Biology subjects

Nagle, R.

Publications and source records attributed to Nagle, R..

2 recordsLinked to original sources

bioRxiv2026

Enzymatic formation of a conserved isoaspartate in ribosomal protein uS11

Isoaspartate (isoAsp) formation is typically viewed as a "molecular clock" through nonenzymatic degradation of aspartate or asparagine during protein aging. Here we report a nearly universal enzymatic pathway for the formation of a conserved isoAsp in the bacterial ribosomal protein uS11. Proteome-wide protein-protein interaction scans using AlphaFold3 identified YbeY as a candidate enzyme from Escherichia coli. NMR spectroscopy supported a stable YbeY-uS11 complex from Thermotoga maritima. Biochemical assays indicated that EcYbeY catalysis is zinc-dependent and prefers the conserved Asn-Gly motif for isoAsp formation. A high-resolution cryo-electron microscopy structure of the 70S ribosome from E. coli {Delta}ybeY revealed that loss of isoAsp alters contacts with the 16S rRNA groove and bS21. Phylogenetic analysis indicated that YbeY is present in almost all bacteria, and its absence is correlated to changes in the Asn-Gly motif of uS11. Additionally, our structural analyses implicate Fap7 as the functional counterpart in archaea and eukaryotes.

biochemistry↗

bioRxiv2025

Improving RNA Secondary Structure Prediction Through ExpandedTraining Data

In recent years, deep learning has revolutionized protein structure prediction, achieving remarkable speed and accuracy. RNA structure prediction, however, has lagged behind. Although several methods have shown some success in predicting RNA secondary and tertiary structures, none have reached the accuracy observed with contemporary protein models. The lack of success of these RNA structure prediction models has been proposed to be due to limited high-quality structural information that can be used as training data. To probe this proposed limitation, we developed a large and diverse dataset comprising paired RNA sequences and their corresponding secondary structures. We assess the utility of this enhanced dataset by retraining on a deep learning model, SincFold. We find that SincFold exhibited improved generalization to some previously unseen RNA families, enhancing its capability to predict accurate de novo RNA secondary structures. The RNASSTR dataset provides a substantial advance for RNA structure modeling, laying a strong foundation for the development of future RNA secondary structure prediction algorithms.

bioinformatics↗