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Journal articles on the topic 'Protein'

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Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2024

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1

Akhter, Tahmin, S. Kanamaru, and F. Arisaka. "2P043 Protein interactions among neck proteins, gp13/gp14, and the connector protein, gp15, of bacteriophage T4." Seibutsu Butsuri 45, supplement (2005): S130. http://dx.doi.org/10.2142/biophys.45.s130_3.

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2

Cao, Yi, Teri Yoo, Shulin Zhuang, and Hongbin Li. "Protein–Protein Interaction Regulates Proteins’ Mechanical Stability." Journal of Molecular Biology 378, no. 5 (May 2008): 1132–41. http://dx.doi.org/10.1016/j.jmb.2008.03.046.

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3

Nawas, Mariam T., Evan J. Walker, Megan B. Richie, Andrew A. White, and Gerald Hsu. "A Protean Protein." Journal of Hospital Medicine 14, no. 2 (February 2019): 117–22. http://dx.doi.org/10.12788/jhm.3102.

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4

Campbell, P. "Proteinâprotein recognition." Biochemistry and Molecular Biology Education 29, no. 5 (September 2001): 211–12. http://dx.doi.org/10.1016/s1470-8175(01)00067-4.

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5

Gómez, Antonio, Sergio Hernández, Isaac Amela, Jaume Piñol, Juan Cedano, and Enrique Querol. "Do protein–protein interaction databases identify moonlighting proteins?" Molecular BioSystems 7, no. 8 (2011): 2379. http://dx.doi.org/10.1039/c1mb05180f.

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6

Busler, Valerie J., Victor J. Torres, Mark S. McClain, Oscar Tirado, David B. Friedman, and Timothy L. Cover. "Protein-Protein Interactions among Helicobacter pylori Cag Proteins." Journal of Bacteriology 188, no. 13 (July 1, 2006): 4787–800. http://dx.doi.org/10.1128/jb.00066-06.

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ABSTRACT Many Helicobacter pylori isolates contain a 40-kb region of chromosomal DNA known as the cag pathogenicity island (PAI). The risk for development of gastric cancer or peptic ulcer disease is higher among humans infected with cag PAI-positive H. pylori strains than among those infected with cag PAI-negative strains. The cag PAI encodes a type IV secretion system that translocates CagA into gastric epithelial cells. To identify Cag proteins that are expressed by H. pylori during growth in vitro, we compared the proteomes of a wild-type H. pylori strain and an isogenic cag PAI deletion mutant using two-dimensional difference gel electrophoresis (2D-DIGE) in multiple pH ranges. Seven Cag proteins were identified by this approach. We then used a yeast two-hybrid system to detect potential protein-protein interactions among 14 Cag proteins. One heterotypic interaction (CagY/7 with CagX/8) and two homotypic interactions (involving H. pylori VirB11/ATPase and Cag5) were similar to interactions previously reported to occur among homologous components of the Agrobacterium tumefaciens type IV secretion system. Other interactions involved Cag proteins that do not have known homologues in other bacterial species. Biochemical analysis confirmed selected interactions involving five of the proteins that were identified by 2D-DIGE. Protein-protein interactions among Cag proteins are likely to have an important role in the assembly of the H. pylori type IV secretion apparatus.

7

Kim, J., K. Harter, and A. Theologis. "Protein-protein interactions among the Aux/IAA proteins." Proceedings of the National Academy of Sciences 94, no. 22 (October 28, 1997): 11786–91. http://dx.doi.org/10.1073/pnas.94.22.11786.

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8

Liu, Jun O. "Recruitment of proteins to modulate protein-protein interactions." Chemistry & Biology 6, no. 8 (August 1999): R213—R215. http://dx.doi.org/10.1016/s1074-5521(99)80080-5.

