Biology subjects

Sohrabi-Jahromi, S.

Publications and source records attributed to Sohrabi-Jahromi, S..

4 recordsLinked to original sources

bioRxiv2021

Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins

MotivationUnderstanding 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. ResultsWe present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with two cooperatively binding RNA-binding domains. We show that bivalent binding is a common strategy among RBPs, yielding 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-binding proteins and highlight the value of bipartite motif models in representing the multivalency of protein-RNA interactions. AvailabilityBMF source code is available at https://github.com/soedinglab/bipartite_motif_finder under a GPL license. The BMF web server is accessible at https://bmf.soedinglab.org.

bioinformatics↗

bioRxiv2021

Cooperativity boosts affinity and specificity of proteins with multiple RNA-binding domains

Numerous cellular processes rely on the binding of proteins with high affinity to specific sets of RNAs. Yet most RNA binding domains display low specificity and affinity, to the extent that for most RNA-binding domains, the enrichment of the best binding motif measured by high-throughput RNA SELEX or RNA bind-n-seq is usually below 10-fold, dramatically lower than that of DNA-binding domains. Here, we develop a thermodynamic model to predict the binding affinity for proteins with any number of RNA-binding domains given the affinities of their isolated domains. For the four proteins in which affinities for individual domains have been measured the model predictions are in good agreement with experimental values. The model gives insight into how proteins with multiple RNA-binding domains can reach affinities and specificities orders of magnitude higher than their individual domains. Our results contribute towards resolving the conundrum of missing specificity and affinity of RNA binding proteins and underscore the need for bioinformatic methods that can learn models for multi-domain RNA binding proteins from high-throughput in-vitro and in-vivo experiments.

molecular biology↗

bioRxiv2019

A high-throughput screen for transcription activation domains reveals their sequence characteristics and permits reliable prediction by deep learning

Transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that permit their dynamic behavior, fuzzy interactions and target specificity. We screened a large set of random 30-mer peptides for AD function and trained a deep neural network (ADpred) on the AD-positive and negative sequences. ADpred correctly identifies known ADs within protein sequences and accurately predicts the consequences of mutations. We show that functional ADs are (1) located within intrinsically disordered regions with biased amino acid composition, (2) contain clusters of hydrophobic residues near acidic side chains, (3) are enriched or depleted for particular dipeptide sequences, and (4) have higher helical propensity than surrounding regions. Taken together, our findings fit the model of "fuzzy" binding through hydrophobic protein-protein interfaces, where activator-coactivator binding takes place in a dynamic hydrophobic environment rather than through combinations of sequence-specific interactions.

molecular biology↗

bioRxiv2019

Mechanisms of active regulation of biomolecular condensates

Liquid-liquid phase separation is a key organizational principle in eukaryotic cells, on par with intracellular membranes. It allows cells to concentrate specific proteins into condensates, increasing reaction rates and achieving switch-like regulation. However, it is unclear how cells trigger condensate formation or dissolution and regulate their sizes. We predict from first principles two mechanisms of active regulation by post-translational modifications such as phosphorylation: In enrichment-inhibition, the regulating modifying enzyme enriches in condensates and the modifications of proteins inhibit their interactions. Stress granules, Cajal bodies, P granules, splicing speckles, and synapsin condensates obey this model. In localization-induction, condensates form around an immobilized modifying enzyme, whose modifications strengthen protein interactions. Spatially targeted condensates formed during transmembrane signaling, microtubule assembly, and actin polymerization conform to this model. The two models make testable predictions that can guide studies into the many emerging roles of biomolecular condensates.

cell biology↗