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arXiv 2020年

作者： Grigas, Alex T. Mei, Zhe Treado, John D. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute signifficantly to their stability. As a dataset of decoys to compare with real protein structures, we studied submissions to the bi-annual CASP competition (speciffically CASP11, 12, and 13), in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence. Our analysis reveals that many of the submissions possess cores that do not recapitulate the features that define real proteins. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a deep learning method, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoys from the CASP competitions equally well, if not better than, state-of-the-art methods that incorporate many additional features. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins

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Analyses of protein cores reveal fundamental differences between solution and crystal structures

arXiv

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arXiv 2019年

作者： Mei, Zhe Treado, John D. Grigas, Alex T. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

There have been several studies suggesting that protein structures solved by NMR spectroscopy and x-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures|in the root-mean-square deviations of the Cα atomic positions, identities of core amino acids, backbone and sidechain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modelling protein cores as jammed packings of amino-acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by x-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and x-ray crystallography. Copyright © 2019, The Authors. All rights reserved.

关键词： X ray crystallography

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A complete data processing workflow for CryoET and subtomogram averaging

arXiv

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arXiv 2019年

作者： Chen, Muyuan Bell, James M. Shi, Xiaodong Sun, Stella Y. Wang, Zhao Ludtke, Steven J. Verna Marrs and McLean Department of Biochemistry and Molecular Biology Baylor College of Medicine HoustonTX United States Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine HoustonTX United States Jiangsu Province Key Laboratory of Anesthesiology and Jiangsu Province Key Laboratory of Anesthesia and Analgesia Application Xuzhou Medical University Xuzhou Jiangsu China Department of Bioengineering Stanford University StanfordCA United States CryoEM Core at Baylor College of Medicine HoustonTX77030 United States

Electron cryotomography (CryoET) is currently the only method capable of visualizing cells in 3D at nanometer resolutions. While modern instruments produce massive amounts of tomography data containing extremely rich structural information, the data processing is very labor intensive and results are often limited by the skills of the personnel rather than the data. We present an integrated workflow that covers the entire tomography data processing pipeline, from automated tilt series alignment to subnanometer resolution subtomogram averaging. This workflow greatly reduces human effort and increases throughput, and is capable of determining protein structures at state-of-the-art resolutions for both purified macromolecules and cells. Copyright © 2019, The Authors. All rights reserved.

关键词： Tomography

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The 3.5-Å CryoEM Structure of Nanodisc-Reconstituted Yeast Vacuolar ATPase V_o Proton Channel

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Molecular Cell 2018年第6期69卷 993-1004.e3页

作者： Roh, Soung-Hun Stam, Nicholas J. Hryc, Corey F. Couoh-Cardel, Sergio Pintilie, Grigore Chiu, Wah Wilkens, Stephan Department of Bioengineering and James H. Clark Center Stanford University Stanford 94305 CA United States Biosciences Division SLAC National Accelerator Laboratory Menlo Park 94025 CA United States Department of Biochemistry and Molecular Biology SUNY Upstate Medical University Syracuse 13210 NY United States Graduate Program in Quantitative and Computational Biosciences Baylor College of Medicine Houston 77030 TX United States Verna and Marrs McLean Department of Biochemistry and Molecular Biology Baylor College of Medicine Houston 77030 TX United States

The molecular mechanism of transmembrane proton translocation in rotary motor ATPases is not fully understood. Here, we report the 3.5-Å resolution cryoEM structure of the lipid nanodisc-reconstituted V_o proton channel of the yeast vacuolar H⁺-ATPase, captured in a physiologically relevant, autoinhibited state. The resulting atomic model provides structural detail for the amino acids that constitute the proton pathway at the interface of the proteolipid ring and subunit a. Based on the structure and previous mutagenesis studies, we propose the chemical basis of transmembrane proton transport. Moreover, we discovered that the C terminus of the assembly factor Voa1 is an integral component of mature V_o. Voa1’s C-terminal transmembrane α helix is bound inside the proteolipid ring, where it contributes to the stability of the complex. Our structure rationalizes possible mechanisms by which mutations in human V_o can result in disease phenotypes and may thus provide new avenues for therapeutic interventions. Here, we report the 3.5-Å resolution cryoEM structure of lipid nanodisc-reconstituted yeast vacuolar H⁺-ATPase V_o proton channel. The resulting atomic model provides insight into the chemical basis of transmembrane proton transport. Moreover, we discovered that the C terminus of the assembly factor Voa1 is an integral component of mature V_o. © 2018 Elsevier Inc.

关键词： cryoEM lipid nanodisc membrane protein structure proton pumping reversible disassembly V-ATPase assembly Vo proton channel vacuolar H+-ATPase Voa1

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AmberTools

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Journal of Chemical Information and Modeling 2023年第20期63卷 6183-6191页

