Gotowa bibliografia na temat „Protein association”

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Artykuły w czasopismach na temat "Protein association"

1

Grueninger, D., N. Treiber, M. O. P. Ziegler, J. W. A. Koetter, M. S. Schulze, and G. E. Schulz. "Designed Protein-Protein Association." Science 319, no. 5860 (2008): 206–9. http://dx.doi.org/10.1126/science.1150421.

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Camacho, Carlos J., and Sandor Vajda. "Protein–protein association kinetics and protein docking." Current Opinion in Structural Biology 12, no. 1 (2002): 36–40. http://dx.doi.org/10.1016/s0959-440x(02)00286-5.

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Pan, Albert C., Daniel Jacobson, Konstantin Yatsenko, Duluxan Sritharan, Thomas M. Weinreich, and David E. Shaw. "Atomic-level characterization of protein–protein association." Proceedings of the National Academy of Sciences 116, no. 10 (2019): 4244–49. http://dx.doi.org/10.1073/pnas.1815431116.

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Despite the biological importance of protein–protein complexes, determining their structures and association mechanisms remains an outstanding challenge. Here, we report the results of atomic-level simulations in which we observed five protein–protein pairs repeatedly associate to, and dissociate from, their experimentally determined native complexes using a molecular dynamics (MD)–based sampling approach that does not make use of any prior structural information about the complexes. To study association mechanisms, we performed additional, conventional MD simulations, in which we observed numerous spontaneous association events. A shared feature of native association for these five structurally and functionally diverse protein systems was that if the proteins made contact far from the native interface, the native state was reached by dissociation and eventual reassociation near the native interface, rather than by extensive interfacial exploration while the proteins remained in contact. At the transition state (the conformational ensemble from which association to the native complex and dissociation are equally likely), the protein–protein interfaces were still highly hydrated, and no more than 20% of native contacts had formed.
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Giles, K. "Interactions underlying subunit association in cholinesterases." Protein Engineering Design and Selection 10, no. 6 (1997): 677–85. http://dx.doi.org/10.1093/protein/10.6.677.

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5

Erickson, Harold P. "Co-operativity in protein-protein association." Journal of Molecular Biology 206, no. 3 (1989): 465–74. http://dx.doi.org/10.1016/0022-2836(89)90494-4.

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Lumry, R., and R. B. Gregory. "Dynamical factors in protein-protein association." Journal of Molecular Liquids 42 (October 1989): 113–44. http://dx.doi.org/10.1016/0167-7322(89)80029-7.

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Karplus, M., and J. Janin. "Comment on: `The entropy cost of protein association'." Protein Engineering, Design and Selection 12, no. 3 (1999): 185–86. http://dx.doi.org/10.1093/protein/12.3.185.

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8

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 (2000): 239–45. http://dx.doi.org/10.1093/protein/13.4.239.

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Suratanee, Apichat, and Kitiporn Plaimas. "Heterogeneous Network Model to Identify Potential Associations Between Plasmodium vivax and Human Proteins." International Journal of Molecular Sciences 21, no. 4 (2020): 1310. http://dx.doi.org/10.3390/ijms21041310.

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Integration of multiple sources and data levels provides a great insight into the complex associations between human and malaria systems. In this study, a meta-analysis framework was developed based on a heterogeneous network model for integrating human-malaria protein similarities, a human protein interaction network, and a Plasmodium vivax protein interaction network. An iterative network propagation was performed on the heterogeneous network until we obtained stabilized weights. The association scores were calculated for qualifying a novel potential human-malaria protein association. This method provided a better performance compared to random experiments. After that, the stabilized network was clustered into association modules. The potential association candidates were then thoroughly analyzed by statistical enrichment analysis with protein complexes and known drug targets. The most promising target proteins were the succinate dehydrogenase protein complex in the human citrate (TCA) cycle pathway and the nicotinic acetylcholine receptor in the human central nervous system. Promising associations and potential drug targets were also provided for further studies and designs in therapeutic approaches for malaria at a systematic level. In conclusion, this method is efficient to identify new human-malaria protein associations and can be generalized to infer other types of association studies to further advance biomedical science.
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10

Zheng, W., N. P. Schafer, A. Davtyan, G. A. Papoian, and P. G. Wolynes. "Predictive energy landscapes for protein-protein association." Proceedings of the National Academy of Sciences 109, no. 47 (2012): 19244–49. http://dx.doi.org/10.1073/pnas.1216215109.

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