检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Wang, Yuanqing Fass, Josh Kaminow, Benjamin Herr, John E. Rufa, dominic Zhang, Ivy Pulido, Iván Henry, Mike Chodera, John d. Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medical College Cornell University New YorkNY10065 United States Physiology Biophysics and System Biology Ph.D. Program Weill Cornell Medical College Cornell University New YorkNY10065 United States Program in Creative Writing Division of Humanities and Arts The City College of New York The City University of New York New YorkNY10031 United States

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with little inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset

关键词： Atoms

来源：评论

学校读者我要写书评

暂无评论

Stochastic aggregation in graph neural networks

arXiv

引用

arXiv 2021年

作者： Wang, Yuanqing Karaletsos, Theofanis Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Physiology Biophysics and System Biology Ph.D. Program New YorkNY10065 United States M.F.A. Program in Creative Writing City College of New York City University of New York New YorkNY10031 United States Facebook Inc. Menlo ParkCA94025 United States

Graph neural networks (GNNs) manifest pathologies including over-smoothing and limited discriminating power as a result of suboptimally expressive aggregating mechanisms. We herein present a unifying framework for stochastic aggregation (STAG) in GNNs, where noise is (adaptively) injected into the aggregation process from the neighborhood to form node embeddings. We provide theoretical arguments that STAG models, with little overhead, remedy both of the aforementioned problems. In addition to fixed-noise models, we also propose probabilistic versions of STAG models and a variational inference framework to learn the noise posterior. We conduct illustrative experiments clearly targeting oversmoothing and multiset aggregation limitations. Furthermore, STAG enhances general performance of GNNs demonstrated by competitive performance in common citation and molecule graph benchmark datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Application of 3-d nonlinear wave-load and structural-response simulations in naval ship design

引用

NAVAL ENGINEERS JOURNAL 1997年第3期109卷 253-266页

作者： Engle, A Lin, W Salvesen, N Shin, Y Allen H. Engle:is a naval architect in the Hydrodynamic Division of the Naval Sea Systems /Command (NavSea). He received his B.S. degree in engineering scinece from the State University of New York at Buffalo in 1977 and his M.S. degree in ocean engineering from the Unviersity of Hawaii in 1979. Corrently Mr. Engle is the program director of the Navy's Hydrodynamic Loads Technology Development Program. Mr. Engle is Chairman of SNAME Panel on Hull Loadings (HS-1) and contributing member to the International Ship and Offshore Structures Congress's Committee on Dynamics. Moei-Min Lin:is a senior research scientist at the Ship technology Division of Sciences Applications International Corporation (SAIC). He received his B.S. degree in naval architecture and marine engineering from the National Cheng-Kung University Taiwan in 1977 and his Ph.D. degree in ocean engineering from MIT in 1985. He is an expert in computational ship hydrodynamics. He has been the P.I. for development of the Large Amplitude Motions Program (LAMP) system. Nils Salvesen:is the manager of the Ship Technology Division of the Science Application International Corporation (SAIC).He received his B.S. and Ph.D. degree from the Development Naval Architecture and Marine Engineering of the University of Michigan in 1960 and 1966 respectively. He has more then thirty years of experience in the development of ship motion simulation and wave load prediction methods. More than twenty-five years ago he developed the original strip-theory code on which the Navy's ship motions program (SMP) is based. Yung-Sup Shin:is the principal engineer responsible for the hydrodynamic projects in the Research and Development Department of the American Bureau of Shipping New York. He received a Ph.D. degree in hydrodynamic from the Department of Naval Architecture and Marine Engineering of the University of Michigan. In the past eighteen years at ABS he has been involved in the theortical development and analysis of various marine hydrodynamic problems. In recent year

despite the limits inherent within linearized frequency-domain ship motion and wave load computer codes, strip theory has been found to provide the design community with a fairly robust, practical design tool with reasonable accuracy for most conventional displacement monohulls. However, the advent of new design concepts including multi-hulls and application of new materials as well as the push to incorporate reliability methods within surface ship structural design criteria has highlighted the need for more rigorous methods of developing a lifetime load spectrum. In this paper, a multilevel computation system for predicting ship motions and wave loads, up through and including extreme sea conditions, is presented. This system includes a traditional strip theory approach and newly developed linear and nonlinear three-dimensional time-domain methods. The new nonlinear methods are currently in the process of being validated by the U.S. Navy. The status of the current development is presented. Sample numerical results from the new nonlinear methods are compared with both linear frequency domain predictions and model tests.

