iCn3D was initially developed as a web-based 3D molecular viewer. It then evolved from visualization into a full-featured interactive structural analysis software. It became a collaborative research instrument through the sharing of permanent, shortened URLs that encapsulate not only annotated visual molecular scenes, but also all underlying data and analysis scripts in a FAIR manner. More recently, with the growth of structural databases, the need to analyze large structural datasets systematically led us to use Python scripts and convert the code to be used in Node. js scripts. We showed a few examples of Python scripts at https://github.com/ncbi/icn3d/tree/master/icn3dpython to export secondary structures or PNG images from iCn3D. Users just need to replace the URL in the Python scripts to export other annotations from iCn3D. Furthermore, any interactive iCn3D feature can be converted into a Node. js script to be run in batch mode, enabling an interactive analysis performed on one or a handful of protein complexes to be scaled up to analysis features of large ensembles of structures. Currently available Node. js analysis scripts examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode. This development will enable ensemble analyses on growing structural databases such as AlphaFold or RoseTTAFold on one hand and Electron Microscopy on the other. In this paper, we also review new features such as DelPhi electrostatic potential, 3D view of mutations, alignment of multiple chains, assembly of multiple structures by realignment, dynamic symmetry calculation, 2D cartoons at different levels, interactive contact maps, and use of iCn3D in Jupyter Notebook as described at https://pypi.org/project/icn3dpy.
Background: Prostate cancer (PCa) is one of the most common malignancies, and many studies have shown that PCa has a poor prognosis, which varies across different ethnicities. This variability is caused by genetic diversity. High-throughput omics technologies have identified and shed some light on the mechanisms of its progression and finding new biomarkers. Still, a systems biology approach is needed for a holistic molecular perspective. In this study, we applied a multi-omics approach to data analysis using different publicly available omics data sets from diverse populations to better understand the PCa disease etiology. Methodology: Our study used multiple omic datasets which included genomic, transcriptomic and metabolomic datasets to better identify drivers for PCa. We first perform an individual omics analysis based on the standard pipeline for each dataset. Furthermore, we applied a novel multi-omics pathways algorithm to integrate all the individual omics datasets. This algorithm applies the p-values of enriched pathways from individual omics data types, which are then combined using the MiniMax statistic to prioritize pathways dysregulated in the omics datasets. Result: The single omics result indicated an association between up-regulated genes in RNA-Seq data and the metabolomics data. Glucose and pyruvate are the primary metabolites, and the associated pathways are glycolysis, gluconeogenesis, pyruvate kinase deficiency, and the Warburg effect pathway. Conclusion: From the interim result, the identified genes in RNA-Seq single omics analysis are linked with the significant pathways from the metabolomics analysis. The multi-omics pathway will eventually enable the identification of biomarkers shared amongst these different omics datasets to ease prostate cancer prognosis.
iCn3D was originally released as a web-based 3D viewer, which allows users to create a custom view in a life-long, shortened URL to share with colleagues. Recently, iCn3D was converted to use JavaScript classes and could be used as a library to write Node.js scripts. Any interactive features in iCn3D can be converted to Node.js scripts to run in batch mode for a large data set. Currently the following Node.js script examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode: ligand-protein interaction, protein-protein interaction, change of interactions due to residue mutations, DelPhi electrostatic potential, and solvent accessible surface area. iCn3D PNG images can also be exported in batch mode using a Python script. Other recent features of iCn3D include the alignment of multiple chains from different structures, realignment, dynamic symmetry calculation for any subsets, 2D cartoons at different levels, and interactive contact maps. iCn3D can also be used in Jupyter Notebook as described at https://pypi.org/project/icn3dpy.
The cancer burden statistics have been on the rise between the years 2008 and 2018. There has been an increase in the incidence and mortalities of cancers within this period. These statistics justify the sustained increase in the study and exploration of more agents with significant anticancer activity. Compounds of natural origin, such as caffeine, a widely known member of the xanthine family of purines fall under this category. It is a popular component of beverages and medications used in contemporary times. This review aims to elucidate on the anticancer activities attributed to caffeine. Caffeine consumption has been shown to have anticancer benefits from epidemiological evidences. It prevents the initiation of carcinogenesis and has antitumor activity. Caffeine also has significant anticancer activity against animal and cultured cell line models of cancer. The cytotoxic effects of some anticancer drugs have also been improved by combination with caffeine. Furthermore, caffeine and related xanthine derivatives have also been applied in experimental chemotherapy. Some molecular pathways have been implicated in these activities related to apoptosis and DNA damage repair pathways. These studies outline the beneficial anticancer effects of caffeine against the different stages of cancer development.
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