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Journal articles on the topic 'Protein flexibility'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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1

Ragone, R., F. Facchiano, A. Facchiano, A. M. Facchiano, and G. Colonna. "Flexibility plot of proteins." "Protein Engineering, Design and Selection" 2, no. 7 (1989): 497–504. http://dx.doi.org/10.1093/protein/2.7.497.

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2

Vihinen, Mauno. "Relationship of protein flexibility to thermostability." "Protein Engineering, Design and Selection" 1, no. 6 (1987): 477–80. http://dx.doi.org/10.1093/protein/1.6.477.

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3

Cioni, Patrizia, and Giovanni B. Strambini. "Pressure Effects on Protein Flexibility Monomeric Proteins." Journal of Molecular Biology 242, no. 3 (September 1994): 291–301. http://dx.doi.org/10.1006/jmbi.1994.1579.

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4

Wang, Chu, Philip Bradley, and David Baker. "Protein–Protein Docking with Backbone Flexibility." Journal of Molecular Biology 373, no. 2 (October 2007): 503–19. http://dx.doi.org/10.1016/j.jmb.2007.07.050.

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5

Greene, Lesley H., Jay A. Grobler, Vladimir A. Malinovskii, Jie Tian, K. Ravi Acharya, and Keith Brew. "Stability, activity and flexibility in α-lactalbumin." Protein Engineering, Design and Selection 12, no. 7 (July 1999): 581–87. http://dx.doi.org/10.1093/protein/12.7.581.

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6

Teplyakov, Alexey, Thomas J. Malia, Galina Obmolova, Steven A. Jacobs, Karyn T. O'Neil, and Gary L. Gilliland. "Conformational flexibility of an anti-IL-13 DARPin†." Protein Engineering, Design and Selection 30, no. 1 (December 15, 2016): 31–37. http://dx.doi.org/10.1093/protein/gzw059.

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Abstract Designed ankyrin repeat proteins (DARPin®) are artificial non-immunoglobulin binding proteins with potential applications as therapeutic molecules. DARPin 6G9 binds interleukin-13 with high affinity and blocks the signaling pathway and as such is promising for the treatment of asthma and other atopic diseases. The crystal structures of DARPin 6G9 in the unbound form and in complex with IL-13 were determined at high resolution. The DARPin competes for the same epitope as the IL-13 receptor chain 13Rα1 but does not interfere with the binding of the other receptor chain, IL-4Rα. Analysis of multiple copies of the DARPin molecule in the crystal indicates the conformational instability in the N-terminal cap that was predicted from molecular dynamics simulations. Comparison of the DARPin structures in the free state and in complex with IL-13 reveals a concerted movement of the ankyrin repeats upon binding resulted in the opening of the binding site. The induced-fit mode of binding employed by DARPin 6G9 is very unusual for DARPins since they were designed as particularly stable and rigid molecules. This finding shows that DARPins can operate by various binding mechanisms and suggests that some flexibility in the scaffold may be an advantage.

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7

Berynskyy, Mykhaylo, and Rebecca C. Wade. "Treating Conformational Flexibility in Protein-Protein Docking." Current Physical Chemistry 3, no. 1 (January 1, 2013): 27–35. http://dx.doi.org/10.2174/1877946811303010006.

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8

Fayos, Rosa, Giuseppe Melacini, Marceen G. Newlon, Lora Burns, John D. Scott, and Patricia A. Jennings. "Induction of Flexibility through Protein-Protein Interactions." Journal of Biological Chemistry 278, no. 20 (February 25, 2003): 18581–87. http://dx.doi.org/10.1074/jbc.m300866200.

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9

Vasilache, Simina, Nazanin Mirshahi, Soo-Yeon Ji, James Mottonen, Donald J. Jacobs, and Kayvan Najarian. "A Signal Processing Method to Explore Similarity in Protein Flexibility." Advances in Bioinformatics 2010 (December 20, 2010): 1–8. http://dx.doi.org/10.1155/2010/454671.

