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Gotowe bibliografie tematyczne / Protein Structure Networks (PSNs)

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Artykuły w czasopismach

Gotowa bibliografia na temat „Protein Structure Networks (PSNs)”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Protein Structure Networks (PSNs)"

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Duong, Vy T., Elizabeth M. Diessner, Gianmarc Grazioli, Rachel W. Martin, and Carter T. Butts. "Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures." Biomolecules 11, no. 12 (2021): 1788. http://dx.doi.org/10.3390/biom11121788.

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Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure netwo

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Newaz, Khalique, Mahboobeh Ghalehnovi, Arash Rahnama, Panos J. Antsaklis, and Tijana Milenković. "Network-based protein structural classification." Royal Society Open Science 7, no. 6 (2020): 191461. http://dx.doi.org/10.1098/rsos.191461.

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Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct three-dimensional (3D) structure-based protein features. By contrast, we first model 3D structures of proteins as protei

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Yan, Wenying, Daqing Zhang, Chen Shen, Zhongjie Liang, and Guang Hu. "Recent Advances on the Network Models in Target-based Drug Discovery." Current Topics in Medicinal Chemistry 18, no. 13 (2018): 1031–43. http://dx.doi.org/10.2174/1568026618666180719152258.

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With the advancement of “proteomics” data and systems biology, new techniques are needed to meet the new era of drug discovery. Network theory is increasingly applied to describe complex biological systems, thus implying its essential roles in system-based drug design. In this review, we first summarized general network parameters used in describing biological systems, and then gave some recent applications of these network parameters as topological indices in drug design in terms of Protein Structure Networks (PSNs), Protein-Protein Interaction Networks (PPINs) including related structural PP

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Aydınkal, Rasim Murat, Onur Serçinoğlu, and Pemra Ozbek. "ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism." Nucleic Acids Research 47, W1 (2019): W471—W476. http://dx.doi.org/10.1093/nar/gkz390.

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AbstractProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a P

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Felline, Angelo, Michele Seeber, and Francesca Fanelli. "webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules." Nucleic Acids Research 48, W1 (2020): W94—W103. http://dx.doi.org/10.1093/nar/gkaa397.

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Abstract A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all

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Ha, Tae Won, Ji Hun Jeong, HyeonSeok Shin, et al. "Characterization of Endoplasmic Reticulum (ER) in Human Pluripotent Stem Cells Revealed Increased Susceptibility to Cell Death upon ER Stress." Cells 9, no. 5 (2020): 1078. http://dx.doi.org/10.3390/cells9051078.

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Human pluripotent stem cells (hPSCs), such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), have a well-orchestrated program for differentiation and self-renewal. However, the structural features of unique proteostatic-maintaining mechanisms in hPSCs and their features, distinct from those of differentiated cells, in response to cellular stress remain unclear. We evaluated and compared the morphological features and stress response of hPSCs and fibroblasts. Compared to fibroblasts, electron microscopy showed simpler/fewer structures with fewer networks in the endoplas

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Puspitasari, Ira, Shukor Sanim Mohd Fauzi, and Cheng-Yuan Ho. "Factors Driving Users’ Engagement in Patient Social Network Systems." Informatics 8, no. 1 (2021): 8. http://dx.doi.org/10.3390/informatics8010008.

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Participatory medicine and e-health help to promote health literacy among non-medical professionals. Users of e-health systems actively participate in a patient social network system (PSNS) to share health information and experiences with other users with similar health conditions. Users’ activities provide valuable healthcare resources to develop effective participatory medicine between patients, caregivers, and medical professionals. This study aims to investigate the factors of patients’ engagement in a PSNS by integrating and modifying an existing behavioral model and information system mo

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Deng, Yu Qiao, and Ge Song. "A Verifiable Visual Cryptography Scheme Using Neural Networks." Advanced Materials Research 756-759 (September 2013): 1361–65. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1361.

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This paper proposes a new verifiable visual cryptography scheme for general access structures using pi-sigma neural networks (VVCSPSN), which is based on probabilistic signature scheme (PSS), which is considered as security and effective verification method. Compared to other high-order networks, PSN has a highly regular structure, needs a much smaller number of weights and less training time. Using PSNs capability of large-scale parallel classification, VCSPSN reduces the information communication rate greatly, makes best known upper bound polynomial, and distinguishes the deferent informatio

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Greene, L. H. "Protein structure networks." Briefings in Functional Genomics 11, no. 6 (2012): 469–78. http://dx.doi.org/10.1093/bfgp/els039.

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Hase, T., Y. Suzuki, S. Ogisima, and H. Tanaka. "Hierarchical Structure of Protein Protein Interaction Networks." Seibutsu Butsuri 43, supplement (2003): S244. http://dx.doi.org/10.2142/biophys.43.s244_1.

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