Biology subjects

Meskys, R.

Publications and source records attributed to Meskys, R..

3 recordsLinked to original sources

bioRxiv2026

ASCH Domain-Containing Proteins Act as tRNA N4-acetylcytidine Erasers

N4-acetylcytidine (ac4C) is a conserved RNA modification that enhances RNA stability and translation accuracy. Emerging evidence suggests that ac4C levels can change in response to cellular and environmental triggers. Despite these indications of regulatory dynamics, the enzymes responsible for removing ac4C, beyond the recently proposed rRNA deacetylase SIRT7, remain largely unknown. Here, we examined 19 ASCH domain-containing proteins from bacteria, archaea, and humans to determine their possible activity in tRNA deacetylation. Despite differences in their sequences, structures, and nucleic-acid binding properties, all tested proteins were capable of removing ac4C from tRNA, revealing a conserved deacetylase activity across diverse species. The proteins were found to vary in nucleic acid recognition, including an archaeal specific helix-turn-helix domain that promotes strong tRNA binding. Together, these findings establish ASCH proteins as a widespread and previously unrecognized family of tRNA deacetylases, suggesting that enzymatic ac4C turnover may require complex regulation within the cells. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/708502v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@880691org.highwire.dtl.DTLVardef@674a13org.highwire.dtl.DTLVardef@1304045org.highwire.dtl.DTLVardef@cba7f6_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

RudS: Bacterial Desulfidase Responsible for tRNA 4-Thiouridine De-modification

In this study, we present a comprehensive analysis of a widespread group of bacterial tRNA de-modifying enzymes, dubbed RudS, which consist of a TudS desulfidase fused to a domain of unknown function (DUF1722). RudS enzymes exhibit specific de-modification activity towards the 4-thiouridine modification (s4U) in tRNA molecules, as indicated by our experimental findings. Notably, heterologous overexpression of RudS genes in Escherichia coli leads to a significant reduction in tRNA 4-thiouridine content, highlighting the enzymes role in tRNA s4U modification regulation. Through a combination of protein modeling, docking studies, and molecular dynamics simulations, we have identified amino acid residues involved in catalysis and tRNA binding. Experimental validation through targeted mutagenesis confirms the TudS domain as the catalytic core of RudS, with the DUF1722 domain facilitating tRNA binding in the anticodon region. Our results suggest a potential role for RudS tRNA modification eraser proteins in prokaryotic tRNA regulation pathways. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/593862v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@d6a91dorg.highwire.dtl.DTLVardef@f8d54dorg.highwire.dtl.DTLVardef@7bdcc9org.highwire.dtl.DTLVardef@1dc3860_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2019

Expanding functional protein sequence space using generative adversarial networks

De novo protein design for catalysis of any desired chemical reaction is a long standing goal in protein engineering, due to the broad spectrum of technological, scientific and medical applications. Currently, mapping protein sequence to protein function is, however, neither computationionally nor experimentally tangible 1,2. Here we developed ProteinGAN, a specialised variant of the generative adversarial network 3 that is able to learn natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase as a template enzyme, we show that 24% of the ProteinGAN-generated and experimentally tested sequences are soluble and display wild-type level catalytic activity in the tested conditions in vitro, even in highly mutated (>100 mutations) sequences. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse novel functional proteins within the allowed biological constraints of the sequence space.

synthetic biology↗