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Abstract Allosteric drugs offer a new avenue for modern drug design. However, the identification of cryptic allosteric sites presents a formidable challenge. Following the allostery nature of residue-driven conformation transition, we propose a state-of-the-art computational pipeline by developing a residue-intuitive hybrid machine learning (RHML) model coupled with molecular dynamics (MD) simulation, through which we can efficiently identify the allosteric site and allosteric modulator as well as reveal their regulation mechanism. For the clinical target β2-adrenoceptor (β2AR), we discover an additional allosteric site located around residues D792.50, F2826.44, N3187.45 and S3197.46 and one putative allosteric modulator ZINC5042. Using Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and protein structure network (PSN), the allosteric potency and regulation mechanism are probed to further improve identification accuracy. Benefiting from sufficient computational evidence, the experimental assays then validate our predicted allosteric site, negative allosteric potency and regulation pathway, showcasing the effectiveness of the identification pipeline in practice. We expect that it will be applicable to other target proteins.

Youngia lushanensis (Asteraceae), a new species of Youngia, is described and illustrated from Lushan in Sichuan, Southwest China. Its systematic position is evaluated based on a molecular phylogenetic analysis of the nuclear ribosomal ITS and on morphological comparison with related species. It is morphologically similar to Y. purpimea and Y. szechuanica, with purplish red abaxially leaves. But it differs from the latter two in that its caudex are glabrous, leaf margins are sparsely dentate, and being glabrous on both surfaces, 9-11 florets in each capitulum, and inner phyllaries are crested. Phylogenetic analysis based on ITS also showed this new species is closely related with Y. purpimea and Y. szechuanica. The three species Y. lushanensis Y. purpimea and Y. szechuanica, formed a basal group of Youngia, which is distributed in moist slopes in Southwest China.

Summary: High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R2 of 0.98 and MAE of 8.8 kJ mol−1, significantly outperforming competitive models.

Here, the authors investigate naphthalimide-based compounds with cold triplet excitons utilized by triplet-triplet annihilation-mediated high-lying reverse intersystem crossing, and realize rate constant of over 109 s−1 with exciton utilization efficiency of 46.7% in organic light-emitting diodes.

Background: Hypoxia is an essential cause of fatigue and aging, and is associated with the occurrence and development of many diseases. Polygonatum kingianum (PK) is a deficiency-nourishing Chinese herbal medicine utilized as both medicine and food, and it has long been used to ameliorate human conditions associated with fatigue and aging over 2000 years in China. PK is an important genuine-medicinal-materials cultivated in Yunnan, China, and is used by the Bai, Wa, and Zhuang nationalities as a traditional medicine for enhancing immunity, anti-fatigue, and anti-aging, while the preventive effect of PK on hypoxia-induced injury and the underlying mechanism are indefinite. Aim of the study: The present study aimed to evaluate the anti-hypoxia efficacy and understand the corresponding mechanism of PK water extract. Materials and methods: The main active ingredients and targets of PK were predicted using network pharmacology, and the anti-hypoxia activities of Gracillin and Liquiritigenin were verified by in vitro experiments. The pharmacodynamic experiments were conducted to evaluate the major signal pathways of PK for detecting anti-hypoxia activity. Results: Fifty active ingredients and 371 potential targets were screened by network pharmacology, then, we confirmed that Gracillin and Liquiritigenin were the main active components of PK to exert anti-hypoxia effect in vitro. The pharmacodynamic experiments revealed that PK enhanced the extension rate of the survival time (ERST) and regulated the targets-related biochemical parameters of rats under hypoxia, showing significant anti-hypoxia effects on rats. Conclusion: The network pharmacology results suggested that PK exerts its anti-hypoxia effect through a multi-component and multi-target manner. Simultaneously, we also observed that Gracillin (saponins) and Liquiritigenin (flavonoids) are the main active components of PK to play a role in anti-hypoxia. The anti-hypoxia effect of PK could be associated with scavenging excess free radicals, maintaining the activities of antioxidant enzymes, and inhibiting oxidative stress due to lipid peroxidation. These findings provide insight into the Polygonatum kingianum as promising medicines or healthcare products for preventing and treating hypoxia.

