Huanglian-Wendan Decoction Inhibits NF-κB/NLRP3 Inflammasome Activation in Liver and Brain of Rats Exposed to Chronic Unpredictable Mild Stress.

Depression is a common mental disorder in modern society. A traditional Chinese medicine Huanglian-Wendan decoction with potential anti-inflammation is used as a clinical antidepressant. Our previous study showed central and peripheral inflammatory responses in a rat model of depression developed by chronic unpredictable mild stress (CUMS). Here, we investigated the anti-inflammatory activity and mechanism of Huanglian-Wendan decoction in CUMS rats. LC-MS/MS and HPLC were performed to determine the major compounds in water extract of this decoction. This study showed that Huanglian-Wendan decoction significantly increased sucrose consumption and reduced serum levels of interleukin-1 beta (IL-1ß), IL-6, and alanine aminotransferase (ALT) in CUMS rats. Moreover, this decoction inhibited nuclear entry of nuclear factor-kappa B (NF-κB) with the reduction of phosphorylated protein of NF-κB (p-NF-κB) and inhibitor of NF-κB alpha (p-IκBα) and downregulated protein of nod-like receptor family pyrin domain-containing 3 (NLRP3), apoptosis-associated speck-like protein containing CARD (ASC), cysteinyl aspartate-specific proteinase-1 (Caspase-1), and IL-1ß in liver and brain regions of CUMS rats. These findings demonstrated that Huanglian-Wendan decoction had antidepressant activity with hepatoprotection in CUMS rats coinciding with its anti-inflammation in both periphery and central. The inhibitory modulation of NF-κB and NLRP3 inflammasome activation by Huanglian-Wendan decoction may mediate its antidepressant action.

Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels of generalization capability and interpretability. Intuitively, the inductive bias of an ML-based model for PLA prediction should fit in with biological mechanisms relevant for binding to achieve good predictions with meaningful reasons. To this end, we propose an interaction-based inductive bias to restrict neural networks to functions relevant for binding with two assumptions: (1) A protein-ligand complex can be naturally expressed as a heterogeneous graph with covalent and non-covalent interactions; (2) The predicted PLA is the sum of pairwise atom-atom affinities determined by non-covalent interactions. The interaction-based inductive bias is embodied by an explainable heterogeneous interaction graph neural network (EHIGN) for explicitly modeling pairwise atom-atom interactions to predict PLA from 3D structures. Extensive experiments demonstrate that EHIGN achieves better generalization capability than other state-of-the-art ML-based baselines in PLA prediction and structure-based virtual screening. More importantly, comprehensive analyses of distance-affinity, pose-affinity, and substructure-affinity relations suggest that the interaction-based inductive bias can guide the model to learn atomic interactions that are consistent with physical reality. As a case study to demonstrate practical usefulness, our method is tested for predicting the efficacy of Nirmatrelvir against SARS-CoV-2 variants. EHIGN successfully recognizes the changes in the efficacy of Nirmatrelvir for different SARS-CoV-2 variants with meaningful reasons.

Nanoparticles for inducing Gaucher disease-like damage in cancer cells.

Nutrient avidity is one of the most distinctive features of tumours. However, nutrient deprivation has yielded limited clinical benefits. In Gaucher disease, an inherited metabolic disorder, cells produce cholesteryl-glucoside which accumulates in lysosomes and causes cell damage. Here we develop a nanoparticle (AbCholB) to emulate natural-lipoprotein-carried cholesterol and initiate Gaucher disease-like damage in cancer cells. AbCholB is composed of a phenylboronic-acid-modified cholesterol (CholB) and albumin. Cancer cells uptake the nanoparticles into lysosomes, where CholB reacts with glucose and generates a cholesteryl-glucoside-like structure that resists degradation and aggregates into microscale crystals, causing Gaucher disease-like damage in a glucose-dependent manner. In addition, the nutrient-sensing function of mTOR is suppressed. It is observed that normal cells escape severe damage due to their inferior ability to compete for nutrients compared with cancer cells. This work provides a bioinspired strategy to selectively impede the metabolic action of cancer cells by taking advantage of their nutrient avidity.

Geometric Interaction Graph Neural Network for Predicting Protein-Ligand Binding Affinities from 3D Structures (GIGN).

Predicting protein-ligand binding affinities (PLAs) is a core problem in drug discovery. Recent advances have shown great potential in applying machine learning (ML) for PLA prediction. However, most of them omit the 3D structures of complexes and physical interactions between proteins and ligands, which are considered essential to understanding the binding mechanism. This paper proposes a geometric interaction graph neural network (GIGN) that incorporates 3D structures and physical interactions for predicting protein-ligand binding affinities. Specifically, we design a heterogeneous interaction layer that unifies covalent and noncovalent interactions into the message passing phase to learn node representations more effectively. The heterogeneous interaction layer also follows fundamental biological laws, including invariance to translations and rotations of the complexes, thus avoiding expensive data augmentation strategies. GIGN achieves state-of-the-art performance on three external test sets. Moreover, by visualizing learned representations of protein-ligand complexes, we show that the predictions of GIGN are biologically meaningful.