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9

Lin, Ya-Ling, Chia-Yi Chen, Ching-Ping Cheng, and Long-Sen Chang. "Protein–protein interactions of KChIP proteins and Kv4.2." Biochemical and Biophysical Research Communications 321, no. 3 (August 2004): 606–10. http://dx.doi.org/10.1016/j.bbrc.2004.07.006.

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10

Lin, Hening, and Virginia W. Cornish. "In Vivo Protein-Protein Interaction Assays: Beyond Proteins." Angewandte Chemie International Edition 40, no. 5 (March 2, 2001): 871–75. http://dx.doi.org/10.1002/1521-3773(20010302)40:5<871::aid-anie871>3.0.co;2-s.

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11

Qiu, Jiajun, Michael Bernhofer, Michael Heinzinger, Sofie Kemper, Tomas Norambuena, Francisco Melo, and Burkhard Rost. "ProNA2020 predicts protein–DNA, protein–RNA, and protein–protein binding proteins and residues from sequence." Journal of Molecular Biology 432, no. 7 (March 2020): 2428–43. http://dx.doi.org/10.1016/j.jmb.2020.02.026.

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12

Velesinović, Aleksandar, and Goran Nikolić. "Protein-protein interaction networks and protein-ligand docking: Contemporary insights and future perspectives." Acta Facultatis Medicae Naissensis 38, no. 1 (2021): 5–17. http://dx.doi.org/10.5937/afmnai38-28322.

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Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry - protein-ligand docking and the prediction of protein-protein interaction networks.

13

Sharif, Shahin Behrouz, Nina Zamani, and Brian P. Chadwick. "BAZ1B the Protean Protein." Genes 12, no. 10 (September 28, 2021): 1541. http://dx.doi.org/10.3390/genes12101541.

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The bromodomain adjacent to the zinc finger domain 1B (BAZ1B) or Williams syndrome transcription factor (WSTF) are just two of the names referring the same protein that is encoded by the WBSCR9 gene and is among the 26–28 genes that are lost from one copy of 7q11.23 in Williams syndrome (WS: OMIM 194050). Patients afflicted by this contiguous gene deletion disorder present with a range of symptoms including cardiovascular complications, developmental defects as well as a characteristic cognitive and behavioral profile. Studies in patients with atypical deletions and mouse models support BAZ1B hemizygosity as a contributing factor to some of the phenotypes. Focused analysis on BAZ1B has revealed this to be a versatile nuclear protein with a central role in chromatin remodeling through two distinct complexes as well as being involved in the replication and repair of DNA, transcriptional processes involving RNA Polymerases I, II, and III as well as possessing kinase activity. Here, we provide a comprehensive review to summarize the many aspects of BAZ1B function including its recent link to cancer.

14

Requena, Jesús R. "The protean prion protein." PLOS Biology 18, no. 6 (June 25, 2020): e3000754. http://dx.doi.org/10.1371/journal.pbio.3000754.

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15

Acuner Ozbabacan, S. E., H. B. Engin, A. Gursoy, and O. Keskin. "Transient protein-protein interactions." Protein Engineering Design and Selection 24, no. 9 (June 15, 2011): 635–48. http://dx.doi.org/10.1093/protein/gzr025.

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16

Kukar, Thomas, Sarah Eckenrode, Yunrong Gu, Wei Lian, Mike Megginson, Jin-Xiong She, and Donghai Wu. "Protein Microarrays to Detect Protein–Protein Interactions Using Red and Green Fluorescent Proteins." Analytical Biochemistry 306, no. 1 (July 2002): 50–54. http://dx.doi.org/10.1006/abio.2002.5614.

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17

Ryu, Jae-Woon, Tae-Ho Kang, Jae-Soo Yoo, and Hak-Yong Kim. "Analysis of Essential Proteins in Protein-Protein Interaction Networks." Journal of the Korea Contents Association 8, no. 6 (June 28, 2008): 74–81. http://dx.doi.org/10.5392/jkca.2008.8.6.074.

