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Статті в журналах з теми "Protein Structure Networks (PSN)"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

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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Анотація:

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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2

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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Анотація:

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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3

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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4

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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Анотація:

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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5

Fanelli, Francesca, Angelo Felline, Francesco Raimondi, and Michele Seeber. "Structure network analysis to gain insights into GPCR function." Biochemical Society Transactions 44, no. 2 (April 11, 2016): 613–18. http://dx.doi.org/10.1042/bst20150283.

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Анотація:

G protein coupled receptors (GPCRs) are allosteric proteins whose functioning fundamentals are the communication between the two poles of the helix bundle. Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM–NMA). The present review article describes the application of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding, dimerization and activation are described. Focus is put on the ENM–NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signalling active (meta II (MII)) states, highlighting changes in structure network and centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

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6

Chasapis, Christos T., and Alexios Vlamis-Gardikas. "Probing Conformational Dynamics by Protein Contact Networks: Comparison with NMR Relaxation Studies and Molecular Dynamics Simulations." Biophysica 1, no. 2 (April 8, 2021): 157–67. http://dx.doi.org/10.3390/biophysica1020012.

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Protein contact networks (PCNs) have been used for the study of protein structure and function for the past decade. In PCNs, each amino acid is considered as a node while the contacts among amino acids are the links/edges. We examined the possible correlation between the closeness centrality measure of amino acids within PCNs and their mobility as known from NMR spin relaxation experiments and molecular dynamic (MD) simulations. The pivotal observation was that plasticity within a protein stretch correlated inversely to closeness centrality. Effects on protein conformational plasticity caused by the formation of disulfide bonds or protein–protein interactions were also identified by the PCN analysis measure closeness centrality and the hereby introduced percentage of closeness centrality perturbation (% CCP). All the comparisons between PCN measures, NMR data, and MDs were performed in a set of proteins of different biological functions and structures: the core protease domain of anthrax lethal factor, the N-terminal RING domain of E3 Ub ligase Arkadia, the reduced and oxidized forms of human thioredoxin 1, and the ubiquitin molecules (Ub) of the catalytic Ub–RING–E3–E2–Ub complex of E3 ligase Ark2.The graph theory analysis of PCNs could thus provide a general method for assessing the conformational dynamics of free proteins and putative plasticity changes between different protein forms (apo/complexed or reduced/oxidized).

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7

Mahmud, Khandakar Abu Hasan Al, Fuad Hasan, Md Ishak Khan, and Ashfaq Adnan. "Shock-Induced Damage Mechanism of Perineuronal Nets." Biomolecules 12, no. 1 (December 22, 2021): 10. http://dx.doi.org/10.3390/biom12010010.

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The perineuronal net (PNN) region of the brain’s extracellular matrix (ECM) surrounds the neural networks within the brain tissue. The PNN is a protective net-like structure regulating neuronal activity such as neurotransmission, charge balance, and action potential generation. Shock-induced damage of this essential component may lead to neuronal cell death and neurodegenerations. The shock generated during a vehicle accident, fall, or improvised device explosion may produce sufficient energy to damage the structure of the PNN. The goal is to investigate the mechanics of the PNN in reaction to shock loading and to understand the mechanical properties of different PNN components such as glycan, GAG, and protein. In this study, we evaluated the mechanical strength of PNN molecules and the interfacial strength between the PNN components. Afterward, we assessed the PNN molecules’ damage efficiency under various conditions such as shock speed, preexisting bubble, and boundary conditions. The secondary structure altercation of the protein molecules of the PNN was analyzed to evaluate damage intensity under varying shock speeds. At a higher shock speed, damage intensity is more elevated, and hyaluronan (glycan molecule) is most likely to break at the rigid junction. The primary structure of the protein molecules is least likely to fail. Instead, the molecules’ secondary bonds will be altered. Our study suggests that the number of hydrogen bonds during the shock wave propagation is reduced, which leads to the change in protein conformations and damage within the PNN structure. As such, we found a direct connection between shock wave intensity and PNN damage.

