Evidence map›Paper›PMID 39309126›Full record

ReviewBiophysical reviews2024

Navigating the complexity of p53-DNA binding: implications for cancer therapy.

Kelly M Thayer, Sean Stetson, Fernando Caballero, Christopher Chiu, In Sub Mark Han

Abstract readReview
In one paragraph

Review in Biophysical reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Biophysical reviews · 2024
    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

5 authors.

Kelly M ThayerCollege of Integrative Sciences, Wesleyan University, Middletown, CT 06457 USA.
Sean StetsonDepartment of Chemistry, Wesleyan University, Middletown, CT 06457 USA.
Fernando CaballeroCollege of Integrative Sciences, Wesleyan University, Middletown, CT 06457 USA.
Christopher ChiuDepartment of Mathematics and Computer Science, Wesleyan University, Middletown, CT 06457 USA.
In Sub Mark HanMolecular Biophysics Program, Wesleyan University, Middletown, CT 06457 USA.

Funding

Ensemble Networks for Allosteric Drug DesignR15GM128102 · NIGMS · WESLEYAN UNIVERSITY · PI THAYER, KELLY MARIE · 2018 to 2021
$976k
NIGMS NIH HHS R15 GM128102
6 · The paper itself

Abstract

Abstract: The tumor suppressor protein p53, a transcription factor playing a key role in cancer prevention, interacts with DNA as its primary means of determining cell fate in the event of DNA damage. When it becomes mutated, it opens damaged cells to the possibility of reproducing unchecked, which can lead to formation of cancerous tumors. Despite its critical role, therapies at the molecular level to restore p53 native function remain elusive, due to its complex nature. Nevertheless, considerable information has been amassed, and new means of investigating the problem have become available. Objectives: We consider structural, biophysical, and bioinformatic insights and their implications for the role of direct and indirect readout and how they contribute to binding site recognition, particularly those of low consensus. We then pivot to consider advances in computational approaches to drug discovery. Materials and methods: We have conducted a review of recent literature pertinent to the p53 protein. Results: Considerable literature corroborates the idea that p53 is a complex allosteric protein that discriminates its binding sites not only via consensus sequence through direct H-bond contacts, but also a complex combination of factors involving the flexibility of the binding site. New computational methods have emerged capable of capturing such information, which can then be utilized as input to machine learning algorithms towards the goal of more intelligent and efficient de novo allosteric drug design. Conclusions: Recent improvements in machine learning coupled with graph theory and sector analysis hold promise for advances to more intelligently design allosteric effectors that may be able to restore native p53-DNA binding activity to mutant proteins. Clinical relevance: The ideas brought to light by this review constitute a significant advance that can be applied to ongoing biophysical studies of drugs for p53, paving the way for the continued development of new methodologies for allosteric drugs. Our discoveries hold promise to provide molecular therapeutics which restore p53 native activity, thereby offering new insights for cancer therapies. Graphical Abstract: Structural representation of the p53 DBD (PDBID 1TUP). DNA consensus sequence is shown in gray, and the protein is shown in blue. Red beads indicate hotspot residue mutations, green beads represent DNA interacting residues, and yellow beads represent both.

Indexed as

AllosteryDrug designGraph theoryMachine learningMD-MSMMD sectorp53

Identifiers

PMID39309126
PMCPMC11415564

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.