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18

Burbelo, Peter D., Adam E. Kisailus, and Jeremy W. Peck. "Detecting Protein-Protein Interactions Using Renilla Luciferase Fusion Proteins." BioTechniques 33, no. 5 (November 2002): 1044–50. http://dx.doi.org/10.2144/02335st05.

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19

Dong, Yun Yuan, and Xian Chun Zhang. "Nonessential-Nonhub Proteins in the Protein-Protein Interaction Network." Advanced Materials Research 934 (May 2014): 159–64. http://dx.doi.org/10.4028/www.scientific.net/amr.934.159.

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Protein-protein interaction (PPI) networks provide a simplified overview of the web of interactions that take place inside a cell. According to the centrality-lethality rule, hub proteins (proteins with high degree) tend to be essential in the PPI network. Moreover, there are also many low degree proteins in the PPI network, but they have different lethality. Some of them are essential proteins (essential-nonhub proteins), and the others are not (nonessential-nonhub proteins). In order to explain why nonessential-nonhub proteins don’t have essentiality, we propose a new measure n-iep (the number of essential neighbors) and compare nonessential-nonhub proteins with essential-nonhub proteins from topological, evolutionary and functional view. The comparison results show that there are statistical differences between nonessential-nonhub proteins and essential-nonhub proteins in centrality measures, clustering coefficient, evolutionary rate and the number of essential neighbors. These are reasons why nonessential-nonhub proteins don’t have lethality.

20

Cheng, Miaomiao, Lizhen Liu, Hanshi Wang, Chao Du, and Wei Song. "Essential Proteins Discovery from Weighted Protein–Protein Interaction Networks." Journal of Bionanoscience 8, no. 4 (August 1, 2014): 293–97. http://dx.doi.org/10.1166/jbns.2014.1239.

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21

Dimitrova, Maria, Isabelle Imbert, Marie Paule Kieny, and Catherine Schuster. "Protein-Protein Interactions between Hepatitis C Virus Nonstructural Proteins." Journal of Virology 77, no. 9 (May 1, 2003): 5401–14. http://dx.doi.org/10.1128/jvi.77.9.5401-5414.2003.

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ABSTRACT Replication of the hepatitis C virus (HCV) genome has been proposed to take place close to the membrane of the endoplasmic reticulum in membrane-associated replicase complexes, as is the case with several other plus-strand RNA viruses, such as poliovirus and flaviviruses. The most obvious benefits of this property are the possibility of coupling functions residing in different polypeptidic chains and the sequestration of viral proteins and nucleic acids in a distinct cytoplasmic compartment with high local concentrations of viral components. Indeed, HCV nonstructural (NS) proteins were clearly colocalized in association with membranes derived from the endoplasmic reticulum. This observation, together with the demonstration of the existence of several physical interactions between HCV NS proteins, supports the idea of assembly of a highly ordered multisubunit protein complex(es) probably involved in the replication of the viral genome. The objective of this study, therefore, was to examine all potential interactions between HCV NS proteins which could result in the formation of a replication complex(es). We identified several interacting viral partners by using a glutathione S-transferase pull-down assay, by in vitro and ex vivo coimmunoprecipitation experiments in adenovirus-infected Huh-7 cells allowing the expression of HCV NS proteins, and, finally, by using the yeast two-hybrid system. In addition, by confocal laser scanning microscopy, NS proteins were clearly shown to colocalize when expressed together in Huh-7 cells. We have been able to demonstrate the existence of a complex network of interactions implicating all six NS proteins. Our observations confirm previously described associations and identify several novel homo- and heterodimerizations.

22

Lu, T., M. Vandyke, and M. Sawadogo. "Protein-Protein Interaction Studies Using Immobilized Oligohistidine Fusion Proteins." Analytical Biochemistry 213, no. 2 (September 1993): 318–22. http://dx.doi.org/10.1006/abio.1993.1427.

