The QSARome of the receptorome: Quantitative structure-activity relationship modeling of multiple ligand sets acting at multiple receptors

Recent advances in High Throughput Screening (HTS) led to the rapid growth of chemical libraries of small molecules, which calls for improved computational tools and predictive models for Virtual Screening (VS). Thus this dissertation focuses on both the development and application of predictive Quantitative Structure-Activity Relationship (QSAR) models and aims to discover novel therapeutic agents for certain diseases. First, this dissertation adopts the combinatorial QSAR framework created by our lab, including the first application of the Distance Weighted Discrimination (DWD) method that resulted in a set of robust QSAR models for the 5-HT7 receptor. VS using these models, followed by the experimental test of identified compounds, led to the finding of five known drugs as potent 5-HT7 binders. Eventually, droperidol (Ki = 3.5 nM) and perospirone (Ki = 8.6 nM) proved to be strong 5-HT7 antagonists. Second, we intended to enhance VS hit rate. To that end, we developed a cost/benefit ratio as an evaluation performance metric for QSAR models. This metric was applied in the Decision Tree machine learning method in two ways: (1) as a benchmarking criterion to compare the prediction performances of different classifiers and (2) as a target function to build QSAR classification trees. This metric may be more suitable for imbalanced HTS data that include few active but many inactive compounds. Finally, a novel QSAR strategy was developed in response to the polygenic nature of most psychotic disorders, related mainly to G-Protein-Coupled Receptors (GPCRs), one class of molecular targets of greatest interest to the pharmaceutical industry. We curated binding data for thousands of GPCR ligands, and developed predictive QSAR models to assess the GPCR binding profiles of untested compounds that could be used to identify potential drug candidates. This comprehensive study yielded a compendium of validated QSAR predictors (the GPCR QSARome), providing effective in silico tools to search for novel antipsychotic drugs. The advances in results and procedures achieved in these studies will be integrated into the current computational strategies for rational drug design and discovery boosted by our lab, so that predictive QSAR modeling will become a reliable support tool for drug discovery programs

SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration

Point cloud registration is to estimate a transformation to align point clouds collected in different perspectives. In learning-based point cloud registration, a robust descriptor is vital for high-accuracy registration. However, most methods are susceptible to noise and have poor generalization ability on unseen datasets. Motivated by this, we introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration. In our method, first, the spheroid generator builds a geometric domain based on spherical voxelization to encode initial features. Then, the spherical interpolation of the sphere is introduced to realize robustness against noise. Finally, a new spherical convolutional neural network with spherical integrity padding completes the extraction of descriptors, which reduces the loss of features and fully captures the geometric features. To evaluate our methods, a new benchmark 3DMatch-noise with strong noise is introduced. Extensive experiments are carried out on both indoor and outdoor datasets. Under high-intensity noise, SphereNet increases the feature matching recall by more than 25 percentage points on 3DMatch-noise. In addition, it sets a new state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with 93.5\% and 75.6\% registration recall and also has the best generalization ability on unseen datasets.Comment: 15 pages, under review for IEEE Transactions on Circuits and Systems for Video Technolog

Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars

The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses. In this paper, the deep learning classification network, IICnet (Infrared Image Classification network), is designed to classify sources from WISE images to achieve a more accurate classification goal. IICnet shows good ability on the feature extraction of the WISE sources. Experiments demonstrates that the classification results of IICnet are superior to some other methods; it has obtained 96.2% accuracy for galaxies, 97.9% accuracy for quasars, and 96.4% accuracy for stars, and the Area Under Curve (AUC) of the IICnet classifier can reach more than 99%. In addition, the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16, GoogleNet, ResNet34, MobileNet, EfficientNetV2, and RepVGG-fewer parameters and faster inference. The above proves that IICnet is an effective method to classify infrared sources

Antitumor Agents 268. Design, Synthesis, and Mechanistic Studies of New 9-Substituted Phenanthrene-Based Tylophorine Analogues as Potent Cytotoxic Agents

Nineteen new phenanthrene-based tylophorine analogs with various functional groups on the piperidine moiety were designed, synthesized and evaluated for in vitro anticancer activity against four human tumor cell lines. Analogs 15 and 21 showed approximately two-fold enhanced inhibitory activity as compared with our prior lead compound (PBT-1). Analogs 23 and 24 with S- and R-configured substituents, respectively, at the piperidine 3’-position exhibited comparable cytotoxicity to that of PBT-1. Furthermore, mechanistic studies to investigate the effects of the new compounds on Akt protein in lung cancer cells and the NF-kB signaling pathway suggested that the compounds may exert their inhibitory activity on tumor cells through inhibition of activation of both Akt and NF-kB signaling pathway

