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arXiv 2020年

作者： Tuckman, Philip J. VanderWerf, Kyle Yuan, Ye Zhang, Shiyun Zhang, Jerry Shattuck, Mark D. O’Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department City College of New York New YorkNY10031 United States Department of Physics Yale University Department of Applied New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

We investigate the mechanical response of packings of purely repulsive, frictionless disks to quasistatic deformations. The deformations include simple shear strain at constant packing fraction and at constant pressure, "polydispersity" strain (in which we change the particle size distribution) at constant packing fraction and at constant pressure, and isotropic compression. For each deformation, we show that there are two classes of changes in the interparticle contact networks: jump changes and point changes. Jump changes occur when a contact network becomes mechanically unstable, particles "rearrange", and the potential energy (when the strain is applied at constant packing fraction) or enthalpy (when the strain is applied at constant pressure) and all derivatives are discontinuous. During point changes, a single contact is either added to or removed from the contact network. For repulsive linear spring interactions, second- and higher-order derivatives of the potential energy/enthalpy are discontinuous at a point change, while for Hertzian interactions, third- and higher-order derivatives of the potential energy/enthalpy are discontinuous. We illustrate the importance of point changes by studying the transition from a hexagonal crystal to a disordered crystal induced by applying polydispersity strain. During this transition, the system only undergoes point changes, with no jump changes. We emphasize that one must understand point changes, as well as jump changes, to predict the mechanical properties of jammed packings. Copyright © 2020, The Authors. All rights reserved.

关键词： Potential energy

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Current md forcefields fail to capture key features of protein structure and fluctuations: a case study of cyclophilin a and t4 lysozyme

arXiv

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arXiv 2020年

作者： Mei, Zhe Grigas, Alex T. Treado, John D. Corres, Gabriel Melendez Vuorte, Maisa Sammalkorpi, Maria Regan, Lynne Levine, Zachary A. O’Hern, Corey S. Department of Chemistry Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Biology UPR Humacao Puerto RicoHumacao00792 Puerto Rico Department of Chemistry and Materials Science School of Chemical Engineering Aalto University Aalto Finland Department of Bioproducts and Biosystems School of Chemical Engineering Aalto University Aalto Finland Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Edinburgh United Kingdom Department of Pathology Yale School of Medicine New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Globular proteins undergo thermal fluctuations in solution, while maintaining an overall well-defined folded structure. In particular, studies have shown that the core structure of globular proteins differs in small, but significant ways when they are solved by x-ray crystallography versus solution-based NMR spectroscopy. Given these discrepancies, it is unclear whether molecular dynamics (MD) simulations can accurately recapitulate protein conformations. We therefore perform extensive MD simulations across multiple force fields and sampling techniques to investigate the degree to which computer simulations can capture the ensemble of conformations observed in experiments. By analyzing fluctuations in the atomic coordinates and core packing, we show that conformations sampled in MD simulations both move away from and sample a larger conformational space than the ensemble of structures observed in NMR experiments. However, we find that adding inter-residue distance restraints that match those obtained via Nuclear Overhauser Effect measurements enables the MD simulations to sample more NMR-like conformations, though significant differences between the core packing features in restrained MD and the NMR ensemble remain. Given that the protein structures obtained from the MD simulations possess smaller and less dense protein cores compared to those solved by NMR, we suggest that future improvements to MD forcefields should aim to increase the packing of hydrophobic residues in protein cores. © 2020, CC BY.

关键词： Molecular dynamics

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Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2

arXiv

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arXiv 2020年

作者： Law, Jeffrey N. Akers, Kyle Tasnina, Nure Della Santina, Catherine M. Deutsch, Shay Kshirsagar, Meghana Klein-Seetharaman, Judith Crovella, Mark Rajagopalan, Padmavathy Kasif, Simon Murali, T.M. Interdisciplinary Ph.D. Program in Genetics Bioinformatics and Computational Biology BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Department of Biomedical Engineering Boston University BostonMA United States Department of Mathematics University of California Los AngelesCA United States AI for Good Lab Microsoft RedmondWA United States Department of Chemistry Colorado School of Mines GoldenCO United States Department of Computer Science Boston University BostonMA United States Department of Chemical Engineering Virginia Tech BlacksburgVA United States

Background: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. Results: We design a network propagation framework with two novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. Conclusions: We examine how our provenance tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly-emerging viruses. © 2020, CC BY-NC-SA.