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23

Paul, Sanjoy, and Ravindra Venkatramani. "Dynamical Metrics to Fingerprint Proteins and Protein-Protein Interactions." Biophysical Journal 118, no. 3 (February 2020): 306a. http://dx.doi.org/10.1016/j.bpj.2019.11.1730.

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24

Win, Debora, Amanda Streeter, Yakira Jack, and Julia R. Koeppe. "Protein-protein interactions of complement proteins C3 and CFH." Biophysical Journal 123, no. 3 (February 2024): 476a. http://dx.doi.org/10.1016/j.bpj.2023.11.2889.

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25

Schaeffer, R. D., and V. Daggett. "Protein folds and protein folding." Protein Engineering Design and Selection 24, no. 1-2 (November 3, 2010): 11–19. http://dx.doi.org/10.1093/protein/gzq096.

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26

Gaines, J. C., S. Acebes, A. Virrueta, M. Butler, L. Regan, and C. S. O'Hern. "Comparing side chain packing in soluble proteins, protein-protein interfaces, and transmembrane proteins." Proteins: Structure, Function, and Bioinformatics 86, no. 5 (February 26, 2018): 581–91. http://dx.doi.org/10.1002/prot.25479.

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27

Finkelstein, A. V. "Can protein unfolding simulate protein folding?" Protein Engineering Design and Selection 10, no. 8 (August 1, 1997): 843–45. http://dx.doi.org/10.1093/protein/10.8.843.

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28

Sear, Richard P. "Specific protein–protein binding in many-component mixtures of proteins." Physical Biology 1, no. 2 (April 29, 2004): 53–60. http://dx.doi.org/10.1088/1478-3967/1/2/001.

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29

Huang, Hsien-Da, Tzong-Yi Lee, Li-Cheng Wu, Feng-Mao Lin, Hsueh-Fen Juan, Jorng-Tzong Horng, and Ann-Ping Tsou. "MultiProtIdent: Identifying Proteins Using Database Search and Protein−Protein Interactions." Journal of Proteome Research 4, no. 3 (June 2005): 690–97. http://dx.doi.org/10.1021/pr0498335.

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30

Wadahama, Hiroyuki, Shinya Kamauchi, Masao Ishimoto, Teruo Kawada, and Reiko Urade. "Protein disulfide isomerase family proteins involved in soybean protein biogenesis." FEBS Journal 274, no. 3 (December 20, 2006): 687–703. http://dx.doi.org/10.1111/j.1742-4658.2006.05613.x.

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31

LI, MIN, JIAN-XIN WANG, HUAN WANG, and YI PAN. "IDENTIFICATION OF ESSENTIAL PROTEINS FROM WEIGHTED PROTEIN–PROTEIN INTERACTION NETWORKS." Journal of Bioinformatics and Computational Biology 11, no. 03 (June 2013): 1341002. http://dx.doi.org/10.1142/s0219720013410023.

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Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein–protein interactions has produced unprecedented opportunities for detecting protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. Unfortunately, the protein–protein interactions produced by high-throughput experiments generally have high false positives. Moreover, most of centrality measures based on network topology are sensitive to false positives. We therefore propose a new method for evaluating the confidence of each interaction based on the combination of logistic regression-based model and function similarity. Nine standard centrality measures in weighted network were redefined in this paper. The experimental results on a yeast protein interaction network shows that the weighting method improved the performance of centrality measures considerably. More essential proteins were discovered by the weighted centrality measures than by the original centrality measures used in the unweighted network. Even about 20% improvements were obtained from closeness centrality and subgraph centrality.

32

Garapati, Hita Sony, Gurranna Male, and Krishnaveni Mishra. "Predicting subcellular localization of proteins using protein-protein interaction data." Genomics 112, no. 3 (May 2020): 2361–68. http://dx.doi.org/10.1016/j.ygeno.2020.01.007.