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8

Lubovac, Zelmina. "Investigating Topological and Functional Features of Multimodular Proteins." Journal of Biomedicine and Biotechnology 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/472415.

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Анотація:

To generate functional modules as functionally and structurally cohesive formations in protein interaction networks (PINs) constitutes an important step towards understanding how modules communicate on a higher level of the PIN organisation that underlies cell functionality. However, we need to understand how individual modules communicate and are organized into the higher-order structure(s) of the PIN organization that underlies cell functionality. In an attempt to contribute to this understanding, we make an assumption that the proteins reappearing in several modules, termed here as multimodular proteins (MMPs), may be useful in building higher-order structure(s) as they may constitute communication points between different modules. In this paper, we investigate common properties shared by these proteins and compare them with the properties of so-called single-modular proteins (SMPs) by analyzing three aspects: functional aspect, that is, annotation of the proteins, topological aspect that is betweenness centrality of the proteins, and lethality. Furthermore, we investigate the interconnectivity role of some proteins that are identified as functionally and topologically important.

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9

Drago, Valentina, Luisa Di Paola, Claire Lesieur, Renato Bernardini, Claudio Bucolo, and Chiara Bianca Maria Platania. "In-Silico Characterization of von Willebrand Factor Bound to FVIII." Applied Sciences 12, no. 15 (August 4, 2022): 7855. http://dx.doi.org/10.3390/app12157855.

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Factor VIII belongs to the coagulation cascade and is expressed as a long pre-protein (mature form, 2351 amino acids long). FVIII is deficient or defective in hemophilic A patients, who need to be treated with hemoderivatives or recombinant FVIII substitutes, i.e., biologic drugs. The interaction between FVIII and von Willebrand factor (VWF) influences the pharmacokinetics of FVIII medications. In vivo, full-length FVIII (FL-FVIII) is secreted in a plasma-inactive form, which includes the B domain, which is then proteolyzed by thrombin protease activity, leading to an inactive plasma intermediate. In this work, we analyzed through a computational approach the binding of VWF with two structure models of FVIII (secreted full-length with B domain, and B domain-deleted FVIII). We included in our analysis the atomic model of efanesoctocog alfa, a novel and investigational recombinant FVIII medication, in which the VWF is covalently linked to FVIII. We carried out a structural analysis of VWF/FVIII interfaces by means of protein–protein docking, PISA (Proteins, Interfaces, Structures and Assemblies), and protein contact networks (PCN) analyses. Accordingly, our computational approaches to previously published experimental data demonstrated that the domains A3-C1 of B domain-deleted FVIII (BDD-FVIII) is the preferential binding site for VWF. Overall, our computational approach applied to topological analysis of protein–protein interface can be aimed at the rational design of biologic drugs other than FVIII medications.

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10

DANICH, V. M., and S. M. SHEVCHENKO. "FORMALIZATION OF THE CONCEPT OF SOCIAL SPACE OF THE SUBJECT THROUGH THE CONCEPT OF SOCIAL NETWORKS." REVIEW OF TRANSPORT ECONOMICS AND MANAGEMENT, no. 4(20) (November 30, 2020): 182–94. http://dx.doi.org/10.15802/rtem2020/228878.

