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  • Table of Content
      Aug. 2016, Volume 2 Issue 5-6 Previous Issue   
    Cover Story
    3dRPC is a computational method designed for three-dimensional RNA-protein complex structure prediction. Starting from a protein structure and a RNA structure, 3dRPC first generates presumptive complex structures by RPDOCK and then evaluates the structures by RPRANK. RPDOCK is an FFTbased docking algorithm that takes features of RNA-protein interactions into consideration, and RPRANK is a knowledge-based potential using root mean square deviation as a measure. Here the authors give a detailed description of t [Detail] ...
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    CONTENTS
    Biophysics Reports. 2016, 2 (5-6): 0-0.  
    Abstract   HTML   PDF (4843KB) ( 34 )
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    OPINION
    Thermodynamic aspects of ATP hydrolysis of actomyosin complex
    Xuejun C. Zhang, Wei Feng
    Biophysics Reports. 2016, 2 (5-6): 87-94.   DOI: 10.1007/s41048-016-0032-5
    Abstract   HTML   PDF (677KB) ( 74 )
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    RESOURCE
    Using 3dRPC for RNA-protein complex structure prediction
    Yangyu Huang, Haotian Li, Yi Xiao
    Biophysics Reports. 2016, 2 (5-6): 95-99.   DOI: 10.1007/s41048-017-0034-y
    Abstract   HTML   PDF (901KB) ( 74 )
    3dRPC is a computational method designed for three-dimensional RNA-protein complex structure prediction. Starting from a protein structure and a RNA structure,3dRPC first generates presumptive complex structures by RPDOCK and then evaluates the structures by RPRANK. RPDOCK is an FFT-based docking algorithm that takes features of RNA-protein interactions into consideration, and RPRANK is a knowledgebased potential using root mean square deviation as a measure. Here we give a detailed description of the usage of 3dRPC. The source code is available at http://biophy.hust.edu.cn/3dRPC.html.
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    RESEARCH ARTICLE
    Simulated microgravity potentiates generation of reactive oxygen species in cells
    Fanlei Ran, Lili An, Yingjun Fan, Haiying Hang, Shihua Wang
    Biophysics Reports. 2016, 2 (5-6): 100-105.   DOI: 10.1007/s41048-016-0029-0
    Abstract   HTML   PDF (500KB) ( 85 )
    Microgravity (MG) and space radiation are two major environmental factors of space environment. Ionizing radiation generates reactive oxygen species (ROS) which plays a key role in radiation-induced DNA damage. Interestingly, simulated microgravity (SMG) also increases ROS production in various cell types. Thus, it is important to detect whether SMG could potentiate ROS production induced by genotoxins including radiation, especially at a minimal level not sufficient to induce detectable ROS. In this study, we treated mouse embryonic stem (MES) cells with H2O2 and SMG for 24 h. The concentration of H2O2 used was within 30 μmol/L at which intracellular ROS was the same as that in untreated cells. Exposure of cells to SMG for 24 h did not induce significantly higher levels of intracellular ROS than that of control cells either. Simultaneous exposure of cells to both SMG- and H2O2- induced ROS and apoptosis in MES cells. Although incubation in medium containing 5 or 30 lmol/L H2O2 induced a small enhancement of DNA double-strand breaks (DSBs), the addition of SMG treatment dramatically increased DSB levels. Taken together, SMG can significantly potentiate the effects of H2O2 at a low concentration that induce a small or negligible change in cells on ROS, apoptosis, and DNA damage. The results were discussed in relation to the combined effects of space radiation and MG on human body in this study.
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    MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets
    Xilin Xu, Aiping Wu, Xinlei Zhang, Mingming Su, Taijiao Jiang, Zhe-Ming Yuan
    Biophysics Reports. 2016, 2 (5-6): 106-115.   DOI: 10.1007/s41048-016-0033-4
    Abstract   HTML   PDF (986KB) ( 37 )
    High-throughput sequencing-based metagenomics has garnered considerable interest in recent years. Numerous methods and tools have been developed for the analysis of metagenomic data. However, it is still a daunting task to install a large number of tools and complete a complicated analysis, especially for researchers with minimal bioinformatics backgrounds. To address this problem, we constructed an automated software named MetaDP for 16S rRNA sequencing data analysis, including data quality control, operational taxonomic unit clustering, diversity analysis, and disease risk prediction modeling. Furthermore, a support vector machine-based prediction model for intestinal bowel syndrome (IBS) was built by applying MetaDP to microbial 16S sequencing data from 108 children. The success of the IBS prediction model suggests that the platform may also be applied to other diseases related to gut microbes, such as obesity, metabolic syndrome, or intestinal cancer, among others (http://metadp.cn:7001/).
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