关键词： SARS

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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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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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Erratum to 'Core Biopsies from Prostate Cancer Patients in Active Surveillance Protocols Harbor PTEN and MYC Alterations'[European Urology Oncology 2 (2019) 277-285]

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European urology oncology 2021年第1期4卷 129页

作者： Paolo Gandellini Nicola Casiraghi Tiziana Rancati Matteo Benelli Valentina Doldi Alessandro Romanel Maurizio Colecchia Cristina Marenghi Riccardo Valdagni Francesca Demichelis Nadia Zaffaroni Molecular Pharmacology Unit Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Laboratory of Computational and Functional Oncology Centre for Integrative Biology (CIBIO) University of Trento Trento Italy. Prostate Cancer Program Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Bioinformatics Unit Hospital of Prato Prato Italy. Laboratory of Bioinformatics and Computational Genomics Centre for Integrative Biology (CIBIO) University of Trento Trento Italy. Department of Pathology and Laboratory Medicine Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Department of Radiation Oncology 1 Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Department of Oncology and Hemato-oncology Università degli Studi di Milano Milan Italy. Laboratory of Computational and Functional Oncology Centre for Integrative Biology (CIBIO) University of Trento Trento Italy. Electronic address: f.demichelis@unitn.it. Molecular Pharmacology Unit Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Electronic address: nadia.zaffaroni@istitutotumori.mi.it.

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The IBEX Knowledge-Base: Achieving more together with open science

arXiv

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arXiv 2024年

作者： Radtke, Andrea J. Anidi, Ifeanyichukwu Arakkal, Leanne Arroyo-Mejias, Armando Beuschel, Rebecca T. Börner, Katy Chu, Colin J. Clark, Beatrice Clatworthy, Menna R. Colautti, Jake Croteau, Joshua Denha, Saven Dever, Rose Dutra, Walderez O. Fritzsche, Sonja Fullam, Spencer Gerner, Michael Y. Gola, Anita Gollob, Kenneth J. Hernandez, Jonathan M. Hor, Jyh Liang Ichise, Hiroshi Jing, Zhixin Jonigk, Danny Kandov, Evelyn Kastenmüller, Wolfgang Koenig, Joshua F.E. Kothurkar, Aanandita Kreins, Alexandra Y. Lamborn, Ian Lin, Yuri Morais, Katia Luciano Pereira Lunich, Aleksandra Luz, Jean C.S. MacDonald, Ryan B. Makranz, Chen Maltez, Vivien I. Moriaty, Ryan V. Ocampo-Godinez, Juan M. Olyntho, Vitoria M. Padhan, Kartika Remmert, Kirsten Richoz, Nathan Schrom, Edward C. Shang, Wanjing Shi, Lihong Shih, Rochelle M. Speranza, Emily Stierli, Salome Teichmann, Sarah A. Veres, Tibor Z. Vierhout, Megan Wachter, Brianna T. Wade-Vallance, Adam K. Williams, Margaret Zangger, Nathan Germain, Ronald N. Yaniv, Ziv Lymphocyte Biology Section Center for Advanced Tissue Imaging Laboratory of Immune System Biology National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Critical Care Medicine and Pulmonary Branch National Heart Lung and Blood Institute National Institutes of Health BethesdaMD United States Lymphocyte Biology Section Laboratory of Immune System Biology National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Department of Intelligent Systems Engineering Indiana University BloomingtonIN United States UCL Institute of Ophthalmology NIHR Moorfields Biomedical Research Centre London United Kingdom Cambridge Institute for Therapeutic Immunology and Infectious Diseases University of Cambridge Department of Medicine Molecular Immunity Unit Laboratory of Molecular Biology Cambridge United Kingdom McMaster Immunology Research Centre Schroeder Allergy and Immunology Research Institute Department of Medicine Faculty of Health Sciences McMaster University HamiltonON Canada Department of Business Development BioLegend Inc. San DiegoCA United States Functional Immunogenomics Unit National Institute of Arthritis and Musculoskeletal and Skin Diseases National Institutes of Health BethesdaMD United States Laboratory of Cell-Cell Interactions Department of Morphology Institute of Biological Sciences Universidade Federal de Minas Gerais MG Belo Horizonte Brazil Spatial Proteomics Group Berlin Germany Division of Rheumatology Rush University Medical Center ChicagoIL United States Department of Immunology University of Washington School of Medicine SeattleWA United States Robin Chemers Neustein Laboratory of Mammalian Cell Biology and Development The Rockefeller University New YorkNY United States Hospital Israelita Albert Einstein SP Sao Paulo Brazil Surgical Oncology Program National Cancer Institute National Institutes of Health BethesdaMD United St

