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

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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 (November 30, 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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Newaz, Khalique, Mahboobeh Ghalehnovi, Arash Rahnama, Panos J. Antsaklis, and Tijana Milenković. "Network-based protein structural classification." Royal Society Open Science 7, no. 6 (June 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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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 (October 4, 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 (May 22, 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 (May 19, 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, Hyun Kyu Kim, Jeong Suk Im, Byung Hoo Song, Jacob Hanna, 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 (April 26, 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 (February 9, 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 (October 4, 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 (December 1, 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, in a way analogous to the sampling performed in large-scale yeast two-hybrid searches, gives degree distributions that differ subtly from the power law and that fit the observed data better than the power law itself. Our results show that the observation of approximate power law behaviour in a sampled sub-graph does not imply that the underlying graph follows a power law.

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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 (December 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 (August 31, 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 (August 1, 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 (June 26, 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 (January 2012): 226a. http://dx.doi.org/10.1016/j.bpj.2011.11.1240.

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19

Lyu, Guizhen, Dongbing Li, Hui Xiong, Langtao Xiao, Jianhua Tong, Chanjuan Ning, Ping Wang, and Shaoshan Li. "Quantitative Proteomic Analyses Identify STO/BBX24 -Related Proteins Induced by UV-B." International Journal of Molecular Sciences 21, no. 7 (April 3, 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)-based proteomic analysis to explore differential proteins that depend on STO and UV-B signaling. A total of 8212 proteins were successfully identified, 221 of them were STO-dependent proteins in UV-B irradiated plants. The abundances of STO-dependent PSB and LHC (light-harvesting complex) proteins in sto mutants decreased under UV-B radiation, suggesting that STO is necessary to maintain the normal accumulation of photosynthetic system complex under UV-B radiation to facilitate photosynthesis photon capture. The abundance of phenylalanine lyase-1 (PAL1), chalcone synthetase (CHS), and flavonoid synthetase (FLS) increased significantly after UV-B irradiation, suggesting that the accumulation of flavonoids do not require STO, but UV-B is needed. Under UV-B radiation, STO stabilizes the structure of antenna protein complex by maintaining the accumulation of PSBs and LHCs, thereby enhancing the non-photochemical quenching (NPQ) ability, releasing extra energy, protecting photosynthesis, and ultimately promoting the elongation of hypocotyl. The accumulation of flavonoid synthesis key proteins is independent of STO under UV-B radiation. Overall, our results provide a comprehensive regulatory network of STO in UV-B signaling.

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20

Hu, Ke, Jing-Bo Hu, Liang Tang, Ju Xiang, Jin-Long Ma, Yuan-Yuan Gao, Hui-Jia Li, and Yan Zhang. "Predicting disease-related genes by path structure and community structure in protein–protein networks." Journal of Statistical Mechanics: Theory and Experiment 2018, no. 10 (October 26, 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 (January 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 (April 9, 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 (December 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 (January 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 (September 14, 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 structure, and show that allosteric networks in a PDZ domain are highly dependent on the supertertiary structure in which they are present. This striking sensitivity of allosteric networks to the presence of adjacent protein domains is likely a common property of supertertiary structures in proteins. Our findings have general implications for prediction of allosteric networks from primary and tertiary structures and for quantitative descriptions of allostery.

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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 (September 22, 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 Learning Networks. Objective is to review motivate and facilitatethese deep learn the network for predicting protein sequences using Deep Learning.

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27

Lappe, M., and L. Holm. "Algorithms for protein interaction networks." Biochemical Society Transactions 33, no. 3 (June 1, 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 distinctly from random networks, these functional maps contain a signal that can be used for predictive purposes. Protein function and structure can be predicted by matching interaction patterns, without the requirement of sequence similarity. Moving on to a higher level definition of protein function, the question arises how to decompose complex networks into meaningful subsets. An algorithm will be demonstrated, which extracts whole signal-transduction pathways from noisy graphs derived from text-mining the biological literature. Finally, an algorithmic strategy is formulated that enables the proteomics community to build a reliable scaffold of the interactome in a fraction of the time compared with uncoordinated efforts.

