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Mapping the interaction surface between Ca_Vβ and actin and its role in calcium channel clearance

Nature Communications 2025年第1期16卷 1-21页

作者： Castilla, Francisco Lugo, Victor Miranda-Laferte, Erick Jordan, Nadine Huesgen, Pitter F. Santiago-Schübel, Beatrix Alfonso-Prieto, Mercedes Hidalgo, Patricia Institute of Biological Information Processing (IBI-1)—Molecular and Cellular Physiology Forschungszentrum Jülich Jülich Germany Graduate Program Faculty of Mathematics and Natural Sciences Heinrich-Heine University Düsseldorf Germany Central Institute of Engineering Electronics and Analytics (ZEA-3) Forschungszentrum Jülich Jülich Germany Institute of Biological Information Processing (IBI-7)—Structural Biochemistry Forschungszentrum Jülich Jülich Germany Institute of Neuroscience and Medicine (INM-9)—Computational Biomedicine Forschungszentrum Jülich Jülich Germany Cécile and Vogt Institute for Brain Research Medical Faculty Heinrich-Heine University Düsseldorf Germany Institute of Biochemistry Heinrich-Heine University Düsseldorf Germany Biologics Analytical Research and Development AbbVie Deutschland GmbH & Co. KG Ludwigshafen Germany Institute of Biology II University of Freiburg Freiburg Germany

Defective ion channel turnover and clearance of damaged proteins are associated with aging and neurodegeneration. The L-type CaV1.2 voltage-gated calcium channel mediates depolarization-induced calcium signals in hear...

Defective ion channel turnover and clearance of damaged proteins are associated with aging and neurodegeneration. The L-type Ca_V1.2 voltage-gated calcium channel mediates depolarization-induced calcium signals in heart and brain. Here, we determined the interaction surface between actin and two calcium channel subunits, Ca_Vβ₂ and Ca_Vβ₄, using cross-linking mass spectrometry and protein-protein docking, and uncovered a role in replenishing conduction-defective Ca_V1.2 channels. computational and in vitro mutagenesis identified hotspots in Ca_Vβ that decreased the affinity for actin but not for Ca_V1.2. When coexpressed with Ca_V1.2, none of the tested actin-association-deficient Ca_Vβ mutants altered the single-channel properties or the total number of channels at the cell surface. However, coexpression with the Ca_Vβ₂ hotspot mutant downregulated current amplitudes, and with a concomitant reduction in the number of functionally available channels, indicating that current inhibition resulted from a build-up of conduction silent channels. Our findings established Ca_Vβ₂–actin interaction as a key player for clearing the plasma membrane of corrupted Ca_V1.2 proteins to ensure the maintenance of a functional pool of channels and proper calcium signal transduction. The Ca_Vβ–actin molecular model introduces a potentially druggable protein-protein interface to intervene Ca_V-mediated signaling processes. © The Author(s) 2025.

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The newly emerged SARS-Like coronavirus HCoV-EMC also has an “Achilles’ heel”: current effective inhibitor targeting a 3C-like protease

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Protein & Cell 2013年第4期4卷 248-250页

作者： Zhilin Ren Liming Yan Ning Zhang Yu Guo Cheng Yang Zhiyong Lou Zihe Rao College of Pharmacy and State Key Laboratory of Medicinal Chemical Biology Nankai UniversityTianjin 300071China Structural Biology Laboratory and MOE Laboratory of Protein Science School of Medicine and Life ScienceTsinghua UniversityBeijing 100084China National Laboratory of Macromolecules Institute of BiophysicsChinese Academy of ScienceBeijing 100101China Emerging Infection Disease Program High-throughput Molecular Drug Discovery CenterTianjin Joint Academy of Biomedicine and TechnologyTianjin 300457China

From the global outbreak of SARS-CoV caused infection disease in 2003,coronaviruses(CoVs)are known to be a great threat to the human ***,a new SARS-like coronavirus,human betacoronavirus 2c EMC/2012(HCoV-EMC),has been identified and the appearance of this new CoV raises concerns that a new spread of CoV may occurs in the *** solving the crystal structure of HCoV-EMC main protease with a wide-spectrum anti-CoV inhibitor N3,we confi rmed that that N3 blocks the function of HCoV-EMC main protease through a similar mechanism to other *** with the good pharmaceutical features,N3 is conceivable to be effective to HCoV-EMC and other CoVs appearing in the *** fi ndings make it convincing that CoVs will not be a threat to human health.