Iterative Bleaching Extends multipleXity (IBEX) is a versatile method for highly multiplexed imaging of diverse tissues. Based on open science principles, we created the IBEX Knowledge-Base, a resource for reagents, p... 详细信息

关键词： Cleaning

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Visualizing structure and transitions in high-dimensional biological data (vol 54, pg 781, 2019)

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NATURE BIOTECHNOLOGY 2020年第1期38卷 108-108页

作者： Moon, Kevin R. van Dijk, David Wang, Zheng Gigante, Scott Burkhardt, Daniel B. Chen, William S. Yim, Kristina van den Elzen, Antonia Hirn, Matthew J. Coifman, Ronald R. Ivanova, Natalia B. Wolf, Guy Krishnaswamy, Smita Department of Mathematics and Statistics Utah State University Logan USA Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven USA Department of Computer Science Yale University New Haven USA School of Basic Medicine Qingdao University Qingdao China Yale Stem Cell Center Department of Genetics Yale University New Haven USA Computational Biology and Bioinformatics Program Yale University New Haven USA Department of Genetics Yale University New Haven USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing USA Department of Mathematics Michigan State University East Lansing USA Applied Mathematics Program Yale University New Haven USA Department of Genetics Center for Molecular Medicine University of Georgia Athens USA Department of Mathematics and Statistics Université de Montréal Montréal Canada Mila—Quebec Artificial Intelligence Institute Montréal Canada

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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Machine learning for early prediction of circulatory failure in the intensive care unit

arXiv

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arXiv 2019年

作者： Hyland, Stephanie L. Faltys, Martin Hüser, Matthias Lyu, Xinrui Gumbsch, Thomas Esteban, Cristóbal Bock, Christian Horn, Max Moor, Michael Rieck, Bastian Zimmermann, Marc Bodenham, Dean Borgwardt, Karsten Rätsch, Gunnar Merz, Tobias M. Department of Computer Science ETH Zürich Zürich Switzerland Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Medical Informatics Group Zürich University Hospital Zürich Switzerland Department of Intensive Care Medicine University Hospital University of Bern Bern Switzerland Department of Biosystems and Engineering ETH Zürich Basel Switzerland Department of Biology ETH Zürich Zürich Switzerland Swiss Institute for Bioinformatics Lausanne Switzerland Cardiovascular Intensive Care Unit Auckland City Hospital Auckland New Zealand