Equivariant Flexible Modeling of the Protein-Ligand Binding Pose with Geometric Deep Learning.

Flexible modeling of the protein-ligand complex structure is a fundamental challenge for in silico drug development. Recent studies have improved commonly used docking tools by incorporating extra-deep learning-based steps. However, such strategies limit their accuracy and efficiency because they retain massive sampling pressure and lack consideration for flexible biomolecular changes. In this study, we propose FlexPose, a geometric graph network capable of direct flexible modeling of complex structures in Euclidean space without the following conventional sampling and scoring strategies. Our model adopts two key designs: scalar-vector dual feature representation and SE(3)-equivariant network, to manage dynamic structural changes, as well as two strategies: conformation-aware pretraining and weakly supervised learning, to boost model generalizability in unseen chemical space. Benefiting from these paradigms, our model dramatically outperforms all tested popular docking tools and recently advanced deep learning methods, especially in tasks involving protein conformation changes. We further investigate the impact of protein and ligand similarity on the model performance with two conformation-aware strategies. Moreover, FlexPose provides an affinity estimation and model confidence for postanalysis.

Hijacking Self-Assembly to Establish Intracellular Functional Nanoparticles.

The targeted transport of nanomedicines is often impeded by various biological events in the body. Viruses can hijack host cells and utilize intracellular transcription and translation biological events to achieve their replication. Inspired by this, a strategy to hijack endogenous products of biological events to assemble into intracellular functional nanoparticles is established. It has been shown that, following tumor vessel destruction therapy, injected cell permeable small molecule drugs bisphosphonate can hijack the hemorrhagic product iron and self-assemble into peroxidase-like nanoparticles within tumor-infiltrating macrophages. Unlike free drugs, the generated intercellular nanoparticles can specifically stress mitochondria, resulting in immune activation of macrophages in vitro and polarizing tumor-associated macrophages (TAMs) from immunosuppressive to tumoricidal and increasing the recruitment of T cells deep within tumor. The hijacking self-assembly strategy significantly inhibits tumor growth compared with the treatment of vascular-disrupting agents alone. Using bisphosphonate to hijack the metabolite associated with hemorrhage, iron, to fabricate functional nanoparticles within specific cells, which may open up new nanotechnology for drug delivery and small molecular drug development.

Accurate Estimation of Solvent Accessible Surface Area for Coarse-Grained Biomolecular Structures with Deep Learning.

Coarse-grained (CG) models of biomolecules have been widely used in protein/ribonucleic acid (RNA) three-dimensional structure prediction, docking, drug design, and molecular simulations due to their superiority in computational efficiency. Most of these applications strongly depend on the reasonable estimation of solvation free energy, which requires the accurate calculation of solvent accessible surface area (SASA). Although algorithms for SASA calculations with all-atom protein and RNA structures have been well-established, accurately estimating the SASA based on CG structures is extremely challenging. In this work, we developed a deep learning-based SASA estimator (DeepCGSA), which can provide almost perfect SASA estimation based on CG structures of protein and RNA molecules. Extensive testing analysis showed that for three types of widely used CG protein models, including the Cα-based, Cα-Cß, and Martini models, the correlation coefficients between the predicted values and the reference values can be as high as 0.95-0.99, which perform dramatically better than available methods. In addition, the new method can be used for CG RNA structures and unfolded protein structures with much improved accuracy. We anticipate that DeepCGSA will be highly useful in the protein/RNA structure prediction, drug design, and other applications, in which accurate estimations of SASA for CG biomolecular structures are critically important.

E. coli Membrane Vesicles as a Catalase Carrier for Long-Term Tumor Hypoxia Relief to Enhance Radiotherapy.

Hypoxia is one of the most important factors that limit the effect of radiotherapy, and the abundant H2O2 in tumor tissues will also aggravate hypoxia-induced radiotherapy resistance. Delivering catalase to decompose H2O2 into oxygen is an effective strategy to relieve tumor hypoxia and radiotherapy resistance. However, low stability limits catalase's in vivo application, which is one of the most common limitations for almost all proteins' internal utilization. Here, we develop catalase containing E. coli membrane vesicles (EMs) with excellent protease resistance to relieve tumor hypoxia for a long time. Even treated with 100-fold of protease, EMs showed higher catalase activity than free catalase. After being injected into tumors post 12 h, EMs maintained their hypoxia relief ability while free catalase lost its activity. Our results indicate that EMs might be an excellent catalase delivery for tumor hypoxia relief. Combined with their immune stimulation features, EMs could enhance radiotherapy and induce antitumor immune memory effectively.