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Artigos de revistas sobre o tema "Protein"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 25 de julho de 2025

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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 (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 (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 (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 (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 m

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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 (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 (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 (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 (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, et al. "ProNA2020 predicts protein–DNA, protein–RNA, and protein–protein binding proteins and residues from sequence." Journal of Molecular Biology 432, no. 7 (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 p

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13

Sharif, Shahin Behrouz, Nina Zamani, and Brian P. Chadwick. "BAZ1B the Protean Protein." Genes 12, no. 10 (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

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14

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

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15

Kukar, Thomas, Sarah Eckenrode, Yunrong Gu, et al. "Protein Microarrays to Detect Protein–Protein Interactions Using Red and Green Fluorescent Proteins." Analytical Biochemistry 306, no. 1 (2002): 50–54. http://dx.doi.org/10.1006/abio.2002.5614.

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16

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

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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 (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 (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 numb

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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 (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 (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 wer

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22

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

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23

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

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24

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

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25

El Hefnawi, Mahmoud M., Mohamed E. Hasan, Amal Mahmoud, et al. "Prediction and Analysis of Three-Dimensional Structure of the p7- Transactivated Protein1 of Hepatitis C Virus." Infectious Disorders - Drug Targets 19, no. 1 (2019): 55–66. http://dx.doi.org/10.2174/1871526518666171215123214.

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Background:The p7-transactivated protein1 of Hepatitis C virus is a small integral membrane protein of 127 amino acids, which is crucial for assembly and release of infectious virions. Ab initio or comparative modelling, is an essential tool to solve the problem of protein structure prediction and to comprehend the physicochemical fundamental of how proteins fold in nature.Results:Only one domain (1-127) of p7-transactivated protein1 has been predicted using the systematic in silico approach, ThreaDom. I-TASSER was ranked as the best server for full-length 3-D protein structural predictions of

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26

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

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27

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 (2018): 581–91. http://dx.doi.org/10.1002/prot.25479.

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28

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

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29

Смирнова, Ирина, Irina Smirnova, Николай Гутов, Nikolay Gutov, Андрей Лукин, and Andrey Lukin. "Research of composition of milk protein concentrates." Food Processing: Techniques and Technology 48, no. 1 (2019): 85–90. http://dx.doi.org/10.21603/2074-9414-2018-1-85-90.

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Emergence of the dairy products enriched with milky proteinaceous concentrates is connected with low level of consumption of protein the population. Results of a research of structure of two samples of milk protein concentrates – Promilk 852 FBI and Promilk Kappa Optimum for the purpose of their further application in production of dairy products are presented in article. Fractions of proteins of milk protein concentrates with use of size of molecular weight are defined. As a result of electrophoretic division of fractions of proteins the method of a free electrophoresis by means of a cell for

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30

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

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31

Huang, Hsien-Da, Tzong-Yi Lee, Li-Cheng Wu, et al. "MultiProtIdent: Identifying Proteins Using Database Search and Protein−Protein Interactions." Journal of Proteome Research 4, no. 3 (2005): 690–97. http://dx.doi.org/10.1021/pr0498335.

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32

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 (2006): 687–703. http://dx.doi.org/10.1111/j.1742-4658.2006.05613.x.

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33

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 (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 sensit

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34

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

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35

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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36

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

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37

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 (2011): 101–3. http://dx.doi.org/10.4161/auto.7.1.13935.

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38

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

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39

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 (2014): 456–65. http://dx.doi.org/10.1007/s11262-014-1100-x.

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40

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 (1998): 679–85. http://dx.doi.org/10.1006/bbrc.1998.9368.

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41

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

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42

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

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43

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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44

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

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45

Kotthoff, Ian, Petras J. Kundrotas, and Ilya A. Vakser. "DOCKGROUND membrane protein-protein set." PLOS ONE 17, no. 5 (2022): e0267531. http://dx.doi.org/10.1371/journal.pone.0267531.

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Membrane proteins are significantly underrepresented in Protein Data Bank despite their essential role in cellular mechanisms and the major progress in experimental protein structure determination. Thus, computational approaches are especially valuable in the case of membrane proteins and their assemblies. The main focus in developing structure prediction techniques has been on soluble proteins, in part due to much greater availability of the structural data. Currently, structure prediction of protein complexes (protein docking) is a well-developed field of study. However, the generic protein

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46

ESPEJO, Alexsandra, Jocelyn CÔTÉ, Andrzej BEDNAREK, Stephane RICHARD, and Mark T. BEDFORD. "A protein-domain microarray identifies novel protein–protein interactions." Biochemical Journal 367, no. 3 (2002): 697–702. http://dx.doi.org/10.1042/bj20020860.

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Protein domains mediate protein—protein interactions through binding to short peptide motifs in their corresponding ligands. These peptide recognition modules are critical for the assembly of multiprotein complexes. We have arrayed glutathione S-transferase (GST) fusion proteins, with a focus on protein interaction domains, on to nitrocellulose-coated glass slides to generate a protein-domain chip. Arrayed protein-interacting modules included WW (a domain with two conserved tryptophans), SH3 (Src homology 3), SH2, 14.3.3, FHA (forkhead-associated), PDZ (a domain originally identified in PSD-95

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47

Bharathwaj, J. Deepak Venkataraman N.* Charumathi P. Lakshminarasimman S. Purushothaman V. M. Sudharsan S. "An Overview of Basics, Types, Approaches, Applications, Advantages and Disadvantages of Docking." International Journal of Pharmaceutical Sciences 3, no. 3 (2025): 437–45. https://doi.org/10.5281/zenodo.14992121.

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Background-Molecular docking serves as an effective method for exploring the molecular targets of nutraceuticals in the treatment of diseases. Objectives-This review focuses on understanding the basics, types, approaches, applications, advantages and disadvantages of docking. Discussion-The basics of docking involve study of the ligands and proteins. The types of docking encompass Rigid Docking, Flexible-Rigid Docking and Flexible Docking. The approaches include determination of the energy profile for the docked conformer of the ligand target and determination of the complementarity of surface

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48

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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49

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 (2014): 2472–82. http://dx.doi.org/10.1002/prot.24611.

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50

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 (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 Fo

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