Solid-state nanopore detection of partially denatured dsDNA with single-strand binding protein: a preliminary analysis
Howald, N.; Klotz, A.
Show abstract
In this work we investigate the use of a nanopore sensor to detect single-strand binding protein (SSB) attached to AT-rich denaturation bubbles in genomic double-stranded (ds) DNA. DNA from the{lambda} bacteriophage was heated in the presence of E. coli SSB at temperatures predicted to open denaturation bubbles near the center of the molecule. A solid state nanopore sensor measured the ionic current as the DNA-SSB solution flowed through the pore, detecting blockades due to the translocation of biomolecules. Large current spikes were observed in the translocating DNA molecules, consistent with SSB binding. However, spikes were largely localized at either end of the DNA molecule, rather than at the predicted sites. We discuss the physico-chemical effects behind this disagreement and prospects for the future use of this technique for genomic mapping.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Single molecule studies characterize the kinetic mechanism of tetrameric p53 binding to different native response elements 94%
- High-throughput Kinetics using Capillary Electrophoresis and Robotics (HiKER) platform used to Study T7, T3, and Sp6 RNA Polymerase Misincorporation 93%
- Bleaching correction for DNA-measurements in highly diluted solutions using confocal microscopy 93%
Similar papers in this journal
Similar papers in this journal
- Single-cell transcriptome profiling simulation reveals the impact of sequencing parameters and algorithms on clustering 88%
- Microgravity modulates effects of chemotherapeutic drugs on cancer cell migration 88%
- Investigation of cell mechanics and migration on DDR2-expressing neuroblastoma cell line 87%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.