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Journal articles on the topic 'Protein association'

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Author: Grafiati

Published: 6 September 2023

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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 (January 11, 2008): 206–9. http://dx.doi.org/10.1126/science.1150421.

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2

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

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3

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 (February 13, 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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4

Giles, K. "Interactions underlying subunit association in cholinesterases." Protein Engineering Design and Selection 10, no. 6 (June 1, 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 (April 1989): 465–74. http://dx.doi.org/10.1016/0022-2836(89)90494-4.

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6

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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7

Karplus, M., and J. Janin. "Comment on: `The entropy cost of protein association'." Protein Engineering, Design and Selection 12, no. 3 (March 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 (April 2000): 239–45. http://dx.doi.org/10.1093/protein/13.4.239.

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9

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 (February 15, 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 (November 5, 2012): 19244–49. http://dx.doi.org/10.1073/pnas.1216215109.

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11

Schreiber, G., G. Haran, and H. X. Zhou. "Fundamental Aspects of Protein−Protein Association Kinetics." Chemical Reviews 109, no. 3 (March 11, 2009): 839–60. http://dx.doi.org/10.1021/cr800373w.

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12

Helms, Volkhard, Mazen Ahmad, Alexander Spaar, and Wei Gu. "Computer Simulation of Protein-Protein Association Processes." Biophysical Journal 96, no. 3 (February 2009): 75a. http://dx.doi.org/10.1016/j.bpj.2008.12.288.

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13

Pan, Albert C., Daniel Jacobson, Konstantin Borisov, Duluxan Sritharan, Thomas M. Weinreich, and David E. Shaw. "Atomic-Level Characterization of Protein-Protein Association." Biophysical Journal 114, no. 3 (February 2018): 557a. http://dx.doi.org/10.1016/j.bpj.2017.11.3045.

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14

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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15

Brems, David N., Leila A. Alter, Michael J. Beckage, Ronald E. Chance, Richard D. DiMarchi, L. Kenney Green, Harlan B. Long, Allen H. Pekar, James E. Shields, and Bruce H. Frank. "Altering the association properties of insulin by amino acid replacement." "Protein Engineering, Design and Selection" 5, no. 6 (1992): 527–33. http://dx.doi.org/10.1093/protein/5.6.527.

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16

Hope, John N., Hao-Chia Chen, and J. Fidding Hejtmancik. "βA3/Al-crystallin association: role of the N-terminal arm." "Protein Engineering, Design and Selection" 7, no. 3 (1994): 445–51. http://dx.doi.org/10.1093/protein/7.3.445.

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17

Bethea, Deidra, Sheng-Jiun Wu, Jinquan Luo, Linus Hyun, Eilyn R. Lacy, Alexey Teplyakov, Steven A. Jacobs, Karyn T. O'Neil, Gary L. Gilliland, and Yiqing Feng. "Mechanisms of self-association of a human monoclonal antibody CNTO607." Protein Engineering, Design and Selection 25, no. 10 (August 22, 2012): 531–38. http://dx.doi.org/10.1093/protein/gzs047.

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18

Gabdoulline, Razif R., and Rebecca C. Wade. "Protein-protein association: investigation of factors influencing association rates by Brownian dynamics simulations." Journal of Molecular Biology 306, no. 5 (March 2001): 1139–55. http://dx.doi.org/10.1006/jmbi.2000.4404.

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19

Ramly, Balqis, Nor Afiqah-Aleng, and Zeti-Azura Mohamed-Hussein. "Protein–Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome." International Journal of Molecular Sciences 20, no. 12 (June 18, 2019): 2959. http://dx.doi.org/10.3390/ijms20122959.

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Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein–protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher’s exact test and comorbidity data were used to identify PCOS–disease subnetworks. Pathway enrichment analysis was performed on the PCOS–disease subnetworks to identify significant pathways that are highly involved in the PCOS–disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.

