Evidence map›Paper›PMID 31820194›Full record

ArticleJournal of computer-aided molecular design2020

Sequence specificity in DNA-drug intercalation: MD simulation and density functional theory approaches.

Lakshmi Maganti, Dhananjay Bhattacharyya

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of computer-aided molecular design, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lakshmi MagantiComputational Science Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata, 700064, India.
Dhananjay BhattacharyyaComputational Science Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata, 700064, India. dhananjay.bhattacharyya@saha.ac.in.ORCID 0000-0001-9227-6490

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA is an essential target for the treatment of various pathologies, especially cancer. Hence targeting DNA double helix for alteration of its function has been attempted by several ways. Drug-DNA intercalation, one such biophysical process, could not be studied extensively as this requires significant deformation of the receptor DNA. Here we report thorough theoretical investigation of intercalation process in daunomycin-DNA interaction, by performing molecular dynamics simulations of the drug-DNA complexes for various DNA sequences, followed by Free-energy analysis and density functional theory (DFT) based studies to understand the binding preference. The classical energy based analyses indicate that the drug prefers to bind to TC/GA sequence over others. The DFT based energies of supra-molecular complexes are always contaminated with basis set superposition error (BSSE), which can be corrected by counterpoise method. This method is quite effective for systems containing two molecular fragments but is not appropriate for studying interaction between two base pair fragments and the drug intercalated between them. We have adopted an extension of the counterpoise method for BSSE corrected interaction energy calculation. These interaction energies, along with the energy penalty due to un-stacking of the base pairs, also indicate TC/GA sequence is the most preferred sequence for binding.

Indexed as

Antibiotics, AntineoplasticDaunorubicinDensity Functional TheoryDNAIntercalating AgentsMolecular Dynamics SimulationNucleic Acid ConformationThermodynamicsAntibiotics, AntineoplasticDaunorubicinDNAIntercalating AgentsBase pair stackingBSSE correctionMM-PBSAMolecular recognition

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.