Summary DNA polymerase stalling activates the ATR checkpoint kinase, which in turn suppresses fork collapse and breakage. Herein, we describe use of ATR inhibition (ATRi) as a means to identify genomic sites of problematic DNA replication in murine and human cells. Over 500 high-resolution ATR-dependent sites were ascertained using two distinct methods (RPA-ChIP and BrITL). The genomic feature most strongly associated with ATR dependence was repetitive DNA that exhibited high structure-forming potential. Repeats most reliant on ATR for stability included structure-forming microsatellites, inverted retroelement repeats, and quasi-palindromic AT-rich repeats. Notably, these categories of repeats differed both in structure formation and in their ability to stimulate RPA accumulation and breakage, implying that the causes and character of replication fork collapse under ATR inhibition can vary in a DNA structure-specific manner. Collectively, these studies identify key sources of endogenous replication stress that rely on ATR for stability.
CryoDRGN is a machine learning system for heterogeneous cryo-EM reconstruction of proteins and protein complexes from single particle cryo-EM data. Central to this approach is a deep generative model for heterogeneous cryo-EM density maps, which we empirically find effectively models both discrete and continuous forms of structural variability. Once trained, cryoDRGN is capable of generating an arbitrary number of 3D density maps, and thus interpreting the resulting ensemble is a challenge. Here, we showcase interactive and automated processing approaches for analyzing cryoDRGN results. Specifically, we detail a step-by-step protocol for analysis of the assembling 50S ribosome dataset (Davis et al., EMPIAR-10076), including preparation of inputs, network training, and visualization of the resulting ensemble of density maps. Additionally, we describe and implement methods to comprehensively analyze and interpret the distribution of volumes with the assistance of an associated atomic model. This protocol is appropriate for structural biologists familiar with processing single particle cryo-EM datasets and with moderate experience programming in Python and navigating Jupyter notebooks. It requires 3-4 days to complete..
The G-quadruplex (GQ) is a well-studied non-canonical DNA structure formed by G-rich sequences found at telomeres and gene promoters. Biological studies suggest that GQs may play roles in regulating gene expression, DNA replication, and DNA repair. Small molecule ligands were shown to alter GQ structure and stability and thereby serve as novel therapies, particularly against cancer. In this work, we investigate the interaction of a G-rich sequence, 5’-GGGTTGGGTTGGGTTGGG-3’ (T1), with a water-soluble porphyrin, N-methyl mesoporphyrin IX (NMM) via biophysical and X-ray crystallographic studies. UV-vis and fluorescence titrations, as well as a Job plot, revealed a 1:1 binding stoichiometry with an impressively tight binding constant of 30–50 μM-1 and ΔG298 of -10.3 kcal/mol. Eight extended variants of T1 (named T2 –T9) were fully characterized and T7 was identified as a suitable candidate for crystallographic studies. We solved the crystal structures of the T1- and T7-NMM complexes at 2.39 and 2.34 Å resolution, respectively. Both complexes form a 5’-5’ dimer of parallel GQs capped by NMM at the 3’ G-quartet, supporting the 1:1 binding stoichiometry. Our work provides invaluable details about GQ-ligand binding interactions and informs the design of novel anticancer drugs that selectively recognize specific GQs and modulate their stability for therapeutic purposes.
Cryo-electron tomography (cryo-ET) allows one to observe macromolecular complexes in their native, spatially contextualized environment. Tools to visualize such complexes at nanometer resolution via iterative alignment and averaging are well-developed but rely on assumptions of structural homogeneity among the complexes under consideration. Recently developed downstream analysis tools allow for some assessment of macromolecular diversity but have limited capacity to represent highly heterogeneous macromolecules, including those undergoing continuous conformational changes. Here, we extend the highly expressive cryoDRGN deep learning architecture, originally created for cryo-electron microscopy single particle analysis, to sub-tomograms. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct a large, heterogeneous ensemble of structures supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET data. We additionally illustrate tomoDRGN's efficacy in analyzing an exemplar dataset, using it to reveal extensive structural heterogeneity among ribosomes imaged in situ.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.