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20

Rajagopal, Nandhini, and Shikha Nangia. "Obtaining Protein Association Energy Landscape for Integral Membrane Proteins." Journal of Chemical Theory and Computation 15, no. 11 (October 8, 2019): 6444–55. http://dx.doi.org/10.1021/acs.jctc.9b00626.

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21

Zhou, Chun, Qimeng Wu, Ziliang Ye, Mengyi Liu, Zhuxian Zhang, Yuanyuan Zhang, Huan Li, et al. "Inverse Association Between Variety of Proteins With Appropriate Quantity From Different Food Sources and New-Onset Hypertension." Hypertension 79, no. 5 (May 2022): 1017–27. http://dx.doi.org/10.1161/hypertensionaha.121.18222.

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The relationships of the variety and quantity of different sources of dietary proteins with hypertension remain uncertain. We aimed to investigate associations between the variety and quantity of proteins intake from 8 major food sources and new-onset hypertension among 12 177 participants from the China Health and Nutrition Survey. Dietary intake was measured by 3 consecutive 24-hour dietary recalls combined with a household food inventory. The variety score of protein sources was defined as the number of protein sources consumed at the appropriate level, accounting for types and quantity of proteins. New-onset hypertension was defined as systolic blood pressure ≥140 mm Hg and diastolic blood pressure ≥90 mm Hg, or physician-diagnosed hypertension or receiving antihypertensive treatment, during the follow-up. During a median follow-up of 6.1 years, there were U-shaped associations of percentages energy from total, unprocessed or processed red meat-derived, whole grain-derived, and poultry-derived proteins with new-onset hypertension; an reverse J-shaped association of fish-derived protein with new-onset hypertension; L-shaped associations of eggs-derived and legumes-derived proteins with new-onset hypertension; and an reverse L-shaped association of refined grain-derived protein with new-onset hypertension (all P values for nonlinearity <0.001). That is, for each protein, there is a window of consumption (appropriate level) where the risk of hypertension is lower. Moreover, a significantly lower risk of new-onset hypertension was found in those with higher variety score of protein sources (per score increment, hazard ratio, 0.74 [95% CI, 0.72–0.76]). In summary, there was an inverse association between the variety of proteins with appropriate quantity from different food sources and new-onset hypertension.

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22

Ben-Naim, Arieh. "On the driving forces for protein-protein association." Journal of Chemical Physics 125, no. 2 (July 14, 2006): 024901. http://dx.doi.org/10.1063/1.2205860.

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23

Qin, Sanbo, and Huan-Xiang Zhou. "Automated Prediction of Protein-Protein Association Rate Constants." Biophysical Journal 100, no. 3 (February 2011): 386a. http://dx.doi.org/10.1016/j.bpj.2010.12.2295.

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24

Kovalenko, I. B., A. M. Abaturova, A. N. Diakonova, O. S. Knyazeva, D. M. Ustinin, S. S. Khruschev, G. Yu Riznichenko, and A. B. Rubin. "Computer Simulation of Protein-Protein Association in Photosynthesis." Mathematical Modelling of Natural Phenomena 6, no. 7 (2011): 39–54. http://dx.doi.org/10.1051/mmnp/20116704.

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25

Gilmore, Jason M., Deanna L. Auberry, Julia L. Sharp, Amanda M. White, Kevin K. Anderson, and Don S. Daly. "A Bayesian estimator of protein–protein association probabilities." Bioinformatics 24, no. 13 (May 22, 2008): 1554–55. http://dx.doi.org/10.1093/bioinformatics/btn238.

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26

Elefsinioti, Antigoni, Ömer Sinan Saraç, Anna Hegele, Conrad Plake, Nina C. Hubner, Ina Poser, Mihail Sarov, et al. "Large-scaleDe NovoPrediction of Physical Protein-Protein Association." Molecular & Cellular Proteomics 10, no. 11 (August 11, 2011): M111.010629. http://dx.doi.org/10.1074/mcp.m111.010629.