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Understanding mechanisms of protein flexibility is of great importance to structural biology. The ability to detect similarities between proteins and their patterns is vital in discovering new information about unknown protein functions. A Distance Constraint Model (DCM) provides a means to generate a variety of flexibility measures based on a given protein structure. Although information about mechanical properties of flexibility is critical for understanding protein function for a given protein, the question of whether certain characteristics are shared across homologous proteins is difficult to assess. For a proper assessment, a quantified measure of similarity is necessary. This paper begins to explore image processing techniques to quantify similarities in signals and images that characterize protein flexibility. The dataset considered here consists of three different families of proteins, with three proteins in each family. The similarities and differences found within flexibility measures across homologous proteins do not align with sequence-based evolutionary methods.

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10

Ehrlich, Lutz P., Michael Nilges, and Rebecca C. Wade. "The impact of protein flexibility on protein-protein docking." Proteins: Structure, Function, and Bioinformatics 58, no. 1 (October 28, 2004): 126–33. http://dx.doi.org/10.1002/prot.20272.

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11

Gupta, S. P. "Protein Flexibility and Drug Design." Current Chemical Biology 12, no. 1 (July 16, 2018): 2. http://dx.doi.org/10.2174/221279681201180716130139.

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12

Zhao, Qinyi. "Protein Flexibility as a Biosignal." Critical Reviews™ in Eukaryotic Gene Expression 20, no. 2 (2010): 157–70. http://dx.doi.org/10.1615/critreveukargeneexpr.v20.i2.60.

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13

Radivojac, P. "Protein flexibility and intrinsic disorder." Protein Science 13, no. 1 (January 1, 2004): 71–80. http://dx.doi.org/10.1110/ps.03128904.

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14

Kumar, S., H. J. Wolfson, and R. Nussinov. "Protein flexibility and electrostatic interactions." IBM Journal of Research and Development 45, no. 3.4 (May 2001): 499–512. http://dx.doi.org/10.1147/rd.453.0499.

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15

Teilum, Kaare, Johan G. Olsen, and Birthe B. Kragelund. "Functional aspects of protein flexibility." Cellular and Molecular Life Sciences 66, no. 14 (March 24, 2009): 2231–47. http://dx.doi.org/10.1007/s00018-009-0014-6.

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16

Hespenheide, Brandon M., Michael F. Thorpe, and Leslie A. Kuhn. "Protein flexibility and unfolding pathways." Journal of Molecular Graphics and Modelling 18, no. 4-5 (2000): 550. http://dx.doi.org/10.1016/s1093-3263(00)80113-8.

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17

Berjanskii, Mark, and David S. Wishart. "NMR: prediction of protein flexibility." Nature Protocols 1, no. 2 (July 6, 2006): 683–88. http://dx.doi.org/10.1038/nprot.2006.108.

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18

Seidl, Michael F., and Jörg Schultz. "Evolutionary flexibility of protein complexes." BMC Evolutionary Biology 9, no. 1 (2009): 155. http://dx.doi.org/10.1186/1471-2148-9-155.

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19

Teilum, Kaare, Johan G. Olsen, and Birthe B. Kragelund. "Protein stability, flexibility and function." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1814, no. 8 (August 2011): 969–76. http://dx.doi.org/10.1016/j.bbapap.2010.11.005.

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20

Vihinen, Mauno, Esa Torkkila, and Pentti Riikonen. "Accuracy of protein flexibility predictions." Proteins: Structure, Function, and Genetics 19, no. 2 (June 1994): 141–49. http://dx.doi.org/10.1002/prot.340190207.

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21

Grünberg, Raik, Michael Nilges, and Johan Leckner. "Flexibility and Conformational Entropy in Protein-Protein Binding." Structure 14, no. 4 (April 2006): 683–93. http://dx.doi.org/10.1016/j.str.2006.01.014.

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22

Grünberg, Raik, Michael Nilges, and Johan Leckner. "Flexibility and Conformational Entropy in Protein-Protein Binding." Structure 14, no. 7 (July 2006): 1205. http://dx.doi.org/10.1016/j.str.2006.06.003.

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23

Bastard, Karine, Chantal Prévost, and Martin Zacharias. "Accounting for loop flexibility during protein-protein docking." Proteins: Structure, Function, and Bioinformatics 62, no. 4 (December 21, 2005): 956–69. http://dx.doi.org/10.1002/prot.20770.

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24

Li, Liwei, Shide Liang, Meaghan M. Pilcher, and Samy O. Meroueh. "Incorporating receptor flexibility in the molecular design of protein interfaces." Protein Engineering, Design and Selection 22, no. 9 (July 30, 2009): 575–86. http://dx.doi.org/10.1093/protein/gzp042.

