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.

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.

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.

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 ifnβ 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.

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.

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.

Diets varying in ratio of sweet sorghum silage to corn silage for lactating dairy cows: Feed intake, milk production, blood biochemistry, ruminal fermentation, and ruminal microbial community

The objective of this study was to investigate the effects of partial substitution of corn silage (CS) with sweet sorghum silage (SS) in the diets of lactating dairy cows on dry matter (DM) intake, milk yield and composition, blood biochemistry, and ruminal fermentation and microbial community. Thirty mid-lactation Holstein dairy cows [mean ± standard deviation; 639 ± 42.0 kg of body weight; 112 ± 24.0 d in milk (DIM)] were assigned to 3 groups (n = 10/treatment) by considering parity, milk yield, and DIM. The cows were fed ad libitum total mixed rations containing 55% forage and 45% concentrate, with only the proportion of CS and SS varying in 3 treatments (DM basis): SS0 (0% substitution of CS), 40% CS and 0% SS; SS25 (25% substitution of CS)_, 30% CS and 10% SS; and SS50 (50% substitution of CS)_, 20% CS and 20% SS. Dry matter intake and milk protein concentration tended to linearly decrease with increasing proportion of SS in the diet. Yields of milk (mean ± standard deviation, 30.9 ± 1.12 kg/d), 4% fat-corrected milk (30.0 ± 0.81 kg/d), energy-corrected milk, milk protein, lactose, and total solids, concentrations of milk fat, lactose, somatic cell counts, and milk efficiency did not differ among diets. The concentrations in blood of urea nitrogen, phosphorus, aspartate aminotransferase, and malondialdehyde linearly increased with increasing SS proportion. Blood IgA decreased with increasing SS substitution rate, but blood IgG and IgM were not different among diets. Ruminal pH did not differ among diets, whereas ruminal NH₃-N concentration quadratically changed such that it was greater for SS50 than for SS0 and SS25. Molar proportions of propionate and acetate to propionate ratio were less for SS25 than for SS0. Although the diversity and general ruminal microbial community structure were not altered by partially replacing CS with SS, the relative abundances of predominant bacteria were affected by diets at the phylum and genus levels. Firmicutes and Bacteroidetes were dominant phyla in the ruminal bacterial community for all diets, and their relative abundance linearly decreased and increased, respectively, with increasing SS substitution rate. Prevotella_1 and Ruminococcaceae_NK4A214_group were detected as the most and the second most abundant genera, with their relative abundance linearly increased and decreased, respectively, with increasing SS substitution rate. The relative abundance of Fibrobacter linearly increased with increasing dietary SS proportion, with greater abundance observed for SS25 and SS50 than for SS0. These results suggest that substitution of CS with SS altered the relative abundances of some predominant bacteria; however, these changes had little effect on ruminal fermentation and milk yield. Under the current experimental conditions, substituting up to 50% of CS with SS had no negative effects on milk yield, indicating that SS can partially replace CS in the diets of high-producing lactating dairy cows without adding extra grain, when diets are fed for a short time. As the effects of substituting CS with SS depend upon the chemical composition and digestibility of these silages and the nutrient requirements of the cows, additional grain may be required in some cases to compensate for the lower starch content of SS.

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.

Profiling PRMT methylome reveals roles of hnRNPA1 arginine methylation in RNA splicing and cell growth

Numerous substrates have been identified for Type I and II arginine methyltransferases (PRMTs). However, the full substrate spectrum of the only type III PRMT, PRMT7, and its connection to type I and II PRMT substrates remains unknown. Here, we use mass spectrometry to reveal features of PRMT7-regulated methylation. We find that PRMT7 predominantly methylates a glycine and arginine motif; multiple PRMT7-regulated arginine methylation sites are close to phosphorylations sites; methylation sites and proximal sequences are vulnerable to cancer mutations; and methylation is enriched in proteins associated with spliceosome and RNA-related pathways. We show that PRMT4/5/7-mediated arginine methylation regulates hnRNPA1 binding to RNA and several alternative splicing events. In breast, colorectal and prostate cancer cells, PRMT4/5/7 are upregulated and associated with high levels of hnRNPA1 arginine methylation and aberrant alternative splicing. Pharmacological inhibition of PRMT4/5/7 suppresses cancer cell growth and their co-inhibition shows synergistic effects, suggesting them as targets for cancer therapy.

