Literature DB >> 20211134

Protein dynamics in drug combinations: a linear superposition of individual-drug responses.

Naama Geva-Zatorsky1, Erez Dekel, Ariel A Cohen, Tamar Danon, Lydia Cohen, Uri Alon.   

Abstract

Drugs and drug combinations have complex biological effects on cells and organisms. Little is known about how drugs affect protein dynamics that determine these effects. Here, we use a dynamic proteomics approach to accurately follow 15 protein levels in human cells in response to 13 different drugs. We find that protein dynamics in response to combinations of drugs are described accurately by a linear superposition (weighted sum) of their response to individual drugs. The weights in this superposition describe the relative impact of each drug on each protein. Using these weights, we show that one can predict the dynamics in a three-drug or four-drug combination on the basis of the dynamics in drug pairs. Our approach might eliminate the need to increase the number of experiments exponentially with the number of drugs and suggests that it might be possible to rationally control protein dynamics with specific drug combinations. (c) 2010 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20211134     DOI: 10.1016/j.cell.2010.02.011

Source DB:  PubMed          Journal:  Cell        ISSN: 0092-8674            Impact factor:   41.582


  53 in total

1.  ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments.

Authors:  Alexander Lachmann; Huilei Xu; Jayanth Krishnan; Seth I Berger; Amin R Mazloom; Avi Ma'ayan
Journal:  Bioinformatics       Date:  2010-08-13       Impact factor: 6.937

2.  Drug discovery: engineering drug combinations.

Authors:  Scott J Dixon; Brent R Stockwell
Journal:  Nat Chem Biol       Date:  2010-05       Impact factor: 15.040

3.  Mechanism-independent method for predicting response to multidrug combinations in bacteria.

Authors:  Kevin Wood; Satoshi Nishida; Eduardo D Sontag; Philippe Cluzel
Journal:  Proc Natl Acad Sci U S A       Date:  2012-07-05       Impact factor: 11.205

Review 4.  Models of signalling networks - what cell biologists can gain from them and give to them.

Authors:  Kevin A Janes; Douglas A Lauffenburger
Journal:  J Cell Sci       Date:  2013-05-01       Impact factor: 5.285

5.  Paring down signaling complexity.

Authors:  Kevin A Janes
Journal:  Nat Biotechnol       Date:  2010-07       Impact factor: 54.908

6.  Defining principles of combination drug mechanisms of action.

Authors:  Justin R Pritchard; Peter M Bruno; Luke A Gilbert; Kelsey L Capron; Douglas A Lauffenburger; Michael T Hemann
Journal:  Proc Natl Acad Sci U S A       Date:  2012-12-18       Impact factor: 11.205

Review 7.  Encoding and decoding cellular information through signaling dynamics.

Authors:  Jeremy E Purvis; Galit Lahav
Journal:  Cell       Date:  2013-02-28       Impact factor: 41.582

8.  Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators.

Authors:  Bernd Bodenmiller; Eli R Zunder; Rachel Finck; Tiffany J Chen; Erica S Savig; Robert V Bruggner; Erin F Simonds; Sean C Bendall; Karen Sachs; Peter O Krutzik; Garry P Nolan
Journal:  Nat Biotechnol       Date:  2012-09       Impact factor: 54.908

Review 9.  Status of PI3K/Akt/mTOR pathway inhibitors in lymphoma.

Authors:  Jason R Westin
Journal:  Clin Lymphoma Myeloma Leuk       Date:  2014-02-07

10.  Rough set soft computing cancer classification and network: one stone, two birds.

Authors:  Yue Zhang
Journal:  Cancer Inform       Date:  2010-07-15
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.