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Journal articles on the topic 'Biomolecular Visualization'

Author: Grafiati
Published: 6 September 2023

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1

DOI, Junta. "Biomolecular Visualization." Journal of the Visualization Society of Japan 10, no. 39 (1990): 222–27. http://dx.doi.org/10.3154/jvs.10.222.

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Duncan, Bruce S., Tom J. Macke, and Arthur J. Olson. "Biomolecular visualization using AVS." Journal of Molecular Graphics 13, no. 5 (October 1995): 271–82. http://dx.doi.org/10.1016/0263-7855(95)00067-4.

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Song, Cheng Long, Chen Zou, Wen Ke Wang, and Si Kun Li. "An Integrated Framework for Biological Data Visualization." Advanced Materials Research 846-847 (November 2013): 1145–48. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1145.

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In the field of bioinformatics visualization, integrating software and data in different levels is the development trend. This paper presents an integration framework for biomolecular structure and genome sequences visualization. The framework can effectively support the data and software interoperability of biomolecular structure / genome sequences visualization. Based on the framework, we developed an integrated visualization system, which provides some new comprehensive visualization functions. Preliminary trial showed that the framework has a good prospect in the research of bioinformatics.
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Perlasca, Paolo, Marco Frasca, Cheick Tidiane Ba, Jessica Gliozzo, Marco Notaro, Mario Pennacchioni, Giorgio Valentini, and Marco Mesiti. "Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools." PLOS ONE 15, no. 12 (December 22, 2020): e0244241. http://dx.doi.org/10.1371/journal.pone.0244241.

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The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.
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Xie, Jiang, Zhonghua Zhou, Kai Lu, Luonan Chen, and Wu Zhang. "Visualization of biomolecular networks' comparison on cytoscape." Tsinghua Science and Technology 18, no. 5 (October 2013): 515——521. http://dx.doi.org/10.1109/tst.2013.6616524.

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He, Weiwei, Yen-Lin Chen, Serdal Kirmizialtin, and Lois Pollack. "Visualization of biomolecular structures by WAXS and MD." Acta Crystallographica Section A Foundations and Advances 77, a1 (July 30, 2021): a124. http://dx.doi.org/10.1107/s0108767321098755.

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Yi Ronggui, Xie Jiang, Zhang Huiran, Zhang Wu, and Shigeo Kawata. "BNVC: A Web-Oriented Biomolecular Network Visualization Platform." Journal of Next Generation Information Technology 4, no. 3 (May 31, 2013): 151–59. http://dx.doi.org/10.4156/jnit.vol4.issue3.18.

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8

Kozlíková, B., M. Krone, M. Falk, N. Lindow, M. Baaden, D. Baum, I. Viola, J. Parulek, and H. C. Hege. "Visualization of Biomolecular Structures: State of the Art Revisited." Computer Graphics Forum 36, no. 8 (November 18, 2016): 178–204. http://dx.doi.org/10.1111/cgf.13072.

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Ando, Toshio, Takayuki Uchihashi, Noriyuki Kodera, Daisuke Yamamoto, Atsushi Miyagi, Masaaki Taniguchi, and Hayato Yamashita. "High-speed AFM and nano-visualization of biomolecular processes." Pflügers Archiv - European Journal of Physiology 456, no. 1 (December 20, 2007): 211–25. http://dx.doi.org/10.1007/s00424-007-0406-0.

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10

You, Qian, Shiaofen Fang, and Jake Yue Chen. "Gene Terrain: Visual Exploration of Differential Gene Expression Profiles Organized in Native Biomolecular Interaction Networks." Information Visualization 9, no. 1 (March 6, 2008): 1–12. http://dx.doi.org/10.1057/ivs.2008.3.

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We propose a new network visualization technique using scattered data interpolation and surface rendering, based upon a foundation layout of a scalar field. Contours of the interpolated surfaces are generated to support multi-scale visual interaction for data exploration. Our framework visualizes quantitative attributes of nodes in a network as a continuous surface by interpolating the scalar field, therefore avoiding scalability issues typical in conventional network visualizations while also maintaining the topological properties of the original network. We applied this technique to the study of a bio-molecular interaction network integrated with gene expression data for Alzheimer's Disease (AD). In this application, differential gene expression profiles obtained from the human brain are rendered for AD patients with differing degrees of severity and compared to healthy individuals. We show that this alternative visualization technique is effective in revealing several types of molecular biomarkers, which are traditionally difficult to detect due to ‘noises’ in data derived from DNA microarray experiments.
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Zhang, Hui Ran, Xiao Long Shen, Jiang Xie, and Dong Bo Dai. "A Web-Based Tool for Visualization of Biomolecular Network Comparison." Applied Mechanics and Materials 556-562 (May 2014): 5482–87. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5482.