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27

Ben-Naim, A. "Solvent effects on protein association and protein folding." Biopolymers 29, no. 3 (February 15, 1990): 567–96. http://dx.doi.org/10.1002/bip.360290312.

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28

Ruvinsky, Anatoly M., Tatsiana Kirys, Alexander V. Tuzikov, and Ilya A. Vakser. "Side-Chain Conformational Changes upon Protein–Protein Association." Journal of Molecular Biology 408, no. 2 (April 2011): 356–65. http://dx.doi.org/10.1016/j.jmb.2011.02.030.

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29

Prat-Gay, Gonzalo de. "Association of complementary fragments and the elucidation of protein folding pathways." "Protein Engineering, Design and Selection" 9, no. 10 (1996): 843–47. http://dx.doi.org/10.1093/protein/9.10.843.

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30

Hejtmancik, J. F., P. T. Wingfield, C. Chambers, P. Russell, H. C. Chen, Y. V. Sergeev, and J. N. Hope. "Association properties of betaB2- and betaA3-crystallin: ability to form dimers." Protein Engineering Design and Selection 10, no. 11 (November 1, 1997): 1347–52. http://dx.doi.org/10.1093/protein/10.11.1347.

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31

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 (May 1, 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 were clearly colocalized in association with membranes derived from the endoplasmic reticulum. This observation, together with the demonstration of the existence of several physical interactions between HCV NS proteins, supports the idea of assembly of a highly ordered multisubunit protein complex(es) probably involved in the replication of the viral genome. The objective of this study, therefore, was to examine all potential interactions between HCV NS proteins which could result in the formation of a replication complex(es). We identified several interacting viral partners by using a glutathione S-transferase pull-down assay, by in vitro and ex vivo coimmunoprecipitation experiments in adenovirus-infected Huh-7 cells allowing the expression of HCV NS proteins, and, finally, by using the yeast two-hybrid system. In addition, by confocal laser scanning microscopy, NS proteins were clearly shown to colocalize when expressed together in Huh-7 cells. We have been able to demonstrate the existence of a complex network of interactions implicating all six NS proteins. Our observations confirm previously described associations and identify several novel homo- and heterodimerizations.

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32

Lee, Dong Heon, Chen Yao, Arunoday Bhan, Thorsten Schlaeger, Joshua Keefe, Benjamin A. T. Rodriguez, Shih-Jen Hwang, Ming-Huei Chen, Daniel Levy, and Andrew D. Johnson. "Integrative Genomic Analysis Reveals Four Protein Biomarkers for Platelet Traits." Circulation Research 127, no. 9 (October 9, 2020): 1182–94. http://dx.doi.org/10.1161/circresaha.119.316447.

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Rationale: Mean platelet volume (MPV) and platelet count (PLT) are platelet measures that have been linked to cardiovascular disease (CVD) and mortality risk. Identifying protein biomarkers for these measures may yield insights into CVD mechanisms. Objective: We aimed to identify causal protein biomarkers for MPV and PLT among 71 CVD-related plasma proteins measured in FHS (Framingham Heart Study) participants. Methods and Results: We conducted integrative analyses of genetic variants associated with PLT/MPV with protein quantitative trait locus variants associated with plasma proteins followed by Mendelian randomization to infer causal relations of proteins for PLT/MPV. We also tested protein-PLT/MPV association in FHS participants. Using induced pluripotent stem cell-derived megakaryocyte clones that produce functional platelets, we conducted RNA-sequencing and analyzed expression differences between low- and high-platelet producing clones. We then performed small interfering RNA gene knockdown experiments targeting genes encoding proteins with putatively causal platelet effects in megakaryocyte clones to examine effects on platelet production. In protein-trait association analyses, ten proteins were associated with MPV and 31 with PLT. Mendelian randomization identified 4 putatively causal proteins for MPV and 4 for PLT. GP-5 (Glycoprotein V), GRN (granulin), and MCAM (melanoma cell adhesion molecule) were associated with PLT, while MPO (myeloperoxidase) showed significant association with MPV in both analyses. RNA-sequencing analysis results were directionally concordant with observed and Mendelian randomization-inferred associations for GP-5, GRN, and MCAM. In siRNA gene knockdown experiments, silencing GP-5, GRN, and MPO decreased PLTs. Genome-wide association study results suggest several of these may be linked to CVD risk. Conclusions: We identified 4 proteins that are causally linked to PLTs. These proteins may also have roles in the pathogenesis of CVD via a platelet/blood coagulation-based mechanism.