关键词： EMC SARS Together

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Refinement of under-determined loops of human prion protein by database-derived distance constraints

Refinement of under-determined loops of human prion protein ...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Feng Cui Kriti Mukhopadhyay Won-Bin Young Robert L. Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics USA University Research Park Ames IA USA Department of Biochemistry Biophysics and Molecular Biology Iowa State University USA Program on Bioinformatics and Computational Biology USA

computational simulations of the conversion from the normal cellular prion (PrP c ) to the scrapie prion (PrP Sc ) are usually based on the structures determined by NMR because of the difficulties in crystallizing prion protein. Due to insufficient experimental restraints, a biologically critical loop region in PrP c (residues 167-171), which is a potential binding site for Protein X, is under-determined in most mammalian species. Here, we show that by adding information about distance constraints derived from a database of high-resolution protein structures, this under-determined loop as well as other secondary structural elements of the E200K variant of human prion protein (hPrP c ), a disease-related isoform, can be refined into more realistic structures in the structural ensemble with improved quality and increased accuracy. In particular, the ensemble becomes more compact after the refinement and the percentage of residues in the most favourable region of the Ramachandran diagram is increased to about 90% in the refined structures from the 80 to 85% range in the previously reported structures. Our results not only provide significantly improved structures of the prion protein and hence would facilitate insights into its conversion in the spongiform encephalopathies, but also demonstrate the strong potential for using databases of known protein structures for structure determination and refinement.

关键词： Humans Proteins Databases Diseases Nuclear magnetic resonance Genetic mutations computational biology Brain Pathogens Amino acids

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P.R.E.S.S. — An R-package for exploring residual-level protein structural statistics

P.R.E.S.S. — An R-package for exploring residual-level prot...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Yuanyuan Huang Steve Bonett Andrzej Kloczkowski Robert Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics Iowa State University Ames IA USA Summer REU Program on Computational Systems Biology Iowa State University Ames IA USA Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA Program on Bioinformatics and Computational Biology Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA

P.R.E.S.S. is an R package developed to allow researchers to get access to and manipulate on a large set of statistical data on protein residue-level structural properties such as residue-level virtual bond lengths, virtual bond angles, and virtual torsion angles. A large set of high-resolution protein structures are downloaded and surveyed. Their residue-level structural properties are calculated and documented. The statistical distributions and correlations of these properties can be queried and displayed. Tools are also provided for modeling and analyzing a given structure in terms of its residue-level structural properties. In particular, new tools for computing residue-level statistical potentials and displaying residue-level Ramachandran-like plots are developed for structural analysis and refinement. P.R.E.S.S. will be released in R as an open source software package, with a user-friendly GUI, accessible and executable by a public user in any R environment.

关键词： Correlation Proteins Graphical user interfaces Potential energy Protein engineering Software Graphics

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DNA repair after oxidative stress: Current challenges

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Current Opinion in Toxicology 2018年 7卷 9-16页

作者： Van Houten, Bennett Santa-Gonzalez, Gloria A. Camargo, Mauricio Program in Molecular Biophysics and Structural Biology University of Pittsburgh Pittsburgh 15261 PA United States Hillman Cancer Center Pittsburgh 15213 PA United States Department of Pharmacology and Chemical Biology University of Pittsburgh Pittsburgh 15261 PA United States University Research Center and Biology Institute Genetics Regeneration and Cancer Laboratory SIU Lab 432 Universidad de Antioquia Medellin Colombia

Reactive oxygen and nitrogen species damage cellular macromolecules including DNA. Cells have a robust base excision repair pathway to deal with this damage in both nuclear and mitochondrial genomes. However, mitochondria lack nucleotide excision repair. Evidence suggests that chronic oxidative stress can induce protective pathways lowering genotoxicity. Understanding oxidant injury to DNA and its repair is critical for our understanding the pathophysiology of a wide range of human disorders. © 2017 Elsevier B.V.