关键词： Naval vessels

来源：评论

学校读者我要写书评

暂无评论

EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment

arXiv

引用

arXiv 2023年

作者： Wang, Yuanqing Pulido, Iván Takaba, Kenichiro Kaminow, Benjamin Scheen, Jenke Wang, Lily Chodera, John d. Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Physiology Biophysics and System Biology Ph.D. Program Weill Cornell Medical College Cornell University New YorkNY10065 United States Pharmaceutical Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medical College Cornell University New YorkNY10065 United States Open Molecular Sciences Foundation DavisCA95618 United States

Atomic partial charges are crucial parameters in molecular dynamics (Md) simulation, dictating the electrostatic contributions to intermolecular energies, and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC, and is expensive for large systems or large numbers of molecules. We propose a hybrid physical / graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserves total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling, for the first time, the use of fully consistent charge models for small molecules and biopolymers for the construction of nextgeneration self-consistent biomolecular force fields. Implemented in the free and open source package espaloma_charge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://***/choderalab/espaloma_charge. © 2023, CC BY.

关键词： Molecules

来源：评论

学校读者我要写书评

暂无评论

Structural insights into the role of N-terminal integrity in phoSL for core-fucosylated N-glycan recognition

引用

International Journal of Biological Macromolecules 2024年 255卷 128309页

作者： Lou, Yuan-Chao Tu, Cheng-Fen Chou, Chun-Chi Yeh, Hsin-Hong Chien, Chia-Yu Sadotra, Sushant Chen, Chinpan Yang, Ruey-Bing Hsu, Chun-Hua Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan Department of Agricultural Chemistry National Taiwan University Taipei106 Taiwan Chemical Biology and Molecular Biophysics Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu300 Taiwan Biomedical Translation Research Center Academia Sinica Taipei115 Taiwan Ph.D. Program in Drug Discovery and Development Industry College of Pharmacy Taipei Medical University Taipei110 Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei106 Taiwan Institute of Biochemical Sciences National Taiwan University Taipei106 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei106 Taiwan

phoSL (pholiota squarrosa Lectin) has an exceptional binding affinity for biomolecules with core-fucosylated N-glycans. This modification involves the addition of fucose to the inner N-acetylglucosamine within the N-glycan structure and is known to influence many physiological processes. Nevertheless, the molecular interactions underlying high-affinity binding of native phoSL to core-fucosylated N-glycans remain largely unknown. In this study, we devised a strategy to produce phoSL with the essential structural characteristics of the native protein (n-phoSL). To do so, a fusion protein was expressed in E. coli and purified. Then, enzymatic cleavage and incubation with glutathione were utilized to recapitulate the native primary structure and disulfide bonding pattern. Subsequently, we identified the residues crucial for n-phoSL binding to core-fucosylated chitobiose (N2F) via NMR spectroscopy. Additionally, crystal structures were solved for both apo n-phoSL and its N2F complex. These analyses suggested a pivotal role of the N-terminal amine in maintaining the integrity of the binding pocket and actively contributing to core-fucose recognition. In support of this idea, the inclusion of additional residues at the N-terminus considerably reduced binding affinity and phoSL cytotoxicity toward breast cancer cells. Taken together, these findings can facilitate the utilization of phoSL in basic research, diagnostics and therapeutic strategies. © 2023 Elsevier B.V.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

arXiv

引用

arXiv 2023年

作者： Takaba, Kenichiro Pulido, Iván Behara, Pavan Kumar Cavender, Chapin E. Friedman, Anika J. Henry, Michael M. Macdermott-Opeskin, Hugo Iacovella, Christopher R. Nagle, Arnav M. Payne, Alexander Matthew Shirts, Michael R. Mobley, david L. Chodera, John d. Wang, Yuanqing Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Pharmaceuticals Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Center for Neurotherapeutics Department of Pathology and Laboratory Medicine University of California IrvineCA92697 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Open Molecular Software Foundation DavisCA95618 United States Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Pharmaceutical Sciences University of California IrvineCA92697 United States Tri-Institutional Ph.D. Program in Chemical Biology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University New YorkNY10004 United States

The development of reliable and extensible molecular mechanics (MM) force fields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. © 2023, CC BY.

