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arXiv 2018年

作者： Warrell, Jonathan Mohsen, Hussein Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University Molecular Biophysics and Biochemistry Yale University Department of Computer Science Yale University

A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be interested in picking out a prioritized collection of subsets of the inputs across a range of scales according to their importance for an output node, and not simply a prioritized ranking across the inputs as singletons. Such a situation may arise in biological applications, for instance, where we are interested in epistatic effects between groups of genes in determining a trait of interest. Here, we outline a flexible framework which may be used to generate multiscale network interpretations, using any previously defined scoring function. We demonstrate the ability of our method to pick out biologically important genes and gene sets in the domains of cancer and psychiatric genomics. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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Author Correction: Cryo-EM structure and biochemical analysis reveal the basis of the functional difference between human PI3KC3-C1 and -C2

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Cell research 2020年第6期30卷 551-552页

作者： Meisheng Ma Jun-Jie Liu Yan Li Yuwei Huang Na Ta Yang Chen Hua Fu Ming-Da Ye Yuehe Ding Weijiao Huang Jia Wang Meng-Qiu Dong Li Yu Hong-Wei Wang The State Key Laboratory of Membrane Biology Tsinghua University-Peking University Joint Center for Life Sciences School of Life Sciences Tsinghua University Beijing 100084 China. Ministry of Education Key Laboratory of Protein Sciences Tsinghua-Peking Joint Center for Life Sciences Beijing Advanced Innovation Center for Structural Biology School of Life Sciences Tsinghua University Beijing 100084 China. Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Tsinghua University Beijing 100084 China. Molecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. MOH Key Laboratory of Systems Biology of Pathogens Institute of Pathogen Biology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China. National Institute of Biological Sciences Beijing 102206 China. The State Key Laboratory of Membrane Biology Tsinghua University-Peking University Joint Center for Life Sciences School of Life Sciences Tsinghua University Beijing 100084 China. liyulab@***. Ministry of Education Key Laboratory of Protein Sciences Tsinghua-Peking Joint Center for Life Sciences Beijing Advanced Innovation Center for Structural Biology School of Life Sciences Tsinghua University Beijing 100084 China. hongweiwang@***.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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molecular imaging with optical coherence tomography and its biomedical applications

Molecular imaging with optical coherence tomography and its ...

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Optical Tomography and Spectroscopy, OTS 2018

作者： de la Zerda, Adam Departments of Structural Biology and Electrical Engineering Stanford University StanfordCA94305 United States Molecular Imaging Program at Stanford StanfordCA94305 United States The Bio-X Program StanfordCA94305 United States Biophysics Program at Stanford StanfordCA94305 United States The Chan Zuckerberg Biohub San FranciscoCA94158 United States

We developed a way to turn OCT into a molecular imaging modality through the development of novel nanoparticles. We explored its application in neurosurgery and in visualizing the role of the immune system in tumors. ... 详细信息

ISBN: (纸本)9781943580415

关键词： molecular imaging

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Publisher Correction: Atomistic mechanisms underlying the activation of the G protein-coupled sweet receptor heterodimer by sugar alcohol recognition

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Scientific reports 2019年第1期9卷 18996页

作者： Panupong Mahalapbutr Nitchakan Darai Wanwisa Panman Aunchan Opasmahakul Nawee Kungwan Supot Hannongbua Thanyada Rungrotmongkol Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Program in Biotechnology Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Multidisciplinary Program of Petrochemistry and Polymer Science Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Computational Chemistry Center of Excellent Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Department of Chemistry Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand. Center of Excellence in Materials Science and Technology Chiang Mai University Chiang Mai 50200 Thailand. Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. t.rungrotmongkol@***. Ph.D. Program in Bioinformatics and Computational Biology Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. t.rungrotmongkol@***. Molecular Sensory Science Center Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. t.rungrotmongkol@***.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Void distributions reveal structural link between jammed packings and protein cores

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Physical Review E 2019年第2期99卷 022416-022416页

作者： John D. Treado Zhe Mei Lynne Regan Corey S. O'Hern Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical & Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Chemistry Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics & Biochemistry Yale University New Haven Connecticut 06520 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction ϕ≈0.56, which is the same as dense, random packing of amino-acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino-acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. We measure the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation. We find that the cluster size statistics are similar for void percolation in packings of amino-acid-shaped particles and randomly placed spheres, but different from that for void percolation in jammed sphere packings. We propose that the connected void regions are a defining structural feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino-acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations.

