A 212-nt long RNA structure in the Tobacco necrosis virus-D RNA genome is resistant to Xrn degradation

Plus-strand RNA viruses can accumulate viral RNA degradation products during infections. Some of these decay intermediates are generated by the cytosolic 5′-to-3′ exoribonuclease Xrn1 (mammals and yeast) or Xrn4 (plants) and are formed when the enzyme stalls on substrate RNAs upon encountering inhibitory RNA structures. Many Xrn-generated RNAs correspond to 3′-terminal segments within the 3′-UTR of viral genomes and perform important functions during infections. Here we have investigated a 3′-terminal small viral RNA (svRNA) generated by Xrn during infections with Tobacco necrosis virus-D (family Tombusviridae). Our results indicate that (i) unlike known stalling RNA structures that are compact and modular, the TNV-D structure encompasses the entire 212 nt of the svRNA and is not functionally transposable, (ii) at least two tertiary interactions within the RNA structure are required for effective Xrn blocking and (iii) most of the svRNA generated in infections is derived from viral polymerase-generated subgenomic mRNA1. In vitro and in vivo analyses allowed for inferences on roles for the svRNA. Our findings provide a new and distinct addition to the growing list of Xrn-resistant viral RNAs and stalling structures found associated with different plant and animal RNA viruses.York University Librarie

Bloom filters for molecules

Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present in a set using either string or fingerprint representations. Bloom filters are small enough to hold billions of molecules in just a few GB of memory and check membership in sub milliseconds. We found string representations can have a false positive rate below 1% and require significantly less storage than using fingerprints. Canonical SMILES with Bloom filters with the simple FNV hashing function provide fast and accurate membership tests with small memory requirements. We provide a general implementation and specific filters for detecting if a molecule is purchasable, patented, or a natural product according to existing databases at https://github.com/whitead/molbloo

Symmetric Molecular Dynamics

We derive a formulation of molecular dynamics that generates only symmetric configurations. We implement it for all 2D planar and 3D space groups. An atlas of 2D Lennard-Jones crystals under all planar groups is created with symmetric molecular dynamics

Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks

Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accuracy. Data-driven approaches, such as deep learning, offer improved accuracy and computational efficiency but typically lack uncertainty quantification. Additionally, ease of use remains a concern for any computational technique, resulting in the sustained popularity of group-based contribution methods. In this work, we addressed these problems with a deep learning model with predictive uncertainty that runs on a static website (without a server). This approach moves computing needs onto the website visitor without requiring installation, removing the need to pay for and maintain servers. Our model achieves satisfactory results in solubility prediction. Furthermore, we demonstrate how to create molecular property prediction models that balance uncertainty and ease of use. The code is available at \url{https://github.com/ur-whitelab/mol.dev}, and the model is usable at \url{https://mol.dev}