Evidence map›Paper›PMID 40343619›Full record

ArticleJournal of computer-aided molecular design2025

Theoretical investigation of AKT1 mutations in breast cancer: a computational approach to structural and functional insights.

Balu Kamaraj, George Priya Doss C

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Article in Journal of computer-aided molecular design, 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. 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

2 authors.

Balu KamarajDepartment of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. bkranganayaki@iau.edu.sa.
George Priya Doss CSchool of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, India. georgepriyadoss@vit.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is a complex disease primarily driven by genetic mutations that disrupt crucial signaling pathways, with the AKT1 gene playing a central role in its progression. This study explores the impact of AKT1 mutations using Whole Exome Sequencing (WES), bioinformatics, and computational modeling. Using WES, we identified and prioritized significant mutations in patient samples, specifically D3N, V337M, and D3N-E169G. Comprehensive sequence and structural analyses were conducted to understand how these mutations affect specific functional domains of the AKT1 protein. To investigate the molecular consequences, molecular docking studies were performed to assess the binding affinity of AKT1 mutations with MK2206, a known allosteric inhibitor of AKT1. The docking results revealed substantial differences in interaction energies, indicating impaired inhibitor binding due to these mutations. Additionally, molecular dynamics simulations over a 500-nanosecond trajectory provided detailed insights into the structural perturbations caused by these mutations. This integrated study, combining genomic and computational approaches, offers a comprehensive understanding of how AKT1 mutations contribute to BC pathogenesis. These findings enhance our knowledge of the molecular mechanisms underlying the disease and support the development of targeted therapies to address the altered behavior of mutated AKT1, advancing personalized treatment strategies for BC.

Indexed as

Breast NeoplasmsMutationProto-Oncogene Proteins c-aktComputational BiologyExome SequencingFemaleHeterocyclic Compounds, 3-RingHumansMolecular Docking SimulationMolecular Dynamics SimulationProtein BindingAKT1 protein, humanHeterocyclic Compounds, 3-RingMK 2206Proto-Oncogene Proteins c-aktAKT1 mutationsBreast cancerMK2206 inhibitorMolecular dockingMolecular dynamics simulationsWES

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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.