Arginine methyltransferases (PRMTs) are involved in the regulation of various physiological and pathological conditions. Using proteomics, the authors here profile the methylation substrates of PRMTs 4, 5 and 7 and characterize the roles of these enzymes in cancer-associated splicing regulation.

Scene perception based visual navigation of mobile robot in indoor environment

Only vision-based navigation is the key of cost reduction and widespread application of indoor mobile robot. Consider the unpredictable nature of artificial environments, deep learning techniques can be used to perform navigation with its strong ability to abstract image features. In this paper, we proposed a low-cost way of only vision-based perception to realize indoor mobile robot navigation, converting the problem of visual navigation to scene classification. Existing related research based on deep scene classification network has lower accuracy and brings more computational burden. Additionally, the navigation system has not yet been fully assessed in the previous work. Therefore, we designed a shallow convolutional neural network (CNN) with higher scene classification accuracy and efficiency to process images captured by a monocular camera. Besides, we proposed an adaptive weighted control (AWC) algorithm and combined with regular control (RC) to improve the robot's motion performance. We demonstrated the capability and robustness of the proposed navigation method by performing extensive experiments in both static and dynamic unknown environments. The qualitative and quantitative results showed that the system performs better compared to previous related work in unknown environments.

Effects of grain type and conditioning temperature during pelleting on growth performance, ruminal fermentation, meat quality and blood metabolites of fattening lambs

Ruminants can tolerate moderate concentrations of dietary tannin, making it feasible to replace corn with sorghum in ruminant diets; however, conditioning temperature of pelleted total mixed ration (PTMR) greatly affects nutrient digestibility. The objective was to determine effects of grain type and conditioning temperature during pelleting on growth performance, ruminal fermentation, meat quality and blood metabolites of fattening lambs. This was a 2 × 3 factorial study, with corn and sorghum and three conditioning temperatures (65, 75 and 85 °C) in a randomized complete design, with 36 lambs (120 ± 10.2 d and 24.9 ± 3.3 kg) grouped by weight and randomly allocated. The resulting six PTMRs were referred to as 65-S, 75-S and 85-S for sorghum-based diets, and 65-C, 75-C and 85-C for corn-based diets, for low, medium and high pelleting temperatures, respectively. There was no grain type × conditioning temperature (Grain × Temp) interaction on growth performance and apparent nutrient digestibility. Furthermore, grain type did not affect DM intake (DMI), average daily gain (ADG) or feed conversion ratio (FCR) of fattening lambs. Pelleting at 75 °C improved ADG (P < 0.03) and FCR (P < 0.02) of fattening lambs compared to other temperatures. There was a Grain × Temp interaction (P < 0.01) on ruminal pH (lowest in lambs fed 75-S). There tended (P = 0.07) to be a Grain × Temp interaction for total volatile fatty acid (VFA), and there were Grain × Temp interactions for molar proportions of acetate (P < 0.04), butyrate (P < 0.03) and branch-chained VFA (P < 0.01). Lambs fed sorghum-based PTMR had greater molar proportion of propionate (P < 0.03) and lower acetate to propionate ratio (A:P, P < 0.04). Lambs fed sorghum-based PTMR had higher plasma concentrations of urea nitrogen (N) (P < 0.03), glucose (P < 0.01) and alkaline phosphatase (P < 0.05), whereas other blood metabolites were not affected by treatments. There were Grain × Temp (P < 0.03) interactions for color coordinates of longissimus and mid-gluteal muscle. Lambs fed sorghum-based PTMR had lower (P < 0.01) dressing percentage and meat quality than those fed corn-based PTMR. We concluded that sorghum can replace corn in lamb diets without compromising growth performance and feed efficiency; furthermore, feeding sorghum vs corn improved rumen fermentation, with reduced A:P ratio and enhanced N and glucose utilization. Finally, pelleting at 75 °C increased feeding value of either sorghum- or corn-based PTMR for fattening lambs.