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Analyzing similarities and differences between biomolecular networks comparison through website intuitively could be a convenient and effective way for researchers. Although several network comparison visualization tools have been developed, none of them can be integrated into websites. In this paper, a web-based tool kit named dynamically adaptive Visualization of Biomolecular Network Comparison (Bio-NCV) is designed and developed. The proposed tool is based on Cytyoscape.js – a popular open-source library for analyzing and visualizing networks. Bio-NCV integrates arjor.js which including the Barnes-Hut algorithm and the Traer Physics library for processing in order to express the dynamically adaptive initialization. In addition, in order to maintain consistency, the counterparts in other networks will change while the nodes and edges in one of the compared networks change. Furthermore, Bio-NCV can deal with both directed and undirected graphs.
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12

Lyubchenko, Yuri L. "Direct AFM visualization of the nanoscale dynamics of biomolecular complexes." Journal of Physics D: Applied Physics 51, no. 40 (August 20, 2018): 403001. http://dx.doi.org/10.1088/1361-6463/aad898.

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13

Haiying Wang, F. Azuaje, and N. Black. "Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks." IEEE Transactions on Nanobioscience 1, no. 4 (December 2002): 146–66. http://dx.doi.org/10.1109/tnb.2003.809465.

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14

Wang, Quan, and W. E. Moerner. "Single-molecule motions enable direct visualization of biomolecular interactions in solution." Nature Methods 11, no. 5 (March 9, 2014): 555–58. http://dx.doi.org/10.1038/nmeth.2882.

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Kokkinopoulou, Maria, Johanna Simon, Katharina Landfester, Volker Mailänder, and Ingo Lieberwirth. "Visualization of the protein corona: towards a biomolecular understanding of nanoparticle-cell-interactions." Nanoscale 9, no. 25 (2017): 8858–70. http://dx.doi.org/10.1039/c7nr02977b.

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16

Stone, John E., Ryan McGreevy, Barry Isralewitz, and Klaus Schulten. "GPU-accelerated analysis and visualization of large structures solved by molecular dynamics flexible fitting." Faraday Discuss. 169 (2014): 265–83. http://dx.doi.org/10.1039/c4fd00005f.

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Hybrid structure fitting methods combine data from cryo-electron microscopy and X-ray crystallography with molecular dynamics simulations for the determination of all-atom structures of large biomolecular complexes. Evaluating the quality-of-fit obtained from hybrid fitting is computationally demanding, particularly in the context of a multiplicity of structural conformations that must be evaluated. Existing tools for quality-of-fit analysis and visualization have previously targeted small structures and are too slow to be used interactively for large biomolecular complexes of particular interest today such as viruses or for long molecular dynamics trajectories as they arise in protein folding. We present new data-parallel and GPU-accelerated algorithms for rapid interactive computation of quality-of-fit metrics linking all-atom structures and molecular dynamics trajectories to experimentally-determined density maps obtained from cryo-electron microscopy or X-ray crystallography. We evaluate the performance and accuracy of the new quality-of-fit analysis algorithmsvis-à-visexisting tools, examine algorithm performance on GPU-accelerated desktop workstations and supercomputers, and describe new visualization techniques for results of hybrid structure fitting methods.
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Wang, Lincong, Hui Qiao, Chen Cao, Shutan Xu, and Shuxue Zou. "An Accurate Model for Biomolecular Helices and Its Application to Helix Visualization." PLOS ONE 10, no. 6 (June 30, 2015): e0129653. http://dx.doi.org/10.1371/journal.pone.0129653.

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18

Ando, Toshio, Takayuki Uchihashi, and Takeshi Fukuma. "High-speed atomic force microscopy for nano-visualization of dynamic biomolecular processes." Progress in Surface Science 83, no. 7-9 (November 2008): 337–437. http://dx.doi.org/10.1016/j.progsurf.2008.09.001.

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19

Jin, Gang, Pentti Tengvall, Ingemar Lundström, and Hans Arwin. "A Biosensor Concept Based on Imaging Ellipsometry for Visualization of Biomolecular Interactions." Analytical Biochemistry 232, no. 1 (November 1995): 69–72. http://dx.doi.org/10.1006/abio.1995.9959.