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25

Huber, Robert. "Flexibility and Rigidity of Proteins and Protein–Pigment Complexes." Angewandte Chemie International Edition in English 27, no. 1 (January 1988): 79–88. http://dx.doi.org/10.1002/anie.198800791.

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26

Dong, Qiwen, Kai Wang, Bin Liu, and Xuan Liu. "Characterization and Prediction of Protein Flexibility Based on Structural Alphabets." BioMed Research International 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/4628025.

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Motivation.To assist efforts in determining and exploring the functional properties of proteins, it is desirable to characterize and predict protein flexibilities.Results.In this study, the conformational entropy is used as an indicator of the protein flexibility. We first explore whether the conformational change can capture the protein flexibility. The well-defined decoy structures are converted into one-dimensional series of letters from a structural alphabet. Four different structure alphabets, including the secondary structure in 3-class and 8-class, the PB structure alphabet (16-letter), and the DW structure alphabet (28-letter), are investigated. The conformational entropy is then calculated from the structure alphabet letters. Some of the proteins show high correlation between the conformation entropy and the protein flexibility. We then predict the protein flexibility from basic amino acid sequence. The local structures are predicted by the dual-layer model and the conformational entropy of the predicted class distribution is then calculated. The results show that the conformational entropy is a good indicator of the protein flexibility, but false positives remain a problem. The DW structure alphabet performs the best, which means that more subtle local structures can be captured by large number of structure alphabet letters. Overall this study provides a simple and efficient method for the characterization and prediction of the protein flexibility.

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27

Peters, G. H., and R. P. Bywater. "Computational analysis of chain flexibility and fluctuations in Rhizomucor miehei lipase." Protein Engineering, Design and Selection 12, no. 9 (September 1999): 747–54. http://dx.doi.org/10.1093/protein/12.9.747.

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28

Opron, Kristopher, Kelin Xia, Zach Burton, and Guo-Wei Wei. "Flexibility-rigidity index for protein-nucleic acid flexibility and fluctuation analysis." Journal of Computational Chemistry 37, no. 14 (March 1, 2016): 1283–95. http://dx.doi.org/10.1002/jcc.24320.

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29

Hrabe, Thomas, Zhanwen Li, Mayya Sedova, Piotr Rotkiewicz, Lukasz Jaroszewski, and Adam Godzik. "PDBFlex: exploring flexibility in protein structures." Nucleic Acids Research 44, no. D1 (November 28, 2015): D423—D428. http://dx.doi.org/10.1093/nar/gkv1316.

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30

Andersen, Claus A. F., Arthur G. Palmer, Søren Brunak, and Burkhard Rost. "Continuum Secondary Structure Captures Protein Flexibility." Structure 10, no. 2 (February 2002): 175–84. http://dx.doi.org/10.1016/s0969-2126(02)00700-1.

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31

Maguid, Sandra, Sebastián Fernández-Alberti, Gustavo Parisi, and Julián Echave. "Evolutionary Conservation of Protein Backbone Flexibility." Journal of Molecular Evolution 63, no. 4 (October 2006): 448–57. http://dx.doi.org/10.1007/s00239-005-0209-x.

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32

Günther, Stefan, Kristian Rother, and Cornelius Frömmel. "Molecular flexibility in protein–DNA interactions." Biosystems 85, no. 2 (August 2006): 126–36. http://dx.doi.org/10.1016/j.biosystems.2005.12.007.

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33

Peskin, Charles S., Dwight You., and Timothy C. Elston. "Protein Flexibility and the Correlation Ratchet." SIAM Journal on Applied Mathematics 61, no. 3 (January 2000): 776–91. http://dx.doi.org/10.1137/s0036139999353942.

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34

Teodoro, Miguel L., George N. Phillips, and Lydia E. Kavraki. "Understanding Protein Flexibility through Dimensionality Reduction." Journal of Computational Biology 10, no. 3-4 (June 2003): 617–34. http://dx.doi.org/10.1089/10665270360688228.

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35

Mandell, Daniel J., and Tanja Kortemme. "Backbone flexibility in computational protein design." Current Opinion in Biotechnology 20, no. 4 (August 2009): 420–28. http://dx.doi.org/10.1016/j.copbio.2009.07.006.

