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Cholesterol allosteric modulation of the oxytocin receptor

Biophysical Journal 2025年第12期124卷 1930-1943页

作者： Marlow, Brennica Vogel, Alexander Kuenze, Georg Pankonin, Maik Reinhardt, Franziska Stadler, Peter F. Hildebrand, Peter W. Meiler, Jens Center for Structural Biology Vanderbilt University Nashville TN United States Institute for Drug Discovery Medical Faculty Leipzig University Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Leipzig University Leipzig Germany Interdisciplinary Center for Bioinformatics Leipzig University Leipzig Germany Chemical and Physical Biology Program Vanderbilt University Nashville TN United States Department of Chemistry Vanderbilt University Nashville TN United States Institute of Medical Physics and Biophysics Medical Faculty Leipzig University Leipzig Germany Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Leipzig Germany Max-Planck-Institute for Mathematics in the Sciences Leipzig Germany Institute of Theoretical Chemistry University of Vienna Wien Austria Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Institute New Mexico Santa Fe Mexico

G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric cross talk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our molecular dynamics approach, also coincide with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand-mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration. © 2025 Biophysical Society

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data Extraction for Associative Classification using Mined Rules in Pediatric Intensive Care data

Data Extraction for Associative Classification using Mined R...

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2023 Datenbanksysteme fur Business, Technologie und Web, BTW 2023 - 2023 database Systems for Business, Technology and Web, BTW 2023

作者： Das, Pronaya Prosun Mast, Marcel Wiese, Lena Jack, Thomas Wulff, Antje Bode, Louisa Frank, Torsten Marschollek, Michael Schamer, Sven Rathert, Henning Haller, Andreas Beerbaum, Philipp Rübsamen, Nicole Böhnke, Julia Karch, André Das, Pronaya Prosun Groszweski-Anders, Christian Fraunhofer Institute for Toxicology and Experimental Medicine Hannover Germany Peter L. Reichertz Institute for Medical Informatics TU Braunschweig Hannover Medical School Hannover Germany Institute of Computer Science Goethe University Frankfurt Frankfurt a. M. Germany Department of Pediatric Cardiology and Intensive Care Medicine Hannover Medical School Hannover Germany Big Data in Medicine Department of Health Services Research School of Medicine and Health Sciences Carl von Ossietzky University Oldenburg Oldenburg Germany Institute of Epidemiology and Social Medicine University of Muenster Muenster Germany Research Group Bioinformatics Fraunhofer Institute for Toxicology and Experimental Medicine Hannover Germany Medisite GmbH Hannover Germany

ISBN: (纸本)9783885797258

Based on the characteristics of health and medical informatics, data mining techniques that were designed to tackle healthcare problems are faced with new challenges. One such challenge is to prepare medical data for pattern mining or machine learning. In this paper, we present a feature engineering technique for the Associative Classification of the Systemic Inflammatory Response Syndrome (SIRS) in severely ill children by mining Associative Rules. SIRS is characterized as the body's excessive defense response due to malevolent stressors such as trauma, acute inflammation, infection, malignancy, and surgery. It can have an impact on the clinical outcome and elevate vulnerability for organ dysfunctions. We aim to extract the features from given datasets using the described extraction process. After the transformation, those features are used to mine rules using Association Rule Mining. Those rules are used to perform Associative Classification and evaluated with the result generated by SIRS criteria defined by the experienced clinicians. The mined rules provide better control over sensitivity and specificity than the SIRS criteria used in everyday medical practice.. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： data mining

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Efficient Algorithms for Co-folding of Multiple RNAs 13th

Efficient Algorithms for Co-folding of Multiple RNAs

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13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020

作者： Lorenz, Ronny Flamm, Christoph Hofacker, Ivo L. Stadler, Peter F. Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 Wien1090 Austria Bioinformatics and Computational Biology Faculty of Computer Science University of Vienna Währingerstraße 29 Wien1090 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics and Competence Center for Scalable Data Services and Solutions Dresden/Leipzig Universität Leipzig Härtelstraße 16-18 Leipzig04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

