A CARM1 Inhibitor Potently Suppresses Breast Cancer Both In Vitro and In Vivo.

CARM1, belonging to the protein arginine methyltransferase (PRMT) family, is intricately associated with the progression of cancer and is viewed as a promising target for both cancer diagnosis and therapy. However, the number of specific and potent CARM1 inhibitors is limited. We herein discovered a CARM1 inhibitor, iCARM1, that showed better specificity and activity toward CARM1 compared to the known CARM1 inhibitors, EZM2302 and TP-064. Similar to CARM1 knockdown, iCARM1 suppressed the expression of oncogenic estrogen/ERα-target genes, whereas activated type I interferon (IFN) and IFN-induced genes (ISGs) in breast cancer cells. Consequently, iCARM1 potently suppressed breast cancer cell growth both in vitro and in vivo. The combination of iCARM1 with either endocrine therapy drugs or etoposide demonstrated synergistic effects in inhibiting the growth of breast tumors. In summary, targeting CARM1 by iCARM1 effectively suppresses breast tumor growth, offering a promising therapeutic approach for managing breast cancers in clinical settings.

Peptide Inhibitor Targeting the Extraterminal Domain in BRD4 Potently Suppresses Breast Cancer Both In Vitro and In Vivo.

BRD4 is associated with a variety of human diseases, including breast cancer. The crucial roles of amino-terminal bromodomains (BDs) of BRD4 in binding with acetylated histones to regulate oncogene expression make them promising drug targets. However, adverse events impede the development of the BD inhibitors. BRD4 adopts an extraterminal (ET) domain, which recruits proteins to drive oncogene expression. We discovered a peptide inhibitor PiET targeting the ET domain to disrupt BRD4/JMJD6 interaction, a protein complex critical in oncogene expression and breast cancer. The cell-permeable form of PiET, TAT-PiET, and PROTAC-modified TAT-PiET, TAT-PiET-PROTAC, potently inhibits the expression of BRD4/JMJD6 target genes and breast cancer cell growth. Combination therapy with TAT-PiET/TAT-PiET-PROTAC and JQ1, iJMJD6, or Fulvestrant exhibits synergistic effects. TAT-PiET or TAT-PiET-PROTAC treatment overcomes endocrine therapy resistance in ERα-positive breast cancer cells. Taken together, we demonstrated that targeting the ET domain is effective in suppressing breast cancer, providing a therapeutic avenue in the clinic.

A cross-sectional study of the association between breastfeeding history and overweight/obesity in postmenopausal women.

OBJECTIVES: This study endeavors to augment comprehension of the association between breastfeeding and maternal weight within Asian populations. METHODS: Data were obtained from the comprehensive 2011 research titled "Risk Evaluation of Cancers in Chinese Diabetic Individuals (REACTION): a longitudinal analysis," focusing specifically on postmenopausal women residing in the metropolitan precincts of Guiyang. It presents a cross-sectional study involving 5,987 parous postmenopausal women, aged 60.1 ± 6.9 years, who underwent assessments of body mass index and waist-to-height ratio. The probability of excessive weight or obesity was evaluated in relation to the aggregate duration of breastfeeding, using single-factor and multivariate logistic regression analyses. RESULTS: Following multiple adjustments for different confounders, the odds ratios (ORs) demonstrated that women who had borne a single child and breastfed for more than 12 months exhibited an increased prevalence of excessive weight (body mass index ≥24 kg/m 2 ) in contrast to those who abstained from breastfeeding (model I: OR, 1.481; 95% confidence interval, 1.124-1.952; P = 0.005; model II: OR, 1.471; 95% confidence interval, 1.113-1.944; P = 0.007). Conversely, among the subset of women who had given birth to two or more children, no noteworthy associations emerged between breastfeeding duration and the propensity for excessive weight or obesity (all models). CONCLUSION: In the Asian population, the duration of breastfeeding does not appear to be necessarily linked to the prevalence of overweight or obesity in postmenopausal women.

Mass Flow and Metabolic Pathway of Nonaeration Greywater Treatment in an Oxygenic Microalgal-Bacterial Biofilm.