Introduction: Papillary thyroid cancer (PTC) is the most common endocrine malignancy. However, different PTC variants reveal high heterogeneity at histological, cytological, molecular and clinicopathological levels, which complicates the precise diagnosis and management of PTC. Alternative splicing (AS) has been reported to be potential cancer biomarkers and therapeutic targets.Method: Here, we aim to find a more sophisticated molecular subclassification and characterization for PTC by integrating AS profiling. Based on six differentially expressed alternative splicing (DEAS) events, a new molecular subclassification was proposed to reclassify PTC into three new groups named as Cluster0, Cluster1 and Cluster2 respectively.Results: An in silico prediction was performed for accurate recognition of new groups with the average accuracy of 91.2%. Moreover, series of analyses were implemented to explore the differences of clinicopathology, molecular and immune characteristics across them. It suggests that there are remarkable differences among them, but Cluster2 was characterized by poor prognosis, higher immune heterogeneity and more sensitive to anti-PD1 therapy. The splicing correlation networks proved the complicated regulation relationships between AS events and splicing factors (SFs). An independent prognostic indicator for PTC overall survival (OS) was established. Finally, three compounds (orantinib, tyrphostin-AG-1295 and AG-370) were discovered to be the potential therapeutic agents.Discussion: Overall, the six DEAS events are not only potential biomarkers for precise diagnosis of PTC, but also the probable prognostic predictors. This research would be expected to highlight the effect of AS events on PTC characterization and also provide new insights into refining precise subclassification and improving medical therapy for PTC patients.

Experimental determination of new cocrystals remains challenging due to the need of a systematic screening with a large range of coformers. Here the authors develop a flexible deep learning framework based on graph neural network demonstrated to quickly predict the formation of co-crystals.

IntroductionAs a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM.Methods and resultsIn the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents.ConclusionOverall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

Despite the increase in the global prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD), no approved drug currently exists for the disease. Poria cocos (Schw.) Wolf (P. cocos) is a medicinal mushroom belonging to a family of polyporaceae widely used in TCM clinics to protect the liver and treat obesity. However, its efficacy, practical components, and underlying mechanism against MAFLD are yet to be determined. In this study, we evaluated the effects of Poria cocos (P. cocos) ethanol extract (EPC) on hepatic dyslipidemia, steatosis, and inflammation by both bioinformatics analysis and MAFLD rats induced by HFD feeding. We found EPC treatment dramatically reduced lipid accumulation, inflammatory cell infiltration, and liver injury. EPC reduced serum TC, TG levels, and hepatic TG, TBA, and NEFA contents. UHPLC Q-Trap/MS examination of BA profiles in serum and feces showed that EPC increased fecal conjugated BAs, decreased free BAs, and improved BA metabolism in HFD-fed rats. Western blot and RT-qPCR analysis showed that EPC could activate hepatic FXR and PPARα expression and reduce CYP7A1 and SREBP-1c expression. Systemic pharmacology combined with molecular docking suggested that poricoic acid B and polyporenic acid C, the major active compounds in EPC, could ameliorate lipid homeostasis by activating the nuclear receptor PPARα. We further confirmed their inhibition effects of lipid droplet deposition in steatized L-02 hepatocytes. In summary, EPC alleviated HFD-induced MAFLD by regulating lipid homeostasis and BA metabolism via the FXR/PPARα-SREBPs signaling pathway. P. cocos triterpenes, such as poricoic acid B and polyporenic acid C, were the characteristic substances of P. cocos for the treatment of MAFLD.

The pollen morphology of 20 species from Blumea and Cyathocline Cass. was investigated using a light microscope (LM) and scanning electron microscopy (SEM) to explore their taxonomic significance. This study showed that pollen grains of these species were usually tricolporate, rarely tetracolporate (B. sinuata). Nine pollen types were distinguishable through the exine sculpture characters and the number of apertures. It was easily distinguished Cyathocline from species of Blumea s. str. by its much smaller size (15.04 μm × 15.07 μm) and sparse and longer spines (24 spines, spine length 4.23 μm) with acute apex, which suggest that C. purpurea might not belong to the genus Blumea s. str. The palynological characteristics indicated that Section Macrophllae and Section Paniculatae of Blumea were not monophyletic groups. The pollen morphology differentiation of B. lacera clade is consistent with the interspecific relationship revealed by the molecular phylogenetic tree. However, the pollen morphology of the Blumea densiflora clade is inconsistent with the interspecific relationship based on molecular phylogenetic analysis. This palynology research can only partly support the previously published molecular phylogeny of Blumea s. str.

Prostate cancer (PRAD) is a common and fatal malignancy. It is difficult to manage clinically due to drug resistance and poor prognosis, thus creating an urgent need for novel therapeutic targets and prognostic biomarkers. Although G protein-coupled receptors (GPCRs) have been most attractive for drug development, there have been lack of an exhaustive assessment on GPCRs in PRAD like their molecular features, prognostic and therapeutic values. To close this gap, we herein systematically investigate multi-omics profiling for GPCRs in the primary PRAD by analyzing somatic mutations, somatic copy-number alterations (SCNAs), DNA methylation and mRNA expression. GPCRs exhibit low expression levels and mutation frequencies while SCNAs are more prevalent. 46 and 255 disease-related GPCRs are identified by the mRNA expression and DNA methylation analysis, respectively, complementing information lack in the genome analysis. In addition, the genomic alterations do not exhibit an observable correlation with the GPCR expression, reflecting the complex regulatory processes from DNA to RNA. Conversely, a tight association is observed between the DNA methylation and mRNA expression. The virtual screening and molecular dynamics simulation further identify four potential drugs in repositioning to PRAD. The combination of 3 clinical characteristics and 26 GPCR molecular features revealed by the transcriptome and genome exhibit good performance in predicting progression-free survival in patients with the primary PRAD, providing candidates as new biomarkers. These observations from the multi-omics analysis on GPCRs provide new insights into the underlying mechanism of primary PRAD and potential of GPCRs in developing therapeutic strategies on PRAD.