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An integrated multiscale computational framework deciphers SARS-CoV-2 resistance to sotrovimab

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Biophysical journal 2025年

作者： Akshit Sharma Shweata Maurya Shivank Kumar Timir Tripathi Rajiv K Kar Aditya K Padhi Laboratory for Computational Biology & Biomolecular Design School of Biochemical Engineering Indian Institute of Technology (BHU) Varanasi 221005 Uttar Pradesh India. Molecular and Structural Biophysics Laboratory Department of Zoology North-Eastern Hill University Shillong India. Electronic address: timir.tripathi@***. Jyoti and Bhupat Mehta School of Health Sciences and Technology Indian Institute of Technology Guwahati Assam India Center for Nanotechnology Indian Institute of Technology Guwahati Assam India. Electronic address: rajivkar@iitg.ac.in. Laboratory for Computational Biology & Biomolecular Design School of Biochemical Engineering Indian Institute of Technology (BHU) Varanasi 221005 Uttar Pradesh India. Electronic address: aditya.bce@iitbhu.ac.in.

The emergence of resistance mutations in the SARS-CoV-2 spike (S) protein presents a challenge for monoclonal antibody treatments like sotrovimab. Understanding the structural, dynamics, and molecular features of these mutations is essential for therapeutic advancements. However, the intricate landscape of potential mutations and critical residues conferring resistance to mAbs like sotrovimab remains elusive. This study introduces an integrated framework that combines interface protein design, machine learning, hybrid quantum mechanics/molecular mechanics (QM/MM) methodologies, all-atom and coarse-grained molecular dynamics (MD) simulations, and correlation analysis. Beginning with the interface-based design and analysis, this framework elucidates the interaction between sotrovimab and the S-protein, identifying pivotal residues and plausible resistance mutations. Machine learning algorithms then facilitate the identification of potential resistance mutations using structural-sequence-binding affinity-energetics features. The hybrid QM/MM approach subsequently evaluates the role of mutational residues as quantum regions, assessing their impact on stabilizing the macromolecular complex. To gain a deeper understanding of the dynamic behaviour of these mutations, multiscale simulations comprising all-atom and coarse-grained MD simulations were performed, revealing their structural, biophysical and energetic impacts. These simulations complemented the static predictions, capturing the conformational dynamics and stability of the mutants in presence of glycan in the S-protein. The accuracy of the predictions is validated by correlating identified resistance mutations with clinical-sequencing data and empirical evidence from sotrovimab-treated patients. Notably, two residues, E340 at the S-protein-sotrovimab interface and Y508 distal from it, and their designs, align with clinically observed resistance mutations. Furthermore, machine learning approaches predict novel S-pr

关键词： Coarse-grained simulations computational biology Drug resistance Integrated framework Machine learning molecular modeling Monoclonal antibody Protein conformation Protein design Protein‐protein interaction Quantum chemistry Resistance mutations SARS-CoV-2 Sotrovimab Spike protein

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Approximations of MINFLUX Localization Precision with Background

arXiv

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arXiv 2024年

作者： Marin, Zach Ries, Jonas Vienna Austria University of Vienna Center for Molecular Biology Department of Structural and Computational Biology Vienna Austria European Molecular Biology Laboratory Cell Biology and Biophysics Heidelberg Germany University of Vienna Faculty of Physics Vienna Austria

MINFLUX [1] is an emerging super-resolution technology that measures the position of single fluorophores with nanometer precision using fewer photons than any other fluorescence imaging or tracking technique. Here, we derive simple and instructive analytical equations for MINFLUX localization precision with a special focus on background photons. A fluorescence background, either arising from an imperfect zero of the MINFLUX excitation point spread function (PSF) or from auto- or out-of-focus fluorescence, ultimately limits the resolution achievable with MINFLUX. Building on previous work [1–3], we try to improve our understanding of the influence of background, especially when it is unknown, through a new set of expressions for the localization precision, based on an explicit background term instead of signal-to-background ratio. We use these equations to generate an intuitive understanding of how fluorescence background affects MINFLUX measurements and illustrate that: • The precision of an emitter position estimate depends on the gradient of its excitation profile. • Knowledge of the fluorescence background, obtained during post-processing of MINFLUX traces or through separate measurements, provides a better localization precision than in the case of unknown background. • In diffraction-limited systems, localization with a PSF that features a near-zero minimum outperforms localization with a maximum. We also present an analytical expression for the localization precision in orbital tracking [4], which we use for comparison to MINFLUX. Non-trivial derivations presented in this paper can be found in the accompanying Mathematica notebook. © 2024, CC BY.