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient-years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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Causes of evolutionary divergence in prostate cancer

arXiv

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arXiv 2025年

作者： Esentürk, Emre Sahli, Atef Haberland, Valeriia Ziuboniewicz, Aleksandra Wirth, Christopher Bova, G. Steven Bristow, Robert G. Brook, Mark N. Brors, Benedikt Butler, Adam Cancel-Tassin, Géraldine Cheng, Kevin C.L. Cooper, Colin S. Corcoran, Niall M. Cussenot, Olivier Eeles, Ros A. Favero, Francesco Gerhauser, Clarissa Gihawi, Abraham Girma, Etsehiwot G. Gnanapragasam, Vincent J. Gruber, Andreas J. Hamid, Anis Hayes, Vanessa M. He, Housheng Hansen Hovens, Christopher M. Imada, Eddie Luidy Jakobsdottir, G. Maria Jung, Chol-Hee Khani, Francesca Kote-Jarai, Zsofia Lamy, Philippe Leeman, Gregory Loda, Massimo Lutsik, Pavlo Marchionni, Luigi Molania, Ramyar Papenfuss, Anthony T. Pellegrina, Diogo Pope, Bernard Queiroz, Lucio R. Rausch, Tobias Reimand, Jüri Robinson, Brian Schlomm, Thorsten Sørensen, Karina D. Uhrig, Sebastian Weischenfeldt, Joachim Xu, Yaobo Yamaguchi, Takafumi N. Zanettini, Claudio Lynch, Andy G. Wedge, David C. Brewer, Daniel S. Woodcock, Dan J. Nuffield Department of Medicine University of Oxford United Kingdom Manchester Cancer Research Centre The University of Manchester United Kingdom Norwich Medical School University of East Anglia United Kingdom Nuffield Department of Surgical Sciences University of Oxford United Kingdom Prostate Cancer Research Center Faculty of Medicine and Health Technology Tampere University Finland Manchester Cancer Research Centre Cancer Research UK Manchester Institute The University of Manchester United Kingdom Christie NHS Foundation Trust United Kingdom Div Cancer Sciences Faculty of Biology Medicine & Health University of Manchester United Kingdom The Institute of Cancer Research United Kingdom Germany Germany Medical Faculty Heidelberg Faculty of Biosciences Heidelberg University Germany Wellcome Sanger Institute United Kingdom France Sorbonne Université GRC n°5 Predictive Onco-Urology APHP Tenon Hospital France Computational Biology Program Ontario Institute for Cancer Research Canada Department of Medical Biophysics University of Toronto Canada Department of Urology Royal Melbourne Hospital Australia Department of Surgery The University of Melbourne Australia Department of Urology Western Health Australia The Royal Marsden NHS Foundation Trust London United Kingdom University of Copenhagen Denmark Finsen Laboratory Copenhagen University Hospital - Rigshospitalet Denmark Germany Cambridge Biomedical Campus Addenbrooke's Hospital Site S Wards Building United Kingdom University of Konstanz Germany School of Medical Sciences University of Sydney Faculty of Medicine & Health Australia School of Health Systems & Public Health University of Pretoria South Africa Manchester Cancer Research Centre University of Manchester United Kingdom Princess Margaret Cancer Centre University Health Network Department of Medical Biophysics University of Toronto Canada Department of Surgery The Collaborative Centre for Genomic Cancer Medicine The University of Melbourne

Cancer progression involves the sequential accumulation of genetic alterations that cumulatively shape the tumour phenotype. In prostate cancer, tumours can follow divergent evolutionary trajectories that lead to distinct subtypes, but the causes of this divergence remain unclear. While causal inference could elucidate the factors involved, conventional methods are unsuitable due to the possibility of unobserved confounders and ambiguity in the direction of causality. Here, we propose a method that circumvents these issues and apply it to genomic data from 829 prostate cancer patients. We identify several genetic alterations that drive divergence as well as others that prevent this transition, locking tumours into one trajectory. Further analysis reveals that these genetic alterations may cause each other, implying a positive-feedback loop that accelerates divergence. Our findings provide insights into how cancer subtypes emerge and offer a foundation for genomic surveillance strategies aimed at monitoring the progression of prostate cancer. © 2025, CC BY-NC-SA.

关键词： Lung cancer

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