A symbiotic microalgal-bacterial biofilm can enable efficient carbon (C) and nitrogen (N) removal during aeration-free wastewater treatment. However, the contributions of microalgae and bacteria to C and N removal remain unexplored. Here, we developed a baffled oxygenic microalgal-bacterial biofilm reactor (MBBfR) for the nonaerated treatment of greywater. A hydraulic retention time (HRT) of 6 h gave the highest biomass concentration and biofilm thickness as well as the maximum removal of chemical oxygen demand (94.8%), linear alkylbenzenesulfonates (LAS, 99.7%), and total nitrogen (97.4%). An HRT of 4 h caused a decline in all of the performance metrics due to LAS biotoxicity. Most of C (92.6%) and N (95.7%) removals were ultimately associated with newly synthesized biomass, with only minor fractions transformed into CO2 (2.2%) and N2 (1.7%) on the function of multifarious-related enzymes in the symbiotic biofilm. Specifically, microalgae photosynthesis contributed to the removal of C and N at 75.3 and 79.0%, respectively, which accounted for 17.3% (C) and 16.7% (N) by bacteria assimilation. Oxygen produced by microalgae favored the efficient organics mineralization and CO2 supply by bacteria. The symbiotic biofilm system achieved stable and efficient removal of C and N during greywater treatment, thus providing a novel technology to achieve low-energy-input wastewater treatment, reuse, and resource recovery.

Development of 3-acetylindole derivatives that selectively target BRPF1 as new inhibitors of receptor activator of NF-κB ligand (RANKL)-Induced osteoclastogenesis.

Bromodomain and PHD finger-containing (BRPF) proteins function as epigenetic readers that specifically recognize acetylated lysine residues on histone tails. The acetyl-lysine binding pocket of BRPF has emerged as an attractive target for the development of protein interaction inhibitors owing to its potential druggability. In this study, we identified 3-acetylindoles as bone antiresorptive agents with a novel scaffold by performing structure-based virtual screening and hit optimization. Among those derivatives, compound 18 exhibited potent and selective inhibitory activities against BRPF1B (IC50 = 102 nM) as well as outstanding inhibitory activity against osteoclastogenesis (73.8% @ 1 µM) and differentiation (IC50 = 0.19 µM) without cytotoxicity. Besides, cellular mechanism assays demonstrated that compound 18 exhibited a strong bone antiresorptive effect by modulating the RANKL/RANK/NFATc1 pathway. Structural and functional studies on BRPF1 inhibitors aid in making advances to understand the epigenetic mechanisms of bone cell development and create innovative therapeutics for treating bone metastases from solid tumors and other bone erosive diseases.

3D based generative PROTAC linker design with reinforcement learning.

Proteolysis targeting chimera (PROTAC), has emerged as an effective modality to selectively degrade disease-related proteins by harnessing the ubiquitin-proteasome system. Due to PROTACs' hetero-bifunctional characteristics, in which a linker joins a warhead binding to a protein of interest (POI), conferring specificity and a E3-ligand binding to an E3 ubiquitin ligase, this could trigger the ubiquitination and transportation of POI to the proteasome, followed by degradation. The rational PROTAC linker design is challenging due to its relatively large molecular weight and the complexity of maintaining the binding mode of warhead and E3-ligand in the binding pockets of counterpart. Conventional linker generation method can only generate linkers in either 1D SMILES or 2D graph, without taking into account the information of ternary structures. Here we propose a novel 3D linker generative model PROTAC-INVENT which can not only generate SMILES of PROTAC but also its 3D putative binding conformation coupled with the target protein and the E3 ligase. The model is trained jointly with the RL approach to bias the generation of PROTAC structures toward pre-defined 2D and 3D based properties. Examples were provided to demonstrate the utility of the model for generating reasonable 3D conformation of PROTACs. On the other hand, our results show that the associated workflow for 3D PROTAC conformation generation can also be used as an efficient docking protocol for PROTACs.

An extensive benchmark study on biomedical text generation and mining with ChatGPT.

MOTIVATION: In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks and benchmarks and got excellent results. With exciting performance on daily chat, researchers began to explore the capacity of ChatGPT on expertise that requires professional education for human and we are interested in the biomedical domain. RESULTS: To evaluate the performance of ChatGPT on biomedical-related tasks, this article presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions, and so on. Typical NLP tasks like named entity recognization, relation extraction, sentence similarity, question and answering, and document classification are included. Overall, ChatGPT got a BLURB score of 58.50 while the state-of-the-art model had a score of 84.30. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation, and the limitation of ChatGPT build on GPT-3.5. AVAILABILITY AND IMPLEMENTATION: All the datasets are available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The prompts are described in the article.

MAVI1, an endoplasmic reticulum-localized microprotein, suppresses antiviral innate immune response by targeting MAVS on mitochondrion.