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33

Szczepaniak, Andrzej, Karin Frank, and Jacek Rybka. "Membrane Association of the Rieske Iron-Sulfur Protein." Zeitschrift für Naturforschung C 50, no. 7-8 (August 1, 1995): 535–42. http://dx.doi.org/10.1515/znc-1995-7-811.

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Abstract The mode of membrane attachment of the Rieske iron -sulfur proteins from cytochrome b6ƒ complex of pea thylakoids and from cytochrome bc1 complex of yeast mitochondria has been studied using biochemical approaches. The relative sensitivity of the Rieske protein to trypsin in the thylakoid membrane shows that all trypsin sites of the Rieske protein are on the lumen side of the thylakoid membrane. In contrast to cytochrome / the chloroplast Rieske protein was extracted from thylakoids using chaotropic agents (NaSCN, urea), an alkaline pH and relatively low concentrations of Trinon X-100. The cytochrome bc1 complex Rieske protein from mitochondrial membranes of yeast was also released by NaSCN and alkaline treatment. The results presented here led us to the conclusion, that the mitochondrial and chloroplast Rieske proteins are extrinsic and that their association with the rest of the complex involves hydrophobic interactions.

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34

Mayer, Melanie L., and Philip Hieter. "Protein networks—built by association." Nature Biotechnology 18, no. 12 (December 2000): 1242–43. http://dx.doi.org/10.1038/82342.

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35

Mukhopadhyay, Somnath, and Allyn C. Howlett. "CB1 receptor-G protein association." European Journal of Biochemistry 268, no. 3 (February 2001): 499–505. http://dx.doi.org/10.1046/j.1432-1327.2001.01810.x.

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36

Goldman, Nick, Jeffrey L. Thorne, and David T. Jones. "Assessing the Impact of Secondary Structure and Solvent Accessibility on Protein Evolution." Genetics 149, no. 1 (May 1, 1998): 445–58. http://dx.doi.org/10.1093/genetics/149.1.445.

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Abstract Empirically derived models of amino acid replacement are employed to study the association between various physical features of proteins and evolution. The strengths of these associations are statistically evaluated by applying the models of protein evolution to 11 diverse sets of protein sequences. Parametric bootstrap tests indicate that the solvent accessibility status of a site has a particularly strong association with the process of amino acid replacement that it experiences. Significant association between secondary structure environment and the amino acid replacement process is also observed. Careful description of the length distribution of secondary structure elements and of the organization of secondary structure and solvent accessibility along a protein did not always significantly improve the fit of the evolutionary models to the data sets that were analyzed. As indicated by the strength of the association of both solvent accessibility and secondary structure with amino acid replacement, the process of protein evolution—both above and below the species level—will not be well understood until the physical constraints that affect protein evolution are identified and characterized.

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37

Kabli, Fatima, Reda Mohamed Hamou, and Abdelmalek Amine. "Protein Classification Using N-gram Technique and Association Rules." International Journal of Software Innovation 6, no. 2 (April 2018): 77–89. http://dx.doi.org/10.4018/ijsi.2018040106.