A hypermethylation strategy utilized by enhancer-bound CARM1 to promote estrogen receptor α-dependent transcriptional activation and breast carcinogenesis

While protein arginine methyltransferases (PRMTs) and PRMT-catalyzed protein methylation have been well-known to be involved in a myriad of biological processes, their functions and the underlying molecular mechanisms in cancers, particularly in estrogen receptor alpha (ERα)-positive breast cancers, remain incompletely understood. Here we focused on investigating PRMT4 (also called coactivator associated arginine methyltransferase 1, CARM1) in ERα-positive breast cancers due to its high expression and the associated poor prognosis.

Methods: ChIP-seq and RNA-seq were employed to identify the chromatin-binding landscape and transcriptional targets of CARM1, respectively, in the presence of estrogen in ERα-positive MCF7 breast cancer cells. High-resolution mass spectrometry analysis of enriched peptides from anti-monomethyl- and anti-asymmetric dimethyl-arginine antibodies in SILAC labeled wild-type and CARM1 knockout cells were performed to globally map CARM1 methylation substrates. Cell viability was measured by MTS and colony formation assay, and cell cycle was measured by FACS analysis. Cell migration and invasion capacities were examined by wound-healing and trans-well assay, respectively. Xenograft assay was used to analyze tumor growth in vivo.

Results: CARM1 was found to be predominantly and specifically recruited to ERα-bound active enhancers and essential for the transcriptional activation of cognate estrogen-induced genes in response to estrogen treatment. Global mapping of CARM1 substrates revealed that CARM1 methylated a large cohort of proteins with diverse biological functions, including regulation of intracellular estrogen receptor-mediated signaling, chromatin organization and chromatin remodeling. A large number of CARM1 substrates were found to be exclusively hypermethylated by CARM1 on a cluster of arginine residues. Exemplified by MED12, hypermethylation of these proteins by CARM1 served as a molecular beacon for recruiting coactivator protein, tudor-domain-containing protein 3 (TDRD3), to CARM1-bound active enhancers to activate estrogen/ERα-target genes. In consistent with its critical role in estrogen/ERα-induced gene transcriptional activation, CARM1 was found to promote cell proliferation of ERα-positive breast cancer cells in vitro and tumor growth in mice.

Conclusions: our study uncovered a “hypermethylation” strategy utilized by enhancer-bound CARM1 in gene transcriptional regulation, and suggested that CARM1 can server as a therapeutic target for breast cancer treatment.

Bayesian machine learning to discover Bruton's tyrosine kinase inhibitors

Bruton's tyrosine kinase (BTK) has a crucial role in multiple cell signaling pathways including B-cell antigen receptor (BCR) and Fc receptor (FcR) signaling cascades, which has attracted much attention to find BTK inhibitors to treat autoimmune diseases. In this work, we constructed a Bayesian classification model for virtually seeking novel BTK inhibitors, which showed good performance in terms of screening efficiency and accuracy. Through searching for several chemical libraries including Chembl_17 (1,317,484 compounds), Chembridge (103,473 compounds), and Chemdiv (700,000 compounds) using this model followed by molecular docking and activity prediction, 52 compounds with novel scaffolds were acknowledged as potential BTK inhibitors, which could be promising starting points for further exploration. This study also provided a guide to construct an efficient and effective protocol for virtual screening by integrating machine learning methods.

In Silico Discovery of JMJD6 Inhibitors for Cancer Treatment

The 2-oxoglutarate (2OG)-dependent oxygenase JMJD6 is emerging as a potential anticancer target, but its inhibitors have not been reported so far. In this study, we reported an in silico protocol to discover JMJD6 inhibitors targeting the druggable 2OG-binding site. Following this protocol, one compound, which we named as WL12, was found to be able to inhibit JMJD6 enzymatic activity and JMJD6-dependent cell proliferation. To our best knowledge, this is the first case in drug discovery targeting JMJD6.

