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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Zhirui Liao Xiaodi Huang Hiroshi Mamitsuka Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University Albury NSW Australia Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Shanghai China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781665429825

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI).

关键词： Drugs Proteins Deep learning Solid modeling Three-dimensional displays Conferences Predictive models

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Landscape of the regulatory elements for lysine 2-hydroxyisobutyrylation pathway

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细胞研究（英文版） 2018年第1期28卷 111-125页

作者： He Huang Zhouqing Luo Shankang Qi Jing Huang Peng Xu Xiuxuan Wang Li Gao Fangyi Li Jian Wang Wenhui Zhao Wei Gu Zhucheng Chen Lunzhi Dai Junbiao Dai Yingming Zhao Ben May Department for Cancer Research The University of Chicago Chicago IL 60637 USA Center for Synthetic Biology Engineering Research Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong 518055China MOE Key Laboratory of Bioinformatics and Center for Synthetic and Systems Biology School of Life Sciences Tsinghua University Beijing 100084 China MOE Key Laboratory of Bioinformatics and Center for Synthetic and Systems Biology School of Life Sciences Tsinghua University Beijing 100084 China Department of General Practice and Lab of PTM State Key Laboratory of Biotherapy West China Hospital Sichuan University Collaborative Innovation Center of Biotherapy Chengdu Sichuan 610041 China School of Pharmaceutical Sciences Tsinghua University Beijing 100084 China Department of Biochemistry and Molecular Biology Health Science Center and Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function Peking University Beijing 100191 China Institute for Cancer Genetics Department of Pathology and Cell Biology Herbert Irving Comprehensive Cancer Center College of Physicians and Surgeons Columbia University 1130 Nicholas Avenue New York NY 10032 USA

Short-chain fatty acids and their corresponding acyl-CoAs sit at the crossroads of metabolic pathways and play important roles in diverse cellular *** are also precursors for protein post-translational lysine acylation modifications.A noteworthy example is the newly identified lysine 2-hydroxyisobutyrylation (Khib) that is derived from 2-hydroxyisobutyrate and *** Khib has been shown to be associated with active gene expression in spermatogenic ***,the key elements that regulate this post-translational lysine acylation pathway remain *** has hindered characterization of the mechanisms by which this modification exerts its biological *** we show that Esalp in budding yeast and its homologue Tip60 in human could add Khib to substrate proteins both in vitro and in *** addition,we have identified HDAC2 and HDAC3 as the major enzymes to remove ***,we report the first global profiling of Khib proteome in mammalian cells,identifying 6 548 Khib sites on 1 725 substrate *** study has thus discovered both the "writers" and "erasers" for histone Khib marks,and major Khib protein *** results not only illustrate the landscape of this new lysine acylation pathway,but also open new avenues for studying diverse functions of cellular metabolites associated with this pathway.

关键词： post-translational modification lysine 2-hydroxyisobutyrylation regulatory element acyltransferase deacylase substrate

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The DrugPattern tool for drug set enrichment analysis and its prediction for beneficial effects of oxLDL on type 2 diabetes

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Journal of Genetics and Genomics 2018年第7期45卷 389-397页

作者： Chuanbo Huang Weili Yang Junpei wang Yuan Zhou Bin Geng Georgios Kararigas Jichun Yang Qinghua Cui Department of Biomedical Informatics Department of Physiology and Pathophysiology MOE Key Lab of Cardiovascular Sciences School of Basic Medical Sciences Center for Non-Coding RNA Medicine Peking UniversityBeijing 100191China School of Mathematics Sciences Huaqiao University Quanzhou 362021 China Hypertension Center Fuwai Hospital Chinese Academy of Medical Sciences and Peking Union Medical CollegeState Key Laboratory of Cardiovascular Disease National Center for Cardiovascular Diseases Beijing 100037 China Charit-Universitatsmedizin Berlin. Corporate Member of Freie Universitat Berlin Humboldt-Universitat zu Berlin Berlin Institute of Health Institute of Gender in Medicine and Center for Cardiovascular Research. DZHK (German Centre for Cardiovascular Research) 10115 Berlin Germany Center of Bioinformatics Key Laboratory for Neuro-lnformation of Ministry of Education School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 610054 China