作者： Case, David A. Aktulga, Hasan Metin Belfon, Kellon Cerutti, David S. Cisneros, G. Andrés Cruzeiro, Vinícius Wilian D. Forouzesh, Negin Giese, Timothy J. Götz, Andreas W. Gohlke, Holger Izadi, Saeed Kasavajhala, Koushik Kaymak, Mehmet C. King, Edward Kurtzman, Tom Lee, Tai-Sung Li, Pengfei Liu, Jian Luchko, Tyler Luo, Ray Manathunga, Madushanka Machado, Matias R. Nguyen, Hai Minh O’Hearn, Kurt A. Onufriev, Alexey V. Pan, Feng Pantano, Sergio Qi, Ruxi Rahnamoun, Ali Risheh, Ali Schott-Verdugo, Stephan Shajan, Akhil Swails, Jason Wang, Junmei Wei, Haixin Wu, Xiongwu Wu, Yongxian Zhang, Shi Zhao, Shiji Zhu, Qiang Cheatham, Thomas E. Roe, Daniel R. Roitberg, Adrian Simmerling, Carlos York, Darrin M. Nagan, Maria C. Merz, Kenneth M. Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Computer Science and Engineering Michigan State University East LansingMI48824-1322 United States FOG Pharmaceuticals Inc. CambridgeMA02140 United States Psivant 451 D Street Suite 205 BostonMA02210 United States Department of Physics Department of Chemistry and Biochemistry University of Texas at Dallas RichardsonTX75801 United States Department of Chemistry and The PULSE Institute Stanford University StanfordCA94305 United States Department of Computer Science California State University Los AngelesCA90032 United States Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States San Diego Supercomputer Center University of California San Diego La Jolla CA92093-0505 United States Institute for Pharmaceutical and Medicinal Chemistry Heinrich Heine University Düsseldorf Düsseldorf40225 Germany Forschungszentrum Jülich GmbH Jülich52425 Germany Pharmaceutical Development Genentech Inc. South San FranciscoCA94080 United States Laufer Center for Physical and Quantitative Biology Department of Chemistry Stony Brook University Stony BrookNY11794 United States Departments of Molecular Biology and Biochemistry Chemical and Biomolecular Engineering Materials Science and Engineering and Biomedical Engineering Graduate Program in Chemical and Materials Physics University of California IrvineCA92697 United States Ph.D. Programs in Chemistry Biochemistry and Biology The Graduate Center of the City University of New York 365 Fifth Avenue New YorkNY10016 United States Department of Chemistry Lehman College 250 Bedford Park Blvd West BronxNY10468 United States Department of Chemistry and Biochemistry Loyola University Chicago ChicagoIL60660 United States Beijing National Laboratory for Molecular Science

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note. ... 详细信息

关键词： Free energy Ligands Molecular mechanics Molecular modeling QM/MM

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ColabFold - making protein folding accessible to all

Research Square

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Research Square 2021年

作者： Mirdita, Milot Schütze, Konstantin Moriwaki, Yoshitaka Heo, Lim Ovchinnikov, Sergey Steinegger, Martin Quantitative and Computational Biology Max Planck Institute for Biophysical Chemistry Göttingen Germany School of Biological Sciences Seoul National University Seoul Korea Republic of Department of Biotechnology Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Collaborative Research Institute for Innovative Microbiology The University of Tokyo Tokyo Japan Department of Biochemistry and Molecular Biology Michigan State University East LansingMI48824 United States JHDSF Program Harvard University CambridgeMA02138 United States FAS Division of Science Harvard University CambridgeMA02138 United States Artificial Intelligence Institute Seoul National University Seoul Korea Republic of

ColabFold offers accelerated protein structure and complex predictions by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 20−30x faster search and optimized model use allows predicting thousands of proteins per day on a server with one GPU. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at ***/sokrypton/ColabFold. Its novel environmental databases are available at *** © 2021, CC BY.

关键词： Protein folding

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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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An oncogenic enhancer encodes selective selenium dependency (vol 29, 386.e1, 2022)

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CELL STEM CELL 2022年第4期29卷 650-650页

作者： Eagle, Kenneth Jiang, Yajian Shi, Xiangguo Li, Minhua Obholzer, Nikolaus P. Hu, Tianyuan Perez, Monika W. Koren, Jost Vrabic Kitano, Ayumi Yi, Joanna S. Lin, Charles Y. Nakada, Daisuke Department of Molecular and Human Genetics Baylor College of Medicine 1 Baylor Plaza Houston TX 77030 USA Program in Quantitative and Computational Biosciences Baylor College of Medicine 1 Baylor Plaza Houston TX 77030 USA Program in Developmental Biology Baylor College of Medicine 1 Baylor Plaza Houston TX 77030 USA Development Disease Models & Therapeutics Graduate Program Baylor College of Medicine 1 Baylor Plaza Houston TX 77030 USA Department of Pediatrics Baylor College of Medicine 1 Baylor Plaza Houston TX 77030 USA Texas Children's Cancer and Hematology Centers 1102 Bates Avenue Houston TX 77030 USA Therapeutic Innovation Center Verna and Marrs McLean Department of Biochemistry and Molecular Biology Baylor College of Medicine 1 Baylor Plaza Houston TX 77030 USA Dan L. Duncan Comprehensive Cancer Center Baylor College of Medicine 1 Baylor Plaza Houston TX 77030 USA Kronos Bio 301 Binney St. 2nd Floor East Cambridge MA 02139 USA

Deregulation of transcription is a hallmark of acute myeloid leukemia (AML) that drives oncogenic expression programs and presents opportunities for therapeutic targeting. By integrating comprehensive pan-cancer enhancer landscapes with genetic dependency mapping, we find that AML-enriched enhancers encode for more selective tumor dependencies. We hypothesized that this approach could identify actionable dependencies downstream of oncogenic driver events and discovered a MYB-regulated AML-enriched enhancer regulating SEPHS2 , a key component of the selenoprotein production pathway. Using a combination of patient samples and mouse models, we show that this enhancer upregulates SEPHS2 , promoting selenoprotein production and antioxidant function required for AML survival. SEPHS2 and other selenoprotein pathway genes are required for AML growth in vitro . SEPHS2 knockout and selenium dietary restriction significantly delay leukemogenesis in vivo with little effect on normal hematopoiesis. These data validate the utility of enhancer mapping in target identification and suggest that selenoprotein production is an actionable target in AML.

关键词： AML selenium enhancer MYB SEPHS2 hematopoiesis hematopoietic stem cell leukemia stem cell selenocysteine

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Publisher Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第5期55卷 893页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Cambridge University Hospitals NHS Foundation Trust Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@***. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. hliang1@***. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson

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