Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/17003
Title: Computationally designed high specificity inhibitors delineate the roles of BCL2 family proteins in cancer
Austin Authors: Berger, Stephanie;Procko, Erik;Margineantu, Daciana;Lee, Erinna F;Shen, Betty W;Zelter, Alex;Silva, Daniel-Adriano;Chawla, Kusum;Herold, Marco J;Garnier, Jean-Marc;Johnson, Richard;MacCoss, Michael J;Lessene, Guillaume;Davis, Trisha N;Stayton, Patrick S;Stoddard, Barry L;Fairlie, W Douglas;Hockenbery, David M;Baker, David
Affiliation: Austin Health, Heidelberg, Victoria, Australia
University of Washington, WA, United States
University of Illinois, IL, United States
Fred Hutchinson Cancer Research Center, United States
LaTrobe Institute for Molecular Science, Australia
Olivia Newton-John Cancer and Wellness Centre, Heidelberg, Victoria, Australia
La Trobe University, Australia
The Walter and Eliza Hall Institute of Medical Research, Australia
University of Melbourne, Australia
Issue Date: 2-Nov-2016
metadata.dc.date: 2016-11-02
Publication information: eLIFE 2016; 5: e20352
Abstract: Many cancers overexpress one or more of the six human pro-survival BCL2 family proteins to evade apoptosis. To determine which BCL2 protein or proteins block apoptosis in different cancers, we computationally designed three-helix bundle protein inhibitors specific for each BCL2 pro-survival protein. Following in vitro optimization, each inhibitor binds its target with high picomolar to low nanomolar affinity and at least 300-fold specificity. Expression of the designed inhibitors in human cancer cell lines revealed unique dependencies on BCL2 proteins for survival which could not be inferred from other BCL2 profiling methods. Our results show that designed inhibitors can be generated for each member of a closely-knit protein family to probe the importance of specific protein-protein interactions in complex biological processes.
URI: http://ahro.austin.org.au/austinjspui/handle/1/17003
DOI: 10.7554/eLife.20352
PubMed URL: https://pubmed.ncbi.nlm.nih.gov/27805565
Type: Journal Article
Subjects: Bcl-2
Cancer
cancer biology
computational biology
computational protein design
human
systems biology
Appears in Collections:Journal articles

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