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Journal articles on the topic 'Protein-protein interactions'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 July 2024

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1

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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2

Legrain, Pierre. "Protein–Protein Interactions: Protein interactions contribute to protein function." Trends in Genetics 18, no. 8 (August 2002): 432. http://dx.doi.org/10.1016/s0168-9525(02)02710-5.

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3

Lederman, Lynne. "Protein-Protein Interactions." BioTechniques 40, no. 5 (May 2006): 567–69. http://dx.doi.org/10.2144/06405tn01.

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4

Hirata, Rosário D. C. "Protein-Protein Interactions." Revista Brasileira de Ciências Farmacêuticas 40, no. 1 (March 2004): 111–12. http://dx.doi.org/10.1590/s1516-93322004000100017.

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5

Williamson, Mike P., and Michael J. Sutcliffe. "Protein–protein interactions." Biochemical Society Transactions 38, no. 4 (July 26, 2010): 875–78. http://dx.doi.org/10.1042/bst0380875.

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In the present article, we describe the two standard high-throughput methods for identification of protein complexes: two-hybrid screens and TAP (tandem affinity purification) tagging. These methods have been used to characterize the interactome of Saccharomyces cerevisiae, showing that the majority of proteins are part of complexes, and that complexes typically consist of a core to which are bound ‘party’ and ‘dater’ proteins. Complexes typically are merely the sum of their parts. A particularly interesting type of complex is the metabolon, containing enzymes within the same metabolic pathway. There is reasonably good evidence that metabolons exist, but they have not been detected using high-thoughput assays, possibly because of their fragility.

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6

Halperin, Inbal, Haim Wolfson, and Ruth Nussinov. "Protein-Protein Interactions." Structure 12, no. 6 (June 2004): 1027–38. http://dx.doi.org/10.1016/j.str.2004.04.009.

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7

Janin, Joël, and Alexandre MJJ Bonvin. "Protein–protein interactions." Current Opinion in Structural Biology 23, no. 6 (December 2013): 859–61. http://dx.doi.org/10.1016/j.sbi.2013.10.003.

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8

Ottmann, Christian. "Protein–Protein Interactions." Drug Discovery Today: Technologies 24 (June 2017): 1–2. http://dx.doi.org/10.1016/j.ddtec.2017.11.008.

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9

NG, SEE-KIONG, and SOON-HENG TAN. "DISCOVERING PROTEIN–PROTEIN INTERACTIONS." Journal of Bioinformatics and Computational Biology 01, no. 04 (January 2004): 711–41. http://dx.doi.org/10.1142/s0219720004000600.

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The ongoing genomics and proteomics efforts have helped identify many new genes and proteins in living organisms. However, simply knowing the existence of genes and proteins does not tell us much about the biological processes in which they participate. Many major biological processes are controlled by protein interaction networks. A comprehensive description of protein–protein interactions is therefore necessary to understand the genetic program of life. In this tutorial, we provide an overview of the various current high-throughput methods for discovering protein–protein interactions, covering both the conventional experimental methods and new computational approaches.

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10

CHUA, HON NIAN, KANG NING, WING-KIN SUNG, HON WAI LEONG, and LIMSOON WONG. "USING INDIRECT PROTEIN–PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION." Journal of Bioinformatics and Computational Biology 06, no. 03 (June 2008): 435–66. http://dx.doi.org/10.1142/s0219720008003497.

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Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein–protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein–protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.

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11

Liu, Peng, Lei Yang, Daming Shi, and Xianglong Tang. "Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/259157.

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A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptivek-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

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12

Saeed S, M. G., S. U. Abdullah, S. A. Sayeed, and R. Ali. "Food protein: Food colour interactions and its application in rapid protein assay." Czech Journal of Food Sciences 28, No. 6 (December 13, 2010): 506–13. http://dx.doi.org/10.17221/112/2009-cjfs.

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The uniform distribution of colours as additives in a majority of the food systems is a reliable indication that one or more components of foods are able to bind with colour molecules and act as their carriers. However, the food components acting as the colour carriers have not been identified. The present paper describes the binding capacity of Carmoisine with a variety of food proteins, our results have shown that the intensity, staining, and sharpness of the stained protein bands were excellent as compared to Coomassie Brilliant Blue-R-250, which is an established staining agent for visualising electrophoretically resolved proteins. The data illustrates that Carmoisine is a fast reacting dye forming colour-complexes with all types of food proteins including curry leaves proteins. The protein bands are visualised within an hour which is useful for the initial immediate protein identifications. The experiments related to the staining of the resolved proteins with Carmoisine have shown that the dye is highly sensitive, rapid, and lasting. The food-dye can provide a quick protein assay as often desired in research works, the results may be later confirmed by using Coomassie if so required. In view of its strong binding with almost all proteins, it was thought that human proteases present in the digestive tract may not hydrolyse the bound proteins completely and may restrict the proteolytic digestion. However, the experiments based on the tryptic digestibility in vitro revealed that colour binding has no adverse effect on hydrolysis of peptide bonds by the intestinal proteases.