Pattern recognition receptor-mediated innate immunity is critical for host defense against viruses. A growing number of coding and noncoding genes are found to encode microproteins. However, the landscape and functions of microproteins in responsive to virus infection remain uncharacterized. Here, we systematically identified microproteins that are responsive to vesicular stomatitis virus infection. A conserved and endoplasmic reticulum-localized membrane microprotein, MAVI1 (microprotein in antiviral immunity 1), was found to interact with mitochondrion-localized MAVS protein and inhibit MAVS aggregation and type I interferon signaling activation. The importance of MAVI1 was highlighted that viral infection was attenuated and survival rate was increased in Mavi1-knockout mice. A peptide inhibitor targeting the interaction between MAVI1 and MAVS activated the type I interferon signaling to defend viral infection. Our findings uncovered that microproteins play critical roles in regulating antiviral innate immune responses, and targeting microproteins might represent a therapeutic avenue for treating viral infection.

Biofilm bioactivity affects nitrogen metabolism in a push-flow microalgae-bacteria biofilm reactor during aeration-free greywater treatment.

Non-aeration microalgae-bacteria biofilm has attracted increasing interest for its application in low cost wastewater treatment. However, it is unclear the quantified biofilm characteristics dynamics and how biofilm bioactivity affects performance and nitrogen metabolisms during wastewater treatment. In this work, a push-flow microalgae-bacteria biofilm reactor (PF-MBBfR) was developed for aeration-free greywater treatment. Comparatively, organic loading at 1.27 ± 0.10 kg COD/(m3⋅d) gave the highest biofilm concentration, density, specific oxygen generation (SOGR) and consumption rates (SOCR), and pollutants removal rates. Contributed to low residual linear alkylbenzene sulfonates and bioactivity, reactor downstream showed low bacteria and protein concentrations and SOCR (12.8 mg O2/g TSS·h), but high microalgae, carbohydrate, biofilm density, SOGR (49.4 mg O2/g TSS·h) and pollutants removal rates. Dissolved organic nitrogen (DON) showed higher molecular weight, CHONS and fraction with 4 atoms of N in reactor upstream. Most of nitrogen was fixed to newly synthesized biomass during assimilation process by related functional enzymes, minor contributed to denitrification due to low N2 emission. High nitrogen assimilation by microalgae showed high SOGR, which favored efficient multiple pollutants removal and reduced DON emission. Our findings favor the practical application of PF-MBBfR based on biofilm bioactivity, enhancing efficiency and reducing DON emission for low- energy-input wastewater treatment.

The recent progress of deep-learning-based in silico prediction of drug combination.

Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.

Structure of the human respiratory complex II.

Human complex II is a key protein complex that links two essential energy-producing processes: the tricarboxylic acid cycle and oxidative phosphorylation. Deficiencies due to mutagenesis have been shown to cause mitochondrial disease and some types of cancers. However, the structure of this complex is yet to be resolved, hindering a comprehensive understanding of the functional aspects of this molecular machine. Here, we have determined the structure of human complex II in the presence of ubiquinone at 2.86 Å resolution by cryoelectron microscopy, showing it comprises two water-soluble subunits, SDHA and SDHB, and two membrane-spanning subunits, SDHC and SDHD. This structure allows us to propose a route for electron transfer. In addition, clinically relevant mutations are mapped onto the structure. This mapping provides a molecular understanding to explain why these variants have the potential to produce disease.

Application of deep generative model for design of Pyrrolo[2,3-d] pyrimidine derivatives as new selective TANK binding kinase 1 (TBK1) inhibitors.

The deep conditional transformer neural network SyntaLinker was applied to identify compounds with pyrrolo[2,3-d]pyrimidine scaffold as potent selective TBK1 inhibitor. Further medicinal chemistry optimization campaign led to the discovery of the most potent compound 7l, which exhibited strong enzymatic inhibitory activity against TBK1 with an IC50 value of 22.4 nM 7l had a superior inhibitory activity in human monocytic THP1-Blue cells reporter gene assay than MRT67307. Furthermore, 7l significantly inhibited TBK1 downstream target genes cxcl10 and ifnß expression in THP1 and RAW264.7 cells induced by poly (I:C) and lipopolysaccharide, respectively. This study suggested that combination of deep conditional transformer neural network SyntaLinker and transfer learning could be a powerful tool for scaffold hopping in drug discovery.

