Literature DB >> 22510467

Unmasking functional motifs within disordered regions of proteins.

Rahul K Das1, Albert H Mao, Rohit V Pappu.   

Abstract

Eukaryotic proteins often possess long stretches that fail to adopt well-defined, three-dimensional structures. These intrinsically disordered regions are associated with cell signaling through the enrichment of hub proteins of networks and as targets for posttranslational modifications. Although disordered regions are readily identified because of their distinct sequence characteristics, it is difficult to predict the functions associated with these regions. This is because disordered regions often house short (two- to five-residue) linear motifs that mediate intermolecular interactions. Predicting their function requires the ability to identify the functionally relevant motifs. If one assumes that functional motifs are highly conserved as compared to background sequence contexts, then a suitable comparative genomics approach proves to be powerful in unmasking functional motifs that are part of disordered regions. This approach has successfully identified known functional motifs and predicted a set of new motifs that might yield important insights regarding previously unknown functionalities for disordered regions. Given knowledge of highly conserved motifs, one can assess whether the rapidly changing sequence contexts are actuators of the functionalities of short linear motifs within disordered regions. This should have important implications for engineering and targeting hub proteins in signaling networks.

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Year:  2012        PMID: 22510467     DOI: 10.1126/scisignal.2003091

Source DB:  PubMed          Journal:  Sci Signal        ISSN: 1945-0877            Impact factor:   8.192


  15 in total

Review 1.  Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment.

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Journal:  Brief Bioinform       Date:  2015-10-22       Impact factor: 11.622

2.  Intrinsically disordered proteins drive enamel formation via an evolutionarily conserved self-assembly motif.

Authors:  Tomas Wald; Frantisek Spoutil; Adriana Osickova; Michaela Prochazkova; Oldrich Benada; Petr Kasparek; Ladislav Bumba; Ophir D Klein; Radislav Sedlacek; Peter Sebo; Jan Prochazka; Radim Osicka
Journal:  Proc Natl Acad Sci U S A       Date:  2017-02-14       Impact factor: 11.205

3.  A sequence-specific transcription activator motif and powerful synthetic variants that bind Mediator using a fuzzy protein interface.

Authors:  Linda Warfield; Lisa M Tuttle; Derek Pacheco; Rachel E Klevit; Steven Hahn
Journal:  Proc Natl Acad Sci U S A       Date:  2014-08-13       Impact factor: 11.205

4.  Transcription Activation Domains of the Yeast Factors Met4 and Ino2: Tandem Activation Domains with Properties Similar to the Yeast Gcn4 Activator.

Authors:  Derek Pacheco; Linda Warfield; Michelle Brajcich; Hannah Robbins; Jie Luo; Jeff Ranish; Steven Hahn
Journal:  Mol Cell Biol       Date:  2018-04-30       Impact factor: 4.272

5.  Kinetic Insights into the Binding between the nSH3 Domain of CrkII and Proline-Rich Motifs in cAbl.

Authors:  Danyun Zeng; Veer S Bhatt; Qingliang Shen; Jae-Hyun Cho
Journal:  Biophys J       Date:  2016-11-01       Impact factor: 4.033

Review 6.  Describing sequence-ensemble relationships for intrinsically disordered proteins.

Authors:  Albert H Mao; Nicholas Lyle; Rohit V Pappu
Journal:  Biochem J       Date:  2013-01-15       Impact factor: 3.857

7.  Cryptic sequence features within the disordered protein p27Kip1 regulate cell cycle signaling.

Authors:  Rahul K Das; Yongqi Huang; Aaron H Phillips; Richard W Kriwacki; Rohit V Pappu
Journal:  Proc Natl Acad Sci U S A       Date:  2016-05-02       Impact factor: 11.205

Review 8.  An intrinsically disordered linker plays a critical role in bacterial cell division.

Authors:  P J Buske; Anuradha Mittal; Rohit V Pappu; Petra Anne Levin
Journal:  Semin Cell Dev Biol       Date:  2014-10-13       Impact factor: 7.727

Review 9.  Classification of intrinsically disordered regions and proteins.

Authors:  Robin van der Lee; Marija Buljan; Benjamin Lang; Robert J Weatheritt; Gary W Daughdrill; A Keith Dunker; Monika Fuxreiter; Julian Gough; Joerg Gsponer; David T Jones; Philip M Kim; Richard W Kriwacki; Christopher J Oldfield; Rohit V Pappu; Peter Tompa; Vladimir N Uversky; Peter E Wright; M Madan Babu
Journal:  Chem Rev       Date:  2014-04-29       Impact factor: 60.622

10.  A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning.

Authors:  Ariel Erijman; Lukasz Kozlowski; Salma Sohrabi-Jahromi; James Fishburn; Linda Warfield; Jacob Schreiber; William S Noble; Johannes Söding; Steven Hahn
Journal:  Mol Cell       Date:  2020-05-15       Impact factor: 17.970

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