关键词： Optical transfer function

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MINFLUX - molecular resolution with minimal photons

arXiv

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arXiv 2024年

作者： Scheiderer, Lukas Marin, Zach Ries, Jonas Vienna Austria University of Vienna Center for Molecular Biology Department of Structural and Computational Biology Vienna Austria European Molecular Biology Laboratory Cell Biology and Biophysics Heidelberg Germany University of Vienna Faculty of Physics Vienna Austria

Optical super-resolution microscopy is a key technology for structural biology that optimally complements electron microscopy because of its high contrast and live-cell compatibility. MINFLUX is an emerging super-resolution technique that measures the precise position of single fluorophores by targeted scanning of a patterned excitation beam featuring a minimum. It achieves unprecedented localization precision for a given number of detected photons. In combination with switchable fluorophores, it can be used for super-resolution imaging with nanometer resolution. For tracking of single fluorophores, it greatly outperforms camera-based techniques and achieves nanometer spatial and sub-millisecond temporal resolution over long tracks. Here, we introduce the basic principle of MINFLUX and explain how it can reach such high photon efficiencies. We then discuss the potential and limitations of MINFLUX for superresolution imaging and single-fluorophore tracking, describe recent extensions and variations of MINFLUX and give an outlook to future developments. © 2024, CC BY.

关键词： Scanning electron microscopy

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Knowledge-based versus experimentally acquired distance and angle constraints for NMR structure refinement

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Journal of Bioinformatics and computational biology 2008年第2期6卷 283-300页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Laboratory of Cell Biology National Cancer Institute National Institutes of Health Bethesda MD 20892 United States Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Iowa State University Ames IA 50011 United States

Nuclear Overhauser effects (NOE) distance constraints and torsion angle constraints are major conformational constraints for nuclear magnetic resonance (NMR) structure refinement. In particular, the number of NOE constraints has been considered as an important determinant for the quality of NMR structures. Of course, the availability of torsion angle constraints is also critical for the formation of correct local conformations. In our recent work, we have shown how a set of knowledge-based short-range distance constraints can also be utilized for NMR structure refinement, as a complementary set of conformational constraints to the NOE and torsion angle constraints. In this paper, we show the results from a series of structure refinement experiments by using different types of conformational constraints - NOE, torsion angle, or knowledge-based constraints - or their combinations, and make a quantitative assessment on how the experimentally acquired constraints contribute to the quality of structural models and whether or not they can be combined with or substituted by the knowledge-based constraints. We have carried out the experiments on a small set of NMR structures. Our preliminary calculations have revealed that the torsion angle constraints contribute substantially to the quality of the structures, but require to be combined with the NOE constraints to be fully effective. The knowledge-based constraints can be functionally as crucial as the torsion angle constraints, although they are statistical constraints after all and are not meant to be able to replace the latter. © 2008 Imperial College Press.

关键词： Knowledge-based distance constraints NMR structure refinement NOE distance constraints Precision and accuracy of NMR structures Torsion angle constraints

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Higher-Order Generalization Bounds: Learning Deep Probabilistic programs via PAC-Bayes Objectives

arXiv

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arXiv 2022年

作者： Warrell, Jonathan Gerstein, Mark Program in Computational Biology and Bioinformatics Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States

Deep Probabilistic programming (DPP) allows powerful models based on recursive computation to be learned using efficient deep-learning optimization techniques. Additionally, DPP offers a unified perspective, where inference and learning algorithms are treated on a par with models as stochastic programs. Here, we offer a framework for representing and learning flexible PAC-Bayes bounds as stochastic programs using DPP-based methods. In particular, we show that DPP techniques may be leveraged to derive generalization bounds that draw on the compositionality of DPP representations. In turn, the bounds we introduce offer principled training objectives for higher-order probabilistic programs. We offer a definition of a higher-order generalization bound, which naturally encompasses single- and multi-task generalization perspectives (including transfer- and meta-learning) and a novel class of bound based on a learned measure of model complexity. Further, we show how modified forms of all higher-order bounds can be efficiently optimized as objectives for DPP training, using variational techniques. We test our framework using single- and multi-task generalization settings on synthetic and biological data, showing improved performance and generalization prediction using flexible DPP model representations and learned complexity measures. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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