Cathepsin S inhibitor reduces high-fat-induced adipogenesis, inflammatory infiltration, and hepatic lipid accumulation in obese mice.

Background: Obesity, which results from a caloric intake and energy expenditure imbalance, is highly prevalent worldwide. Cathepsin S (CTSS), which is a cysteine protease, is elevated in obesity and may regulate a variety of physiological processes. This study sought to investigate the functional role of CTSS in obesity. Methods: Mice were administrated 60 mg/kg of RO5444101 in vivo and fed a high-fat diet (HFD) to induce obesity. The weights of the mice fed a normal-chow diet and a HFD were measured. The expression levels of total triglycerides (TG), total cholesterol (TC), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and monocyte chemoattractant protein-1 (MCP-1) were assessed using appropriate corresponding assay kits. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to estimate the messenger ribonucleic acid (mRNA) expression of CTSS in the serum and the release of M1- and M2-type cytokines, and western blot was used to measure the phosphorylated-nuclear factor kappaB (NF-kappaB) p65 and NF-κB p65 proteins. The mRNA and protein expressions of sterol regulatory element-binding protein 1 (SREBP1), fatty acid synthase (FASN), leptin, and adiponectin were also evaluated by RT-qPCR and western blot. Further, hematoxylin and eosin (H&E), immunohistochemical, and red oil O staining were employed to detect the pathological changes of the epididymal white adipose tissue (eWAT), the macrophage infiltration in the eWAT, and lipid accumulation, respectively. Results: We found that CTSS was elevated in the plasma, visceral adipose, and liver tissues of the obese mice. After the administration of 60 mg/kg of RO5444101, the weight of the obese mice decreased, insulin resistance was inhibited, and adipocyte formation was suppressed. The CTSS inhibitor also decreased the level of macrophage infiltration in the eWAT, MCP-1 expression, and the release of M1- and M2-type cytokines in the HFD-induced mice. The CTSS inhibitor appeared to improve the hepatic function parameters and lipid accumulation of the HFD-induced mice. The CTSS inhibitor also appeared to improve the inflammatory damage in the HFD-induced mice. Conclusions: CTSS inhibitor helped to protect against HFD-induced adipogenesis, inflammatory infiltration, and hepatic lipid accumulation in obese mice.

A specific JMJD6 inhibitor potently suppresses multiple types of cancers both in vitro and in vivo.

Jumonji C-domain-containing protein 6 (JMJD6), an iron (Fe2+) and α-ketoglutarate (α-KG)-dependent oxygenase, is expressed at high levels, correlated with poor prognosis, and considered as a therapeutic target in multiple cancer types. However, specific JMJD6 inhibitors that are potent in suppressing tumorigenesis have not been reported so far. We herein report that iJMJD6, a specific small-molecule inhibitor of JMJD6 with favorable physiochemical properties, inhibits the enzymatic activity of JMJD6 protein both in vitro and in cultured cells. iJMJD6 is effective in suppressing cell proliferation, migration, and invasion in multiple types of cancer cells in a JMJD6-dependent manner, while it exhibits minimal toxicity in normal cells. Mechanistically, iJMJD6 represses the expression of oncogenes, including Myc and CCND1, in accordance with JMJD6 function in promoting the transcription of these genes. iJMJD6 exhibits suitable pharmacokinetic properties and suppresses tumor growth in multiple cancer cell line- and patient-derived xenograft models safely. Furthermore, combination therapy with iJMJD6 and BET protein inhibitor (BETi) JQ1 or estrogen receptor antagonist fulvestrant exhibits synergistic effects in suppressing tumor growth. Taken together, we demonstrate that inhibition of JMJD6 enzymatic activity by using iJMJD6 is effective in suppressing oncogene expression and cancer development, providing a therapeutic avenue for treating cancers that are dependent on JMJD6 in the clinic.

Identification and structural analysis of a selective tropomyosin receptor kinase C (TRKC) inhibitor.

