Evidence map›Paper›PMID 31665426›Full record

ArticleNucleic acids research2019

New insights into protein-DNA binding specificity from hydrogen bond based comparative study.

Maoxuan Lin, Jun-Tao Guo

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

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

27 citing papers in PubMed, 71 citations in OpenAlex.

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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 at 1 institution in 1 country.

Maoxuan LinDepartment of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Jun-Tao GuoDepartment of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
University of North Carolina at Charlotte · US

Funding

Structural Features of Specificity in Protein-DNA RecognitionR15GM110618 · NIGMS · UNIVERSITY OF NORTH CAROLINA CHARLOTTE · PI GUO, JUN-TAO · 2015 to 2015
$342k
NIGMS NIH HHS R15 GM110618
6 · The paper itself

Abstract

Knowledge of protein-DNA binding specificity has important implications in understanding DNA metabolism, transcriptional regulation and developing therapeutic drugs. Previous studies demonstrated hydrogen bonds between amino acid side chains and DNA bases play major roles in specific protein-DNA interactions. In this paper, we investigated the roles of individual DNA strands and protein secondary structure types in specific protein-DNA recognition based on side chain-base hydrogen bonds. By comparing the contribution of each DNA strand to the overall binding specificity between DNA-binding proteins with different degrees of binding specificity, we found that highly specific DNA-binding proteins show balanced hydrogen bonding with each of the two DNA strands while multi-specific DNA binding proteins are generally biased towards one strand. Protein-base pair hydrogen bonds, in which both bases of a base pair are involved in forming hydrogen bonds with amino acid side chains, are more prevalent in the highly specific protein-DNA complexes than those in the multi-specific group. Amino acids involved in side chain-base hydrogen bonds favor strand and coil secondary structure types in highly specific DNA-binding proteins while multi-specific DNA-binding proteins prefer helices.

Indexed as

Models, MolecularAmino AcidsBase PairingBinding SitesDNADNA-Binding ProteinsHydrogen BondingNucleic Acid ConformationProtein Structure, SecondaryAmino AcidsDNADNA-Binding Proteins

Identifiers

PMID31665426
PMCPMC6868434
OpenAlexW2982501585

What OpenQuestion holds

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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.