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20

Sehnal, David, Sebastian Bittrich, Mandar Deshpande, Radka Svobodová, Karel Berka, Václav Bazgier, Sameer Velankar, Stephen K. Burley, Jaroslav Koča, and Alexander S. Rose. "Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures." Nucleic Acids Research 49, W1 (May 6, 2021): W431—W437. http://dx.doi.org/10.1093/nar/gkab314.

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Abstract Large biomolecular structures are being determined experimentally on a daily basis using established techniques such as crystallography and electron microscopy. In addition, emerging integrative or hybrid methods (I/HM) are producing structural models of huge macromolecular machines and assemblies, sometimes containing 100s of millions of non-hydrogen atoms. The performance requirements for visualization and analysis tools delivering these data are increasing rapidly. Significant progress in developing online, web-native three-dimensional (3D) visualization tools was previously accomplished with the introduction of the LiteMol suite and NGL Viewers. Thereafter, Mol* development was jointly initiated by PDBe and RCSB PDB to combine and build on the strengths of LiteMol (developed by PDBe) and NGL (developed by RCSB PDB). The web-native Mol* Viewer enables 3D visualization and streaming of macromolecular coordinate and experimental data, together with capabilities for displaying structure quality, functional, or biological context annotations. High-performance graphics and data management allows users to simultaneously visualise up to hundreds of (superimposed) protein structures, stream molecular dynamics simulation trajectories, render cell-level models, or display huge I/HM structures. It is the primary 3D structure viewer used by PDBe and RCSB PDB. It can be easily integrated into third-party services. Mol* Viewer is open source and freely available at https://molstar.org/.
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21

Walter, Peter, Sam Ansari, and Volkhard Helms. "The ABC (Analysing Biomolecular Contacts)-database." Journal of Integrative Bioinformatics 4, no. 1 (March 1, 2007): 31–39. http://dx.doi.org/10.1515/jib-2007-50.

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Abstract As protein-protein interactions are one of the basic mechanisms in most cellular processes, it is desirable to understand the molecular details of protein-protein contacts and ultimately be able to predict which proteins interact. Interface areas on a protein surface that are involved in protein interactions exhibit certain characteristics. Therefore, several attempts were made to distinguish protein interactions from each other and to categorize them. One way of classification are the groups of transient and permanent interactions. Previously two of the authors analysed several properties for transient complexes such as the amino acid and secondary structure element composition and pairing preferences. Certainly, interfaces can be characterized by many more possible attributes and this is a subject of intense ongoing research. Although several freely available online databases exist that illuminate various aspects of protein-protein interactions, we decided to construct a new database collecting all desired interface features allowing for facile selection of subsets of complexes. As database-server we applied MySQL and the program logic was written in JAVA. Furthermore several class extensions and tools such as JMOL were included to visualize the interfaces and JfreeChart for the representation of diagrams and statistics. The contact data is automatically generated from standard PDB files by a tcl/tk-script running through the molecular visualization package VMD. Currently the database contains 536 interfaces extracted from 479 PDB files and it can be queried by various types of parameters. Here, we describe the database design and demonstrate its usefulness with a number of selected features. Availability: The ABC-database can be accessed on http://service.bioinformatik.unisaarland.de/abc.
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22

Haghizadeh, Anahita, Mariam Iftikhar, Shiba S. Dandpat, and Trey Simpson. "Looking at Biomolecular Interactions through the Lens of Correlated Fluorescence Microscopy and Optical Tweezers." International Journal of Molecular Sciences 24, no. 3 (January 31, 2023): 2668. http://dx.doi.org/10.3390/ijms24032668.

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Understanding complex biological events at the molecular level paves the path to determine mechanistic processes across the timescale necessary for breakthrough discoveries. While various conventional biophysical methods provide some information for understanding biological systems, they often lack a complete picture of the molecular-level details of such dynamic processes. Studies at the single-molecule level have emerged to provide crucial missing links to understanding complex and dynamic pathways in biological systems, which are often superseded by bulk biophysical and biochemical studies. Latest developments in techniques combining single-molecule manipulation tools such as optical tweezers and visualization tools such as fluorescence or label-free microscopy have enabled the investigation of complex and dynamic biomolecular interactions at the single-molecule level. In this review, we present recent advances using correlated single-molecule manipulation and visualization-based approaches to obtain a more advanced understanding of the pathways for fundamental biological processes, and how this combination technique is facilitating research in the dynamic single-molecule (DSM), cell biology, and nanomaterials fields.
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23

R. Shaw, Olivia, and Jodi A. Hadden-Perilla. "TactViz: A VMD Plugin for Tactile Visualization of Protein Structures." Journal of Science Education for Students with Disabilities 23, no. 1 (October 21, 2020): 1–8. http://dx.doi.org/10.14448/jsesd.12.0015.