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The knowledge extraction process from biological data is increasingly being considered, it addresses general issues such as grouping, classification and association; The Protein classification is an important activity for the biologist to respond to biological needs. For this reason, the authors present a global framework inspired by the knowledge extraction process from biological data to classified proteins from their primary structure based on the association rules. This framework has three main steps: The first one is, the pre-processing phase, consists of extracting descriptors by N-Gram technique. The second is the extraction of associations rules, applying the Apriori algorithm. The third step is selecting the relevant rules, and applied the classifier. The experiments of this technique were performed on five classes of protein, extracted from UniProt data bank and compared with five classification methods in the WEKA platform. The obtained results satisfied the authors' purpose to propose an effective protein classifier supported by the N-gram technique and the Apriori algorithm.

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38

Dong, C., Y. Mahamat-Saleh, A. Racine, P. Jantchou, S. Chan, A. Hart, F. Carbonnel, and M. C. Boutron-Ruault. "OP17 Protein intakes and risk of inflammatory bowel disease in the European Prospective Investigation into Cancer and Nutrition cohort (EPIC-IBD)." Journal of Crohn's and Colitis 14, Supplement_1 (January 2020): S015. http://dx.doi.org/10.1093/ecco-jcc/jjz203.016.

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Abstract Background Diet may contribute to inflammatory bowel disease (IBD) pathogenesis. In a previous French cohort, we found an association between high protein intake and increased risk of IBD. We aimed to investigate this relationship in the EPIC-IBD (European Prospective Investigation into Cancer and Nutrition – Inflammatory Bowel Diseases) cohort. Methods 413 593 participants from 8 European countries were included. Dietary data were collected at baseline from validated food frequency questionnaires. Mean daily intake of nutrients was assessed using the EPIC nutrient database. To reduce bias in the estimation of relative risks, calibrated dietary data were obtained from the country and sex-specific calibration models for all participants. Associations between proteins (total, animal, and vegetable) or food sources of animal proteins, and IBD risk were estimated by Cox proportional hazard models. Results After a mean follow-up of 16 years, 595 incident cases of IBD were identified, including 177 Crohn’s disease (CD) and 418 ulcerative colitis (UC) cases. No association was observed between total protein intake and IBD risk (adjusted HR for the fourth vs. the first quartile = 1.25; CI 95% = 0.89–1.77, P-trend = 0.33). There was a significant association between the calibrated continuous variable of animal protein intake and IBD risk (adjusted HR per 10 g/day: 1.10; 95% CI = 1.004–1.21) although no association was found for extreme quartiles (HR: 0.99; 95% CI = 0.73–1.34; P-trend = 0.91). There was no association between vegetable protein intake and IBD risk. There was an association between meat consumption and IBD risk (adjusted HR for the fourth vs.. the first quartile = 1.37; CI95% = 1.02–1.82, P-trend = 0.003) and between red meat consumption and IBD risk (adjusted HR for the fourth vs. the first quartile = 1.41; CI95% = 1.03–1.92, P-trend = 0.006). In separate analyses for CD and UC, there was an association between total meat and UC risk, and between red meat and UC risk. No association was found between food sources of animal proteins and CD risk. Conclusion Animal protein intake is associated with IBD risk in the EPIC-IBD cohort. Observed associations between meat consumption and IBD or UC risk, and between red meat consumption and IBD or UC risk deserve further investigation.

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39

Chasman, D. I., and R. D. Kornberg. "GAL4 protein: purification, association with GAL80 protein, and conserved domain structure." Molecular and Cellular Biology 10, no. 6 (June 1990): 2916–23. http://dx.doi.org/10.1128/mcb.10.6.2916-2923.1990.

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Expression of the yeast Saccharomyces cerevisiae GAL4 protein under its own (galactose-inducible) control gave 5 to 10 times the level of protein observed when the GAL4 gene was on a high-copy plasmid. Purification of GAL4 by a procedure including affinity chromatography on a GAL4-binding DNA column yielded not only GAL4 but also a second protein, shown to be GAL80 by its reaction with an antipeptide antibody. Sequence comparisons of GAL4 and other members of a family of proteins sharing homologous cysteine finger motifs identified an additional region of homology in the middle of these proteins shown by genetic analysis to be important for GAL4 function. GAL4 could be cleaved proteolytically at the boundary of the conserved region, defining internal and carboxy-terminal folded domains.