Enrichment analysis methods, e.g., gene set enrichment analysis, represent one class of important bio- informatical resources for mining patterns in biomedical datasets. However, tools for inferring patterns and rules of a list of drugs are limited. In this study, we developed a web-based tool, DrugPattern, for drug set enrichment analysis. We first collected and curated 7019 drug sets, including indications, adverse reactions, targets, pathways, etc. from public databases. For a list of interested drugs, DrugPat- tern then evaluates the significance of the enrichment of these drugs in each of the 7019 drug sets. To validate DrugPattern, we employed it for the prediction of the effects of oxidized low-density lipoprotein （oxLDL）, a factor expected to be deleterious. We predicted that oxLDL has beneficial effects on some diseases, most of which were supported by evidence in the literature. Because DrugPattern predicted the potential beneficial effects of oxLDL in type 2 diabetes （T2D）, animal experiments were then performed to further verify this prediction. As a result, the experimental evidences validated the DrugPattern prediction that oxLDL indeed has beneficial effects on T2D in the case of energy restriction. These data confirmed the prediction accuracy of our approach and revealed unexpected protective roles for oxLDL in various diseases. This study provides a tool to infer patterns and rules in biomedical datasets based on drug set enrichment analysis.

关键词： Enrichment analysis Drug Data mining oxLDL Type 2 diabetes

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Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

arXiv

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arXiv 2018年

作者： Jia, Chen Qian, Hong Chen, Min Zhang, Michael Q. Department of Mathematical Sciences University of Texas at Dallas RichardsonTX75080 United States Department of Applied Mathematics University of Washington SeattleWA98195 United States Department of Biological Sciences Center for Systems Biology University of Texas at Dallas RichardsonTX75080 United States MOE Key Lab Division of Bioinformatics CSSB TNLIST Tsinghua University Beijing100084 China

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression. Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Meeting Report: Cold Spring Harbor Asia Synthetic Biology Meeting

Meeting Report: Cold Spring Harbor Asia Synthetic Biology Me...

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作者： Hajnal, Ivan MOE Key Lab of Bioinformatics School of Life Sciences Tsinghua-Peking Center for Life Sciences Tsinghua University Beijing100084 China

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Emergent Lévy behavior in single-cell stochastic gene expression

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Physical Review E 2017年第4期96卷 040402(R)-040402(R)页

作者： Chen Jia Michael Q. Zhang Hong Qian Department of Mathematical Sciences University of Texas at Dallas Richardson Texas 75080 USA Department of Biological Sciences University of Texas at Dallas Richardson Texas 75080 USA MOE Key Lab and Division of Bioinformatics CSSB TNLIST Tsinghua University Beijing 100084 China Department of Applied Mathematics University of Washington Seattle Washington 98195 USA

Single-cell gene expression is inherently stochastic; its emergent behavior can be defined in terms of the chemical master equation describing the evolution of the mRNA and protein copy numbers as the latter tends to infinity. We establish two types of “macroscopic limits”: the Kurtz limit is consistent with the classical chemical kinetics, while the Lévy limit provides a theoretical foundation for an empirical equation proposed in N. Friedman et al., Phys. Rev. Lett. 97, 168302 (2006). Furthermore, we clarify the biochemical implications and ranges of applicability for various macroscopic limits and calculate a comprehensive analytic expression for the protein concentration distribution in autoregulatory gene networks. The relationship between our work and modern population genetics is discussed.

关键词： Gene expression Gene regulatory networks Markovian processes Stochastic differential equations

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Identification of the anti-tumor activity and mechanisms of nuciferine through a network pharmacology approach

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中国药理学报（英文版） 2016年第7期37卷 963-972页

作者： Quan QI Rui LI Hui-ying LI Yu-bing CAO Ming BAI Xiao-jing FAN Shu-yan WANG Tianjin State Key Laboratory of Modern Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin 300193China MOE Key Lab of Bioinformatics Bioinformatics Division TNLIST and Department of Automation Tsinghua University Beijing 100084 China MOE Key Lab of Bioinformatics Bioinformatics Division TNLIST and Department of Automation Tsinghua University Beijing 100084 China Tianjin State Key Laboratory of Modern Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin 300193China The Second Hospital Affiliated to Tianjin University of Traditional Chinese Medicine Tianjin 300150 China Tianjin International Joint Academy of Biotechnology & Medicine Tianjin 300457 China

