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

Author: Grafiati

Published: 6 September 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 (2020): W94—W103. http://dx.doi.org/10.1093/nar/gkaa397.

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

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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 (2021): 1788. http://dx.doi.org/10.3390/biom11121788.

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

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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 (2019): W471—W476. http://dx.doi.org/10.1093/nar/gkz390.

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

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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 (2020): 191461. http://dx.doi.org/10.1098/rsos.191461.

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

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

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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 structur

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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 (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

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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 (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

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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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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 multimod

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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 (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 intermedi

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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

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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

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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 (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 Ps

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13

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

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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 informatio

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15

Wu, Xiao-Tong, Zhu-Pei Xiong, Kun-Xiang Chen, et al. "Genome-Wide Identification and Transcriptional Expression Profiles of PP2C in the Barley (Hordeum vulgare L.) Pan-Genome." Genes 13, no. 5 (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

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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 (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,

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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 (2003): 1491–96. http://dx.doi.org/10.1042/bst0311491.

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

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

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27

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

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

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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 (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 (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, et al. "Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma." Biophysics Reviews 3, no. 1 (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 co

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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 (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 (2020): 24294–304. http://dx.doi.org/10.1073/pnas.2007201117.

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

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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 (2018): 168. http://dx.doi.org/10.14419/ijet.v7i4.5.20037.

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

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34

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

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

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35

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

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

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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 (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 (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 rela

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

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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 (2005): 383–97. http://dx.doi.org/10.1142/s0219525905000531.

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

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

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

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