关键词： Jamming Protein structure Packing & jamming problems Proteins Crystal structures Finite-size scaling molecular dynamics Percolation theory

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Proteome Analysis Using Gel-LC-MS/MS

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Current Protocols in Protein Science 2019年第1期96卷 e93页

作者： Goldman, Aaron R. Beer, Lynn A. Tang, Hsin-Yao Hembach, Peter Zayas-Bazan, Delaine Speicher, David W. Proteomics and Metabolomics Facility The Wistar Institute Philadelphia PA United States Center for Systems and Computational Biology and Molecular and Cellular Oncogenesis Program The Wistar Institute Philadelphia PA United States Biochemistry and Biophysics Graduate Group Perelman School of Medicine University of Pennsylvania Philadelphia PA United States

This article describes processing of protein samples using 1D SDS gels prior to protease digestion for proteomics workflows that subsequently utilize reversed-phase nanocapillary ultra-high-pressure liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS). The resulting LC-MS/MS data are used to identify peptides and thereby infer proteins present in samples ranging from simple mixtures to very complex proteomes. Bottom-up proteome studies usually involve quantitative comparisons across several or many samples. For either situation, 1D SDS gels represent a simple, widely available technique that can be used to either fractionate complex proteomes or rapidly clean up low microgram samples with minimal losses. After gel separation and staining/destaining, appropriate gel slices are excised, and in-gel reduction, alkylation, and protease digestion are performed. Digests are then processed for LC-MS/MS analysis. Protocols are described for either sample fractionation with high-throughput processing of many samples or simple cleanup without fractionation. An optional strategy is to conduct in-solution reduction and alkylation prior to running gels, which is advantageous when a large number of samples will be separated into large numbers of fractions. Optimization of trypsin digestion parameters and comparison to in-solution protease digestion are also described. © 2019 by John Wiley & Sons, Inc. © 2019 John Wiley & Sons, Inc.

关键词： gel-LC-MS/MS in-gel digestion LC-MS/MS mass spectrometry proteome fractionation proteomics SDS gels

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Atomistic mechanisms underlying the activation of the G protein-coupled sweet receptor heterodimer by sugar alcohol recognition (vol 9, 10205, 2019)

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SCIENTIFIC REPORTS 2019年第1期9卷 1-11页

作者： Mahalapbutr, Panupong Darai, Nitchakan Panman, Wanwisa Opasmahakul, Aunchan Kungwan, Nawee Hannongbua, Supot Rungrotmongkol, Thanyada Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Program in Biotechnology Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Multidisciplinary Program of Petrochemistry and Polymer Science Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Computational Chemistry Center of Excellent Department of Chemistry Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Department of Chemistry Faculty of Science Chiang Mai University 50200 Chiang Mai Thailand Center of Excellence in Materials Science and Technology Chiang Mai University 50200 Chiang Mai Thailand Ph.D. Program in Bioinformatics and Computational Biology Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Molecular Sensory Science Center Faculty of Science Chulalongkorn University 10330 Bangkok Thailand

The human T1R2-T1R3 sweet taste receptor (STR) plays an important role in recognizing various low-molecular-weight sweet-tasting sugars and proteins, resulting in the release of intracellular heterotrimeric G protein that in turn leads to the sweet taste perception. Xylitol and sorbitol, which are naturally occurring sugar alcohols (polyols) found in many fruits and vegetables, exhibit the potential caries-reducing effect and are widely used for diabetic patients as low-calorie sweeteners. In the present study, computational tools were applied to investigate the structural details of binary complexes formed between these two polyols and the T1R2-T1R3 heterodimeric STR. Principal component analysis revealed that the Venus flytrap domain (VFD) of T1R2 monomer was adapted by the induced-fit mechanism to accommodate the focused polyols, in which α-helical residues 233–268 moved significantly closer to stabilize ligands. This finding likely suggested that these structural transformations might be the important mechanisms underlying polyols-STR recognitions. The calculated free energies also supported the VFD of T1R2 monomer as the preferential binding site for such polyols, rather than T1R3 region, in accord with the lower number of accessible water molecules in the T1R2 pocket. The E302 amino acid residue in T1R2 was found to be the important recognition residue for polyols binding through a strongly formed hydrogen bond. Additionally, the binding affinity of xylitol toward the T1R2 monomer was significantly higher than that of sorbitol, making it a sweeter tasting molecule. [ABSTRACT FROM AUTHOR]