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13

Roche, Jennifer, and Susanna Törnroth-Horsefield. "Aquaporin Protein-Protein Interactions." International Journal of Molecular Sciences 18, no. 11 (October 27, 2017): 2255. http://dx.doi.org/10.3390/ijms18112255.

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14

Larkin,, Michael I. "Characterizing Protein-Protein Interactions." Genetic Engineering & Biotechnology News 31, no. 9 (May 2011): 24–25. http://dx.doi.org/10.1089/gen.31.9.11.

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15

Nadamuni, Sridhar. "Targeting Protein-Protein Interactions." Genetic Engineering & Biotechnology News 33, no. 4 (February 15, 2013): 14, 16–17. http://dx.doi.org/10.1089/gen.33.4.06.

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16

Lakey, Jeremy H., and Elaine M. Raggett. "Measuring protein—protein interactions." Current Opinion in Structural Biology 8, no. 1 (February 1998): 119–23. http://dx.doi.org/10.1016/s0959-440x(98)80019-5.

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17

Crunkhorn, Sarah. "Inhibiting protein–protein interactions." Nature Reviews Drug Discovery 15, no. 4 (April 2016): 234. http://dx.doi.org/10.1038/nrd.2016.54.

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18

Cannataro, Mario, Pietro H. Guzzi, and Pierangelo Veltri. "Protein-to-protein interactions." ACM Computing Surveys 43, no. 1 (November 2010): 1–36. http://dx.doi.org/10.1145/1824795.1824796.

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19

Cho, Sa-Yeon, Sung-Goo Park, Do-Hee Lee, and Byoung-Chul Park. "Protein-protein Interaction Networks: from Interactions to Networks." BMB Reports 37, no. 1 (January 31, 2004): 45–52. http://dx.doi.org/10.5483/bmbrep.2004.37.1.045.

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20

Nguyen, Tuan N., and James A. Goodrich. "Protein-protein interaction assays: eliminating false positive interactions." Nature Methods 3, no. 2 (February 2006): 135–39. http://dx.doi.org/10.1038/nmeth0206-135.

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21

Aachmann, F. L., D. E. Otzen, K. L. Larsen, and R. Wimmer. "Structural background of cyclodextrin-protein interactions." Protein Engineering Design and Selection 16, no. 12 (December 1, 2003): 905–12. http://dx.doi.org/10.1093/protein/gzg137.

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22

Vyncke, Laurens, Delphine Masschaele, Jan Tavernier, and Frank Peelman. "Straightforward Protein-Protein Interaction Interface Mapping via Random Mutagenesis and Mammalian Protein Protein Interaction Trap (MAPPIT)." International Journal of Molecular Sciences 20, no. 9 (April 26, 2019): 2058. http://dx.doi.org/10.3390/ijms20092058.

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The MAPPIT (mammalian protein protein interaction trap) method allows high-throughput detection of protein interactions by very simple co-transfection of three plasmids in HEK293T cells, followed by a luciferase readout. MAPPIT detects a large percentage of all protein interactions, including those requiring posttranslational modifications and endogenous or exogenous ligands. Here, we present a straightforward method that allows detailed mapping of interaction interfaces via MAPPIT. The method provides insight into the interaction mechanism and reveals how this is affected by disease-associated mutations. By combining error-prone polymerase chain reaction (PCR) for random mutagenesis, 96-well DNA prepping, Sanger sequencing, and MAPPIT via 384-well transfections, we test the effects of a large number of mutations of a selected protein on its protein interactions. The entire screen takes less than three months and interactions with multiple partners can be studied in parallel. The effect of mutations on the MAPPIT readout is mapped on the protein structure, allowing unbiased identification of all putative interaction sites. We have thus far analysed 6 proteins and mapped their interfaces for 16 different interaction partners. Our method is broadly applicable as the required tools are simple and widely available.

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23

Fuller, Stephen. "Thermodynamic perspectives on protein interactions protein interactions." Trends in Biotechnology 11, no. 12 (December 1993): 526. http://dx.doi.org/10.1016/0167-7799(93)90041-7.

