scholarly journals Scrambled Prion Domains Form Prions and Amyloid

2004 ◽  
Vol 24 (16) ◽  
pp. 7206-7213 ◽  
Author(s):  
Eric D. Ross ◽  
Ulrich Baxa ◽  
Reed B. Wickner

ABSTRACT The [URE3] prion of Saccharomyces cerevisiae is a self-propagating amyloid form of Ure2p. The amino-terminal prion domain of Ure2p is necessary and sufficient for prion formation and has a high glutamine (Q) and asparagine (N) content. Such Q/N-rich domains are found in two other yeast prion proteins, Sup35p and Rnq1p, although none of the many other yeast Q/N-rich domain proteins have yet been found to be prions. To examine the role of amino acid sequence composition in prion formation, we used Ure2p as a model system and generated five Ure2p variants in which the order of the amino acids in the prion domain was randomly shuffled while keeping the amino acid composition and C-terminal domain unchanged. Surprisingly, all five formed prions in vivo, with a range of frequencies and stabilities, and the prion domains of all five readily formed amyloid fibers in vitro. Although it is unclear whether other amyloid-forming proteins would be equally resistant to scrambling, this result demonstrates that [URE3] formation is driven primarily by amino acid composition, largely independent of primary sequence.

2021 ◽  
Vol 16 ◽  
Author(s):  
Saud Alguwaizani ◽  
Shulei Ren ◽  
De-Shuang Huang ◽  
Kyungsook Han

Aim: Both bacterial infection and viral infection involve a large number of protein-protein interactions (PPIs) between a pathogen and its target host. Background: So far, many computational methods have focused on predicting PPIs within the same species rather than PPIs across different species. Methods: From the extensive analysis of PPIs between Yersinia pestis bacteria and humans, we recently discovered an interesting relation; a linear relation between amino acid composition and sequence length was observed in many proteins involved in PPIs. We have built a support vector machine (SVM) model, which predicts PPIs between human and bacteria using two feature types derived from the relation. The two feature types used in the SVM are the amino acid composition group (AACG) and the difference in amino acid composition between host and pathogen proteins. Result: The SVM model achieved high performance in predicting bacteria-human PPIs. The model showed an accuracy of 96%, sensitivity of 94%, and specificity of 98% in predicting PPIs between humans and Yersinia pestis, in which there is a strong relation between amino acid composition and sequence length. The SVM model was also tested in predicting PPIs between human and viruses, which include Ebola, HCV, and SARSCoV-2, and showed a good performance. Conclusion: The feature types identified in our study are simple yet powerful in predicting pathogen-human PPIs. Although preliminary, our method will be useful for finding unknown target host proteins or pathogen proteins and designing in vitro or in vivo experiments.


Genetics ◽  
2009 ◽  
Vol 183 (3) ◽  
pp. 929-940 ◽  
Author(s):  
Carley D. Ross ◽  
Blake R. McCarty ◽  
Michael Hamilton ◽  
Asa Ben-Hur ◽  
Eric D. Ross

The [URE3] and [PSI+] prions are the infections amyloid forms of the Saccharomyces cerevisiae proteins Ure2p and Sup35p, respectively. Randomizing the order of the amino acids in the Ure2 and Sup35 prion domains while retaining amino acid composition does not block prion formation, indicating that amino acid composition, not primary sequence, is the predominant feature driving [URE3] and [PSI+] formation. Here we show that Ure2p promiscuously interacts with various compositionally similar proteins to influence [URE3] levels. Overexpression of scrambled Ure2p prion domains efficiently increases de novo formation of wild-type [URE3] in vivo. In vitro, amyloid aggregates of the scrambled prion domains efficiently seed wild-type Ure2p amyloid formation, suggesting that the wild-type and scrambled prion domains can directly interact to seed prion formation. To test whether interactions between Ure2p and naturally occurring yeast proteins could similarly affect [URE3] formation, we identified yeast proteins with domains that are compositionally similar to the Ure2p prion domain. Remarkably, all but one of these domains were also able to efficiently increase [URE3] formation. These results suggest that a wide variety of proteins could potentially affect [URE3] formation.


2015 ◽  
Vol 16 ◽  
pp. 94-103 ◽  
Author(s):  
Jae-Young Je ◽  
Soo Yeon Park ◽  
Joung-Youl Hwang ◽  
Chang-Bum Ahn

2008 ◽  
Vol 107 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Xian-Sheng Wang ◽  
Chuan-He Tang ◽  
Xiao-Quan Yang ◽  
Wen-Rui Gao

1965 ◽  
Vol 109 (3) ◽  
pp. 571-578 ◽  
Author(s):  
Ralph A. Bradshaw ◽  
Larry A. Rogers ◽  
Robert L. Hill ◽  
John Buettner-Janusch

1986 ◽  
Vol 22 (3) ◽  
pp. 225-233 ◽  
Author(s):  
M.M. Youssef ◽  
M.A. Hamza ◽  
M.H. Abd El-Aal ◽  
Laila A. Shekib ◽  
A.A. El-Banna

2002 ◽  
Vol 13 (8) ◽  
pp. 2559-2570 ◽  
Author(s):  
Sidney Yu ◽  
Michael G. Roth

ARF GAP1, a 415-amino acid GTPase activating protein (GAP) for ADP-ribosylation factor (ARF) contains an amino-terminal 115-amino acid catalytic domain and no other recognizable features. Amino acids 203–334 of ARF GAP1 were sufficient to target a GFP-fusion protein to Golgi membranes in vivo. When overexpressed in COS-1 cells, this protein domain inhibited protein transport between the ER and Golgi and, in vitro, competed with the full-length ARF GAP1 for binding to membranes. Membrane binding by ARF GAP1 in vitro was increased by a factor in cytosol and this increase was inhibited by IC261, an inhibitor selective for casein kinase Iδ (CKIδ), or when cytosol was treated with antibody to CKIδ. The noncatalytic domain of ARF GAP1 was phosphorylated both in vivo and in vitro by CKI. IC261 blocked membrane binding by ARF GAP1 in vivo and inhibited protein transport in the early secretory pathway. Overexpression of a catalytically inactive CKIδ also inhibited the binding of ARF GAP1 to membranes and interfered with protein transport. Thus, a CKI isoform is required for protein traffic through the early secretory pathway and can modulate the amount of ARF GAP1 that can bind to membranes.


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