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

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

Protein science : a publication of the Protein Society - 1 Jan 2020

Pandurangan Arun Prasad, Blundell Tom L

Abstract excerpt

Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are...

Read the complete abstract on PubMed

Topics

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

Share to Computational Biology
Share to Drug Resistance

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to High-Throughput Nucleotide Sequencing

Share to Machine Learning

Share to Protein Conformation

Share to Protein Stability

Share to Proteins

Share to Software

Source

Open in PubMed Open full text

Identifiers

PMID: 31693276
DOI: 10.1002/pro.3774
PMCID: PMC6933854

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.PMID 31693276Read ↗

Combating mutations in genetic disease and drug resistance: understanding molecular mechanisms to guide drug design.Semantic proximity Read ↗

The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations.Semantic proximity Read ↗

SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.Semantic proximity Read ↗

Further Development of SAMPDI-3D: Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNASemantic proximity Read ↗

Alignment-based protein mutational landscape prediction: doing more with lessSemantic proximity Read ↗

Predicting mutational function using machine learning.Semantic proximity Read ↗

Interpreting the effect of mutations to protein binding sites from large-scale genomic screens.Semantic proximity Read ↗

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs.Semantic proximity Read ↗

Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions.Semantic proximity Read ↗

MAVISp: A modular structure-based framework for protein variant effects.Semantic proximity Read ↗

MechPPI: Binding Mechanism-based Machine-Learning tool for Predicting Protein-Protein Binding Affinity Changes Upon MutationsSemantic proximity Read ↗

MAVISp: A Modular Structure-Based Framework for Protein Variant EffectsSemantic proximity Read ↗

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