DNA double-strand breaks (DSBs) are among the most lethal types of DNA damage and frequently cause genome instability. Sequencing-based methods for mapping DSBs have been developed but they allow measurement only of relative frequencies of DSBs between loci, which limits our understanding of the physiological relevance of detected DSBs. Here we propose quantitative DSB sequencing (qDSB-Seq), a method providing both DSB frequencies per cell and their precise genomic coordinates. We induce spike-in DSBs by a site-specific endonuclease and use them to quantify detected DSBs (labeled, e.g., using i-BLESS). Utilizing qDSB-Seq, we determine numbers of DSBs induced by a radiomimetic drug and replication stress, and reveal two orders of magnitude differences in DSB frequencies. We also measure absolute frequencies of Top1-dependent DSBs at natural replication fork barriers. qDSB-Seq is compatible with various DSB labeling methods in different organisms and allows accurate comparisons of absolute DSB frequencies across samples.
In this work, the components of the protein electrostatic potentials in solution are analyzed with NMR paramagnetic relaxation enhancement experiments and compared with continuum solution theory, and multiscale simulations. To determine the contributions of the solution components, we analyze them at different ionic strengths from 0 to 745 mM. A theoretical approximation allows the determination of the electrostatic potential at a given proton without reference to the protein structure given the ratio of paramagnetic relaxation enhancements rates between a cationic and an anionic probe. The results derived from simulations show good agreement with experiment and simple continuum solvent theory for many of the residues. A discrepancy including a switch of sign of the electrostatic potential was observed for particular residues. By considering the components of the potential, we found the discrepancy is mainly caused by angular correlations of the probe molecules with these residues. The correction for the correlations allows a more accurate analysis of the experiments determining the electrostatic potential of proteins in solution.
Eukaryotic DNA replication is elaborately orchestrated to duplicate the genome timely and faithfully. Replication initiates at multiple origins from which replication forks emanate and travel bi-directionally. The complex spatio-temporal regulation of DNA replication remains incompletely understood. To study it, computational models of DNA replication have been developed in S. cerevisiae. However, in spite of the experimental evidence of forks' speed stochasticity, all models assumed that forks' speeds are the same. Here, we present the first model of DNA replication assuming that speeds vary stochastically between forks. Utilizing data from both wild-type and hydroxyurea-treated yeast cells, we show that our model is more accurate than models assuming constant forks' speed and reconstructs dynamics of DNA replication faithfully starting both from population-wide data and data reflecting fork movement in individual cells. Completion of replication in a timely manner is a challenge due to its stochasticity; we propose an empirically derived modification to replication speed based on the distance to the approaching fork, which promotes timely completion of replication. In summary, our work discovers a key role that stochasticity of the forks' speed plays in the dynamics of DNA replication. We show that without including stochasticity of forks' speed it is not possible to accurately reconstruct movement of individual replication forks, measured by DNA combing. Author summary OPEN ACCESSCitation: Yousefi R, Rowicka M (2019) Stochasticity of replication forks' speeds plays a key role in the dynamics of DNA replication. PLoS Comput Biol 15(12): e1007519. https://doi.org/ 10.simpler than previous model and thus avoids over-learning (fitting noise). We also discover how replication speed may be attuned to timely complete replication. We propose that forks' speed increases with diminishing distance to the approaching fork, which we show promotes timely completion of replication. Such speed up can be e.g. explained by a synergy effect of chromatin unwinding by both forks. Our model can be used to simulate phenomena beyond replication, e.g. DNA double-strand breaks resulting from broken replication forks.Computer simulations reveal stochasticity of DNA replication PLOS Computational Biology | https://doi.
The proximal distribution function (pDF) quantifies the probability of finding a solvent molecule in the vicinity of solutes. The approach constitutes a hierarchically organized theory for constructing approximate solvation structures around solutes. Given the assumption of universality of atom cluster-specific solvation, reconstruction of the solvent distribution around arbitrary molecules provides a computationally convenient route to solvation thermodynamics. Previously, such solvent reconstructions usually considered the contribution of the nearest-neighbor distribution only. We extend the pDF reconstruction algorithm to terms including next-nearest-neighbor contribution. As a test, small molecules (alanine and butane) are examined. The analysis is then extended to include the protein myoglobin in the P6 crystal unit cell. Molecular dynamics simulations are performed, and solvent density distributions around the solute molecules are compared with the results from different pDF reconstruction models. It is shown that the next-nearest-neighbor modification significantly improves the reconstruction of the solvent number density distribution in concave regions and between solute molecules. The probability densities are then used to calculate the solute–solvent non-bonded interaction energies including van der Waals and electrostatic, which are found to be in good agreement with the simulated values.
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