Aim: Nuciferine is an aporphine alkaloid extracted from lotus leaves, which is a raw material in Chinese medicinal herb for weight loss. In this study we used a network pharmacology approach to identify the anti-tumor activity of nuciferine and the underlying ***: The pharmacological activities and mechanisms of nuciferine were identified through target profile prediction, clustering analysis and functional enrichment analysis using our traditional Chinese medicine (TCM) network pharmacology platform. The anti-tumor activity of nuciferine was validated by in vitro and in vivo experiments. The anti-tumor mechanisms of nuciferine were predicted through network target analysis and verified by in vitro ***: The nuciferine target profile was enriched with signaling pathways and biological functions, including “regulation of lipase activity”, “response to nicotine” and “regulation of cell proliferation”. Target profile clustering results suggested that nuciferine to exert anti-tumor effect. In experimental validation, nuciferine (0.8 mg/mL) markedly inhibited the viability of human neuroblastoma SY5Y cells and mouse colorectal cancer CT26 cells in vitro, and nuciferine (0.05 mg/mL) significantly suppressed the invasion of 6 cancer cell lines in vitro. Intraperitoneal injection of nuciferine (9.5 mg/mL, ip, 3 times a week for 3 weeks) significantly decreased the weight of SY5Y and CT26 tumor xenografts in nude mice. Network target analysis and experimental validation in SY5Y and CT26 cells showed that the anti-tumor effect of nuciferine was mediated through inhibiting the PI3K-AKT signaling pathway and IL-1 levels in SY5Y and CT26 ***: By using a TCM network pharmacology method, nuciferine is identified as an anti-tumor agent against human neuroblastoma and mouse colorectal cancer in vitro and in vivo, through inhibiting the PI3K-AKT signaling pathways and IL-1 levels.

关键词： nuciferine TCM network pharmacology anti-tumor agent neuroblastoma colorectal cancer PI3K-AKT IL-1 drugCIPHER

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Stochastic fluctuations can reveal the feedback signs of gene regulatory networks at the single-molecule level

arXiv

引用

arXiv 2017年

作者： Jia, Chen Xie, Peng Chen, Min Zhang, Michael Q. Department of Mathematical Sciences University of Texas at Dallas RichardsonTX75080 United States Department of Biological Sciences Center for Systems Biology University of Texas at Dallas RichardsonTX75080 United States MOE Key Lab Division of Bioinformatics CSSB TNLIST Tsinghua University Beijing100084 China

Understanding the relationship between spontaneous stochastic fluctuations and the topology of the underlying gene regulatory network is of fundamental importance for the study of single-cell stochastic gene expression. Here by solving the analytical steady-state distribution of the protein copy number in a general kinetic model of stochastic gene expression with nonlinear feedback regulation, we reveal the relationship between stochastic fluctuations and feedback topology at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations. Based on such relationship, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data. The theory is demonstrated by numerical simulations and, more importantly, validated quantitatively by single-cell data analysis of a synthetic gene circuit integrated in human kidney cells. Copyright © 2017, The Authors. All rights reserved.

关键词： Gene expression

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Emergent Lévy behavior in single-cell stochastic gene expression

arXiv

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arXiv 2017年

作者： Jia, Chen Zhang, Michael Q. Qian, Hong Department of Mathematical Sciences University of Texas at Dallas RichardsonTX75080 United States Department of Biological Sciences University of Texas at Dallas RichardsonTX75080 United States MOE Key Lab Division of Bioinformatics CSSB TNLIST Tsinghua University Beijing100084 China Department of Applied Mathematics University of Washington SeattleWA98195 United States

Single-cell gene expression is inherently stochastic;its emergent behavior can be defined in terms of the chemical master equation describing the evolution of the mRNA and protein copy numbers as the latter tends to infinity. We establish two types of "macroscopic limits": the Kurtz limit is consistent with the classical chemical kinetics, while the Lévy limit provides a theoretical foundation for an empirical equation proposed in [Phys. Rev. Lett. 97:168302, 2006]. Furthermore, we clarify the biochemical implications and ranges of applicability for various macroscopic limits and calculate a comprehensive analytic expression for the protein concentration distribution in autoregulatory gene networks. The relationship between our work and modern population genetics is discussed. Copyright © 2017, The Authors. All rights reserved.

关键词： Gene expression

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