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Strosberg, A. D., and C. Nahmias. "G-protein-coupled receptor signalling through protein networks." Biochemical Society Transactions 35, no. 1 (January 22, 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 (November 1, 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 (July 1, 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 (August 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 (March 7, 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 (August 19, 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 (January 22, 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 (February 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 appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.

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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 (December 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. coli and the S. cerevisiae PINs are better represented by the stochastic and deterministic hierarchical network models, respectively. The current fruit fly protein–protein interaction dataset does not have convincing evidence in favor of the hierarchical network model. These findings lead us to conclude that, in contrast to scale-free structure, hierarchical structure model applies for certain species' PINs only. We also demonstrate that PINs are robust when subject to random perturbation where up to 50% of the nodes are rewired. Average node degree correlation study supports the fact that nodes of low connectivity are correlated, whereas nodes of high connectivity are not directly linked.

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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 (April 13, 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 (October 2007): 572–82. http://dx.doi.org/10.1109/tcbb.2007.1055.

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Yan, Wenying, Maomin Sun, Guang Hu, Jianhong Zhou, Wenyu Zhang, Jiajia Chen, Biao Chen, and Bairong Shen. "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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Liebman, M. "Neural networks and protein structure-function analysis on the macintosh." Journal of Molecular Graphics 9, no. 1 (March 1991): 42. http://dx.doi.org/10.1016/0263-7855(91)80037-z.

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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 (May 29, 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 quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure parsed from protein structure at each time-step without using any database, which is different than all existed QA methods. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub: https://github.com/caorenzhi/AngularQA

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Pržulj, Nataša, and Desmond J. Higham. "Modelling protein–protein interaction networks via a stickiness index." Journal of The Royal Society Interface 3, no. 10 (August 22, 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 range of network similarity measures, including relative graphlet frequency distance, we find that our model outperforms other random graph classes. In particular, we show that given the underlying degree information, fitting a stickiness model produces better results than simply choosing a degree-matching graph uniformly at random. Therefore, the results lend support to the basic modelling methodology.

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Baker, Charles, Sheelagh Carpendale, Przemyslaw Prusinkiewicz, and Michael Surette. "GeneVis: Simulation and Visualization of Genetic Networks." Information Visualization 2, no. 4 (December 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 the simulation. The network structure representation depicts the genetic network dependencies that are present in the simulation. GeneVis includes several interactive viewing tools. These include animated transitions from the individual protein representation to the protein concentration representation and from the individual protein representation to the network structure representation. Three types of lenses are used to provide different views within a representation: fuzzy lenses, base pair lenses, and the network structure ring lens. With a fuzzy lens an alternate representation can be viewed in a selected region. The base pair lenses allow users to reposition genes for better viewing or to minimize interference during the simulation. The ring lens provides detail-in-context viewing of individual levels in the genetic network structure representation.

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Senior, Andrew W., Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, 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 (November 11, 2019): 1141–48. http://dx.doi.org/10.1002/prot.25834.

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VISHVESHWARA, SARASWATHI, K. V. BRINDA, and N. KANNAN. "PROTEIN STRUCTURE: INSIGHTS FROM GRAPH THEORY." Journal of Theoretical and Computational Chemistry 01, no. 01 (July 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 analyzed and parameters relevant to protein structure are extracted, are explained. The structural and biological information derived from protein structures using these methods is presented.

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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 sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

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Gosline, John M. "Structure and Mechanical Properties of Rubberlike Proteins in Animals." Rubber Chemistry and Technology 60, no. 3 (July 1, 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 polymer networks. Future research on protein sequences should increase our understanding of how polypeptide chains can function as random-coil molecules, and studies into the mechanical state of elastin in arterial tissues may provide important clues about the mechanisms of some forms of human disease.

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