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36

Taghizadeh, Mohammad, and Bahram Goliaei. "A Quantitative Measure of Protein Flexibility." Biophysical Journal 104, no. 2 (January 2013): 228a. http://dx.doi.org/10.1016/j.bpj.2012.11.1286.

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37

Patel, Shruti N., and Steffen P. Graether. "Increased flexibility decreases antifreeze protein activity." Protein Science 19, no. 12 (November 11, 2010): 2356–65. http://dx.doi.org/10.1002/pro.516.

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38

Jacobs, Donald J., A. J. Rader, Leslie A. Kuhn, and M. F. Thorpe. "Protein flexibility predictions using graph theory." Proteins: Structure, Function, and Genetics 44, no. 2 (2001): 150–65. http://dx.doi.org/10.1002/prot.1081.

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39

Rauh, Daniel, Gerhard Klebe, and Milton T. Stubbs. "Understanding Protein–Ligand Interactions: The Price of Protein Flexibility." Journal of Molecular Biology 335, no. 5 (January 2004): 1325–41. http://dx.doi.org/10.1016/j.jmb.2003.11.041.

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40

Wei, Yufeng. "Death Effector Domain Flexibility in Mediating Protein-Protein Interactions." Biophysical Journal 108, no. 2 (January 2015): 513a. http://dx.doi.org/10.1016/j.bpj.2014.11.2809.

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41

Kingsley, Laura J., and Markus A. Lill. "Including ligand-induced protein flexibility into protein tunnel prediction." Journal of Computational Chemistry 35, no. 24 (July 5, 2014): 1748–56. http://dx.doi.org/10.1002/jcc.23680.

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42

Brown, Craig, Karen Campos-León, Madeleine Strickland, Christopher Williams, Victoria Fairweather, R. Leo Brady, Matthew P. Crump, and Kevin Gaston. "Protein flexibility directs DNA recognition by the papillomavirus E2 proteins." Nucleic Acids Research 39, no. 7 (December 3, 2010): 2969–80. http://dx.doi.org/10.1093/nar/gkq1217.

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43

Opron, Kristopher, Kelin Xia, and Guo-Wei Wei. "Fast and anisotropic flexibility-rigidity index for protein flexibility and fluctuation analysis." Journal of Chemical Physics 140, no. 23 (June 21, 2014): 234105. http://dx.doi.org/10.1063/1.4882258.

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44

Ross, Eric D., Philip R. Hardwidge, and L. James Maher. "HMG Proteins and DNA Flexibility in Transcription Activation." Molecular and Cellular Biology 21, no. 19 (October 1, 2001): 6598–605. http://dx.doi.org/10.1128/mcb.21.19.6598-6605.2001.

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ABSTRACT The relative stiffness of naked DNA is evident from measured values of longitudinal persistence length (∼150 bp) and torsional persistence length (∼180 bp). These parameters predict that certain arrangements of eukaryotic transcription activator proteins in gene promoters should be much more effective than others in fostering protein-protein interactions with the basal RNA polymerase II transcription apparatus. Thus, if such interactions require some kind of DNA looping, DNA loop energies should depend sensitively on helical phasing of protein binding sites, loop size, and intrinsic DNA curvature within the loop. Using families of artificial transcription templates where these parameters were varied, we were surprised to find that the degree of transcription activation by arrays of Gal4-VP1 transcription activators in HeLa cell nuclear extract was sensitive only to the linear distance separating a basal promoter from an array of bound activators on DNA templates. We now examine the hypothesis that this unexpected result is due to factors in the extract that act to enhance apparent DNA flexibility. We demonstrate that HeLa cell nuclear extract is rich in a heat-resistant activity that dramatically enhances apparent DNA longitudinal and torsional flexibility. Recombinant mammalian high-mobility group 2 (HMG-2) protein can substitute for this activity. We propose that the abundance of HMG proteins in eukaryotic nuclei provides an environment in which DNA is made sufficiently flexible to remove many constraints on protein binding site arrangements that would otherwise limit efficient transcription activation to certain promoter geometries.

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45

Tanchuk, V. Yu, V. O. Tanin, and A. I. Vovk. "Analysis of conformational flexibility of loop 110-120 of protein tyrosine phosphatase 1B." Ukrainian Biochemical Journal 85, no. 6 (October 28, 2013): 73–80. http://dx.doi.org/10.15407/ubj85.05.073.

