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Journal articles on the topic 'Protein-protein'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

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.

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2

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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3

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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4

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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5

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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6

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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7

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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8

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.

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9

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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10

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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11

Karagoz, G. E., T. Sinnige, O. Hsieh, and S. G. D. Rudiger. "Expressed protein ligation for a large dimeric protein." Protein Engineering Design and Selection 24, no. 6 (February 18, 2011): 495–501. http://dx.doi.org/10.1093/protein/gzr007.

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12

Sayre, T. C., T. M. Lee, N. P. King, and T. O. Yeates. "Protein stabilization in a highly knotted protein polymer." Protein Engineering Design and Selection 24, no. 8 (June 13, 2011): 627–30. http://dx.doi.org/10.1093/protein/gzr024.

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13

Lluis, M. W., J. I. Godfroy, and H. Yin. "Protein engineering methods applied to membrane protein targets." Protein Engineering Design and Selection 26, no. 2 (October 31, 2012): 91–100. http://dx.doi.org/10.1093/protein/gzs079.

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14

Prastiyani, Lien Meilya Muriasti, and Nuryanto Nuryanto. "HUBUNGAN ANTARA ASUPAN PROTEIN DAN KADAR PROTEIN AIR SUSU IBU." Journal of Nutrition College 8, no. 4 (November 26, 2019): 246–53. http://dx.doi.org/10.14710/jnc.v8i4.25838.

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Latar Belakang: ASI merupakan makanan terbaik bayi 0-6 bulan karena mengandung semua unsur zat gizi yang dibutuhkan bayi serta mengandung antibodi untuk melindungi bayi dari penyakit. Kandungan zat gizi ASI salah satunya dipengaruhi oleh asupan zat gizi. Protein merupakan salah satu zat gizi yang berperan dalam pertumbuhan, pembentukan jaringan dan organ penting dan pertahanan tubuh bayi.Tujuan penelitian ini adalah untuk mengetahui hubungan antara asupan protein dengan kadar protein air susu ibu (ASI).Metode: Jenis penelitian ini adalah observasional dengan desain studi cross sectional. Jumlah sampel sebanyak 33 orang ibu menyusui bayi 0-6 bulan di wilayah Kecamatan Candisari dan Kecamatan Tembalang yang dipilih secara acak. Asupan protein diperoleh melalui recall 3x24 jam melalui wawancara dengan metode food recall dengan hari yang berbeda. Kadar protein ASI dianalisis dengan metode Kjeldahl. Hubungan antara asupan protein dengan kadar protein ASI diuji menggunakan uji korelasi Rank Spearman.Hasil: Rerata asupan protein ibu menyusui 58,06±10,51gram dan rerata tingkat asupan protein 49,74±9,29%.Hasil penelitian menunjukkan adanya hubungan signifikan antara asupan protein dengan kadar protein ASI dengan nilai p= 0,029 (p<0,05). Simpulan: Terdapat hubungan signifikan antara asupan protein dengan kadar protein ASI.

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15

Gottschalk, K. E., H. Neuvirth, and G. Schreiber. "A novel method for scoring of docked protein complexes using predicted protein-protein binding sites." Protein Engineering Design and Selection 17, no. 2 (January 20, 2004): 183–89. http://dx.doi.org/10.1093/protein/gzh021.

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16

Xu, D., C. J. Tsai, and R. Nussinov. "Hydrogen bonds and salt bridges across protein-protein interfaces." Protein Engineering Design and Selection 10, no. 9 (September 1, 1997): 999–1012. http://dx.doi.org/10.1093/protein/10.9.999.

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17

Fryszczyn, Bartlomiej G., Nicholas G. Brown, Wanzhi Huang, Miriam A. Balderas, and Timothy Palzkill. "Use of periplasmic target protein capture for phage display engineering of tight-binding protein–protein interactions." Protein Engineering, Design and Selection 24, no. 11 (September 6, 2011): 819–28. http://dx.doi.org/10.1093/protein/gzr043.

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18

Porebski, Benjamin T., and Ashley M. Buckle. "Consensus protein design." Protein Engineering Design and Selection 29, no. 7 (June 5, 2016): 245–51. http://dx.doi.org/10.1093/protein/gzw015.

