Evidence map›Paper›PMID 23274531›Full record

ArticleJournal of molecular modeling2013

Structural and energetic insights into sequence-specific interaction in DNA-drug recognition: development of affinity predictor and analysis of binding selectivity.

Jingheng Ning, Weiwei Chen, Jiaojiao Li, Zaixi Peng, Jianhui Wang, Zhong Ni

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Article in Journal of molecular modeling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 38% of its field
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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 10 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Jingheng NingSchool of Chemical and Biological Engineering, Changsha University of Science and Technology, Changsha 410004, China.
Weiwei Chen
Jiaojiao Li
Zaixi Peng
Jianhui Wang
Zhong Ni
Changsha University of Science and Technology · CNJiangsu University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although the molecular mechanism and thermodynamic profile of a wide variety of chemical agents have been examined intensively in the past decades in terms of specific recognition of their protein receptors, to date the physicochemical nature of DNA-drug recognition and association still remains largely unexplored. The present study focused on understanding the structural basis, energetic landscape, and biological implications underlying the binding of small-molecule ligands to their cognate or non-cognate DNA receptors. First, a new method to capture the structural features of DNA-drug complex architecture was proposed and then used to correlate the extracted features with binding affinity of the complexes. By employing this method, a statistical regression-based predictor was developed to quantitatively evaluate the interaction potency of drug compounds with DNA in a fast and reliable manner. Subsequently, we use the predictor to examine the binding behavior of a number of structure-available, affinity-known DNA-drug complexes as well as a large pool of randomly generated DNA decoys in complex with the same drugs. It was found that (1) as compared with protein-DNA recognition, small-molecule agents have relatively low specificity in selecting their cognate DNA targets from the background of numerous random decoys; (2) the abundance of A-T base pairs in the DNA core motif exhibits a significant positive correlation with the affinity of drug ligand binding to the DNA receptor; and (3) high affinity seems not to be closely related to high selectivity for a DNA-targeting drug, although high-affinity drug entities have a greater possibility of being ranked computationally as top binders. We hope that this work will provide a preliminary insight into the molecular origin of sequence-specific interactions in DNA-drug recognition.

Indexed as

Molecular Docking SimulationBase PairingBenzamidinesBinding SitesCrystallography, X-RayDNAHumansLigandsNetropsinNucleic Acid ConformationQuantitative Structure-Activity RelationshipStatic ElectricityBenzamidinesDNAfuramidineLigandsNetropsinpropamidine

Identifiers

PMID23274531
OpenAlexW2034456653

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Registered trials

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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.