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Scientific disciplines spanning biology, biochemistry, and biophysics involve the study of proteins and their functions. Visualization of protein structures represents a barrier to education and research in these disciplines for students who are blind or visually impaired. Here, we present a software plugin for readily producing variable-height tactile graphics of proteins using the free biomolecular visualization software Visual Molecular Dynamics (VMD) and protein structure data that is publicly available through the Protein Data Bank. Our method also supports interactive tactile visualization of proteins with VMD on electronic refreshable tactile display devices. Employing our method in an academic laboratory has enabled an undergraduate student who is blind to carry out research alongside her sighted peers. By making the study of protein structures accessible to students who are blind or visually impaired, we aim to promote diversity and inclusion in STEM education and research.
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Schlick, Tamar. "Engineering Teams Up with Computer-Simulation and Visualization Tools to Probe Biomolecular Mechanisms." Biophysical Journal 85, no. 1 (July 2003): 1–4. http://dx.doi.org/10.1016/s0006-3495(03)74448-8.

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25

Dries, Daniel R., Diane M. Dean, Laura L. Listenberger, Walter R. P. Novak, Margaret A. Franzen, and Paul A. Craig. "An expanded framework for biomolecular visualization in the classroom: Learning goals and competencies." Biochemistry and Molecular Biology Education 45, no. 1 (August 3, 2016): 69–75. http://dx.doi.org/10.1002/bmb.20991.

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Kurosu, Jun, Kaname Kanai, and Jun’ya Tsutsumi. "Label-free visualization of nano-thick biomolecular binding by electric-double-layer modulation." Sensors and Actuators B: Chemical 382 (May 2023): 133548. http://dx.doi.org/10.1016/j.snb.2023.133548.

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27

Vymětal, Jiří, David Jakubec, Jakub Galgonek, and Jiří Vondrášek. "Amino Acid Interactions (INTAA) web server v2.0: a single service for computation of energetics and conservation in biomolecular 3D structures." Nucleic Acids Research 49, W1 (May 21, 2021): W15—W20. http://dx.doi.org/10.1093/nar/gkab377.

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Abstract Interactions among amino acid residues are the principal contributor to the stability of the three-dimensional structure of a protein. The Amino Acid Interactions (INTAA) web server (https://bioinfo.uochb.cas.cz/INTAA/) has established itself as a unique computational resource, which enables users to calculate the contribution of individual residues in a biomolecular structure to its total energy using a molecular mechanical scoring function. In this update, we describe major additions to the web server which help solidify its position as a robust, comprehensive resource for biomolecular structure analysis. Importantly, a new continuum solvation model was introduced, allowing more accurate representation of electrostatic interactions in aqueous media. In addition, a low-overhead pipeline for the estimation of evolutionary conservation in protein chains has been added. New visualization options were introduced as well, allowing users to easily switch between and interrelate the energetic and evolutionary views of the investigated structures.
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Nemoto, Tomomi. "Visualization and Analysis of Cellular and Biomolecular Dynamics by using Ultra-Short Pulse Laser." Nippon Laser Igakkaishi 30, no. 4 (2009): 435–40. http://dx.doi.org/10.2530/jslsm.30.435.

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29

Baroroh, S.Si., M.Biotek., Umi, Zahra Silmi Muscifa, Wanda Destiarani, Fauzian Giansyah Rohmatullah, and Muhammad Yusuf. "Molecular interaction analysis and visualization of protein-ligand docking using Biovia Discovery Studio Visualizer." Indonesian Journal of Computational Biology (IJCB) 2, no. 1 (July 21, 2023): 22. http://dx.doi.org/10.24198/ijcb.v2i1.46322.

