Evidence map›Paper›PMID 39576589›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Bioinformatics Approaches for Understanding the Binding Affinity of Protein-Nucleic Acid Complexes.

K Harini, Masakazu Sekijima, M Michael Gromiha

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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. TheFrontiers in cellular and infection microbiology · 2025
    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

3 authors.

K HariniDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
Masakazu SekijimaDepartment of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
M Michael GromihaDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India. gromiha@iitm.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-nucleic acid interactions are involved in various biological processes such as gene expression, replication, transcription, translation, and packaging. Understanding the recognition mechanism of the protein-nucleic acid complexes has been investigated from different perspectives, including the binding affinities of protein-DNA and protein-RNA complexes. Experimentally, protein-nucleic acid interactions are analyzed using X-ray crystallography, Isothermal Titration Calorimetry (ITC), DNA/RNA pull-down assays, DNA/RNA footprinting, and systematic evolution of ligands by exponential enrichment (SELEX). On the other hand, numerous databases and computational tools have been developed to study protein-nucleic acid complexes based on their binding sites, specific interactions between them, and binding affinity. In this chapter, we discuss various databases for protein-nucleic acid complex structures and the tools available to extract features from them. Further, we provide details on databases and prediction methods reported for exploring the binding affinity of protein-nucleic acid complexes along with important structure-based parameters, which govern the binding affinity.

Indexed as

Computational BiologyProtein BindingBinding SitesCrystallography, X-RayDatabases, ProteinDNADNA-Binding ProteinsModels, MolecularNucleic AcidsProteinsRNARNA-Binding ProteinsSoftwareDNADNA-Binding ProteinsNucleic AcidsProteinsRNARNA-Binding ProteinsBinding affinityComputational toolsMachine learningPrediction methodsProtein-DNA complexesProtein-RNA complexes

Identifiers

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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.