Literature DB >> 26676746

Balancing Protein Stability and Activity in Cancer: A New Approach for Identifying Driver Mutations Affecting CBL Ubiquitin Ligase Activation.

Minghui Li1, Stephen C Kales2, Ke Ma2, Benjamin A Shoemaker1, Juan Crespo-Barreto2, Andrew L Cangelosi2, Stanley Lipkowitz3, Anna R Panchenko4.   

Abstract

Oncogenic mutations in the monomeric Casitas B-lineage lymphoma (Cbl) gene have been found in many tumors, but their significance remains largely unknown. Several human c-Cbl (CBL) structures have recently been solved, depicting the protein at different stages of its activation cycle and thus providing mechanistic insight underlying how stability-activity tradeoffs in cancer-related proteins-may influence disease onset and progression. In this study, we computationally modeled the effects of missense cancer mutations on structures representing four stages of the CBL activation cycle to identify driver mutations that affect CBL stability, binding, and activity. We found that recurrent, homozygous, and leukemia-specific mutations had greater destabilizing effects on CBL states than random noncancer mutations. We further tested the ability of these computational models, assessing the changes in CBL stability and its binding to ubiquitin-conjugating enzyme E2, by performing blind CBL-mediated EGFR ubiquitination assays in cells. Experimental CBL ubiquitin ligase activity was in agreement with the predicted changes in CBL stability and, to a lesser extent, with CBL-E2 binding affinity. Two thirds of all experimentally tested mutations affected the ubiquitin ligase activity by either destabilizing CBL or disrupting CBL-E2 binding, whereas about one-third of tested mutations were found to be neutral. Collectively, our findings demonstrate that computational methods incorporating multiple protein conformations and stability and binding affinity evaluations can successfully predict the functional consequences of cancer mutations on protein activity, and provide a proof of concept for mutations in CBL. ©2015 American Association for Cancer Research.

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Year:  2015        PMID: 26676746      PMCID: PMC4738050          DOI: 10.1158/0008-5472.CAN-14-3812

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  48 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations.

Authors:  Raphael Guerois; Jens Erik Nielsen; Luis Serrano
Journal:  J Mol Biol       Date:  2002-07-05       Impact factor: 5.469

Review 3.  RINGs hold the key to ubiquitin transfer.

Authors:  Rhesa Budhidarmo; Yoshio Nakatani; Catherine L Day
Journal:  Trends Biochem Sci       Date:  2011-12-10       Impact factor: 13.807

4.  Structural basis for autoinhibition and phosphorylation-dependent activation of c-Cbl.

Authors:  Hao Dou; Lori Buetow; Andreas Hock; Gary J Sibbet; Karen H Vousden; Danny T Huang
Journal:  Nat Struct Mol Biol       Date:  2012-01-22       Impact factor: 15.369

Review 5.  The Cbl interactome and its functions.

Authors:  Mirko H H Schmidt; Ivan Dikic
Journal:  Nat Rev Mol Cell Biol       Date:  2005-12       Impact factor: 94.444

6.  Next-generation sequencing identifies rare variants associated with Noonan syndrome.

Authors:  Peng-Chieh Chen; Jiani Yin; Hui-Wen Yu; Tao Yuan; Minerva Fernandez; Christina K Yung; Quang M Trinh; Vanya D Peltekova; Jeffrey G Reid; Erica Tworog-Dube; Margaret B Morgan; Donna M Muzny; Lincoln Stein; John D McPherson; Amy E Roberts; Richard A Gibbs; Benjamin G Neel; Raju Kucherlapati
Journal:  Proc Natl Acad Sci U S A       Date:  2014-07-21       Impact factor: 11.205

7.  Computational approaches to identify functional genetic variants in cancer genomes.

Authors:  Abel Gonzalez-Perez; Ville Mustonen; Boris Reva; Graham R S Ritchie; Pau Creixell; Rachel Karchin; Miguel Vazquez; J Lynn Fink; Karin S Kassahn; John V Pearson; Gary D Bader; Paul C Boutros; Lakshmi Muthuswamy; B F Francis Ouellette; Jüri Reimand; Rune Linding; Tatsuhiro Shibata; Alfonso Valencia; Adam Butler; Serge Dronov; Paul Flicek; Nick B Shannon; Hannah Carter; Li Ding; Chris Sander; Josh M Stuart; Lincoln D Stein; Nuria Lopez-Bigas
Journal:  Nat Methods       Date:  2013-08       Impact factor: 28.547