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Molecular docking interpretation is one of the crucial part before determining the result. Docking is commonly used to study the biomolecular interaction, usually for protein-ligand interaction, and to study about the molecular mechanism. Analysis of molecular interaction can help user to determine the strengthened of docking results, besides free energy of binding. In this protocol, analysis of molecular interaction as well as the surface characteristic of receptor was discussed in detail. In addition, the visualization to obtain suitable pictures for publications also included. The entire protocol will spend more of less 2 hours.
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Le, Kathy H., Jared Adolf-Bryfogle, Jason C. Klima, Sergey Lyskov, Jason W. Labonte, Steven Bertolani, Shourya S. Roy Burman, et al. "PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design." Biophysicist 2, no. 1 (April 1, 2021): 108–22. http://dx.doi.org/10.35459/tbp.2019.000147.

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ABSTRACT Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of 16 modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications, such as protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a Web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels, as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.
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Wu, Zhaolong, Enbo Chen, Shuwen Zhang, Yinping Ma, and Youdong Mao. "Visualizing Conformational Space of Functional Biomolecular Complexes by Deep Manifold Learning." International Journal of Molecular Sciences 23, no. 16 (August 9, 2022): 8872. http://dx.doi.org/10.3390/ijms23168872.

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The cellular functions are executed by biological macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving the conformational continuum of important biomolecular complexes at the atomic level is essential to understanding their functional mechanisms and guiding structure-based drug discovery. Here, we introduce a deep manifold learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy (cryo-EM) reconstructions that approximately visualize the conformational space of biomolecular complexes of interest. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of pseudo-energy landscapes, which simultaneously improves 3D classification accuracy and reconstruction resolution via an energy-based particle-voting algorithm. In blind assessments using simulated heterogeneous datasets, AlphaCryo4D achieved 3D classification accuracy three times those of alternative methods and reconstructed continuous conformational changes of a 130-kDa protein at sub-3 Å resolution. By applying this approach to analyze several experimental datasets of the proteasome, ribosome and spliceosome, we demonstrate its potential generality in exploring hidden conformational space or transient states of macromolecular complexes that remain hitherto invisible. Integration of this approach with time-resolved cryo-EM further allows visualization of conformational continuum in a nonequilibrium regime at the atomic level, thus potentially enabling therapeutic discovery against highly dynamic biomolecular targets.
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32

Botello-Smith, Wesley M., Qin Cai, and Ray Luo. "Biological applications of classical electrostatics methods." Journal of Theoretical and Computational Chemistry 13, no. 03 (May 2014): 1440008. http://dx.doi.org/10.1142/s0219633614400082.

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Continuum electrostatics modeling of solvation based on the Poisson–Boltzmann (PB) equation has gained wide acceptance in biomolecular applications such as energetic analysis and structural visualization. Successful application of the PB solvent models requires careful calibration of the solvation parameters. Extensive testing and validation is also important to ensure accuracy in their applications. Limitation in the continuum modeling of solvation is also a known issue in certain biomolecular applications. Growing interest in membrane systems has further spurred developmental efforts to allow inclusion of membrane in the PB solvent models. Despite their past successes due to careful parameterization, algorithm development and parallel implementation, there is still much to be done to improve their transferability from the small molecular systems upon which they were developed and validated to complex macromolecular systems as advances in technology continue to push forward, providing ever greater computational resources to researchers to study more interesting biological systems of higher complexity.
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Amyot, Romain, and Holger Flechsig. "BioAFMviewer: An interactive interface for simulated AFM scanning of biomolecular structures and dynamics." PLOS Computational Biology 16, no. 11 (November 18, 2020): e1008444. http://dx.doi.org/10.1371/journal.pcbi.1008444.

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We provide a stand-alone software, the BioAFMviewer, which transforms biomolecular structures into the graphical representation corresponding to the outcome of atomic force microscopy (AFM) experiments. The AFM graphics is obtained by performing simulated scanning over the molecular structure encoded in the corresponding PDB file. A versatile molecular viewer integrates the visualization of PDB structures and control over their orientation, while synchronized simulated scanning with variable spatial resolution and tip-shape geometry produces the corresponding AFM graphics. We demonstrate the applicability of the BioAFMviewer by comparing simulated AFM graphics to high-speed AFM observations of proteins. The software can furthermore process molecular movies of conformational motions, e.g. those obtained from servers which model functional transitions within a protein, and produce the corresponding simulated AFM movie. The BioAFMviewer software provides the platform to employ the plethora of structural and dynamical data of proteins in order to help in the interpretation of biomolecular AFM experiments.
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Gaur, Pankaj, Ajay Kumar, Shalmoli Bhattacharyya, and Subrata Ghosh. "Biomolecular recognition at the cellular level: geometrical and chemical functionality dependence of a low phototoxic cationic probe for DNA imaging." Journal of Materials Chemistry B 4, no. 28 (2016): 4895–900. http://dx.doi.org/10.1039/c6tb00787b.