MiR-137 Targets Estrogen-Related Receptor Alpha and Impairs the Proliferative and Migratory Capacity of Breast Cancer Cells

ERRα is an orphan nuclear receptor emerging as a novel biomarker of breast cancer. Over-expression of ERRα in breast tumor is considered as a prognostic factor of poor clinical outcome. The mechanisms underlying the dysexpression of this nuclear receptor, however, are poorly understood. MicroRNAs (miRNAs) regulate gene expression at the post-transcriptional level and play important roles in tumor initiation and progression. In the present study, we have identified that the expression of ERRα is regulated by miR-137, a potential tumor suppressor microRNA. The bioinformatics search revealed two putative and highly conserved target-sites for miR-137 located within the ERRα 3′UTR at nt 480–486 and nt 596–602 respectively. Luciferase-reporter assay demonstrated that the two predicted target sites were authentically functional. They mediated the repression of reporter gene expression induced by miR-137 in an additive manner. Moreover, ectopic expression of miR-137 down-regulated ERRα expression at both protein level and mRNA level, and the miR-137 induced ERRα-knockdown contributed to the impaired proliferative and migratory capacity of breast cancer cells. Furthermore, transfection with miR-137mimics suppressed at least two downstream target genes of ERRα–CCNE1 and WNT11, which are important effectors of ERRα implicated in tumor proliferation and migration. Taken together, our results establish a role of miR-137 in negatively regulating ERRα expression and breast cancer cell proliferation and migration. They suggest that manipulating the expression level of ERRα by microRNAs has the potential to influence breast cancer progression

Design and Synthesis of Pleuromutilin Derivatives as Antibacterial Agents Using Quantitative Structure–Activity Relationship Model

The quantitative structure–activity relationship (QSAR) is one of the most popular methods for the virtual screening of new drug leads and optimization. Herein, we collected a dataset of 955 MIC values of pleuromutilin derivatives to construct a 2D-QSAR model with an accuracy of 80% and a 3D-QSAR model with a non-cross-validated correlation coefficient (r2) of 0.9836 and a cross-validated correlation coefficient (q2) of 0.7986. Based on the obtained QSAR models, we designed and synthesized pleuromutilin compounds 1 and 2 with thiol-functionalized side chains. Compound 1 displayed the highest antimicrobial activity against both Staphylococcus aureus ATCC 29213 (S. aureus) and Methicillin-resistant Staphylococcus aureus (MRSA), with minimum inhibitory concentrations (MICs) < 0.0625 μg/mL. These experimental results confirmed that the 2D and 3D-QSAR models displayed a high accuracy of the prediction function for the discovery of lead compounds from pleuromutilin derivatives

Plumbagin Alleviates Capillarization of Hepatic Sinusoids In Vitro by Downregulating ET-1, VEGF, LN, and Type IV Collagen

Critical roles for liver sinusoidal endothelial cells (LSECs) in liver fibrosis have been demonstrated, while little is known regarding the underlying molecular mechanisms of drugs delivered to the LSECs. Our previous study revealed that plumbagin plays an antifibrotic role in liver fibrosis. In this study, we investigated whether plumbagin alleviates capillarization of hepatic sinusoids by downregulating endothelin-1 (ET-1), vascular endothelial growth factor (VEGF), laminin (LN), and type IV collagen on leptin-stimulated LSECs. We found that normal LSECs had mostly open fenestrae and no organized basement membrane. Leptin-stimulated LSECs showed the formation of a continuous basement membrane with few open fenestrae, which were the features of capillarization. Expression of ET-1, VEGF, LN, and type IV collagen was enhanced in leptin-stimulated LSECs. Plumbagin was used to treat leptin-stimulated LSECs. The sizes and numbers of open fenestrae were markedly decreased, and no basement membrane production was found after plumbagin administration. Plumbagin decreased the levels of ET-1, VEGF, LN, and type IV collagen in leptin-stimulated LSECs. Plumbagin promoted downregulation of ET-1, VEGF, LN, and type IV collagen mRNA. Altogether, our data reveal that plumbagin reverses capillarization of hepatic sinusoids by downregulation of ET-1, VEGF, LN, and type IV collagen

