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

Author: Grafiati

Published: 6 September 2023

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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 netwo

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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 protei

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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 PP

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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 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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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 endoplas

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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 mo

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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 informatio

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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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11

Thomas, A., R. Cannings, N. A. M. Monk, and C. Cannings. "On the structure of protein–protein interaction networks." Biochemical Society Transactions 31, no. 6 (2003): 1491–96. http://dx.doi.org/10.1042/bst0311491.

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We present a simple model for the underlying structure of protein–protein pairwise interaction graphs that is based on the way in which proteins attach to each other in experiments such as yeast two-hybrid assays. We show that data on the interactions of human proteins lend support to this model. The frequency of the number of connections per protein under this model does not follow a power law, in contrast to the reported behaviour of data from large-scale yeast two-hybrid screens of yeast protein–protein interactions. Sampling sub-graphs from the underlying graphs generated with our model, i

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12

Cotterill, RMJ. "Neural networks applied to protein structure." Journal de Chimie Physique 88 (1991): 2729. http://dx.doi.org/10.1051/jcp/1991882729.

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13

Vijayabaskar, M. S., and Saraswathi Vishveshwara. "Interaction Energy Based Protein Structure Networks." Biophysical Journal 99, no. 11 (2010): 3704–15. http://dx.doi.org/10.1016/j.bpj.2010.08.079.

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14

Naveed, Hammad, and Jingdong J. Han. "Structure-based protein-protein interaction networks and drug design." Quantitative Biology 1, no. 3 (2013): 183–91. http://dx.doi.org/10.1007/s40484-013-0018-y.

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15

Hales, David, and Stefano Arteconi. "Motifs in evolving cooperative networks look like protein structure networks." Networks & Heterogeneous Media 3, no. 2 (2008): 239–49. http://dx.doi.org/10.3934/nhm.2008.3.239.

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16

Head-Gordon, Teresa, and Frank H. Stillinger. "Optimal neural networks for protein-structure prediction." Physical Review E 48, no. 2 (1993): 1502–15. http://dx.doi.org/10.1103/physreve.48.1502.

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17

Milenković, Tijana, Ioannis Filippis, Michael Lappe, and Nataša Pržulj. "Optimized Null Model for Protein Structure Networks." PLoS ONE 4, no. 6 (2009): e5967. http://dx.doi.org/10.1371/journal.pone.0005967.

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18

Johnson, Margaret E., and Gerhard Hummer. "Refining Protein Interaction Networks with Protein Structure and Kinetic Modeling." Biophysical Journal 102, no. 3 (2012): 226a. http://dx.doi.org/10.1016/j.bpj.2011.11.1240.

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19

Lyu, Guizhen, Dongbing Li, Hui Xiong, et al. "Quantitative Proteomic Analyses Identify STO/BBX24 -Related Proteins Induced by UV-B." International Journal of Molecular Sciences 21, no. 7 (2020): 2496. http://dx.doi.org/10.3390/ijms21072496.

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Plants use solar radiation for photosynthesis and are inevitably exposed to UV-B. To adapt to UV-B radiation, plants have evolved a sophisticated strategy, but the mechanism is not well understood. We have previously reported that STO (salt tolerance)/BBX24 is a negative regulator of UV-B-induced photomorphogenesis. However, there is limited knowledge of the regulatory network of STO in UV-B signaling. Here, we report the identification of proteins differentially expressed in the wild type (WT) and sto mutant after UV-B radiation by iTRAQ (isobaric tags for relative and absolute quantitation)-

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20

Hu, Ke, Jing-Bo Hu, Liang Tang, et al. "Predicting disease-related genes by path structure and community structure in protein–protein networks." Journal of Statistical Mechanics: Theory and Experiment 2018, no. 10 (2018): 100001. http://dx.doi.org/10.1088/1742-5468/aae02b.

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21

Rost, Burkhard, and Chris Sander. "EXERCISING MULTI-LAYERED NETWORKS ON PROTEIN SECONDARY STRUCTURE." International Journal of Neural Systems 03, supp01 (1992): 209–20. http://dx.doi.org/10.1142/s0129065792000541.