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Uchihashi, Takayuki, and Simon Scheuring. "Applications of high-speed atomic force microscopy to real-time visualization of dynamic biomolecular processes." Biochimica et Biophysica Acta (BBA) - General Subjects 1862, no. 2 (February 2018): 229–40. http://dx.doi.org/10.1016/j.bbagen.2017.07.010.

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Nersisyan, Lilit, Ruben Samsonyan, and Arsen Arakelyan. "CyKEGGParser: tailoring KEGG pathways to fit into systems biology analysis workflows." F1000Research 3 (August 14, 2014): 145. http://dx.doi.org/10.12688/f1000research.4410.2.

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The KEGG pathway database is a widely accepted source for biomolecular pathway maps. In this paper we present the CyKEGGParser app (http://apps.cytoscape.org/apps/cykeggparser) for Cytoscape 3 that allows manipulation with KEGG pathway maps. Along with basic functionalities for pathway retrieval, visualization and export in KGML and BioPAX formats, the app provides unique features for computer-assisted adjustment of inconsistencies in KEGG pathway KGML files and generation of tissue- and protein-protein interaction specific pathways. We demonstrate that using biological context-specific KEGG pathways created with CyKEGGParser makes systems biology analysis more sensitive and appropriate compared to original pathways.
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Gopal, Sahana, Ciro Chiappini, James P. K. Armstrong, Qu Chen, Andrea Serio, Chia-Chen Hsu, Christoph Meinert, et al. "Immunogold FIB-SEM: Combining Volumetric Ultrastructure Visualization with 3D Biomolecular Analysis to Dissect Cell-Environment Interactions." Advanced Materials 31, no. 32 (June 13, 2019): 1900488. http://dx.doi.org/10.1002/adma.201900488.

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38

Sarkar, Daipayan, Martin Kulke, and Josh V. Vermaas. "LongBondEliminator: A Molecular Simulation Tool to Remove Ring Penetrations in Biomolecular Simulation Systems." Biomolecules 13, no. 1 (January 5, 2023): 107. http://dx.doi.org/10.3390/biom13010107.

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We develop a workflow, implemented as a plugin to the molecular visualization program VMD, that can fix ring penetrations with minimal user input. LongBondEliminator, detects ring piercing artifacts by the long, strained bonds that are the local minimum energy conformation during minimization for some assembled simulation system. The LongBondEliminator tool then automatically treats regions near these long bonds using multiple biases applied through NAMD. By combining biases implemented through the collective variables module, density-based forces, and alchemical techniques in NAMD, LongBondEliminator will iteratively alleviate long bonds found within molecular simulation systems. Through three concrete examples with increasing complexity, a lignin polymer, an viral capsid assembly, and a large, highly glycosylated protein aggrecan, we demonstrate the utility for this method in eliminating ring penetrations from classical MD simulation systems. The tool is available via gitlab as a VMD plugin, and has been developed to be generically useful across a variety of biomolecular simulations.
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39

Kjaergaard, Magnus, Laura Skak Rasmussen, Johan Nygaard Vinther, Kasper Røjkjær Andersen, Ebbe Sloth Andersen, Esben Lorentzen, Søren S. Thirup, Daniel E. Otzen, and Ditlev Egeskov Brodersen. "A Semester-Long Learning Path Teaching Computational Skills via Molecular Graphics in PyMOL." Biophysicist 3, no. 2 (December 1, 2022): 106–14. http://dx.doi.org/10.35459/tbp.2022.000219.