ISBN: (纸本)9783030723781

The simplest class of structures formed by N≥ 2 interacting RNAs consists of all crossing-free base pairs formed over linear arrangements of the constituent RNA sequences. For each permutation of the N strands the structure prediction problem is algorithmically very similar – but not identical – to folding of a single, contiguous RNA. The differences arise from two sources: First, "nicks", i.e., the transitions from one to the next piece of RNA, need to be treated with special care. Second, the connectedness of the structures needs to guaranteed. For the forward recursions, i.e., the computation of folding energies or partition functions, these modifications are rather straightforward and retain the cubic time complexity of the well-known folding algorithms. This is not the case for a straightforward implementation of the corresponding outside recursion, which becomes quartic. Cubic running times, however, can be restored by introducing linear-size auxiliary arrays. Asymptotically, the extra effort over the corresponding algorithms for a single RNA sequence of the same length is negligible in both time and space. An implementation within the framework of the ViennaRNA package conforms to the theoretical performance bounds and provides access to several algorithmic variants, include the handling of user-defined hard and soft constraints. © 2021, Springer Nature Switzerland AG.

关键词： RNA

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Nesting of Touching Polygons

arXiv

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arXiv 2024年

作者： Seemann, Carsten R. Stadler, Peter F. Hellmuth, Marc Swarm Intelligence and Complex Systems Group Department of Computer Science Leipzig University Augustusplatz 10 LeipzigD-04109 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia The Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Polygons are cycles embedded into the plane;their vertices are associated with x- and y-coordinates and the edges are straight lines. Here, we consider a set of polygons with pairwise non-overlapping interior that may touch along their boundaries. Ideas of the sweep line algorithm by Bajaj and Dey for non-touching polygons are adapted to accommodate polygons that share boundary points. The algorithms established here achieves a running time of O(n + N log N), where n is the total number of vertices and N MSC Codes 05C10 Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Prediction of Diblock Copolymer Morphology via Machine Learning

arXiv

引用

arXiv 2023年

作者： Park, Hyun Yu, Boyuan Park, Juhae Sun, Ge Tajkhorshid, Emad de Pablo, Juan J. Schneider, Ludwig Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Pritzker School of Molecular Engineering University of Chicago 5640 Ellis Ave ChicagoIL60637 United States Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

A machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy exploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution over time. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement. As an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes. Copyright © 2023, The Authors. All rights reserved.

关键词： Block copolymers

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MassSpecGym: A benchmark for the discovery and identification of molecules

arXiv

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arXiv 2024年

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of Computing Science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Germany Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag: Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym – the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Recording provenance of workflow runs with RO-Crate

arXiv

引用

arXiv 2023年

作者： Leo, Simone Crusoe, Michael R. Rodríguez-Navas, Laura Sirvent, Raül Kanitz, Alexander De Geest, Paul Wittner, Rudolf Pireddu, Luca Garijo, Daniel Fernández, José M. Colonnelli, Iacopo Gallo, Matej Ohta, Tazro Suetake, Hirotaka Capella-Gutierrez, Salvador de Wit, Renske Kinoshita, Bruno P. Soiland-Reyes, Stian CA Pula Italy Vrije Universiteit Amsterdam Amsterdam Netherlands DTL Projects Netherlands Forschungszentrum Jülich Germany Barcelona Supercomputing Center Barcelona Spain Biozentrum University of Basel Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland VIB Data Core Gent Belgium Faculty of Informatics Masaryk University Brno Czech Republic Institute of Computer Science Masaryk University Brno Czech Republic BBMRI-ERIC Graz Austria Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Computer Science Department Università degli Studi di Torino Torino Italy Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Shizuoka Japan Institute for Advanced Academic Research Chiba University Chiba Japan Sator Incorporated Tokyo Japan Department of Computer Science The University of Manchester Manchester United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands

Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and *** to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain. © 2023, CC BY.

关键词： Workflow management

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Chemically inspired Erdős-Rényi oriented hypergraphs

arXiv

引用

arXiv 2023年

作者： Garcia-Chung, Angel Bermúdez-Montaña, Marisol Stadler, Peter F. Jost, Jürgen Restrepo, Guillermo Tecnológico de Monterrey Escuela de Ingeniería y Ciencias Estado de México52926 Mexico Max Planck Institute for Mathematics in the Sciences Leipzig Germany Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany Bioinformatics Group Department of Computer Science Universität Leipzig Leipzig Germany Institute for Theoretical Chemistry University of Vienna Vienna Austria The Santa Fe Institute Santa FeNM United States Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Leipzig Germany School of Applied Sciences and Engineering EAFIT University Medellín Colombia

High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erdős-Rényi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry. © 2023, CC BY-SA.