8.  Glioma-derived mutations in IDH1 dominantly inhibit IDH1 catalytic activity and induce HIF-1alpha.

Authors:  Shimin Zhao; Yan Lin; Wei Xu; Wenqing Jiang; Zhengyu Zha; Pu Wang; Wei Yu; Zhiqiang Li; Lingling Gong; Yingjie Peng; Jianping Ding; Qunying Lei; Kun-Liang Guan; Yue Xiong
Journal:  Science       Date:  2009-04-10       Impact factor: 47.728

9.  BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations.

Authors:  Yves Dehouck; Jean Marc Kwasigroch; Marianne Rooman; Dimitri Gilis
Journal:  Nucleic Acids Res       Date:  2013-05-30       Impact factor: 16.971

10.  Predicting the Impact of Missense Mutations on Protein-Protein Binding Affinity.

Authors:  Minghui Li; Marharyta Petukh; Emil Alexov; Anna R Panchenko
Journal:  J Chem Theory Comput       Date:  2014-02-27       Impact factor: 6.006

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  12 in total

1.  A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

Authors:  Jianwen Fang
Journal:  Brief Bioinform       Date:  2020-07-15       Impact factor: 11.622

2.  Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols.

Authors:  Minghui Li; Alexander Goncearenco; Anna R Panchenko
Journal:  Methods Mol Biol       Date:  2017

3.  MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions.

Authors:  Minghui Li; Franco L Simonetti; Alexander Goncearenco; Anna R Panchenko
Journal:  Nucleic Acids Res       Date:  2016-05-05       Impact factor: 16.971

4.  Exploring background mutational processes to decipher cancer genetic heterogeneity.

Authors:  Alexander Goncearenco; Stephanie L Rager; Minghui Li; Qing-Xiang Sang; Igor B Rogozin; Anna R Panchenko
Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

5.  AlloSigMA 2: paving the way to designing allosteric effectors and to exploring allosteric effects of mutations.

Authors:  Zhen Wah Tan; Enrico Guarnera; Wei-Ven Tee; Igor N Berezovsky
Journal:  Nucleic Acids Res       Date:  2020-07-02       Impact factor: 16.971

6.  Lack of Correlation between Aberrant p16, RAR-β2, TIMP3, ERCC1, and BRCA1 Protein Expression and Promoter Methylation in Squamous Cell Carcinoma Accompanying Candida albicans-Induced Inflammation.

Authors:  Yui Terayama; Tetsuro Matsuura; Kiyokazu Ozaki
Journal:  PLoS One       Date:  2016-07-13       Impact factor: 3.240

7.  Finding driver mutations in cancer: Elucidating the role of background mutational processes.

Authors:  Anna-Leigh Brown; Minghui Li; Alexander Goncearenco; Anna R Panchenko
Journal:  PLoS Comput Biol       Date:  2019-04-29       Impact factor: 4.475

8.  PremPDI estimates and interprets the effects of missense mutations on protein-DNA interactions.

Authors:  Ning Zhang; Yuting Chen; Feiyang Zhao; Qing Yang; Franco L Simonetti; Minghui Li
Journal:  PLoS Comput Biol       Date:  2018-12-11       Impact factor: 4.475

9.  MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions.

Authors:  Ning Zhang; Yuting Chen; Haoyu Lu; Feiyang Zhao; Roberto Vera Alvarez; Alexander Goncearenco; Anna R Panchenko; Minghui Li
Journal:  iScience       Date:  2020-02-27

Review 10.  Computational Approaches to Prioritize Cancer Driver Missense Mutations.

Authors:  Feiyang Zhao; Lei Zheng; Alexander Goncearenco; Anna R Panchenko; Minghui Li
Journal:  Int J Mol Sci       Date:  2018-07-20       Impact factor: 5.923

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