关键词： Quantum chemistry

来源：评论

学校读者我要写书评

暂无评论

OpenMM 8: Molecular dynamics Simulation with Machine Learning Potentials

arXiv

引用

arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, philip Wang, Yuanqing Zhang, Ivy Chodera, John d. de Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUdA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CdK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Technology dictates algorithms: Recent developments in read alignment

arXiv

引用

arXiv 2020年

作者： Alser, Mohammed Rotman, Jeremy Taraszka, Kodi Shi, Huwenbo Baykal, Pelin Icer Yang, Harry Taegyun Xue, Victor Knyazev, Sergey Singer, Benjamin d. Balliu, Brunilda Koslicki, david Skums, Pavel Zelikovsky, Alex Alkan, Can Mutlu, Onur Mangul, Serghei Computer Science Department ETH Zürich Zürich8092 Switzerland Computer Engineering Department Bilkent University Bilkent Ankara 06800 Turkey Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Department of Epidemiology Harvard T.H. Chan School of Public Health MA02115 United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA02142 United States Department of Computer Science Georgia State University AtlantaGA30302 United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles Los AngelesCA90095 United States Division of Pulmonary and Critical Care Medicine Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Biochemistry & Molecular Genetics Northwestern University Feinberg School of Medicine United States Simpson Querrey Center for Epigenetics Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Computer Science and Engineering Pennsylvania State University University ParkPA16801 United States Biology Department Pennsylvania State University University ParkPA16801 United States The Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16801 United States The Laboratory of Bioinformatics I.M. Sechenov First Moscow State Medical University Moscow119991 Russia Bilkent-Hacettepe Health Sciences and Technologies Program Ankara Turkey Department of Clinical Pharmacy School of Pharmacy University of Southern California Los AngelesCA90089 United States

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to today’s diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

The Broad Institute-Six Years Later

引用

CHEMISTRY & biology 2010年第4期17卷 311-312页

作者： McCarthy, Alice Main Text “In June 2003 the scientific and medical communities at MIT Harvard University and its affiliated hospitals and the Whitehead Institute banded together as collaborating partners to form the Eli and Edythe L. Broad Institute based in Cambridge MA. The Broad Institute established with initial funding from a $100 million philanthropic donation from the Los Angeles-based Broad family was primarily viewed as a marriage between the Whitehead Institute's Center for Genome Research (WICGR) and the Harvard Institute of Chemistry and Cell Biology (ICCB). Eli Broad founder and chairman of AIG SunAmerica Inc. explained “the purpose of the Broad Institute is to create a new type of research institute to build on the accomplishments of the human genome project and to move to clinical applications to both prevent and cure diseases.” Every Thursday morning we meet with perhaps 20 faculty members and 100 other researchers to discuss what we're all doing and should be doing next. -David Altschuler This paragraph was written five years ago when the Broad Institute was in its very earliest days as a life science research community (McCarthy 2005). Since that time “the Broad” as it's known has kept true to Eli Broad's vision having attracted a talented group of researchers faculty trainees and professional staff. This 1600 person research community known internally as “Broadies” includes faculty staff and students from throughout the MIT and Harvard biomedical research communities and beyond with collaborations spanning over a hundred private and public institutions in more than 40 countries worldwide. “What is special about the Broad is that we have people from Harvard MIT and the Harvard hospitals come together and work on problems of shared interest that could not be solved in their own individuals labs” explains David Altshuler M.D. Ph.D. Deputy Director and one of the Broad's six core faculty members. “These problems require expertise beyond any one principal investigator and in

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：