Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking

Molecular docking is a key computational tool utilized to predict the binding conformations of small molecules to protein targets, which is fundamental in the design of novel drugs. Despite recent advancements in geometric deep learning-based approaches leading to improvements in blind docking efficiency, these methods have encountered notable challenges, such as limited generalization performance on unseen proteins, the inability to concurrently address the settings of blind docking and site-specific docking, and the frequent occurrence of physical implausibilities such as inter-molecular steric clash. In this study, we introduce DeltaDock, a robust and versatile framework designed for efficient molecular docking to overcome these challenges. DeltaDock operates in a two-step process: rapid initial complex structures sampling followed by multi-scale iterative refinement of the initial structures. In the initial stage, to sample accurate structures with high efficiency, we develop a ligand-dependent binding site prediction model founded on large protein models and graph neural networks. This model is then paired with GPU-accelerated sampling algorithms. The sampled structures are updated using a multi-scale iterative refinement module that captures both protein-ligand atom-atom interactions and residue-atom interactions in the following stage. Distinct from previous geometric deep learning methods that are conditioned on the blind docking setting, DeltaDock demonstrates superior performance in both blind docking and site-specific docking settings. Comprehensive experimental results reveal that DeltaDock consistently surpasses baseline methods in terms of docking accuracy. Furthermore, it displays remarkable generalization capabilities and proficiency for predicting physically valid structures, thereby attesting to its robustness and reliability in various scenarios.Comment: 13 pages, 8 figure

A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design

Structure-based drug design (SBDD), which utilizes the three-dimensional geometry of proteins to identify potential drug candidates, is becoming increasingly vital in drug discovery. However, traditional methods based on physiochemical modeling and experts' domain knowledge are time-consuming and laborious. The recent advancements in geometric deep learning, which integrates and processes 3D geometric data, coupled with the availability of accurate protein 3D structure predictions from tools like AlphaFold, have significantly propelled progress in structure-based drug design. In this paper, we systematically review the recent progress of geometric deep learning for structure-based drug design. We start with a brief discussion of the mainstream tasks in structure-based drug design, commonly used 3D protein representations and representative predictive/generative models. Then we delve into detailed reviews for each task (binding site prediction, binding pose generation, \emph{de novo} molecule generation, linker design, and binding affinity prediction), including the problem setup, representative methods, datasets, and evaluation metrics. Finally, we conclude this survey with the current challenges and highlight potential opportunities of geometric deep learning for structure-based drug design.Comment: 14 page

UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection

Underwater object detection faces the problem of underwater image degradation, which affects the performance of the detector. Underwater object detection methods based on noise reduction and image enhancement usually do not provide images preferred by the detector or require additional datasets. In this paper, we propose a plug-and-play Underwater joint image enhancement Module (UnitModule) that provides the input image preferred by the detector. We design an unsupervised learning loss for the joint training of UnitModule with the detector without additional datasets to improve the interaction between UnitModule and the detector. Furthermore, a color cast predictor with the assisting color cast loss and a data augmentation called Underwater Color Random Transfer (UCRT) are designed to improve the performance of UnitModule on underwater images with different color casts. Extensive experiments are conducted on DUO for different object detection models, where UnitModule achieves the highest performance improvement of 2.6 AP for YOLOv5-S and gains the improvement of 3.3 AP on the brand-new test set (URPCtest). And UnitModule significantly improves the performance of all object detection models we test, especially for models with a small number of parameters. In addition, UnitModule with a small number of parameters of 31K has little effect on the inference speed of the original object detection model. Our quantitative and visual analysis also demonstrates the effectiveness of UnitModule in enhancing the input image and improving the perception ability of the detector for object features

Relationship between occupational stress and job burnout among rural-to-urban migrant workers in Dongguan, China: a cross-sectional study

Objectives: In China, there have been an increasing number of migrant workers from rural to urban areas, and migrant workers have the highest incidence of occupational diseases. However, few studies have examined the impact of occupational stress on job burnout in these migrant workers. This study aimed to investigate the relationship between occupational stress and job burnout among migrant workers. Design: This study used a cross-sectional survey. Setting: This investigation was conducted in Dongguan city, Guangdong Province, China. Participants: 3806 migrant workers, aged 18–60 years, were randomly selected using multistage sampling procedures. Primary and secondary outcome measures: Multistage sampling procedures were used to examine demographic characteristics, behaviour customs and jobrelated data. Hierarchical linear regression and logistic regression models were constructed to explore the relationship between occupational stress and burnout. Results: Demographics, behaviour customs and jobrelated characteristics significantly affected on burnout. After adjusting for the control variable, a high level of emotional exhaustion was associated with high role overload, high role insufficiency, high role boundary, high physical environment, high psychological strain, high physical strain, low role ambiguity, low responsibility and low vocational strain. A high level of depersonalisation was associated with high role overload, high role ambiguity, high role boundary, high interpersonal strain, high recreation, low physical environment and low social support. A low level of personal accomplishment was associated with high role boundary, high role insufficiency, low responsibility, low social support, low physical environment, low self-care and low interpersonal strain. Compared to the personal resources, the job strain and personal strain were more likely to explain the burnout of rural-to-urban migrant workers in our study. Conclusions: The migrant workers have increased job burnouts in relation to occupational stress. Relieving occupational stress and maintaining an appropriate quantity and quality of work could be important measures for preventing job burnout among these workers