Abstract Background The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to reveal the genome characteristics for downstream analysis for the purpose of understanding the mechanisms of cancers. Most of the conventional machine learning methods involved a gene filtering step, in which tens of thousands of genes were firstly filtered based on the gene expression levels by a statistical method with an arbitrary cutoff. Although gene filtering procedure helps to reduce the feature dimension and avoid overfitting, there is a risk that some pathogenic genes important to the disease will be ignored. Results In this study, we proposed a novel deep learning approach by combining a convolutional neural network with stationary wavelet transform (SWT-CNN) for stratifying cancer patients and predicting their clinical outcomes without gene filtering based on tumor genomic profiles. The proposed SWT-CNN overperformed the state-of-art algorithms, including support vector machine (SVM) and logistic regression (LR), and produced comparable prediction performance to random forest (RF). Furthermore, for all the cancer types, we firstly proposed a method to weight the genes with the scores, which took advantage of the representative features in the hidden layer of convolutional neural network, and then selected the prognostic genes for the Cox proportional-hazards regression. The results showed that risk stratifications can be effectively improved by using the identified prognostic genes as feature, indicating that the representative features generated by SWT-CNN can well correlate the genes with prognostic risk in cancers and be helpful for selecting the prognostic gene signatures. Conclusions Our results indicated that gene expression-based SWT-CNN model can be an excellent tool for stratifying the prognostic risk for cancer patients. In addition, the representative features of SWT-CNN were validated to be useful for evaluating the importance of the genes in the risk stratification and can be further used to identify the prognostic gene signatures.

Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.

Abstract Kaposi’s sarcoma-associated herpesvirus (KSHV) is the etiological agent of Kaposi’s sarcoma, primary effusion lymphoma and multicentric Castleman’s disease, etc. In this study, we firstly systematically constructed the KSHV-encoded miRNA-regulated co-expressed protein-protein interaction network (CePPIN), which display the biological knowledge regarding the mechanism of miRNA-regulated KSHV pathogenesis. Then, we investigated the topological parameters for the proteins in CePPIN, especially for those miRNA targets and we found that cellular target genes of KSHV-encoded miRNAs tend to be hubs and bottlenecks in the network. Then the GO and KEGG pathway analysis suggests that miRNA targets are involved in various cellular processes mostly related to immune regulate and cell cycle. Enrichment analysis was also performed to identify the six important functional modules which are proven to be highly related to KSHV pathogenesis. Finally, difference analysis of common targets and specific targets shows that two kinds of targets are different in terms of both topological properties and enriched functions, thus we can extrapolate that the functions of KSHV-encoded miRNAs can be also classified into two generic groups, one can act as functional mimics of some oncogenic human miRNAs which contribute to tumorigenesis and the other can contribute to maintaining viral survival.

Abstract In contrast to protein-coding genes, long-noncoding RNAs (lncRNAs) are much less well understood, despite increasing evidence indicating a wide range of their biological functions, and possible roles in various cancers. Based on public RNA-seq datasets of four solid cancer types, we here utilize Weighted Correlation Network Analysis (WGCNA) to propose a strategy for exploring the functions of lncRNAs altered in more than two cancer types, which we call onco-lncRNAs. Results indicate that cancer-expressed lncRNAs show high tissue specificity and are weakly expressed, more so than protein-coding genes. Most of the 236 onco-lncRNAs we identified have not been reported to have associations with cancers before. Our analysis exploits co-expression network to reveal that onco-lncRNAs likely play key roles in the multistep development of human cancers, covering a wide range of functions in genome stability maintenance, signaling, cell adhesion and motility, morphogenesis, cell cycle, immune and inflammatory response. These observations contribute to a more comprehensive understanding of cancer-associated lncRNAs, while demonstrating a novel and efficient strategy for subsequent functional studies of lncRNAs.

