Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as model

1 Jan 2020

Abstract excerpt

The wide application of new DNA sequencing technologies is generating vast quantities of genetic variation data at unprecedented speed. Developing methodologies to decode the pathogenicity of the variants is imperatively demanding. We hypothesized that as deleterious variants may function through disturbing structural stability of their affected proteins, information from structural change caused by genetic...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Bacteriophages and microbial interactions
Share to Cancer-related Molecular Pathways
Share to Genomics and Phylogenetic Studies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 33363700
DOI: 10.1016/j.csbj.2020.11.041

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as modelPMID 33363700Read ↗

In Silico Screening, Molecular Dynamics Simulation and Binding Free Energy Identify Single-Point Mutations That Destabilize p53 and Reduce Binding to DNA.Semantic proximity Read ↗

ADDRESS: A Database of Disease-associated Human Variants Incorporating Protein Structure and Folding Stabilities.Semantic proximity Read ↗

Evaluating the performance of protein structure prediction in detecting structural changes of pathogenic nonsynonymous single nucleotide variantsSemantic proximity Read ↗

ROWVA: A Structure-Based Metric for Predicting the Pathogenicity of Protein Variants Using Alphafold2.Semantic proximity Read ↗

Unraveling the Structural Changes in the DNA-Binding Region of Tumor Protein p53 (TP53) upon Hotspot Mutation p53 Arg248 by Comparative Computational Approach.Semantic proximity Read ↗

Investigation of Structural Mechanisms Underlying p53 Dysfunction Caused by 148 Missense Mutations Using AlphaFold3 and Molecular Dynamics SimulationsSemantic proximity Read ↗

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.Semantic proximity Read ↗

Computational identification of pathogenic associated nsSNPs and its structural impact in UROD gene: a molecular dynamics approach.Semantic proximity Read ↗

Applications of computational algorithm tools to identify functional SNPs.Semantic proximity Read ↗

Interpreting the Effects of DNA Polymerase Variants at the Structural Level Using MAVISp and Molecular Dynamics SimulationsSemantic proximity Read ↗

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.Semantic proximity Read ↗

A bioinformatics approach to the identification of novel deleterious mutations of human TPMT through validated screening and molecular dynamics.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.