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  Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins

Sohrabi-Jahromi, S., & Söding, J. (2021). Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins. Bioinformatics, 37(Suppl. 1), i308-i316. doi:doi.org/10.1093/bioinformatics/btab300.

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Sohrabi-Jahromi, S.1, Author           
Söding, J1, Author           
Affiliations:
1Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society, ou_1933286              

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Free keywords: Regulatory and functional genomics
 Abstract: Thermodynamic modeling reveals widespread
multivalent binding by RNA-binding proteins
Salma Sohrabi-Jahromi1 and Johannes So ̈ ding1,2, *
1
Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Go ̈ ttingen 37077, Germany and 2
Campus-
Institut Data Science (CIDAS), Go ̈ ttingen 37077, Germany
*To whom correspondence should be addressed.
Abstract
Motivation: Understanding how proteins recognize their RNA targets is essential to elucidate regulatory processes
in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to
RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins
with a single RNA-binding domain. However, current approaches to de novo discovery of RNA binding motifs do
not take multivalent binding into account.
Results: We present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with two co-
operatively binding RNA-binding domains. We show that bivalent binding is a common strategy among RBPs, yield-
ing higher affinity and sequence specificity. We furthermore illustrate that the spatial geometry between the binding
sites can be learned from bound RNA sequences. These discovered bipartite motifs are consistent with previously
known motifs and binding behaviors. Our results demonstrate the importance of multivalent binding for RNA-bind-
ing proteins and highlight the value of bipartite motif models in representing the multivalency of protein-RNA
interactions.
Availability and implementation: BMF source code is available at https://github.com/soedinglab/bipartite_motif_find
er under a GPL license. The BMF web server is accessible at https://bmf.soedinglab.org

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Language(s): eng - English
 Dates: 2021-07-122021-07
 Publication Status: Published in print
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 Rev. Type: Peer
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Title: Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 37 (Suppl. 1) Sequence Number: - Start / End Page: i308 - i316 Identifier: ISSN: 1367-4803