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40

Chasman, D. I., and R. D. Kornberg. "GAL4 protein: purification, association with GAL80 protein, and conserved domain structure." Molecular and Cellular Biology 10, no. 6 (June 1990): 2916–23. http://dx.doi.org/10.1128/mcb.10.6.2916.

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Expression of the yeast Saccharomyces cerevisiae GAL4 protein under its own (galactose-inducible) control gave 5 to 10 times the level of protein observed when the GAL4 gene was on a high-copy plasmid. Purification of GAL4 by a procedure including affinity chromatography on a GAL4-binding DNA column yielded not only GAL4 but also a second protein, shown to be GAL80 by its reaction with an antipeptide antibody. Sequence comparisons of GAL4 and other members of a family of proteins sharing homologous cysteine finger motifs identified an additional region of homology in the middle of these proteins shown by genetic analysis to be important for GAL4 function. GAL4 could be cleaved proteolytically at the boundary of the conserved region, defining internal and carboxy-terminal folded domains.

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41

Best, Robert B., Wenwei Zheng, and Jeetain Mittal. "Balanced Protein–Water Interactions Improve Properties of Disordered Proteins and Non-Specific Protein Association." Journal of Chemical Theory and Computation 10, no. 11 (October 16, 2014): 5113–24. http://dx.doi.org/10.1021/ct500569b.

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42

Kendellen, Megan F., Katharine S. Barrientos, and Christopher M. Counter. "POT1 Association with TRF2 Regulates Telomere Length." Molecular and Cellular Biology 29, no. 20 (August 3, 2009): 5611–19. http://dx.doi.org/10.1128/mcb.00286-09.

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ABSTRACT Deleting the OB folds encoding the telomeric single-stranded DNA (ssDNA)-binding activity of the human telomeric protein POT1 induces significant telomere elongation, suggesting that at least one critical aspect of the regulation of telomere length is disrupted by this POT1ΔOB mutant protein. POT1 is known to associate with two proteins through the protein interaction domain retained in POT1ΔOB—the telomeric double-stranded DNA-binding protein TRF2 and the telomere-associated protein TPP1. We report that introducing a mutation that reduces association of POT1 with TRF2, but not a mutation that reduces the association with TPP1, abrogates the ability of POT1ΔOB to promote telomere elongation. Mechanistically, expression of POT1ΔOB reduced the association of TRF2 with POT1, RAP1, and TIN2; however, of these proteins, only ectopic expression of POT1 suppressed the telomere elongation induced by POT1ΔOB. Lastly, replacing endogenous POT1 with a full-length POT1 mutant defective in the association with TRF2 induced telomere elongation. Thus, we conclude that the association of POT1 with both ssDNA and TRF2 is critical for telomere length homeostasis.

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Ziaunys, Mantas, Kamile Mikalauskaite, Lukas Krasauskas, and Vytautas Smirnovas. "Conformation-Specific Association of Prion Protein Amyloid Aggregates with Tau Protein Monomers." International Journal of Molecular Sciences 24, no. 11 (May 25, 2023): 9277. http://dx.doi.org/10.3390/ijms24119277.

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Protein aggregation into amyloid fibrils is associated with several amyloidoses, including neurodegenerative Alzheimer’s and Parkinson’s diseases. Despite years of research and numerous studies, the process is still not fully understood, which significantly impedes the search for cures of amyloid-related disorders. Recently, there has been an increase in reports of amyloidogenic protein cross-interactions during the fibril formation process, which further complicates the already intricate process of amyloid aggregation. One of these reports displayed an interaction involving Tau and prion proteins, which prompted a need for further investigation into the matter. In this work, we generated five populations of conformationally distinct prion protein amyloid fibrils and examined their interaction with Tau proteins. We observed that there was a conformation-specific association between Tau monomers and prion protein fibrils, which increased the aggregate self-association and amyloidophilic dye binding capacity. We also determined that the interaction did not induce the formation of Tau protein amyloid aggregates, but rather caused their electrostatic adsorption to the prion protein fibril surface.

