Evidence map›Paper›PMID 40536257›Full record

ArticleProteins2025

Development of a Novel Method for Representing 3D Structures of Nucleotides Using the Concept of the TSR Algorithm and Evaluation of the Method Through Studying Specific Interactions Between DNAs and p53.

Krishna Rauniyar, Tarikul I Milon, Poorya Khajouie, Ramy Alabdulkarim, Yuwu Chen, Sarika Kondra, Vijay Raghavan, Wu Xu

Abstract read
In one paragraph

Article in Proteins, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Krishna RauniyarThe Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, Louisiana, USA.ORCID 0009-0009-9726-1728
Tarikul I MilonDepartment of Chemistry, University of Louisiana at Lafayette, Lafayette, Louisiana, USA.
Poorya KhajouieThe Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, Louisiana, USA.
Ramy AlabdulkarimDepartment of Chemistry, University of Louisiana at Lafayette, Lafayette, Louisiana, USA.
Yuwu ChenHigh Performance Computing, 329 Frey Computing Services Center, Louisiana State University, Baton Rouge, Louisiana, USA.
Sarika KondraThe Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, Louisiana, USA.
Vijay RaghavanThe Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, Louisiana, USA.
Wu XuDepartment of Chemistry, University of Louisiana at Lafayette, Lafayette, Louisiana, USA.ORCID 0000-0002-2982-3276

Funding

Development of a new TSR algorithm to represent sequences of proteins and 3D structures of nucleotides and molecular complexes for mechanistic understanding of protein sequence and structure relationsR15GM144944 · NIGMS · UNIVERSITY OF LOUISIANA AT LAFAYETTE · PI Wu Xu · 2021 to 2026
$963k
NIGMS NIH HHS R15 GM144944NIH NIGMS 1R15GM144944-01
6 · The paper itself

Abstract

Prior evidence has suggested that interactions between transcription factor amino acids and DNA nucleotides follow a recognition code. However, the recognition code remains poorly understood due to the inability of currently available computational methods to quantify and interpret subtle conformational changes of transcription factor amino acids and DNA nucleotides. In this study, we have developed a novel way of representing 3D structures of nucleotides of DNAs or RNAs by adapting the concept of the Triangular Spatial Relationship (TSR) from the TSR-based computational method originally designed for protein 3D structural comparisons. Representing nucleotide 3D structures using a vector of integers (TSR keys) is unique. We chose p53 as an example of a transcription factor to establish the structural basis for comprehending the recognition code. By taking advantage of the proposed representation of nucleotide 3D structures, we were able to demonstrate the structural differences between the nucleotides that interact with p53 and those that do not interact with p53 as well as the structural differences between the amino acids of p53 that interact with DNA and those that do not interact with DNA. In summary, this study demonstrates the capabilities of an advanced computational methodology with notable advantages for representing and quantifying nucleotide structures and for providing a comprehensive understanding of the structural specificity existing between p53 proteins and their binding DNAs. Such an analysis can also be extended to complexes involving other transcription factor-DNA pairs.

Indexed as

AlgorithmsComputational BiologyDNANucleotidesTumor Suppressor Protein p53Binding SitesHumansModels, MolecularNucleic Acid ConformationProtein BindingProtein ConformationRNADNANucleotidesRNATP53 protein, humanTumor Suppressor Protein p533D nucleotide structures3D structure representationDNAsp53structural complementaritystructure comparisontranscription factorsTSR‐based method

Identifiers

PMID40536257
PMCPMC12354323

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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.