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The quality of a multi-layered network predicting the secondary structure of proteins is improved substantially by: (i) using information about evolutionarily conserved amino acids (increase of overall accuracy by six percentage points), (ii) balancing the training dynamics (increase of accuracy for strand), and (iii) combining uncorrelated networks in a jury (increase two percentage points). In addition, appending a second level structure-to-structure network results in better reproduction of the length of secondary structure segments.

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22

Berenstein, Ariel José, Janet Piñero, Laura Inés Furlong, and Ariel Chernomoretz. "Mining the Modular Structure of Protein Interaction Networks." PLOS ONE 10, no. 4 (2015): e0122477. http://dx.doi.org/10.1371/journal.pone.0122477.

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23

Lu, Hui-Chun, Arianna Fornili, and Franca Fraternali. "Protein–protein interaction networks studies and importance of 3D structure knowledge." Expert Review of Proteomics 10, no. 6 (2013): 511–20. http://dx.doi.org/10.1586/14789450.2013.856764.

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24

Fang, Yi, Mengtian Sun, Guoxian Dai, and Karthik Ramain. "The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction." IEEE/ACM Transactions on Computational Biology and Bioinformatics 13, no. 1 (2016): 76–85. http://dx.doi.org/10.1109/tcbb.2015.2456876.

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25

Laursen, Louise, Johanna Kliche, Stefano Gianni, and Per Jemth. "Supertertiary protein structure affects an allosteric network." Proceedings of the National Academy of Sciences 117, no. 39 (2020): 24294–304. http://dx.doi.org/10.1073/pnas.2007201117.

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The notion that protein function is allosterically regulated by structural or dynamic changes in proteins has been extensively investigated in several protein domains in isolation. In particular, PDZ domains have represented a paradigm for these studies, despite providing conflicting results. Furthermore, it is still unknown how the association between protein domains in supramodules, consitituting so-called supertertiary structures, affects allosteric networks. Here, we experimentally mapped the allosteric network in a PDZ:ligand complex, both in isolation and in the context of a supramodular

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26

Chandni, Khatri, Prof Mrudang Pandya, and Dr Sunil Jardosh. "Deep Learning Approaches for Protein Structure Prediction." International Journal of Engineering & Technology 7, no. 4.5 (2018): 168. http://dx.doi.org/10.14419/ijet.v7i4.5.20037.

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In recent years, Machine Learning techniques that are based on Deep Learning networks that show a great promise in research communities.Successful methods for deep learning involve Artificial Neural Networks and Machine Learning. Deep Learning solves severa problems in bioinformatics. Protein Structure Prediction is one of the most important fields that can be solving using Deep Learning approaches.These protein are categorized on basis of occurrence of amino acid patterns occur to extract the feature. In these paper aimed to review work based on protein structure prediction solve using Deep L

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27

Lappe, M., and L. Holm. "Algorithms for protein interaction networks." Biochemical Society Transactions 33, no. 3 (2005): 530–34. http://dx.doi.org/10.1042/bst0330530.

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The functional characterization of all genes and their gene products is the main challenge of the postgenomic era. Recent experimental and computational techniques have enabled the study of interactions among all proteins on a large scale. In this paper, approaches will be presented to exploit interaction information for the inference of protein structure, function, signalling pathways and ultimately entire interactomes. Interaction networks can be modelled as graphs, showing the operation of gene function in terms of protein interactions. Since the architecture of biological networks differs

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28

Strosberg, A. D., and C. Nahmias. "G-protein-coupled receptor signalling through protein networks." Biochemical Society Transactions 35, no. 1 (2007): 23–27. http://dx.doi.org/10.1042/bst0350023.

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This short review provides a broad, and therefore necessarily incomplete and personal, overview of G-protein-coupled receptors, which are often targets for a wide range of important drugs: I will discuss successively their structure, function and interactions with associated proteins. Examples will be drawn from work done over the last 30 years by scientists that worked at different times in my laboratories, mainly in the field of β-adrenoceptors, muscarinic acetylcholine, melatonin and angiotensin receptors.

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29

Sun, Dengdi, and Maolin Hu. "Predicting Protein Function Based on the Topological Structure of Protein Interaction Networks." Journal of Computational and Theoretical Nanoscience 4, no. 7 (2007): 1337–43. http://dx.doi.org/10.1166/jctn.2007.2421.