Effects of replacing wheat bran with palm kernel cake or fermented palm kernel cake on the growth performance, intestinal microbiota and intestinal health of tilapia (GIFT, Oreochromis niloticus)

A nine-week feeding trial was conducted to evaluate the effects of replacing wheat bran (WB) with palm kernel cake (PKC) or fermented palm kernel cake (FPKC) on the growth performance, intestinal microbiota and intestinal health of genetically improved farmed tilapia (GIFT, Oreochromis niloticus) (initial weight 7.00 ± 0.01 g). Eleven isonitrogenous and isolipidic experimental diets were formulated by replacing 0, 20, 40, 60, 80, and 100% of dietary WB with PKC or FPKC. Replacement of WB with PKC concentrations up to 80% had no significant effect on the growth rate of tilapia or feed utilisation (p > 0.05). FPKC improved the growth performance of tilapia, with optimum growth achieved at 40% replacement level (p < 0.05). Complete replacement with PKC significantly decreased the activity of lipase and trypsin, and reduced the height of muscularis and the height of villus (p < 0.05). However, FPKC significantly increased amylase activity and villus height (p < 0.05). The apparent digestibility of dry matter and energy decreased linearly with increasing levels of PKC substitution, while FPKC showed the opposite trend (p < 0.05). PKC replacement of WB by 20% significantly reduced serum diamine oxidase activity and endothelin levels and increased intestinal tight junctions (p < 0.05). However, FPKC significantly decreased diamine oxidase activity and increased intestinal tight junctions (p < 0.05). PKC completely replaced WB, up-regulating the expression of pro-inflammatory factors (il-1β) (p < 0.05). When 40% of WB was replaced with FPKC, the expression of pro-inflammatory factors (il-1β and il-6) was decreased significantly (p < 0.05). Completely replacement of WB with PKC reduced the abundance of Firmicutes and Chloroflexi, while FPKC reduced the abundance of Fusobacteriota and increased the levels of Actinobacteriota. WB can be replaced with PKC up to 80% in tilapia feeds. However, the high percentage of gluten induced intestinal inflammation, impaired gut health, and reduced dietary nutrient utilisation and growth performance. Complete replacement of WB with FPKC promoted intestinal immunity. It also improved dietary nutrient utilisation and growth performance. However, the optimal growth was achieved at a 40% replacement level

A contrast-enhanced CT-based radiomic nomogram for the differential diagnosis of intravenous leiomyomatosis and uterine leiomyoma

ObjectiveUterine intravenous leiomyomatosis (IVL) is a rare and unique leiomyoma that is difficult to surgery due to its ability to extend into intra- and extra-uterine vasculature. And it is difficult to differentiate from uterine leiomyoma (LM) by conventional CT scanning, which results in a large number of missed diagnoses. This study aimed to evaluate the utility of a contrast-enhanced CT-based radiomic nomogram for preoperative differentiation of IVL and LM.Methods124 patients (37 IVL and 87 LM) were retrospectively enrolled in the study. Radiomic features were extracted from contrast-enhanced CT before surgery. Clinical, radiomic, and combined models were developed using LightGBM (Light Gradient Boosting Machine) algorithm to differentiate IVL and LM. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC) and decision curve analysis (DCA).ResultsClinical factors, such as symptoms, menopausal status, age, and selected imaging features, were found to have significant correlations with the differential diagnosis of IVL and LM. A total of 108 radiomic features were extracted from contrast-enhanced CT images and selected for analysis. 29 radiomics features were selected to establish the Rad-score. A clinical model was developed to discriminate IVL and LM (AUC=0.826). Radiomic models were used to effectively differentiate IVL and LM (AUC=0.980). This radiological nomogram combined the Rad-score with independent clinical factors showed better differentiation efficiency than the clinical model (AUC=0.985, p=0.046).ConclusionThis study provides evidence for the utility of a radiomic nomogram integrating clinical and radiomic signatures for differentiating IVL and LM with improved diagnostic accuracy. The nomogram may be useful in clinical decision-making and provide recommendations for clinical treatment

Comprehensive analyses of the citrus WRKY gene family involved in the metabolism of fruit sugars and organic acids

Sugars and organic acids are the main factors determining the flavor of citrus fruit. The WRKY transcription factor family plays a vital role in plant growth and development. However, there are still few studies about the regulation of citrus WRKY transcription factors (CsWRKYs) on sugars and organic acids in citrus fruit. In this work, a genome-wide analysis of CsWRKYs was carried out in the citrus genome, and a total of 81 CsWRKYs were identified, which contained conserved WRKY motifs. Cis-regulatory element analysis revealed that most of the CsWRKY promoters contained several kinds of hormone-responsive and abiotic-responsive cis-elements. Furthermore, gene expression analysis and fruit quality determination showed that multiple CsWRKYs were closely linked to fruit sugars and organic acids with the development of citrus fruit. Notably, transcriptome co-expression network analysis further indicated that three CsWRKYs, namely, CsWRKY3, CsWRKY47, and CsWRKY46, co-expressed with multiple genes involved in various pathways, such as Pyruvate metabolism and Citrate cycle. These CsWRKYs may participate in the metabolism of fruit sugars and organic acids by regulating carbohydrate metabolism genes in citrus fruit. These findings provide comprehensive knowledge of the CsWRKY family on the regulation of fruit quality