E2EDNA
E2EDNA simulates single-stranded (ss)DNA systems and their interactions with small analytes to support molecular biophysics and materials science studies, including DNA aptamer–based sensors.
Key Features:
- Input formats: Accepts DNA sequences in FASTA format and accommodates structures of desired ligands.
- Approximate folding: Performs initial folding of the ssDNA sequence to generate a starting structure.
- Refining step: Refines the folded structure to improve accuracy and structural stability.
- Analyte complexation: Facilitates complexation between DNA and ligands to form DNA–ligand complexes.
- Molecular dynamics sampling: Supports molecular dynamics simulations at various levels of accuracy to sample dynamic behavior over time.
- Advanced force field utilization: Employs the AMOEBA polarizable force field, demonstrated in a DNA-UTP complex case study.
Scientific Applications:
- Aptamer design and optimization: Provides high-accuracy predictions for selected sequences to support practical optimization of DNA aptamers.
- Complementing SELEX analyses: Offers direct simulation access and flexibility in theoretical engine choice beyond secondary structure prediction and motif analysis from SELEX datasets.
- Mechanistic studies in molecular biophysics and materials science: Enables investigation of ssDNA–ligand interaction mechanisms relevant to sensors and nanotechnologies.
Methodology:
The computational workflow proceeds through approximate folding of the ssDNA sequence, refinement of the folded structure, analyte complexation, and molecular dynamics sampling, with optional use of the AMOEBA polarizable force field as demonstrated on a DNA-UTP complex.
Topics
Details
- Cost:
- Free of charge
- Tool Type:
- workflow
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Python
- Added:
- 1/4/2022
- Last Updated:
- 11/24/2024
Operations
Publications
Kilgour M, Liu T, Walker BD, Ren P, Simine L. E2EDNA: Simulation Protocol for DNA Aptamers with Ligands. Journal of Chemical Information and Modeling. 2021;61(9):4139-4144. doi:10.1021/acs.jcim.1c00696. PMID:34435773. PMCID:PMC9536994.
PMID: 34435773
PMCID: PMC9536994
Funding: - Natural Sciences and Engineering Research Council of Canada: RGPIN-2019-24734
- National Institute of General Medical Sciences: R01GM106137