Tropomyosin receptor kinases (TRKs) are a family of TRKA, TRKB and TRKC isoforms. It has been widely reported that TRKs are implicated in a variety of tumors with several Pan-TRK inhibitors currently being used or evaluated in clinical treatment. However, off-target adverse events frequently occur in the clinical use of Pan-TRK inhibitors, which result in poor patient compliance, even drug discontinuation. Although a subtype-selectivity TRK inhibitor may avert the potential off-target adverse events and can act as a more powerful tool compound in the biochemical studies on TRKs, the high sequence similarities of TRKs hinder the development of subtype-selectivity TRK inhibitors. For example, no selective TRKC inhibitor has been reported. Herein, a selective TRKC inhibitor (L13) was disclosed, with potent TRKC inhibitory activity and 107.5-/34.9-fold selectivity over TRKA/B (IC50 TRKA/B/C = 1400 nM, 454 nM, 13 nM, respectively). Extensive molecular dynamics simulations illustrated that key interactions of L13 with the residues and diversely conserved water molecules in the ribose regions of different TRKs may be the structural basis of selectivity. This will provide inspiring insights into the development of subtype-selectivity TRK inhibitors. Moreover, L13 could serve as a tool compound to investigate the distinct biological functions of TRKC and a starting point for further research on drugs specifically targeting TRKC.

Discovery of 1-(5-(1H-benzo[d]imidazole-2-yl)-2,4-dimethyl-1H-pyrrol-3-yl)ethan-1-one derivatives as novel and potent bromodomain and extra-terminal (BET) inhibitors with anticancer efficacy.

As epigenetic readers, bromodomain and extra-terminal domain (BET) family proteins bind to acetylated-lysine residues in histones and recruit protein complexes to promote transcription initiation and elongation. Inhibition of BET bromodomains by small molecule inhibitors has emerged as a promising therapeutic strategy for cancer. Herein, we describe our efforts toward the discovery of a novel series of 1-(5-(1H-benzo[d]imidazole-2-yl)-2,4-dimethyl-1H-pyrrol-3-yl)ethan-1-one derivatives as BET inhibitors. Intensive structural modifications led to the identification of compound 35f as the most active inhibitor of BET BRD4 with selectivity against BET family proteins. Further biological studies revealed that compound 35f can arrest the cell cycle in G0/G1 phase and induce apoptosis via decreasing the expression of c-Myc and other proteins related to cell cycle and apoptosis. More importantly, compound 35f showed favorable pharmacokinetic properties and antitumor efficacy in MV4-11 mouse xenograft model with acceptable tolerability. These results indicated that BET inhibitors could be potentially used to treat hematologic malignancies and some solid tumors.

Structure of Mycobacterium tuberculosis cytochrome bcc in complex with Q203 and TB47, two anti-TB drug candidates.

Pathogenic mycobacteria pose a sustained threat to global human health. Recently, cytochrome bcc complexes have gained interest as targets for antibiotic drug development. However, there is currently no structural information for the cytochrome bcc complex from these pathogenic mycobacteria. Here, we report the structures of Mycobacterium tuberculosis cytochrome bcc alone (2.68 Å resolution) and in complex with clinical drug candidates Q203 (2.67 Å resolution) and TB47 (2.93 Å resolution) determined by single-particle cryo-electron microscopy. M. tuberculosis cytochrome bcc forms a dimeric assembly with endogenous menaquinone/menaquinol bound at the quinone/quinol-binding pockets. We observe Q203 and TB47 bound at the quinol-binding site and stabilized by hydrogen bonds with the side chains of QcrBThr313 and QcrBGlu314, residues that are conserved across pathogenic mycobacteria. These high-resolution images provide a basis for the design of new mycobacterial cytochrome bcc inhibitors that could be developed into broad-spectrum drugs to treat mycobacterial infections.

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches.

The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets.

Deep learning-driven scaffold hopping in the discovery of Akt kinase inhibitors.

Scaffold hopping has been widely used in drug discovery and is a topic of high interest. Here a deep conditional transformer neural network, SyntaLinker, was applied for the scaffold hopping of a phase III clinical Akt inhibitor, AZD5363. A number of novel scaffolds were generated and compound 1a as a proof-of-concept was synthesized and validated by biochemical assay. Further structure-based optimization of 1a led to a novel Akt inhibitor with high potency (Akt1 IC50 = 88 nM) and in vitro antitumor activities.

De Novo Molecule Design Through the Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites.

De novo molecule design through the molecular generative model has gained increasing attention in recent years. Here, a novel generative model was proposed by integrating the three-dimensional (3D) structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of the protein binding pocket is effectively characterized through a coarse-grain strategy and the 3D information of the pocket can be represented by the sorted eigenvalues of the Coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor, DeeplyTough, to train cRNN models and evaluated their performance. It has been shown that the model trained with the constraint of protein environment information has a clear tendency on generating compounds with higher similarity to the original X-ray-bound ligand than the normal RNN model and also better docking scores. Our results demonstrate the potential application of the controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.