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46

Hogues, Hervé, Francis Gaudreault, Christopher R. Corbeil, Christophe Deprez, Traian Sulea, and Enrico O. Purisima. "ProPOSE: Direct Exhaustive Protein–Protein Docking with Side Chain Flexibility." Journal of Chemical Theory and Computation 14, no. 9 (August 14, 2018): 4938–47. http://dx.doi.org/10.1021/acs.jctc.8b00225.

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47

Zhou, Qi, Helin Li, Fang Li, Benpeng Zhang, Xiaojuan Wu, and Wei Wu. "Strategy and Mechanism of Rice Bran Protein Emulsion Stability Based on Rancidity-Induced Protein Oxidation: An Ultrasonic Case Study." Foods 11, no. 23 (December 2, 2022): 3896. http://dx.doi.org/10.3390/foods11233896.

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To provide a strategy for improving the stability of rice bran protein emulsion (RBPE), rice bran proteins (RBPs) with different oxidation extents were prepared from fresh rice bran (RB) stored for different times (0, 1, 3, 5, 10 d), and RBPE was prepared with ultrasonic treatment. The ultrasonic conditions were optimized according to the results of the RBPE’s stability (when RB stored for 0, 1, 3, 5, 10 d, the optimal ultrasonic treatment conditions of RBPE were 500 w and 50 min, 400 w and 30 min, 400 w and 30 min, 300 w and 20 min, 500 w and 50 min, respectively). Additionally, the structural characteristics and the flexibility of RBPE interface protein were characterized, and the results showed that compared with native protein and excessive oxidized protein, the unfolded structure content and flexibility of interface protein of RBPE prepared by moderate oxidized protein under optimal ultrasonic intensity was higher. Furthermore, the correlation analysis showed that the RBPE stability was significantly correlated with the structural characteristics and flexibility of the RBPE interface protein (p < 0.05). In summary, ultrasonic treatment affected the interface protein’s structural characteristics and flexibility, improving the stability of RBPE prepared from oxidized RBP.

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48

MAMONOVA, TATYANA B., ANNA V. GLYAKINA, MARIA G. KURNIKOVA, and OXANA V. GALZITSKAYA. "FLEXIBILITY AND MOBILITY IN MESOPHILIC AND THERMOPHILIC HOMOLOGOUS PROTEINS FROM MOLECULAR DYNAMICS AND FOLDUNFOLD METHOD." Journal of Bioinformatics and Computational Biology 08, no. 03 (June 2010): 377–94. http://dx.doi.org/10.1142/s0219720010004690.

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To function properly protein molecules require both flexibility and rigidity, therefore fast and accurate prediction of protein rigidity/flexibility is one of the important problems in protein science. In this work we used two theoretical approaches to determine flexible regions in four homologous pairs of proteins from thermophilic and mesophilic organisms. Protein pairs chosen in this study were selected to represent four typical folding classes. Our first approach, FoldUnfold, uses amino acid sequence and statistical information on the density of contacts of amino acids in tertiary structures of known globular proteins. The main advantages of such knowledge-based methodology are its computational speed and ability to make predictions in the absence of three-dimensional (3D) structure of a protein. The second approach uses a graph theory-based rigid cluster decomposition termed FIRST, applied together with Molecular Dynamics (MD) simulations of proteins with known structure. While MD simulations are time-consuming, they are the most direct way of studying physical properties of proteins, including their rigidity/flexibility. Flexible regions predicted by both methods in this work were in good agreement with each other. We also showed that high mobility of a site is not necessarily indicative of its high flexibility and vice versa. In our simulations thermophile proteins were less flexible than their mesophilic homologues. Longer flexible loops were found in mesophilic proteins of all classes.

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49

Knegtel, Ronald M. A., Rolf Boelens, and Robert Kaptein. "Monte Carlo docking of protein-DNA complexes: incorporation of DNA flexibility and experimental data." "Protein Engineering, Design and Selection" 7, no. 6 (1994): 761–68. http://dx.doi.org/10.1093/protein/7.6.761.

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50

Merkley, Eric D., William W. Parson, and Valerie Daggett. "Temperature dependence of the flexibility of thermophilic and mesophilic flavoenzymes of the nitroreductase fold." Protein Engineering, Design and Selection 23, no. 5 (January 18, 2010): 327–36. http://dx.doi.org/10.1093/protein/gzp090.

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