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19

Kajihara, Akiro, Hitoshi Komooka, Kenshu Kamiya, and Hideaki Umeyama. "Protein modelling using a chimera reference protein derived from exons." "Protein Engineering, Design and Selection" 6, no. 6 (1993): 615–20. http://dx.doi.org/10.1093/protein/6.6.615.

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20

Koike, A., and T. Takagi. "Prediction of protein-protein interaction sites using support vector machines." Protein Engineering Design and Selection 17, no. 2 (January 20, 2004): 165–73. http://dx.doi.org/10.1093/protein/gzh020.

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21

Jones, Susan, Antoine Marin, and Janet M.Thornton. "Protein domain interfaces: characterization and comparison with oligomeric protein interfaces." Protein Engineering, Design and Selection 13, no. 2 (February 2000): 77–82. http://dx.doi.org/10.1093/protein/13.2.77.

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22

Pazos, Florencio, and Alfonso Valencia. "Similarity of phylogenetic trees as indicator of protein–protein interaction." Protein Engineering, Design and Selection 14, no. 9 (September 2001): 609–14. http://dx.doi.org/10.1093/protein/14.9.609.

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23

Dandekar, Thomas, and Patrick Argos. "Potential of genetic algorithms in protein folding and protein engineering simulations." "Protein Engineering, Design and Selection" 5, no. 7 (1992): 637–45. http://dx.doi.org/10.1093/protein/5.7.637.

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24

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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25

Brandsdal, B. O., and A. O. Smalås. "Evaluation of protein–protein association energies by free energy perturbation calculations." Protein Engineering, Design and Selection 13, no. 4 (April 2000): 239–45. http://dx.doi.org/10.1093/protein/13.4.239.

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26

Wetzel, R. "What is protein engineering?" "Protein Engineering, Design and Selection" 1, no. 1 (1986): 3–5. http://dx.doi.org/10.1093/protein/1.1.3.

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27

Chou, Kuo-Chen, and David W. Elrod. "Protein subcellular location prediction." Protein Engineering, Design and Selection 12, no. 2 (February 1999): 107–18. http://dx.doi.org/10.1093/protein/12.2.107.

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28

Taylor, William R. "Protein structural domain identification." Protein Engineering, Design and Selection 12, no. 3 (March 1999): 203–16. http://dx.doi.org/10.1093/protein/12.3.203.

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29

Kumar, Sandeep, Chung-Jung Tsai, and Ruth Nussinov. "Factors enhancing protein thermostability." Protein Engineering, Design and Selection 13, no. 3 (March 2000): 179–91. http://dx.doi.org/10.1093/protein/13.3.179.

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30

Eroshkin, A. M., V. I. Fomin, P. A. Zhilkin, and V. A. Kulichkov. "Protein fragment variability analysis and some principles of protein engineering of vaccines." "Protein Engineering, Design and Selection" 3, no. 5 (1990): 425–31. http://dx.doi.org/10.1093/protein/3.5.425.

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31

Nguyen, Peter Q., and Jonathan J. Silberg. "A selection that reports on protein–protein interactions within a thermophilic bacterium." Protein Engineering, Design and Selection 23, no. 7 (April 23, 2010): 529–36. http://dx.doi.org/10.1093/protein/gzq024.

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32

Sánchez, Ignacio E., Diego U. Ferreiro, and Gonzalo de Prat Gay. "Mutational analysis of kinetic partitioning in protein folding and protein–DNA binding." Protein Engineering, Design and Selection 24, no. 1-2 (September 27, 2010): 179–84. http://dx.doi.org/10.1093/protein/gzq064.

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33

Fernandez-Fernandez, M. R., and B. Sot. "The relevance of protein-protein interactions for p53 function: the CPE contribution." Protein Engineering Design and Selection 24, no. 1-2 (October 15, 2010): 41–51. http://dx.doi.org/10.1093/protein/gzq074.

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34

Otzen, D. E. "Mapping the folding pathway of the transmembrane protein DsbB by protein engineering." Protein Engineering Design and Selection 24, no. 1-2 (October 25, 2010): 139–49. http://dx.doi.org/10.1093/protein/gzq079.

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35

Khait, R., and G. Schreiber. "FRETex: a FRET-based, high-throughput technique to analyze protein-protein interactions." Protein Engineering Design and Selection 25, no. 11 (September 25, 2012): 681–87. http://dx.doi.org/10.1093/protein/gzs067.