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33

Zhang, Zhaopeng, Jishou Ruan, Jianzhao Gao, and Fang-Xiang Wu. "Predicting essential proteins from protein-protein interactions using order statistics." Journal of Theoretical Biology 480 (November 2019): 274–83. http://dx.doi.org/10.1016/j.jtbi.2019.06.022.

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34

Meier, Matthias, Doron Gerber, and Stephen Quake. "Functional Assignment of Hypothetical Proteins from Protein-Protein Interaction Networks." Biophysical Journal 98, no. 3 (January 2010): 741a. http://dx.doi.org/10.1016/j.bpj.2009.12.4062.

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35

Vos, Michel J., Marianne P. Zijlstra, Serena Carra, Ody C. M. Sibon, and Harm H. Kampinga. "Small heat shock proteins, protein degradation and protein aggregation diseases." Autophagy 7, no. 1 (January 2011): 101–3. http://dx.doi.org/10.4161/auto.7.1.13935.

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36

Wilson, Bridget, Lance A. Liotta, and Emanuel Petricoin III. "Monitoring Proteins and Protein Networks Using Reverse Phase Protein Arrays." Disease Markers 28, no. 4 (2010): 225–32. http://dx.doi.org/10.1155/2010/240248.

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Recent advances in high throughput, high content “omic” technologies coupled with clinical information has lead to the expectation that the complexity of the molecular information generated will lead to more robust scientific research as well as the expectation that overarching therapeutic approaches will be patient-tailored to the underlying specific molecular defects of the disease. As disease understanding progresses and more therapeutics, which predominately target proteins, are developed there is a need to more confidently determine the protein signaling events that can be correlated with drug response since the deranged protein signaling networks are often the drug target itself. In this environment, the Reverse Phase Protein Microarray (RPMA) can be utilized to address the needs of both clinical screening and disease understanding through its ability to provide an unmatched functional and highly multiplexed signaling network level mapping of ongoing signaling activation, coupled with the ability of the platform to provide this information reproducibly from a tiny needle biopsy specimen or fine needle aspirate. This platform has now been utilized for biomarker discovery/validation and advancements in disease understanding both in the clinic and at the bench in the fields of cancer, liver disease, immunological disorders, and bacterial infection.

37

Nchongboh, Chofong Gilbert, Guan-wei Wu, Ni Hong, and Guo-ping Wang. "Protein–protein interactions between proteins of Citrus tristeza virus isolates." Virus Genes 49, no. 3 (July 27, 2014): 456–65. http://dx.doi.org/10.1007/s11262-014-1100-x.

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38

Koike, Manabu, Takashi Miyasaka, Tsuneyo Mimori, and Tadahiro Shiomi. "Subcellular Localization and Protein-Protein Interaction Regions of Ku Proteins." Biochemical and Biophysical Research Communications 252, no. 3 (November 1998): 679–85. http://dx.doi.org/10.1006/bbrc.1998.9368.

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39

Lin, Hening, and Virginia W. Cornish. "ChemInform Abstract: In vivo Protein-Protein Interaction Assays: Beyond Proteins." ChemInform 32, no. 21 (May 26, 2010): no. http://dx.doi.org/10.1002/chin.200121275.

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40

Yadav, Keerti Kumar, and Ajay Kumar Singh. "Topology-based protein–protein interaction analysis of oral cancer proteins." Current Science 123, no. 10 (November 25, 2022): 1216. http://dx.doi.org/10.18520/cs/v123/i10/1216-1224.

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41

Vakser, IIya A. "Main-chain complementarity in protein-protein recognition." "Protein Engineering, Design and Selection" 9, no. 9 (1996): 741–44. http://dx.doi.org/10.1093/protein/9.9.741.

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42

Lei, H., and Y. Duan. "Incorporating intermolecular distance into protein-protein docking." Protein Engineering Design and Selection 17, no. 12 (February 16, 2005): 837–45. http://dx.doi.org/10.1093/protein/gzh100.

