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DNA Replication

DNA Replication

DNA replication involves the separation of the two strands of the double helix, with each strand serving as a template from which the new complementary strand is copied. After replication, each double-stranded DNA includes one parental or “old” strand and one “new” strand. This is known as semiconservative replication. The resulting DNA molecules have the same sequence and are divided equally into the two daughter cells.
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Replication in Eukaryotes

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In eukaryotic cells, DNA replication is highly conserved and tightly regulated. Multiple linear chromosomes must be duplicated with high fidelity before cell division, so there are many proteins that fulfill specialized roles in the replication process. Replication occurs in three phases: initiation, elongation, and termination, and ends with two complete sets of chromosomes in the nucleus.
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The Replisome

The Replisome

DNA replication is carried out by a large complex of proteins that act in a coordinated matter to achieve high-fidelity DNA replication. Together this complex is known as the DNA replication machinery or the replisome.
The synthesis of the leading and lagging strands is a highly coordinated process. To explain this, the “Trombone model” was proposed by Bruce Alberts in 1980. The DNA loop formation starts when a primer is synthesized on the parent lagging strand. The loop grows with...

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Lagging Strand Synthesis

Lagging Strand Synthesis

During replication, the complementary strands in double-stranded DNA are synthesized at different rates. Replication first begins on the leading strand. Replication starts later, occurs more slowly, and proceeds discontinuously on the lagging strand.
There are several major differences between synthesis of the leading strand and synthesis of the lagging strand. 1) Leading strand synthesis happens in the direction of replication fork opening, whereas lagging strand synthesis happens in the...

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Related Experiment Video

Updated: Mar 9, 2026

Direct Observation of Enzymes Replicating DNA Using a Single-molecule DNA Stretching Assay

Direct Observation of Enzymes Replicating DNA Using a Single-molecule DNA Stretching Assay

Published on: March 23, 2010

Sequence-Dependent Persistence Lengths of DNA.

Jonathan S Mitchell¹, Jaroslaw Glowacki¹, Alexandre E Grandchamp¹

¹Ecole Polytechnique Fédérale de Lausanne , Lausanne CH 1273, Switzerland.

Journal of Chemical Theory and Computation

|December 29, 2016

Summary

This summary is machine-generated.

Monte Carlo simulations using the cgDNA model predict DNA persistence length, finding sequence influences stiffness. This provides a new way to analyze DNA shape and flexibility.

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Area of Science:

Computational Biology
Biophysics
Molecular Modeling

Background:

DNA persistence length is a key metric for DNA mechanics.
Existing models often provide sequence-averaged values, potentially masking sequence-specific behaviors.
Understanding sequence-dependent DNA flexibility is crucial for various biological processes.

Purpose of the Study:

To predict DNA persistence length using a coarse-grain rigid-base model (cgDNA) and Monte Carlo simulations.
To investigate the sequence-specific dependence of DNA persistence length.
To develop a method to separate intrinsic DNA shape from stiffness-induced correlations.

Main Methods:

Utilized a Monte Carlo code with the cgDNA coarse-grain rigid-base model for B-form double-stranded DNA.
Performed simulations to calculate sequence-averaged and sequence-dependent persistence lengths.
Developed a factorization to distinguish shape-driven and stiffness-driven contributions to DNA flexibility.

Main Results:

Predicted a sequence-averaged persistence length of 53.5 nm, closely aligning with experimental values.
Demonstrated substantial sequence-dependent variations in persistence length.
Identified specific DNA sequences (e.g., poly(A), poly(TA), A-tracts) with notable deviations in stiffness and bending.

Conclusions:

The cgDNA model accurately predicts DNA persistence length and captures sequence-dependent effects.
The factorization method successfully separates shape and stiffness contributions to DNA flexibility.
Simulation results align well with molecular dynamics and experimental data, validating the model's predictive power.