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Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin

Research Square

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Research Square 2021年

作者： Wang, Guan Kitaoka, Traci Crawford, Ali Mao, Qian Hesketh, Andrew Guppy, Fergus M. Ash, Garrett I. Liu, Jason Gerstein, Mark B. Pitsiladis, Yannis P. Sport and Exercise Science and Sports Medicine Research and Enterprise Group University of Brighton Brighton United Kingdom Centre for Regenerative Medicine and Devices University of Brighton Brighton United Kingdom Illumina San DiegoCA United States BGI Shenzhen China School of Pharmacy and Biomolecular Sciences University of Brighton Brighton United Kingdom Centre for Stress and Age-related Disease University of Brighton Brighton United Kingdom Veterans Affairs Connecticut Healthcare System West HavenCT United States Center for Medical Informatics Yale University New HavenCT United States Program in Computational Biology & Bioinformatics Yale University New HavenCT United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States

RNA-seq has matured and become an important tool for studying RNA biology. Here we compared two RNA-seq (Illumina sequencing by synthesis and MGI DNBSEQTM) and two microarray platforms (Illumina Expression BeadChip and GeneChipTM Human Transcriptome Array 2.0) in healthy individuals administered recombinant human erythropoietin for transcriptome-wide quantification of differential gene expression. The results show that total RNA sequencing combined with DNB-seq produced a multitude of genes of biological relevance and significance in response to recombinant human erythropoietin, in contrast to other platforms. Through data triangulation linking genes to functions, genes representing the processes of erythropoiesis as well as non-erythropoietic functions of erythropoietin were unveiled. This study provides a knowledge base of genes characterising the responses to recombinant human erythropoietin through cross-platform comparison and validation. © 2021, CC BY.

关键词： RNA

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Remembering the work of Phillip L. Geissler: A coda to his scientific trajectory

arXiv

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arXiv 2023年

作者： Bowman, Gregory R. Cox, Stephen J. Dellago, Christoph DuBay, Kateri H. Eaves, Joel D. Fletcher, Daniel A. Frechette, Layne B. Grünwald, Michael Klymko, Katherine Ku, JiYeon Omar, Ahmad K. Rabani, Eran Reichman, David R. Rogers, Julia R. Rosnik, Andreana M. Rotskoff, Grant M. Schneider, Anna R. Schwierz, Nadine Sivak, David A. Vaikuntanathan, Suriyanarayanan Whitelam, Stephen Widmer-Cooper, Asaph Bioengineering Biochemistry and Biophysics University of Pennsylvania PA19104 United States Yusuf Hamied Department of Chemistry University of Cambridge Lensfield Road CambridgeCB2 1EW United Kingdom Faculty of Physics University of Vienna Vienna1090 Austria Department of Chemistry University of Virginia CharlottesvilleVA22903 United States Department of Chemistry University of Colorado Boulder BoulderCO80309 United States Department of Bioengineering & Biophysics Program University of California Berkeley Berkeley United States Division of Biological Systems & Engineering Lawrence Berkeley National Laboratory Berkeley United States Chan Zuckerberg Biohub San Francisco United States Martin A. Fisher School of Physics Brandeis University WalthamMA02454 United States Department of Chemistry University of Utah Salt Lake City84112 United States Lawrence Berkeley National Lab BerkeleyCA94720 United States Co. Ltd Suwon16229 Korea Republic of Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Chemistry University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States The Raymond and Beverly Sackler Center Computational Molecular and Materials Science Tel Aviv University Tel Aviv69978 Israel Department of Chemistry Columbia University New YorkNY United States Department of Systems Biology Columbia University New YorkNY10032 United States Atomwise San FranciscoCA94103 United States Department of Chemistry Stanford University StanfordCA94305 United States Form Energy BerkeleyCA94710 United States Institute of Physics University of Augsburg Germany Department of Physics Simon Fraser University BurnabyBCV5A1S6 Canada Department of Chemistry University of Chicago ChicagoIL6063

Phillip L. Geissler made important contributions to the statistical mechanics of biological polymers, heterogeneous materials, and chemical dynamics in aqueous environments. He devised analytical and computational methods that revealed the underlying organization of complex systems at the frontiers of biology, chemistry, and materials science. In this retrospective, we celebrate his work at these frontiers. Copyright © 2023, The Authors. All rights reserved.

关键词： Statistical mechanics

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