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43

Abdullah, Syahid, Wisnu Ananta Kusuma, and Sony Hartono Wijaya. "Sequence-based prediction of protein-protein interaction using autocorrelation features and machine learning." Jurnal Teknologi dan Sistem Komputer 10, no. 1 (January 4, 2022): 1–11. http://dx.doi.org/10.14710/jtsiskom.2021.13984.

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Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). The prediction models with the three autocorrelation methods can produce predictions with high average accuracy, which is 95.34% for Geary in KNN, 97.43% for Geary in RF, and 97.11% for Geary and Moran in SVM. In addition, the interacting protein pairs tend to have similar autocorrelation characteristics. Thus, the autocorrelation method can be used to predict PPI well.

44

Diansyah, Mohammad Romano, Wisnu Ananta Kusuma, and Annisa Annisa. "Identification of significant protein in protein-protein interaction of Alzheimer disease using top-k representative skyline query." Jurnal Teknologi dan Sistem Komputer 9, no. 3 (April 24, 2021): 126–32. http://dx.doi.org/10.14710/jtsiskom.2021.13985.

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Alzheimer's disease is the most common neurodegenerative disease. This study aims to analyze protein-protein interaction (PPI) to provide a better understanding of multifactorial neurodegenerative diseases and can be used to find proteins that have a significant role in Alzheimer's disease. PPI data were obtained from experimental and computational predictions and analyzed using centrality measures. The Top-k RSP method was applied to find significant proteins in PPI networks using the dominance rule. The method was applied to the PPI data with the interaction sources from the experimental and experiment+prediction. The results indicate that APP and PSEN1 are significant proteins for Alzheimer's disease. This study also showed that both data sources (experiment+prediction) and the Top-k RSP algorithm proved useful for PPI analysis of Alzheimer's disease.

45

HAO, Liyang, Quan PAN, and Shaowu ZHANG. "Prediction of Drug-Target Proteins by Integrating Protein-Protein Interaction Network and Protein Sequence Similarity." Acta Biophysica Sinica 29, no. 9 (2013): 695. http://dx.doi.org/10.3724/sp.j.1260.2013.30042.

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46

Zhang, Changsheng, Bo Tang, Qian Wang, and Luhua Lai. "Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening." Proteins: Structure, Function, and Bioinformatics 82, no. 10 (June 3, 2014): 2472–82. http://dx.doi.org/10.1002/prot.24611.

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47

Leatherbarrow, Robin J., and Alan R. Fersht. "Protein engineering." "Protein Engineering, Design and Selection" 1, no. 1 (1986): 7–16. http://dx.doi.org/10.1093/protein/1.1.7.

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48

Dill, Ken A. "Protein surgery." "Protein Engineering, Design and Selection" 1, no. 5 (1987): 369–71. http://dx.doi.org/10.1093/protein/1.5.369.

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49

Sawyer, Nicholas, Danielle M. Williams, and Lynne Regan. "Protein goldendoodles: Designing new proteins." Biochemist 36, no. 1 (February 1, 2014): 28–33. http://dx.doi.org/10.1042/bio03601028.

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The goldendoodle (Figure 1) is a breed of dog created to combine the desirable features of the golden retriever (calm personality, good with people and an excellent service dog) with those of the poodle (low shedding and hypoallergenic). The result surpasses expectations: not only does the goldendoodle have a great personality and low shedding, but also the animal is exceedingly cute and in great demand. Protein design, the creation of novel proteins either de novo or by extensive mutagenesis of natural proteins, has likewise produced many ‘goldendoodle-esque’ proteins whose unprecedented combination of stability and function have revolutionized academic and clinical research. Here, we discuss the history of protein design and highlight some particularly successful protein designs of this type.

50

Vershon, Andrew K. "Protein interactions of homeodomain proteins." Current Opinion in Biotechnology 7, no. 4 (August 1996): 392–96. http://dx.doi.org/10.1016/s0958-1669(96)80113-3.

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