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30

Ema, Romana Rahman, Akhi Khatun, Md Alam Hossain, Mostafijur Rahman Akhond, Nazmul Hossain, and Md Yasir Arafat. "Protein Secondary Structure Prediction using Hybrid Recurrent Neural Networks." Journal of Computer Science 18, no. 7 (2022): 599–611. http://dx.doi.org/10.3844/jcssp.2022.599.611.

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31

Reczko, M. "Protein Secondary Structure Prediction with Partially Recurrent Neural Networks." SAR and QSAR in Environmental Research 1, no. 2-3 (1993): 153–59. http://dx.doi.org/10.1080/10629369308028826.

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32

Wagner, Andreas. "How the global structure of protein interaction networks evolves." Proceedings of the Royal Society of London. Series B: Biological Sciences 270, no. 1514 (2003): 457–66. http://dx.doi.org/10.1098/rspb.2002.2269.

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33

Mishra, Awdhesh Kumar, Swati Puranik, and Manoj Prasad. "Structure and regulatory networks of WD40 protein in plants." Journal of Plant Biochemistry and Biotechnology 21, S1 (2012): 32–39. http://dx.doi.org/10.1007/s13562-012-0134-1.

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34

Zhou, Shusen, Hailin Zou, Chanjuan Liu, Mujun Zang, and Tong Liu. "Combining Deep Neural Networks for Protein Secondary Structure Prediction." IEEE Access 8 (2020): 84362–70. http://dx.doi.org/10.1109/access.2020.2992084.

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35

Wood, M. J., and J. D. Hirst. "Predicting protein secondary structure by cascade-correlation neural networks." Bioinformatics 20, no. 3 (2004): 419–20. http://dx.doi.org/10.1093/bioinformatics/btg423.

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36

Grazioli, Gianmarc, Vy Duong, Elizabeth Diessner, Rachel W. Martin, and Carter T. Butts. "Reconstructing atomistic structures from residue-level protein structure networks using artificial neural networks." Biophysical Journal 121, no. 3 (2022): 133a. http://dx.doi.org/10.1016/j.bpj.2021.11.2046.

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37

Noor, Amina, Erchin Serpedin, Mohamed Nounou, Hazem Nounou, Nady Mohamed, and Lotfi Chouchane. "An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks." Advances in Bioinformatics 2013 (February 21, 2013): 1–12. http://dx.doi.org/10.1155/2013/953814.

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The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It a

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38

LEE, PO-HAN, CHIEN-HUNG HUANG, JYWE-FEI FANG, HSIANG-CHUAN LIU, and KA-LOK NG. "HIERARCHICAL AND TOPOLOGICAL STUDY OF THE PROTEIN–PROTEIN INTERACTION NETWORKS." Advances in Complex Systems 08, no. 04 (2005): 383–97. http://dx.doi.org/10.1142/s0219525905000531.

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We employ the random graph theory approach to analyze the protein–protein interaction database DIP. Several global topological parameters are used to characterize the protein–protein interaction networks (PINs) for seven organisms. We find that the seven PINs are well approximated by the scale-free networks, that is, the node degree cumulative distribution P cum (k) scales with the node degree k (P cum (k) ~ k-α). We also find that the logarithm of the average clustering coefficient C ave (k) scales with k (C ave (k) ~ k-β), for E. coli and S. cerevisiae. In particular, we determine that the E

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39

Sora, Valentina, Dionisio Sanchez, and Elena Papaleo. "Bcl-xL Dynamics under the Lens of Protein Structure Networks." Journal of Physical Chemistry B 125, no. 17 (2021): 4308–20. http://dx.doi.org/10.1021/acs.jpcb.0c11562.

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40

Ibrahim, Ali Abdulhafidh, and Ibrahim Sabah Yasseen. "Using Neural Networks to Predict Secondary Structure for Protein Folding." Journal of Computer and Communications 05, no. 01 (2017): 1–8. http://dx.doi.org/10.4236/jcc.2017.51001.

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41

Jinmiao Chen and N. S. Chaudhari. "Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction." IEEE/ACM Transactions on Computational Biology and Bioinformatics 4, no. 4 (2007): 572–82. http://dx.doi.org/10.1109/tcbb.2007.1055.

