Relevant bibliographies by topics / Protein Structure Networks (PSNs)

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Academic literature on the topic 'Protein Structure Networks (PSNs)'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Protein Structure Networks (PSNs)"

1

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 networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.
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2

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 protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of approximately 9400 CATH and approximately 12 800 SCOP protein domains (spanning 36 PSN sets), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times. Our data and code are available at https://doi.org/10.5281/zenodo.3787922
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3

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 PPINs, and Elastic Network Models (ENMs). These network models have enabled the development of new drugs relying on allosteric effects, describing anti-cancer targets, targeting hot spots and key proteins at the protein-protein interfaces and PPINs, and helped drug design by modulating conformational flexibility. Accordingly, we highlighted the integration of network models bringing new paradigms into the next-generation target-based drug discovery.
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4

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 Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.
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5

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 PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.
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6

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 endoplasmic reticulum (ER) of hPSCs, as well as lower expression of ER-related genes according to meta-analysis. As hPSCs contain low levels of binding immunoglobulin protein (BiP), an ER chaperone, thapsigargin treatment sharply increased the gene expression of the unfolded protein response. Thus, hPSCs with decreased chaperone function reacted sensitively to ER stress and entered apoptosis faster than fibroblasts. Such ER stress-induced apoptotic processes were abolished by tauroursodeoxycholic acid, an ER-stress reliever. Hence, our results revealed that as PSCs have an underdeveloped structure and express fewer BiP chaperone proteins than somatic cells, they are more susceptible to ER stress-induced apoptosis in response to stress.
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7

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 model (i.e., affective events theory (AET) and self-determination theory (SDT)). The AET is used to model the structure, the affective aspects of the driven behavior, and actual affective manifestation. The SDT is used to model interest and its relations with behavior. The data analysis and model testing are based on structural equation modeling, using responses from 428 users. The results indicate that interest and empathy promote users’ engagement in a PSNS. The findings from this study suggest recommendations to further promote users’ participation in a PSNS from the sociotechnical perspective, which include sensitizing and constructive engagement features. Furthermore, the data generated from a user’s participation in a PSNS could contribute to the study of clinical manifestations of disease, especially an emerging disease.
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8

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 information in secret image.
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9

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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10

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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