关键词： Graph theory

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Molecular docking and molecular dynamics simulation studies of inhibitor candidates against Anopheles gambiae 3-hydroxykynurenine transaminase and implications on vector control

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Heliyon 2025年第1期11卷 e41633页

作者： Adedeji, Eunice O. Ogunlana, Olubanke O. Oduselu, Gbolahan O. Koenig, Rainer Adebiyi, Ezekiel Soremekun, Opeyemi S. Fatumo, Segun Covenant University Bioinformatics Research (CUBRe) Covenant University Ogun State Ota Nigeria Department of Biochemistry Covenant University Ogun State Ota Nigeria Department of Biology University of York York United Kingdom Covenant Applied Informatics and Communication Africa Centre of Excellence Covenant University Ota Nigeria Institute for Infectious Diseases and Infection Control (IIMK RG Systems Biology) Jena University Hospital Am Klinikum 1 Jena 07747 Germany African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala 10218 Uganda Division of Applied Bioinformatics German Cancer Research Center (DKFZ) G200 Im Neuenheimer Feld 280 Heidelberg 69120 Germany The African Computational Genomics (TACG) Research Group MRC/UVRI and LSHTM Entebbe Uganda Precision Healthcare University Research Institute Queen Mary University of London United Kingdom Department of Non-Communicable Disease Epidemiology London School of Hygiene & Tropical Medicine United Kingdom

Isoxazole and oxadiazole derivatives inhibiting 3-hydroxykynurenine transaminase (3HKT) are potential larvicidal candidates. This study aims to identify more suited potential inhibitors of Anopheles gambiae 3HKT (Ag3HKT) through molecular docking and molecular dynamics simulation. A total of 958 compounds were docked against Anopheles gambiae 3HKT (PDB ID: 2CH2) using Autodock vina and Autodock4. The top three identified hits were subjected to 300 ns molecular dynamics simulation using AMBER 18 and ADMET analysis using SWISSADME predictor and ADMETSAR. Replacement of alkyl attachment on C5 of isoxazole or oxadiazole derivative with a cycloalkyl group yielded compounds with lower binding energy than their straight chain counterparts. The top three compounds were brominated compounds, 2‐[3‐(4‐bromophenyl)‐1,2‐oxazol‐5‐yl]cyclopentane‐1‐carboxylic acid, 2-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, 3-[3-(4-bromo-2-methylphenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, and they had binding energies of −8.58, −8.25, and −8.18 kcal/mol in virtual screening against 2CH2 protein target, respectively. These compounds were predicted to be less toxic than temephos, a standard larvicide and more biodegradable than previously reported inhibitors. The three compounds exhibited a greater stabilizing effect on 2CH2 protein target than 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid, a previously reported inhibitor candidate with good larvicidal activity on Aedes aegypti. Further thermodynamic calculations revealed that the top three compounds possessed total binding energies (ΔG_bind) of −26.64 kcal/mol, −24.26 kcal/mol and −14.11 kcal/mol, respectively, as compared to −12.02 kcal/mol for 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid. These compounds could be better larvicides than previously reported isoxazole or oxadiazole derivatives and safer than temephos. © 2025 The Authors

关键词： 1,2,4-Oxadiazole 3HKT Isoxazoles Larvicide Mosquito Vector control

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Every atom-atom map can be explained by electron pushing diagrams

arXiv

引用

arXiv 2023年

作者： Flamm, Christoph Müller, Stefan Stadler, Peter F. Department of Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 WienA-1090 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig School of Embedded Composite Artificial Intelligence Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Facultad de Ciencias Universidad National de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Chemical reactions can be understood as transformations of multigraphs (molecules) that preserve vertex labels (atoms) and degrees (sums of bonding and non-bonding electrons), thereby implying the atom-atom map of a reaction. The corresponding reaction mechanism is often described by an electron pushing diagram that explains the transformation by consecutive local relocations of invidudal edges (electron pairs). Here, we show that every degree-preserving map between multigraphs, and thus every atom-atom map, can be generated by cyclic electron pushing. Moreover, it is always possible to decompose such an explanation into electron pushing diagrams involving only four electron pairs. This in turn implies that every reaction can be decomposed into a sequence of elementary reactions that involve at most two educt molecules and two product molecules. Hence, the requirement of a mechanistic explantion in terms of electron pushing and small imaginary transition states does not impose a combinatorial constraint on the feasibility of hypothetical chemical *** Codes 05C92, 92E10 Copyright © 2023, The Authors. All rights reserved.

关键词： Atoms

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