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ABSTRACT Structural biology describes biological processes at the molecular level and is an integral part of undergraduate study programs in molecular biosciences. Students are often fascinated by the visualizations created by molecular graphics software, which allow them to see the molecular world for the first time. Today, molecular visualization and structural analysis do not require expensive high-end computers but can be performed on the students' own laptops and are therefore highly suited for active learning approaches. We have designed a semester-long learning path that integrates molecular graphics and structural analysis using PyMOL into an undergraduate course in biomolecular structure and function. Compared to stand-alone PyMOL introductions, the semester-long learning path allows for an improved pedagogical design. The path progressively introduces more advanced functions in relevant scientific contexts and allows for spaced repetition. Advanced analysis functions in PyMOL are available only via the command line, so the learning path also teaches basic scripting and serves as an accessible introduction to computational thinking because a few lines of code can produce stunning results. Student surveys carried out at the end of the course suggest that the learning path supported the ability to perform structural analysis to a high degree. Moreover, a simulated exam showed that almost all students were able to carry out basic visualization tasks using PyMOL scripts, while three-quarters could undertake advanced structural analysis after following the course. In summary, integration of molecular graphics software with teaching of structural biochemistry allows a hands-on approach to analyzing molecular mechanisms and introduces biologically oriented students to computational thinking.
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40

Weatherby, Gerard, and Michael Robert Gryk. "Embedding Analytics within the Curation of Scientific Workflows." International Journal of Digital Curation 15, no. 1 (December 31, 2020): 8. http://dx.doi.org/10.2218/ijdc.v15i1.709.

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This paper reports on the ongoing activities and curation practices of the National Center for Biomolecular NMR Data Processing and Analysis1. Over the past several years, the Center has been developing and extending computational workflow management software for use by a community of biomolecular NMR spectroscopists. Previous work had been to refactor the workflow system to utilize the PREMIS framework for reporting retrospective provenance as well as for sharing workflows between scientists and to support data reuse. In this paper, we report on our recent efforts to embed analytics within the workflow execution and within provenance tracking. Important metrics for each of the intermediate datasets are included within the corresponding PREMIS intellectual object, which allows for both inspection of the operation of individual actors as well as visualization of the changes throughout a full processing workflow. These metrics can be viewed within the workflow management system or through standalone metadata widgets. Our approach is to support a hybrid approach of both automated, workflow execution as well as manual intervention and metadata management. In this combination, the workflow system and metadata widgets encourage the domain experts to be avid curators of the data which they create, fostering both computational reproducibility and scientific data reuse.
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41

Abdelmoula, Walid M., Benjamin Balluff, Sonja Englert, Jouke Dijkstra, Marcel J. T. Reinders, Axel Walch, Liam A. McDonnell, and Boudewijn P. F. Lelieveldt. "Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data." Proceedings of the National Academy of Sciences 113, no. 43 (October 10, 2016): 12244–49. http://dx.doi.org/10.1073/pnas.1510227113.

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The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.
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42

Neszmélyi, A., E. László, and J. Holló. "Biomolecular Modelling: An Interactive Program for the Visualization and Modelling of Carbohydrate (Starch and Oligosaccharide) Complexes in Solution." Starch - Stärke 39, no. 11 (1987): 393–96. http://dx.doi.org/10.1002/star.19870391107.

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43

Fujimoto, Hirofumi, Miroslav Pinak, Toshiyuki Nemoto, Kiyotaka Sakamoto, Kazuyuki Yamada, Yoshiyuki Hoshi, and Etsuo Kume. "Large scale MD simulation of 8-oxoguanine and AP site multiple lesioned DNA molecule combined with biomolecular visualization software." Journal of Molecular Structure: THEOCHEM 681, no. 1-3 (July 2004): 1–8. http://dx.doi.org/10.1016/j.theochem.2003.12.053.

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44

Martins, Jorge Emanuel, Davide D’Alimonte, Joana Simões, Sara Sousa, Eduardo Esteves, Nuno Rosa, Maria José Correia, Mário Simões, and Marlene Barros. "MODeLING.Vis: A Graphical User Interface Toolbox Developed for Machine Learning and Pattern Recognition of Biomolecular Data." Symmetry 15, no. 1 (December 23, 2022): 42. http://dx.doi.org/10.3390/sym15010042.

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Many scientific publications that affect machine learning have set the basis for pattern recognition and symmetry. In this paper, we revisit the concept of “Mind-life continuity” published by the authors, testing the symmetry between cognitive and electrophoretic strata. We opted for machine learning to analyze and understand the total protein profile of neurotypical subjects acquired by capillary electrophoresis. Capillary electrophoresis permits a cost-wise solution but lacks modern proteomic techniques’ discriminative and quantification power. To compensate for this problem, we developed tools for better data visualization and exploration in this work. These tools permitted us to examine better the total protein profile of 92 young adults, from 19 to 25 years old, healthy university students at the University of Lisbon, with no serious, uncontrolled, or chronic diseases affecting the nervous system. As a result, we created a graphical user interface toolbox named MODeLING.Vis, which showed specific expected protein profiles present in saliva in our neurotypical sample. The developed toolbox permitted data exploration and hypothesis testing of the biomolecular data. In conclusion, this analysis offered the data mining of the acquired neuroproteomics data in the molecular weight range from 9.1 to 30 kDa. This molecular weight range, obtained by pattern recognition of our dataset, is characteristic of the small neuroimmune molecules and neuropeptides. Consequently, MODeLING.Vis offers a machine-learning solution for probing into the neurocognitive response.
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45