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24

Salminen, H., R. Kivikari, and M. Heinonen. "Protein-lipid interactions during oxidation of liposomes." Czech Journal of Food Sciences 22, SI - Chem. Reactions in Foods V (January 1, 2004): S133—S135. http://dx.doi.org/10.17221/10636-cjfs.

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Oxidation of bovine serum albumin and its interaction with phenolic red raspberry and bilberry extracts (4.2 and 8.4 μg/ml) was investigated in a liposome system. Samples were incubated in the dark at 37°C with copper, and the extent of oxidation was measured by determing the loss of tryptophan fluorescence and the formation of protein carbonyls, conjugated diene hydroperoxides and hexanal. Both red raspberry and bilberry extracts inhibited lipid and protein oxidation. Red raspberry extract in 4.2 μg/ml concentration was the best inhibitor against both lipid and protein oxidation. In conclusion, oxidative deterioration due to protein-lipid oxidation is inhibited by phenolic compounds in berries.

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25

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.

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26

Taudte, Susann, Hong Xin, Anthony J. Bell, and Neville R. Kallenbach. "Interactions between HMG boxes." Protein Engineering, Design and Selection 14, no. 12 (December 2001): 1015–23. http://dx.doi.org/10.1093/protein/14.12.1015.

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27

Otzen, Daniel E., and Alan R. Fersht. "Analysis of protein–protein interactions by mutagenesis: direct versus indirect effects." Protein Engineering, Design and Selection 12, no. 1 (January 1999): 41–45. http://dx.doi.org/10.1093/protein/12.1.41.

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28

Harris, Pernille. "PIPPI – Protein-excipient Interactions and Protein-Protein Interactions in formulation – ERC." Impact 2017, no. 8 (October 20, 2017): 75–77. http://dx.doi.org/10.21820/23987073.2017.8.75.

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29

Taniguchi, Hisaaki. "Protein myristoylation in protein–lipid and protein–protein interactions." Biophysical Chemistry 82, no. 2-3 (December 1999): 129–37. http://dx.doi.org/10.1016/s0301-4622(99)00112-x.

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30

Sánchez Claros, Carmen, and Anna Tramontano. "Detecting Mutually Exclusive Interactions in Protein-Protein Interaction Maps." PLoS ONE 7, no. 6 (June 8, 2012): e38765. http://dx.doi.org/10.1371/journal.pone.0038765.

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31

Cook, Helen, Nadezhda Doncheva, Damian Szklarczyk, Christian von Mering, and Lars Jensen. "Viruses.STRING: A Virus-Host Protein-Protein Interaction Database." Viruses 10, no. 10 (September 23, 2018): 519. http://dx.doi.org/10.3390/v10100519.

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As viruses continue to pose risks to global health, having a better understanding of virus–host protein–protein interactions aids in the development of treatments and vaccines. Here, we introduce Viruses.STRING, a protein–protein interaction database specifically catering to virus–virus and virus–host interactions. This database combines evidence from experimental and text-mining channels to provide combined probabilities for interactions between viral and host proteins. The database contains 177,425 interactions between 239 viruses and 319 hosts. The database is publicly available at viruses.string-db.org, and the interaction data can also be accessed through the latest version of the Cytoscape STRING app.

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32

Al-Nema, Mayasah Y., and Anand Gaurav. "Protein-Protein Interactions of Phosphodiesterases." Current Topics in Medicinal Chemistry 19, no. 7 (May 31, 2019): 555–64. http://dx.doi.org/10.2174/1568026619666190401113803.

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Background: Phosphodiesterases (PDEs) are enzymes that play a key role in terminating cyclic nucleotides signalling by catalysing the hydrolysis of 3’, 5’- cyclic adenosine monophosphate (cAMP) and/or 3’, 5’ cyclic guanosine monophosphate (cGMP), the second messengers within the cell that transport the signals produced by extracellular signalling molecules which are unable to get into the cells. However, PDEs are proteins which do not operate alone but in complexes that made up of a many proteins. Objective: This review highlights some of the general characteristics of PDEs and focuses mainly on the Protein-Protein Interactions (PPIs) of selected PDE enzymes. The objective is to review the role of PPIs in the specific mechanism for activation and thereby regulation of certain biological functions of PDEs. Methods: Methods The article discusses some of the PPIs of selected PDEs as reported in recent scientific literature. These interactions are critical for understanding the biological role of the target PDE. Results: The PPIs have shown that each PDE has a specific mechanism for activation and thereby regulation a certain biological function. Conclusion: Targeting of PDEs to specific regions of the cell is based on the interaction with other proteins where each PDE enzyme binds with specific protein(s) via PPIs.