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The purpose. To study the structure and properties of social space from the point of view of the subject's social networks, to find out the mechanisms of forming social contacts in modern conditions. Methods. The concept of "social network" is studied from the point of view of modern tools for their creation. Mechanisms for forming a personal social network are presented on the example of the "work" group from the list of "friends" of the profile. Highlighting the subject's personal social network made it possible to identify information transmission channels. The analysis of corporate social networks of enterprises, technologies of their implementation and features of functioning is made. The functionality of modern corporate social network services is studied. A survey of social networks of higher educational institutions in the context of distance education, as well as the use of existing social network services by higher educational institutions in the context of distance education, was conducted. Results. Features of forming a list of accounts in the "work" group from the general list of "friends"are revealed. Modern tools for creating social networks of the subject, corporate social networks of enterprises and organizations are studied. The factors that will influence the formation of a web-PSN are highlighted. The structure of web-CSN, disadvantages and advantages of using it, and technologies for their implementation are studied. Changes in the structures of corporate social networks of educational institutions are highlighted. Scientific novelty. The paper defines a personal social network (PSN), web-PSN, aggregate web-PSN, personificator, and personificant. The paper identifies groups of web-PSN objects, elucidates the mechanisms of web-PSN formation, and provides a formal description of them. Corporate social networks are considered, the definition of web-CSN is given, and the problems that an enterprise should solve before implementing web-CSN are formulated. The factors that influence the formation of a personal social network are also identified. Practical significance. Highlighting a web-PSN is relevant for business tasks, such as identifying potential buyers, identifying bots, fake accounts, and so on. The research results are important for other applied tasks, for example, determining the prospects for network expansion, determining the directions and speed of information dissemination, information perception, the ability and possibility of distortion, transmission prospects, and in general, for predicting the dynamics of communication networks of subjects. The study of the mechanisms of forming social contacts is important for formulating tips and suggestions on the processes of creating and developing a pesonal social network for an ordinary user, to ensure the protection of their account and personal data that it contains.

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11

Hu, Guang, Luisa Di Paola, Zhongjie Liang, and Alessandro Giuliani. "Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case." BioMed Research International 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/2483264.

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The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

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12

Lukinova, Nina I., Victoria V. Roussakova, and Mark E. Fortini. "Genetic Characterization of Cytological Region 77A–D Harboring the Presenilin Gene of Drosophila melanogaster." Genetics 153, no. 4 (December 1, 1999): 1789–97. http://dx.doi.org/10.1093/genetics/153.4.1789.

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Abstract We performed a systematic lethal mutagenesis of the genomic region uncovered by Df(3L)rdgC-co2 (cytological interval 77A–D) to isolate mutations in the single known Presenilin (Psn) gene of Drosophila melanogaster. Because this segment of chromosome III has not been systematically characterized before, inter se complementation testing of newly recovered mutants was carried out. A total of 79 lethal mutations were isolated, representing at least 17 lethal complementation groups, including one corresponding to the Psn gene. Fine structure mapping of the genomic region surrounding the Psn transcription unit by transgenic rescue experiments allowed us to localize two of the essential loci together with Psn within an ~12-kb genomic DNA region. One of these loci, located 3′ to Psn, encodes a Drosophila protein related to the yeast 60S ribosomal protein L10 precursor. We also determined which of the newly recovered lethal mutant groups correspond to previously isolated lethal P-element insertions, lethal inversion breakpoints, and lethal polo gene mutants. Point mutations were identified in all five recovered Psn alleles, one of which results in a single amino acid substitution G-E at a conserved residue in the C-terminal cytoplasmic tail of the protein, suggesting an important functional role for this C-terminal domain of Presenilin. In addition, some viable mutations were recovered in the screen, including new alleles of the clipped and inturned loci.

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13

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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14

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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15

Wu, Xiao-Tong, Zhu-Pei Xiong, Kun-Xiang Chen, Guo-Rong Zhao, Ke-Ru Feng, Xiu-Hua Li, Xi-Ran Li, et al. "Genome-Wide Identification and Transcriptional Expression Profiles of PP2C in the Barley (Hordeum vulgare L.) Pan-Genome." Genes 13, no. 5 (May 7, 2022): 834. http://dx.doi.org/10.3390/genes13050834.