Virtual Screening with a Structure-Based Pharmacophore Model to Identify Small-Molecule Inhibitors of CARM1

CARM1 (coactivator-associated arginine methyltransferase 1), also known as PRMT4 (protein arginine N-methyltransferase 4), belongs to the protein arginine methyltransferase (PRMT) family, which has emerged as a potential anticancer drug target. To discover new CARM1 inhibitors, we performed virtual screening against the substrate-binding site in CARM1. Structure-based pharmacophore models, which were generated according to three druggable subpockets embedding critical residues for ligand binding, were applied for virtual screening. The importance of the solvent-exposed substrate-binding cavity was highlighted due to significant hydrophobicity. Aided by molecular docking, 15 compounds structurally distinct from known CARM1 inhibitors were selected to evaluate their inhibitory effects on CARM1 methyltransferase activity, which resulted in seven compounds exhibiting micromolar inhibition, with selectivity over other members in the PRMT protein family. Moreover, three of them exhibited potent antiproliferation activities in breast cancer cells. Particularly, compound NO.2 exhibited potent activity both in vitro and in cultured cells, which will serve as a leading hit for developing CARM1 inhibitors with improved efficacy. The virtual screening strategy in this study will be applicable for the discovery of substrate-competitive inhibitors targeting other members in the PRMT protein family.

Effects of Astragalus glycoprotein on Th17/Treg cells in mice with collagen-induced arthritis

In this study of Th17/Treg cells, the therapeutic effect of Astragalus glycoprotein on collagen-induced arthritis in mice (CIA) was explored, and a basis for the clinical treatment of rheumatoid arthritis is provided. Sixty mice were selected for the establishment of a CIA mouse model, and were then randomly divided into a CIA model group, a hydrocortisone control group, a low, medium, and high dose group of Astragalus glycoprotein, respectively. The same number of control groups with same number of mice was established and after basic immunization, intraperitoneal injections were given once daily for two weeks in the treatment. At the end of the treatment, the mice in each group were selected and the proportion of Th17/Treg cells was detected by flow cytometry. The expression and positive expression of RORt, Foxp3, P-STAT3 and P-STAT5 protein were detected by Western blot and immunohistochemistry. Astragalus glycoprotein was shown to potentially improve the diet and mental state, reduce the arthritis index score and improve the pathological state of synovial membranes in the mice. Moreover, flow cytometry results showed that, compared with the CIA model group, the proportion of Th17 cells in the four other groups of mice decreased, while the proportion of Treg cells increased. This difference was statistically significant (P less than 0.05). From the experiment, the following conclusions were drawn: Astragalus glycoprotein can reduce Th17 cells and their transcription factors in the peripheral blood of CIA mice, up-regulate Treg cells and their transcription factors, and correct the balance of Th17/Treg cells so as to achieve an effective of treatment for CIA mice.

CHARMM36m: an improved force field for folded and intrinsically disordered proteins

The all-atom additive CHARMM36 protein force field is widely used in molecular modeling and simulations. We present its refinement, CHARMM36m (http://mackerell.umaryland.edu/charmm_ff.shtml), with improved accuracy in generating polypeptide backbone conformational ensembles for intrinsically disordered peptides and proteins.

Identification of Covalent Binding Sites Targeting Cysteines Based on Computational Approaches

Covalent drugs have attracted increasing attention in recent years due to good inhibitory activity and selectivity. Targeting noncatalytic cysteines with irreversible inhibitors is a powerful approach for enhancing pharmacological potency and selectivity because cysteines can form covalent bonds with inhibitors through their nucleophilic thiol groups. However, most human kinases have multiple noncatalytic cysteines within the active site; to accurately predict which cysteine is most likely to form covalent bonds is of great importance but remains a challenge when designing irreversible inhibitors. In this work, FTMap was first applied to check its ability in predicting covalent binding site defined as the region where covalent bonds are formed between cysteines and irreversible inhibitors. Results show that it has excellent performance in detecting the hot spots within the binding pocket, and its hydrogen bond interaction frequency analysis could give us some interesting instructions for identification of covalent binding cysteines. Furthermore, we proposed a simple but useful covalent fragment probing approach and showed that it successfully predicted the covalent binding site of seven targets. By adopting a distance-based method, we observed that the closer the nucleophiles of covalent warheads are to the thiol group of a cysteine, the higher the possibility that a cysteine is prone to form a covalent bond. We believe that the combination of FTMap and our distance-based covalent fragment probing method can become a useful tool in detecting the covalent binding site of these targets.

