inhibitor selectivity profile to the STK17B. Our results may advance our understanding of kinase inhibitor selectivity and offer possible implications for the design of highly selective inhibitors for other protein kinases.

Long-Timescale Simulations Revealed Critical Non-Conserved Residues of Phosphodiesterases Affecting Selectivity of BAY60-7550

A major obstacle of the selective inhibitor design for specific human phosphodiesterase (PDE) is that highly conserved catalytic pockets are difficult to be distinguished by inhibitor molecules. To overcome this, a feasible path is to understand the molecular determinants underlying the selectivity of current inhibitors. BAY60-7550 (BAY for short; IC₅₀ = 4.7 nM) is a highly selective inhibitor targeting PDE2A which is a dual-specificity PDE and an attractive target for therapeutic intervention of the central nervous system (CNS) disorders. Recent studies suggest that molecular determinants may be in binding processes of BAY. However, a detailed understanding of these processes are still lacking. To explore these processes, High-Throughput Molecular Dynamics (HTMD) simulations were performed to reproduce the spontaneous association of BAY with catalytic pockets of 4 PDE isoforms; Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD) simulations were performed to reproduce the unbinding-rebinding processes of FKG and 10.13039/100016266MC2, two pyrazolopyrimidinone PDE2A selective inhibitors, in the PDE2A system. The produced molecular trajectories were analyzed by the Markov state model (MSM) and the molecular mechanics/generalized Born surface area (MM/GBSA). The results showed that the non-covalent interactions between the non-conserved residues and BAY, especially the hydrogen bonds, determined the unique binding pathways of BAY on the surface of PDE2A. These pathways were different from those of BAY on the surface of the other three PDE isoforms and the binding pathways of the other two PDE2A inhibitors in PDE2A systems. These differences were ultimately reflected in the high selectivity of this inhibitor for PDE2A. As a result, this study demonstrates the critical role of the binding processes in the selectivity of BAY, and also identifies the key non-conserved residues affecting the binding processes of BAY. Thus, this study provides a new perspective and data support for the further development of BAY-derived inhibitors targeting PDE2A.