Corrigendum to “Context-Dependent Remodeling of Rad51–DNA Complexes by Srs2 Is Mediated by a Specific Protein–Protein Interaction” [J. Mol. Biol. 426 (2014) 1883–1897]

2014 ◽  
Vol 426 (18) ◽  
pp. 3195-3196
Author(s):  
Anna K. Lytle ◽  
Sofia S. Origanti ◽  
Yupeng Qiu ◽  
Jeffrey VonGermeten ◽  
Sua Myong ◽  
...  
2014 ◽  
Vol 426 (9) ◽  
pp. 1883-1897 ◽  
Author(s):  
Anna K. Lytle ◽  
Sofia S. Origanti ◽  
Yupeng Qiu ◽  
Jeffrey VonGermeten ◽  
Sua Myong ◽  
...  

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3326 ◽  
Author(s):  
Benoît Béganton ◽  
Etienne Coyaud ◽  
Estelle M. N. Laurent ◽  
Alain Mangé ◽  
Julien Jacquemetton ◽  
...  

RAS proteins (KRAS, NRAS and HRAS) are frequently activated in different cancer types (e.g., non-small cell lung cancer, colorectal cancer, melanoma and bladder cancer). For many years, their activities were considered redundant due to their high degree of sequence homology (80% identity) and their shared upstream and downstream protein partners. However, the high conservation of the Hyper-Variable-Region across mammalian species, the preferential activation of different RAS proteins in specific tumor types and the specific post-translational modifications and plasma membrane-localization of each paralog suggest they could ensure discrete functions. To gain insights into RAS proteins specificities, we explored their proximal protein–protein interaction landscapes using the proximity-dependent biotin identification technology (BioID) in Flp-In T-REx 293 cell lines stably transfected and inducibly expressing wild type KRAS4B, NRAS or HRAS. We identified more than 800 high-confidence proximal interactors, allowing us to propose an unprecedented comparative analysis of wild type RAS paralogs protein networks. These data bring novel information on poorly characterized RAS functions, e.g., its putative involvement in metabolic pathways, and on shared as well as paralog-specific protein networks that could partially explain the complexity of RAS functions. These networks of protein interactions open numerous avenues to better understand RAS paralogs biological activities.


2002 ◽  
Vol 30 (2) ◽  
pp. 89-92 ◽  
Author(s):  
D. A. Lomas ◽  
A. Lourbakos ◽  
S.-A. Cumming ◽  
D. Belorgey

α1-Antitrypsin functions as a ‘mousetrap’ to inhibit its target proteinase, neutrophil elastase. The common severe Z deficiency variant (Glu342 → Lys) destabilizes the mousetrap to allow a sequential protein-protein interaction between the reactive-centre loop of one molecule and β-sheet A of another. These loop-sheet polymers accumulate within hepatocytes to form inclusion bodies that are associated with juvenile cirrhosis and hepatocellular carcinoma. The lack of circulating protein predisposes the Z α1-antitrypsin homozygote to emphysema. Loop-sheet polymerization is now recognized to underlie deficiency variants of other members of the serine proteinase inhibitor (serpin) superfamily, i.e. antithrombin, C1 esterase inhibitor and α1-anti-chymotrypsin, which are associated with thrombosis, angio-oedema and emphysema respectively. Moreover, we have shown recently that the same process in a neuron-specific protein, neuroserpin, underlies a novel inclusion-body dementia, known as familial encephalopathy with neuroserpin inclusion bodies. Our understanding of the structural basis of polymerization has allowed the development of strategies to prevent the aberrant protein-protein interaction in vitro. This must now be achieved in vivo if we are to treat the associated clinical syndromes.


2015 ◽  
Vol 33 (3) ◽  
pp. 643-656 ◽  
Author(s):  
Wojciech Delewski ◽  
Bogumiła Paterkiewicz ◽  
Mateusz Manicki ◽  
Brenda Schilke ◽  
Bartłomiej Tomiczek ◽  
...  

Plasmid ◽  
2001 ◽  
Vol 45 (2) ◽  
pp. 63-74 ◽  
Author(s):  
Qing Bao Tian ◽  
Makoto Ohnishi ◽  
Takahiro Murata ◽  
Keisuke Nakayama ◽  
Yoshiro Terawaki ◽  
...  

2021 ◽  
Vol 1 ◽  
Author(s):  
Markus Hollander ◽  
Trang Do ◽  
Thorsten Will ◽  
Volkhard Helms

Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.


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