Quantitative structure-activity relationship study on BTK inhibitors by modified multivariate adaptive regression spline and CoMSIA methods

Bruton's tyrosine kinase (BTK) plays a crucial role in B-cell activation and development, and has emerged as a new molecular target for the treatment of autoimmune diseases and B-cell malignancies. In this study, two- and three-dimensional quantitative structure-activity relationship (2D and 3D-QSAR) analyses were performed on a series of pyridine and pyrimidine-based BTK inhibitors by means of genetic algorithm optimized multivariate adaptive regression spline (GA-MARS) and comparative molecular similarity index analysis (CoMSIA) methods. Here, we propose a modified MARS algorithm to develop 2D-QSAR models. The top ranked models showed satisfactory statistical results (2D-QSAR: Q(2) = 0.884, r(2) = 0.929, r(2)pred = 0.878; 3D-QSAR: q(2) = 0.616, r(2) = 0.987, r(2)pred = 0.905). Key descriptors selected by 2D-QSAR were in good agreement with the conclusions of 3D-QSAR, and the 3D-CoMSIA contour maps facilitated interpretation of the structure-activity relationship. A new molecular database was generated by molecular fragment replacement (MFR) and further evaluated with GA-MARS and CoMSIA prediction. Twenty-five pyridine and pyrimidine derivatives as novel potential BTK inhibitors were finally selected for further study. These results also demonstrated that our method can be a very efficient tool for the discovery of novel potent BTK inhibitors.

Insight into the key interactions of bromodomain inhibitors based on molecular docking, interaction fingerprinting, molecular dynamics and binding free energy calculation

The interaction mechanism of bromodomain inhibitors was investigated using interaction fingerprinting and binding free energy based methods.

An efficient multistep ligand-based virtual screening approach for GPR40 agonists

G protein-coupled receptor 40/free fatty acid receptor 1 (GPR40/FFAR1) is a member of the GPCR superfamily, and GPR40 agonists have therapeutic potential for type 2 diabetes. With the crystal structure of GPR40 currently unavailable, various ligand-based virtual screening approaches can be applied to identify novel agonists of GPR40. It is known that each ligand-based method has its own advantages and limitations. To improve the efficiency of individual ligand-based methods, an efficient multistep ligand-based virtual screening approach is presented in this study, including the pharmacophore-based screening, physicochemical property filtering, protein-ligand interaction fingerprint similarity analysis, and 2D-fingerprint structural similarity search. A focused decoy library was generated and used to evaluate the efficiency of this virtual screening protocol. This multistep workflow not only significantly improved the hit rate compared with each individual ligand-based method, but also identified diverse known actives from decoys. This protocol may serve as an efficient virtual screening tool for the targets without crystal structures available to discover novel active compounds.

Molecular modelling on small molecular CDK2 inhibitors: an integrated approach using a combination of molecular docking, 3D-QSAR and pharmacophore modelling

Cyclin-dependent kinase 2 (CDK2) has been identified as an important target for developing novel anticancer agents. Molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) and pharmacophore modelling were combined with the ultimate goal of studying the structure-activity relationship of CDK2 inhibitors. The comparative molecular similarity indices analysis (CoMSIA) model constructed based on a set of 3-aminopyrazole derivatives as CDK2 inhibitors gave statistically significant results (q (2) = 0.700; r (2) = 0.982). A HypoGen pharmacophore model, constructed using diverse CDK2 inhibitors, also showed significant statistics ([Formula: see text]Cost = 61.483; RMSD = 0.53; Correlation coefficient = 0.98). The small residues and error values between the estimated and experimental activities of the training and test set compounds proved their strong capability of activity prediction. The structural insights obtained from these two models were consistent with each other. The pharmacophore model summarized the important pharmacophoric features required for protein-ligand binding. The 3D contour maps in combination with the comprehensive pharmacophoric features helped to better interpret the structure-activity relationship. The results will be beneficial for the discovery and design of novel CDK2 inhibitors. The simplicity of this approach provides expansion to its applicability in optimizing other classes of small molecular CDK2 inhibitors.