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The gene family protein phosphatase 2C (PP2C) is related to developmental processes and stress responses in plants. Barley (Hordeum vulgare L.) is a popular cereal crop that is primarily utilized for human consumption and nutrition. However, there is little knowledge regarding the PP2C gene family in barley. In this study, a total of 1635 PP2C genes were identified in 20 barley pan-genome accessions. Then, chromosome localization, physical and chemical feature predictions and subcellular localization were systematically analyzed. One wild barley accession (B1K-04-12) and one cultivated barley (Morex) were chosen as representatives to further analyze and compare the differences in HvPP2Cs between wild and cultivated barley. Phylogenetic analysis showed that these HvPP2Cs were divided into 12 subgroups. Additionally, gene structure, conserved domain and motif, gene duplication event detection, interaction networks and gene expression profiles were analyzed in accessions Morex and B1K-04-12. In addition, qRT-PCR experiments in Morex indicated that seven HvMorexPP2C genes were involved in the response to aluminum and low pH stresses. Finally, a series of positively selected homologous genes were identified between wild accession B1K-04-12 and another 14 cultivated materials, indicating that these genes are important during barley domestication. This work provides a global overview of the putative physiological and biological functions of PP2C genes in barley. We provide a broad framework for understanding the domestication- and evolutionary-induced changes in PP2C genes between wild and cultivated barley.

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16

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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17

Haryanto, Toto, Rizky Kurniawan, Sony Muhammad, Aziz Kustiyo, and Endang Purnama Giri. "Ekstraksi Fitur Rantai Markov untuk Klasifikasi Famili Protein." Jurnal Ilmiah SINUS 21, no. 2 (July 10, 2023): 79. http://dx.doi.org/10.30646/sinus.v21i2.748.

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As complex molecules, proteins have various roles for living things. Proteins are organic molecules formed from twenty amino acid combinations with various functions for living things, such as transportation systems, a catalyst of chemical reactions for metabolism, and food reserves. This research aims to classify proteins family based on sequences of amino acids as the primary structure. There are 300 amino acid fragments obtained from the Pfam database. The proteins family database subset with three sub-sample classes was obtained, including 1-cysPrx_C, 4HBT, and ABC_Tran. In this research, the first and second order of the Markov chain for extracting features were applied. Moreover, we use a Probabilistic Neural Network (PNN) as a classifier compared to the joint probability technique with Markov assumptions. We evaluate the results by comparing the sensitivity and specificity of both classification techniques. The evaluation results show that overall, PNN has slightly better performance than the joint probability technique for classifying protein families.

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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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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Анотація:

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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28

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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29

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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30

Kulkarni, Prakash, Vitor B. P. Leite, Susmita Roy, Supriyo Bhattacharyya, Atish Mohanty, Srisairam Achuthan, Divyoj Singh, et al. "Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma." Biophysics Reviews 3, no. 1 (March 2022): 011306. http://dx.doi.org/10.1063/5.0080512.

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Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and “promiscuous” interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

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31

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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32

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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33

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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34

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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35

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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36

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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37

Seo, Jung-hyun, and HyeongOk Lee. "Petersen-star networks modeled by optical transpose interconnection system." International Journal of Distributed Sensor Networks 17, no. 11 (November 2021): 155014772110331. http://dx.doi.org/10.1177/15501477211033115.

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One method to create a high-performance computer is to use parallel processing to connect multiple computers. The structure of the parallel processing system is represented as an interconnection network. Traditionally, the communication links that connect the nodes in the interconnection network use electricity. With the advent of optical communication, however, optical transpose interconnection system networks have emerged, which combine the advantages of electronic communication and optical communication. Optical transpose interconnection system networks use electronic communication for relatively short distances and optical communication for long distances. Regardless of whether the interconnection network uses electronic communication or optical communication, network cost is an important factor among the various measures used for the evaluation of networks. In this article, we first propose a novel optical transpose interconnection system–Petersen-star network with a small network cost and analyze its basic topological properties. Optical transpose interconnection system–Petersen-star network is an undirected graph where the factor graph is Petersen-star network. OTIS–PSN n has the number of nodes 102n, degree n+3, and diameter 6 n − 1. Second, we compare the network cost between optical transpose interconnection system–Petersen-star network and other optical transpose interconnection system networks. Finally, we propose a routing algorithm with a time complexity of 6 n − 1 and a one-to-all broadcasting algorithm with a time complexity of 2 n − 1.

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38

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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39

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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40

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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41

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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42

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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43

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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44

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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45

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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Анотація:

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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46

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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47

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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48

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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49

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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50

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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