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42

Yan, Wenying, Maomin Sun, Guang Hu, et al. "Amino acid contact energy networks impact protein structure and evolution." Journal of Theoretical Biology 355 (August 2014): 95–104. http://dx.doi.org/10.1016/j.jtbi.2014.03.032.

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43

Liebman, M. "Neural networks and protein structure-function analysis on the macintosh." Journal of Molecular Graphics 9, no. 1 (1991): 42. http://dx.doi.org/10.1016/0263-7855(91)80037-z.

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44

Conover, Matthew, Max Staples, Dong Si, Miao Sun, and Renzhi Cao. "AngularQA: Protein Model Quality Assessment with LSTM Networks." Computational and Mathematical Biophysics 7, no. 1 (2019): 1–9. http://dx.doi.org/10.1515/cmb-2019-0001.

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AbstractQuality Assessment (QA) plays an important role in protein structure prediction. Traditional multimodel QA method usually suffer from searching databases or comparing with other models for making predictions, which usually fail when the poor quality models dominate the model pool. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quali

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45

Pržulj, Nataša, and Desmond J. Higham. "Modelling protein–protein interaction networks via a stickiness index." Journal of The Royal Society Interface 3, no. 10 (2006): 711–16. http://dx.doi.org/10.1098/rsif.2006.0147.

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What type of connectivity structure are we seeing in protein–protein interaction networks? A number of random graph models have been mooted. After fitting model parameters to real data, the models can be judged by their success in reproducing key network properties. Here, we propose a very simple random graph model that inserts a connection according to the degree, or ‘stickiness’, of the two proteins involved. This model can be regarded as a testable distillation of more sophisticated versions that attempt to account for the presence of interaction surfaces or binding domains. By computing a

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46

Baker, Charles, Sheelagh Carpendale, Przemyslaw Prusinkiewicz, and Michael Surette. "GeneVis: Simulation and Visualization of Genetic Networks." Information Visualization 2, no. 4 (2003): 201–17. http://dx.doi.org/10.1057/palgrave.ivs.9500055.

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GeneVis simulates genetic networks and visualizes the process of this simulation interactively, providing a visual environment for exploring the dynamics of genetic regulatory networks. The visualization environment supports several representational modes, which include: an individual protein representation, a protein concentration representation, and a network structure representation. The individual protein representation shows the activities of the individual proteins. The protein concentration representation illustrates the relative spread and concentrations of the different proteins in th

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47

Senior, Andrew W., Richard Evans, John Jumper, et al. "Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)." Proteins: Structure, Function, and Bioinformatics 87, no. 12 (2019): 1141–48. http://dx.doi.org/10.1002/prot.25834.

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48

VISHVESHWARA, SARASWATHI, K. V. BRINDA, and N. KANNAN. "PROTEIN STRUCTURE: INSIGHTS FROM GRAPH THEORY." Journal of Theoretical and Computational Chemistry 01, no. 01 (2002): 187–211. http://dx.doi.org/10.1142/s0219633602000117.

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The sequence and structure of a large body of proteins are becoming increasingly available. It is desirable to explore mathematical tools for efficient extraction of information from such sources. The principles of graph theory, which was earlier applied in fields such as electrical engineering and computer networks are now being adopted to investigate protein structure, folding, stability, function and dynamics. This review deals with a brief account of relevant graphs and graph theoretic concepts. The concepts of protein graph construction are discussed. The manner in which graphs are analyz

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49

Liu, Peng, Lei Yang, Daming Shi, and Xianglong Tang. "Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/259157.

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A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptivek-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction

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50

Gosline, John M. "Structure and Mechanical Properties of Rubberlike Proteins in Animals." Rubber Chemistry and Technology 60, no. 3 (1987): 417–38. http://dx.doi.org/10.5254/1.3536137.

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Abstract Polymer networks formed from protein molecules that adopt kinetically-free, random-coil conformations are found in many animals, where they play a number of important roles. The 5 rubberlike proteins isolated and studied to date indicate that animal rubbers, like their synthetic counterparts, contain random networks which are usually stabilized by covalent crosslinks. Long-range elasticity in rubberlike proteins is based on changes in the conformational entropy of random-coil molecules. Further, these protein networks show viscoelastic glass transitions similar to all other amorphous

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