Foster, M. R. | bioRxiv Science

bioRxiv2023

The Translesion Polymerase Pol Y1 is a Constitutive Component of the B. subtilis Replication Machinery

Unrepaired DNA damage encountered by the cellular replication machinery can stall DNA replication, ultimately leading to cell death. In the DNA damage tolerance pathway translesion synthesis (TLS), replication stalling is alleviated by the recruitment of specialized polymerases to synthesize short stretches of DNA near a lesion. Although TLS promotes cell survival, most TLS polymerases are low fidelity and must be tightly regulated to avoid harmful mutagenesis. The gram-negative bacterium Escherichia coli has served as the model organism for studies of the molecular mechanisms of bacterial TLS. However, it is poorly understood whether these same mechanisms apply to other bacteria. Here, we use in vivo single-molecule fluorescence microscopy to investigate the TLS polymerase Pol Y1 in the model gram-positive bacterium Bacillus subtilis. We find significant differences in the localization and dynamics of Pol Y1 in comparison to its E. coli homolog, Pol IV. Notably, Pol Y1 is constitutively enriched at or near sites of replication in the absence of DNA damage through interactions with the DnaN clamp; in contrast, Pol IV has been shown to be selectively enriched only upon replication stalling. These results suggest key differences in the roles and mechanisms of regulation of TLS polymerases across different bacterial species.

biochemistry↗

bioRxiv2022

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics

Metabolite annotation continues to be the widely accepted bottleneck in non-targeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas phase collision cross section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable and Reusable) data principles. Another unique aspect of CCSP 2.0 its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73 and 1.87% for the 170 [M-H]-, 155 [M+H]+ and 138 [M+Na]+ adducts tested. For class-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a {Delta}CCS threshold of 2.8% and a mass error of 10 ppm.

biochemistry↗