MF-SuP-pKa: Multi-fidelity modeling with subgraph pooling mechanism for pKa prediction

Acid-base dissociation constant (pKa) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pKa prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pKa (multi-fidelity modeling with subgraph pooling for pKa prediction), a novel pKa prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pKa prediction. To overcome the scarcity of accurate pKa data, low-fidelity data (computational pKa) was used to fit the high-fidelity data (experimental pKa) through transfer learning. The final MF-SuP-pKa model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pKa achieves superior performances to the state-of-the-art pKa prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pKa achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.

Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach

Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a model called auxiliary multi-task graph isomorphism network with uncertainty weighting (AMGU) was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network (MT-GIN) with the auxiliary learning and uncertainty weighting strategy. The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks (GNN) models on the internal test set. Furthermore, it also exhibited much better performance on two external test sets, suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity. Then, a naïve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms, and the consistency of the interpretability results for 5 typical epidermal growth factor receptor (EGFR) inhibitors with their structure‒activity relationships could be observed. Finally, a free online web server called KIP was developed to predict the kinome-wide polypharmacology effects of small molecules (http://cadd.zju.edu.cn/kip).

Metformin activates chaperone-mediated autophagy and improves disease pathologies in an Alzheimer disease mouse model

Chaperone-mediated autophagy (CMA) is a lysosome-dependent selective degradation pathway implicated in the pathogenesis of cancer and neurodegenerative diseases. However, the mechanisms that regulate CMA are not fully understood. Here, using unbiased drug screening approaches, we discover Metformin, a drug that is commonly the first medication prescribed for type 2 diabetes, can induce CMA. We delineate the mechanism of CMA induction by Metformin to be via activation of TAK1-IKKα/β signaling that leads to phosphorylation of Ser85 of the key mediator of CMA, Hsc70, and its activation. Notably, we find that amyloid-beta precursor protein (APP) is a CMA substrate and that it binds to Hsc70 in an IKKα/β-dependent manner. The inhibition of CMA-mediated degradation of APP enhances its cytotoxicity. Importantly, we find that in the APP/PS1 mouse model of Alzheimer's disease (AD), activation of CMA by Hsc70 overexpression or Metformin potently reduces the accumulated brain Aβ plaque levels and reverses the molecular and behavioral AD phenotypes. Our study elucidates a novel mechanism of CMA regulation via Metformin-TAK1-IKKα/β-Hsc70 signaling and suggests Metformin as a new activator of CMA for diseases, such as AD, where such therapeutic intervention could be beneficial.

Discovery of a small molecule inhibitor of cullin neddylation that triggers ER stress to induce autophagy

Protein neddylation is catalyzed by a three-enzyme cascade, namely an E1 NEDD8-activating enzyme (NAE), one of two E2 NEDD8 conjugation enzymes and one of several E3 NEDD8 ligases. The physiological substrates of neddylation are the family members of cullin, the scaffold component of cullin RING ligases (CRLs). Currently, a potent E1 inhibitor, MLN4924, also known as pevonedistat, is in several clinical trials for anti-cancer therapy. Here we report the discovery, through virtual screening and structural modifications, of a small molecule compound HA-1141 that directly binds to NAE in both

Discovery of Novel Androgen Receptor Ligands by Structure-based Virtual Screening and Bioassays / 基因组蛋白质组与生物信息学报·英文版

Androgen receptor (AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of many severe diseases such as prostate cancer, muscle atrophy, and osteoporosis. Binding of ligands to AR triggers the conformational changes in AR that may affect the recruitment of coactivators and downstream response of AR signaling pathway. Therefore, AR ligands have great potential to treat these diseases. In this study, we searched for novel AR ligands by performing a docking-based virtual screening (VS) on the basis of the crystal structure of the AR ligand binding domain (LBD) in complex with its agonist. A total of 58 structurally diverse compounds were selected and subjected to LBD affinity assay, with five of them (HBP1-3, HBP1-17, HBP1-38, HBP1-51, and HBP1-58) exhibiting strong binding to AR-LBD. The IC values of HBP1-51 and HBP1-58 are 3.96 µM and 4.92 µM, respectively, which are even lower than that of enzalutamide (Enz, IC = 13.87 µM), a marketed second-generation AR antagonist. Further bioactivity assays suggest that HBP1-51 is an AR agonist, whereas HBP1-58 is an AR antagonist. In addition, molecular dynamics (MD) simulations and principal components analysis (PCA) were carried out to reveal the binding principle of the newly-identified AR ligands toward AR. Our modeling results indicate that the conformational changes of helix 12 induced by the bindings of antagonist and agonist are visibly different. In summary, the current study provides a highly efficient way to discover novel AR ligands, which could serve as the starting point for development of new therapeutics for AR-related diseases.