Can Human Canonical Glutathione S-Transferases Act as RNA-Binding Proteins?

Glutathione S-transferase (GST) is an enzyme superfamily of particular interest for human health with many functional roles, and it is involved in several cancer types. Under cell stress conditions, their concentrations can increase by up to 10% of cell protein content. Recently, a study describing the landscape of RNA-binding proteins in mammalian spermatogenesis reported evidence of canonical GST–RNA interactions in three mouse mu GSTs. Prompted by this, we searched for available databases and found that RBP2GO, which collects candidate RNA-binding proteins (RBPs) detected in recent human proteomic studies, also lists a few human GST–RNA interactions, without any molecular details. To highlight the molecular features of the GST–RNA interaction, we applied recently developed predictors of RNA–binding sites and validated the results with AutoDock Vina, a docking program that computes binding affinity. Overall, our findings support the notion that a GST–RNA interaction can exist and suggest a potential overlap between RNA binding sites and residues responsible for binding glutathione (GSH), which is the most common GST substrate. Our computational analysis supports the notion that human GSTs can bind RNA that shares the binding region with the glutathione-binding pocket.

Authors

Institutions

Publication Details

Journal
Journal of Computational Biophysics and Chemistry
Published
2025-05-02
DOI
https://doi.org/10.1142/s2737416525500607
Primary Topic
Glutathione Transferases and Polymorphisms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Can Human Canonical Glutathione S-Transferases Act as RNA-Binding Proteins?

Pier Luigi Martelli, Rita Casadio, Gabriele Vazzana
Journal of Computational Biophysics and Chemistry
Glutathione Transferases and Polymorphisms
article

Can Human Canonical Glutathione S-Transferases Act as RNA-Binding Proteins?

Pier Luigi Martelli, Rita Casadio, Gabriele Vazzana
article en

Abstract

Glutathione S-transferase (GST) is an enzyme superfamily of particular interest for human health with many functional roles, and it is involved in several cancer types. Under cell stress conditions, their concentrations can increase by up to 10% of cell protein content. Recently, a study describing the landscape of RNA-binding proteins in mammalian spermatogenesis reported evidence of canonical GST–RNA interactions in three mouse mu GSTs. Prompted by this, we searched for available databases and found that RBP2GO, which collects candidate RNA-binding proteins (RBPs) detected in recent human proteomic studies, also lists a few human GST–RNA interactions, without any molecular details. To highlight the molecular features of the GST–RNA interaction, we applied recently developed predictors of RNA–binding sites and validated the results with AutoDock Vina, a docking program that computes binding affinity. Overall, our findings support the notion that a GST–RNA interaction can exist and suggest a potential overlap between RNA binding sites and residues responsible for binding glutathione (GSH), which is the most common GST substrate. Our computational analysis supports the notion that human GSTs can bind RNA that shares the binding region with the glutathione-binding pocket.

Journal of Computational Biophysics and ChemistryVol. 25(04)
Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IT), University of Bologna (IT)
Zero hunger
Openalex Percentile: Top 92%
Glutathione Transferases and Polymorphisms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.