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Saveanu, Cosmin, Abdelkader Namane, Pierre-Emmanuel Gleizes, Alice Lebreton, Jean-Claude Rousselle, Jacqueline Noaillac-Depeyre, Nicole Gas, Alain Jacquier, and Micheline Fromont-Racine. "Sequential Protein Association with Nascent 60S Ribosomal Particles." Molecular and Cellular Biology 23, no. 13 (July 1, 2003): 4449–60. http://dx.doi.org/10.1128/mcb.23.13.4449-4460.2003.

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ABSTRACT Ribosome biogenesis in eukaryotes depends on the coordinated action of ribosomal and nonribosomal proteins that guide the assembly of preribosomal particles. These intermediate particles follow a maturation pathway in which important changes in their protein composition occur. The mechanisms involved in the coordinated assembly of the ribosomal particles are poorly understood. We show here that the association of preribosomal factors with pre-60S complexes depends on the presence of earlier factors, a phenomenon essential for ribosome biogenesis. The analysis of the composition of purified preribosomal complexes blocked in maturation at specific steps allowed us to propose a model of sequential protein association with, and dissociation from, early pre-60S complexes for several preribosomal factors such as Mak11, Ssf1, Rlp24, Nog1, and Nog2. The presence of either Ssf1 or Nog2 in complexes that contain the 27SB pre-rRNA defines novel, distinct pre-60S particles that contain the same pre-rRNA intermediates and that differ only by the presence or absence of specific proteins. Physical and functional interactions between Rlp24 and Nog1 revealed that the assembly steps are, at least in part, mediated by direct protein-protein interactions.

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Livesay, D. R., and S. Subramaniam. "Conserved sequence and structure association motifs in antibody-protein and antibody-hapten complexes." Protein Engineering Design and Selection 17, no. 5 (June 8, 2004): 463–72. http://dx.doi.org/10.1093/protein/gzh058.

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Xavier, K. Asish, Shawn M. McDonald, J. Andrew McCammon, and Richard C. Willson. "Association and dissociation kinetics of bobwhite quail lysozyme with monoclonal antibody HyHEL-5." Protein Engineering, Design and Selection 12, no. 1 (January 1999): 79–83. http://dx.doi.org/10.1093/protein/12.1.79.

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Dhusia, Kalyani, Zhaoqian Su, and Yinghao Wu. "Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein–Protein Association." Biomolecules 10, no. 7 (July 15, 2020): 1056. http://dx.doi.org/10.3390/biom10071056.

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The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

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Milligan, Graeme. "G protein-coupled receptors: oligomerisation and association with accessory proteins." Seminars in Cell & Developmental Biology 15, no. 3 (June 2004): 261. http://dx.doi.org/10.1016/j.semcdb.2003.12.014.

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Sartor, O., J. H. Sameshima, and K. C. Robbins. "Differential association of cellular proteins with family protein-tyrosine kinases." Journal of Biological Chemistry 266, no. 10 (April 1991): 6462–66. http://dx.doi.org/10.1016/s0021-9258(18)38140-7.

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Rapuano, Roberta, and Giuseppe Graziano. "On the Molecular Driving Force of Protein–Protein Association." Biophysica 2, no. 3 (August 25, 2022): 240–47. http://dx.doi.org/10.3390/biophysica2030023.

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The amount of water-accessible-surface-area, WASA, buried upon protein–protein association is a good measure of the non-covalent complex stability in water; however, the dependence of the binding Gibbs free energy change upon buried WASA proves to be not trivial. We assign a precise physicochemical role to buried WASA in the thermodynamics of non-covalent association and perform close scrutiny of the contributions favoring and those contrasting protein–protein association. The analysis indicates that the decrease in solvent-excluded volume, an entropic effect, described by means of buried WASA, is the molecular driving force of non-covalent association in water.

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