Modulating protein–protein interactions (PPIs) with small drug-like molecules targeting it exhibits great promise in modern drug discovery. G protein-coupled receptors (GPCRs) are the largest family of targeted proteins and could form dimers in living biological cells through PPIs. However, compared to drug development of the orthosteric site, there has been lack of investigations on the druggability of the PPI interface for GPCRs and its functional implication on experiments. Thus, in order to address these issues, we constructed a novel computational strategy, which involved in molecular dynamics simulation, virtual screening and protein structure network (PSN), to study one representative GPCR homodimer (CXCR4). One druggable pocket was identified in the PPI interface and one small molecule targeting it was screened, which could strengthen PPI mainly through hydrophobic interaction between the benzene rings of the PPI molecule and TM4 of the receptor. The PSN results further reveals that the PPI molecule could increase the number of the allosteric regulation pathways between the druggable pocket of the dimer interface to the orthostatic site for the subunit A but only play minor role for the other subunit B, leading to the asymmetric change in the volume of the binding pockets for the two subunits (increase for the subunit A and minor change for the subunit B). Consequently, the screening performance of the subunit A to the antagonists is enhanced while the subunit B is unchanged nearly, implying that the PPI molecule may be beneficial to enhance the drug efficacies of the antagonists. In addition, one main regulation pathway with the highest frequency was identified for the subunit A, which consists of Trp1955.34–Tyr190ECL2–Val1965.35–Gln2005.39–Asp2626.58–Cys28N-term, revealing their importance in the allosteric regulation from the PPI molecule. The observations from the work could provide valuable information for the development of the PPI drug-like molecule for GPCRs.

Despite of the low occurrence rate in the entire genomes, de novo mutation is proved to be deleterious and will lead to severe genetic diseases via impacting on the gene function. Considering the fact that the traditional family based linkage approaches and the genome-wide association studies are unsuitable for identifying the de novo mutations, in recent years, several pipelines have been proposed to detect them based on the whole-genome or whole-exome sequencing data and were used for calling them in the rare diseases. However, how the performance of these variant calling pipelines on detecting the de novo mutations is still unexplored. For the purpose of facilitating the appropriate choice of the pipelines and reducing the false positive rate, in this study, we thoroughly evaluated the performance of the commonly used trio calling methods on the detection of the de novo single-nucleotide variants (DNSNVs) by conducting a comparative analysis for the calling results. Our results exhibited that different pipelines have a specific tendency to detect the DNSNVs in the genomic regions with different GC contents. Additionally, to refine the calling results for a single pipeline, our proposed filter achieved satisfied results, indicating that the read coverage at the mutation positions can be used as an effective index to identify the high-confidence DNSNVs. Our findings should be good support for the committees to choose an appropriate way to explore the de novo mutations for the rare diseases.

Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. To address the problem, we propose a novel SAR ship detector based on CNN, which consist of three subnetworks: the Fusion Feature Extractor Network (FFEN), Region Proposal Network (RPN), and Refine Detection Network (RDN). Instead of using a single feature map, we fuse feature maps in bottom–up and top–down ways and generate proposals from each fused feature map in FFEN. Furthermore, we further merge features generated by the region-of-interest (RoI) pooling layer in RDN. Based on the feature representation strategy, the CNN framework constructed can significantly enhance the location and semantics information for the multiscale ships, in particular for the small ships. On the other hand, the residual block is introduced to increase the network depth, through which the detection precision could be further improved. The public SAR ship dataset (SSDD) and China Gaofen-3 satellite SAR image are used to validate the proposed method. Our method shows excellent performance for detecting the multiscale and small-size ships with respect to some competitive models and exhibits high potential in practical application.

BackgroundType III secretion systems (T3SSs) are central to the pathogenesis and specifically deliver their secreted substrates (type III secreted proteins, T3SPs) into host cells. Since T3SPs play a crucial role in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T3SSs. This study reports a novel and effective method for identifying the distinctive residues which are conserved different from other SPs for T3SPs prediction. Moreover, the importance of several sequence features was evaluated and further, a promising prediction model was constructed.ResultsBased on the conservation profiles constructed by a position-specific scoring matrix (PSSM), 52 distinctive residues were identified. To our knowledge, this is the first attempt to identify the distinct residues of T3SPs. Of the 52 distinct residues, the first 30 amino acid residues are all included, which is consistent with previous studies reporting that the secretion signal generally occurs within the first 30 residue positions. However, the remaining 22 positions span residues 30-100 were also proven by our method to contain important signal information for T3SP secretion because the translocation of many effectors also depends on the chaperone-binding residues that follow the secretion signal. For further feature optimisation and compression, permutation importance analysis was conducted to select 62 optimal sequence features. A prediction model across 16 species was developed using random forest to classify T3SPs and non-T3 SPs, with high receiver operating curve of 0.93 in the 10-fold cross validation and an accuracy of 94.29% for the test set. Moreover, when performing on a common independent dataset, the results demonstrate that our method outperforms all the others published to date. Finally, the novel, experimentally confirmed T3 effectors were used to further demonstrate the model's correct application. The model and all data used in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/T3SPs.zip.