Baltoumas, Fotis A., Sofia Zafeiropoulou, Evangelos Karatzas, Mikaela Koutrouli, Foteini Thanati, Kleanthi Voutsadaki, Maria Gkonta, et al. "Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review." Biomolecules 11, no. 8 (August 20, 2021): 1245. http://dx.doi.org/10.3390/biom11081245.

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Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.
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46

Yeung, Enoch, Jongmin Kim, Ye Yuan, Jorge Gonçalves, and Richard M. Murray. "Data-driven network models for genetic circuits from time-series data with incomplete measurements." Journal of The Royal Society Interface 18, no. 182 (September 2021): 20210413. http://dx.doi.org/10.1098/rsif.2021.0413.

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Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro , due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli -based transcriptional event detector.
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47

Huang, Daiyun, Bowen Song, Jingjue Wei, Jionglong Su, Frans Coenen, and Jia Meng. "Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data." Bioinformatics 37, Supplement_1 (July 1, 2021): i222—i230. http://dx.doi.org/10.1093/bioinformatics/btab278.

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Abstract Motivation Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available. Results We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution. Availability implementation The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM Supplementary information Supplementary data are available at Bioinformatics online.
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48

LACROIX, ZOÉ, LOUIQA RASCHID, and BARBARA A. ECKMAN. "TECHNIQUES FOR OPTIMIZATION OF QUERIES ON INTEGRATED BIOLOGICAL RESOURCES." Journal of Bioinformatics and Computational Biology 02, no. 02 (June 2004): 375–411. http://dx.doi.org/10.1142/s0219720004000648.

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Today, scientific data are inevitably digitized, stored in a wide variety of formats, and are accessible over the Internet. Scientific discovery increasingly involves accessing multiple heterogeneous data sources, integrating the results of complex queries, and applying further analysis and visualization applications in order to collect datasets of interest. Building a scientific integration platform to support these critical tasks requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web, as well as data that are locally materialized in warehouses or generated by software. The lack of efficiency of existing approaches can significantly affect the process with lengthy delays while accessing critical resources or with the failure of the system to report any results. Some queries take so much time to be answered that their results are returned via email, making their integration with other results a tedious task. This paper presents several issues that need to be addressed to provide seamless and efficient integration of biomolecular data. Identified challenges include: capturing and representing various domain specific computational capabilities supported by a source including sequence or text search engines and traditional query processing; developing a methodology to acquire and represent semantic knowledge and metadata about source contents, overlap in source contents, and access costs; developing cost and semantics based decision support tools to select sources and capabilities, and to generate efficient query evaluation plans.
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49

DeVore, Kira, and Po-Lin Chiu. "Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity." Biomolecules 12, no. 5 (April 24, 2022): 628. http://dx.doi.org/10.3390/biom12050628.

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Single-particle cryogenic electron microscopy (cryo-EM) has become an indispensable tool to probe high-resolution structural detail of biomolecules. It enables direct visualization of the biomolecules and opens a possibility for averaging molecular images to reconstruct a three-dimensional Coulomb potential density map. Newly developed algorithms for data analysis allow for the extraction of structural heterogeneity from a massive and low signal-to-noise-ratio (SNR) cryo-EM dataset, expanding our understanding of multiple conformational states, or further implications in dynamics, of the target biomolecule. This review provides an overview that briefly describes the workflow of single-particle cryo-EM, including imaging and data processing, and new methods developed for analyzing the data heterogeneity to understand the structural variability of biomolecules.
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Yamamoto, Daisuke, Naoki Nagura, Saeko Omote, Masaaki Taniguchi, and Toshio Ando. "3TP3-06 Streptavidin 2D crystals as solid supports for the visualization of biomolecular processes by high-speed AFM(The 47th Annual Meeting of the Biophysical Society of Japan)." Seibutsu Butsuri 49, supplement (2009): S62. http://dx.doi.org/10.2142/biophys.49.s62_1.

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