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33

L. Armstrong, Clare, Erik Sandqvist, and Maikel C. Rheinstadter. "Protein-Protein Interactions in Membranes." Protein & Peptide Letters 18, no. 4 (April 1, 2011): 344–53. http://dx.doi.org/10.2174/092986611794653941.

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34

Sharma, S., T. Ramsey, and K. Bair. "Protein-Protein Interactions: Lessons Learned." Current Medicinal Chemistry-Anti-Cancer Agents 2, no. 2 (March 1, 2002): 311–30. http://dx.doi.org/10.2174/1568011023354191.

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35

Fukao, Y. "Protein-Protein Interactions in Plants." Plant and Cell Physiology 53, no. 4 (March 1, 2012): 617–25. http://dx.doi.org/10.1093/pcp/pcs026.

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36

Kondo, Akihiko. "Protein-protein interactions and selection." FEBS Journal 277, no. 9 (April 7, 2010): 1981. http://dx.doi.org/10.1111/j.1742-4658.2010.07624.x.

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37

Rydeen, Amy E., Eric M. Brustad, and Gary J. Pielak. "Osmolytes and Protein–Protein Interactions." Journal of the American Chemical Society 140, no. 24 (May 29, 2018): 7441–44. http://dx.doi.org/10.1021/jacs.8b03903.

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38

Jones, S., and J. M. Thornton. "Principles of protein-protein interactions." Proceedings of the National Academy of Sciences 93, no. 1 (January 9, 1996): 13–20. http://dx.doi.org/10.1073/pnas.93.1.13.

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39

Teichmann, S. A. "Principles of protein-protein interactions." Bioinformatics 18, Suppl 2 (October 1, 2002): S249. http://dx.doi.org/10.1093/bioinformatics/18.suppl_2.s249.

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40

Mamitsuka, H. "Mining new protein-protein interactions." IEEE Engineering in Medicine and Biology Magazine 24, no. 3 (May 2005): 103–8. http://dx.doi.org/10.1109/memb.2005.1436467.

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41

Cochran, Andrea G. "Antagonists of protein–protein interactions." Chemistry & Biology 7, no. 4 (April 2000): R85—R94. http://dx.doi.org/10.1016/s1074-5521(00)00106-x.

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42

Milroy, Lech-Gustav, Tom N. Grossmann, Sven Hennig, Luc Brunsveld, and Christian Ottmann. "Modulators of Protein–Protein Interactions." Chemical Reviews 114, no. 9 (April 15, 2014): 4695–748. http://dx.doi.org/10.1021/cr400698c.

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43

Bonvin, Alexandre MJJ, and Özlem Keskin. "Editorial overview: Protein–protein interactions." Current Opinion in Structural Biology 35 (December 2015): vii—ix. http://dx.doi.org/10.1016/j.sbi.2015.11.004.

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44

Gadek, Thomas R., and Denise A. Ockey. "Inhibitors of protein-protein interactions." Expert Opinion on Therapeutic Patents 12, no. 3 (March 2002): 393–400. http://dx.doi.org/10.1517/13543776.12.3.393.

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45

Ackerley, David F., Gregory L. Challis, and Max J. Cryle. "Understanding biosynthetic protein–protein interactions." Natural Product Reports 35, no. 11 (2018): 1118–19. http://dx.doi.org/10.1039/c8np90037j.

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46

Janin, Joël. "Protein-protein interactions and assembly." Current Opinion in Structural Biology 1, no. 1 (February 1991): 42–44. http://dx.doi.org/10.1016/0959-440x(91)90009-i.

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47

Kerman, Kagan, and Heinz-Bernhard Kraatz. "Metals Coordinate Protein-Protein Interactions." Angewandte Chemie International Edition 47, no. 35 (August 18, 2008): 6522–24. http://dx.doi.org/10.1002/anie.200801169.

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48

Chène, Patrick. "Drugs Targeting Protein–Protein Interactions." ChemMedChem 1, no. 4 (April 10, 2006): 400–411. http://dx.doi.org/10.1002/cmdc.200600004.

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49

Mamitsuka, Hiroshi. "Mining from protein-protein interactions." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, no. 5 (August 8, 2012): 400–410. http://dx.doi.org/10.1002/widm.1065.

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50

Von Hippel, Peter H. "Protein interactions." Trends in Biochemical Sciences 18, no. 11 (November 1993): 450. http://dx.doi.org/10.1016/0968-0004(93)90149-h.

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