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36

Sloan, D. J., and H. W. Hellinga. "Structure-based engineering of environmentally sensitive fluorophores for monitoring protein-protein interactions." Protein Engineering Design and Selection 11, no. 9 (September 1, 1998): 819–23. http://dx.doi.org/10.1093/protein/11.9.819.

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37

Martin, Andrew C. R. "The ups and downs of protein topology; rapid comparison of protein structure." Protein Engineering, Design and Selection 13, no. 12 (December 2000): 829–37. http://dx.doi.org/10.1093/protein/13.12.829.

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38

AYYILDIZ, Ezgi, and Vilda PURUTÇUOĞLU. "Is It Necessary to Apply the Outlier Detection for Protein-Protein Interaction Data?" Turkiye Klinikleri Journal of Biostatistics 10, no. 3 (2018): 173–86. http://dx.doi.org/10.5336/biostatic.2018-61547.

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39

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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40

Riemann, Ralph Nico, and Martin Zacharias. "Refinement of protein cores and protein–peptide interfaces using a potential scaling approach." Protein Engineering, Design and Selection 18, no. 10 (September 9, 2005): 465–76. http://dx.doi.org/10.1093/protein/gzi052.

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41

Frare, Erica, Patrizia Polverino de Laureto, Elena Scaramella, Fiorella Tonello, Oriano Marin, Renzo Deana, and Angelo Fontana. "Chemical synthesis of the RGD-protein decorsin: Pro→Ala replacement reduces protein thermostability." Protein Engineering, Design and Selection 18, no. 10 (September 9, 2005): 487–95. http://dx.doi.org/10.1093/protein/gzi054.

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42

Petrausch, U., J. Dernedde, V. Coelho, H. Panjideh, D. Frey, H. Fuchs, E. Thiel, and P. M. Deckert. "A33scFv Green fluorescent protein, a recombinant single-chain fusion protein for tumor targeting." Protein Engineering Design and Selection 20, no. 12 (November 22, 2007): 583–90. http://dx.doi.org/10.1093/protein/gzm043.

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43

Teng, P. K., and D. Eisenberg. "Short protein segments can drive a non-fibrillizing protein into the amyloid state." Protein Engineering Design and Selection 22, no. 8 (July 14, 2009): 531–36. http://dx.doi.org/10.1093/protein/gzp037.

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44

Villoutreix, Bruno O., Anna M. Blom, and Björn Dahlbäck. "Structural prediction and analysis of endothelial cell protein C/activated protein C receptor." Protein Engineering, Design and Selection 12, no. 10 (October 1999): 833–40. http://dx.doi.org/10.1093/protein/12.10.833.

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45

Betts, Matthew J., and Michael J. E. Sternberg. "An analysis of conformational changes on protein–protein association: implications for predictive docking." Protein Engineering, Design and Selection 12, no. 4 (April 1999): 271–83. http://dx.doi.org/10.1093/protein/12.4.271.

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46

George, Richard A., and Jaap Heringa. "An analysis of protein domain linkers: their classification and role in protein folding." Protein Engineering, Design and Selection 15, no. 11 (November 2002): 871–79. http://dx.doi.org/10.1093/protein/15.11.871.

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47

Lesley, Scott A., Jim Graziano, Charles Y. Cho, Mark W. Knuth, and Heath E. Klock. "Gene expression response to misfolded protein as a screen for soluble recombinant protein." Protein Engineering, Design and Selection 15, no. 2 (February 2002): 153–60. http://dx.doi.org/10.1093/protein/15.2.153.

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48

Schulze-Kremer, Steffen, and Ross D. King. "IPSA—Inductive Protein Structure Analysis." "Protein Engineering, Design and Selection" 5, no. 5 (1992): 377–90. http://dx.doi.org/10.1093/protein/5.5.377.

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49

Attwood, T. K., and J. B. C. Findlay. "Fingerprinting G-protein-coupled receptors." "Protein Engineering, Design and Selection" 7, no. 2 (1994): 195–203. http://dx.doi.org/10.1093/protein/7.2.195.

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50

Zehfus, Micheal H. "Binary discontinuous compact protein domains." "Protein Engineering, Design and Selection" 7, no. 3 (1994): 335–40. http://dx.doi.org/10.1093/protein/7.3.335.

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