DPQP: A Detection Pipeline for Quasar Pair Candidates Based on QSO Photometric Images and Spectra

Quasars have an important role in the studies of galaxy evolution and star formation. The rare close projection of two quasars in the sky allows us to study the environment and matter exchange around the foreground quasar (QSOfg) and the background quasar (QSObg). This paper proposes a pipeline DPQP for quasar pair (QP) candidates’ detection based on photometric images and the corresponding spectra. The pipeline consists of three main parts: a target source detector, a regressor, and a discriminator. In the first part, the target source detection network–YOLOv4 (TSD-YOLOv4) and the target source classification network (TSCNet) are used in sequence to detect quasars in SDSS photometric images. In the second part, a depth feature extraction network of quasar images (DE-QNet) is constructed to estimate the redshifts of quasars from photometric images. In the third part, a quasar pair score (Q-Score) metric is proposed based on the spectral analysis. The larger the Q-Score, the greater the possibility of two pairs being a quasar pair. The experimental results show that between redshift 1.0 and 4.0, the MAE of DE-QNet is 0.316, which is 16.1% lower than the existing method. Samples with |Δz| < 0.15 account for 77.1% of the test dataset. A new table with 1025 QP candidates is provided by traversing 50,000 SDSS photometric images

Reconsideration of the optimal minimum lymph node count for young colon cancer patients: a population-based study

Abstract Background Currently, young colon cancer (CC) patients continue to increase and represent a heterogeneous patient group. The aim of this study was to explore the optimal minimum lymph node count after CC resection for young patients. Methods We performed a comprehensive search of the Surveillance, Epidemiology, and End Results (SEER) database, 2360 CC patients aged from 20 to 40 were analyzed. X-tile was used to determine the optimal cut-off point of lymph node based on survival outcomes of young patients. The cancer specific survival (CSS) was estimated with Kaplan-Meier method, the Cox proportional hazards regression model was used to analyse independent prognostic factors and exact 95% confidence intervals (CIs). Results Using X-tile analysis, 22-node measure was identified as the optimal choice for CC patients aged < 40. The 5-year CSS were 85.8% and 80.9% for patients examining ≥22 nodes and < 22 nodes. Furthermore, we identified that examining < 22 nodes was an independent adverse prognostic factor in patients aged < 40. In addition, the revised 22-node measure could examine more positive nodes than the standard 12-node measure in young patients. Conclusions For young colon cancer patients, the lymph node examination should be differently evaluated. We suggest that 22-node measure may be more suitable for CC patients aged < 40. Trial registration Retrospectively registered

Comparative analysis of the organelle genomes of Aconitum carmichaelii revealed structural and sequence differences and phylogenetic relationships

Abstract In this study, we conducted an assembly and analysis of the organelle genomes of Aconitum carmichaelii. Our investigation encompassed the examination of organelle genome structures, gene transfer events, and the environmental selection pressures affecting A. carmichaelii. The results revealed distinct evolutionary patterns in the organelle genomes of A. carmichaelii. Especially, the plastome exhibited a more conserved structure but a higher nucleotide substitution rate (NSR), while the mitogenome displayed a more complex structure with a slower NSR. Through homology analysis, we identified several instances of unidirectional protein-coding genes (PCGs) transferring from the plastome to the mitogenome. However, we did not observe any events which genes moved from the mitogenome to the plastome. Additionally, we observed multiple transposable element (TE) fragments in the organelle genomes, with both organelles showing different preferences for the type of nuclear TE insertion. Divergence time estimation suggested that rapid differentiation occurred in Aconitum species approximately 7.96 million years ago (Mya). This divergence might be associated with the reduction in CO2 levels and the significant uplift of the Qinghai-Tibet Plateau (QTP) during the late Miocene. Selection pressure analysis indicated that the dN/dS values of both organelles were less than 1, suggested that organelle PCGs were subject to purification selection. However, we did not detect any positively selected genes (PSGs) in Subg. Aconitum and Subg. Lycoctonum. This observation further supports the idea that stronger negative selection pressure on organelle genes in Aconitum results in a more conserved amino acid sequence. In conclusion, this study contributes to a deeper understanding of organelle evolution in Aconitum species and provides a foundation for future research on the genetic mechanisms underlying the structure and function of the Aconitum plastome and mitogenome