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Related Concept Videos

Mutations01:35

Mutations

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Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
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Mutations01:39

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Mutations in Microorganisms01:18

Mutations in Microorganisms

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Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
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Mismatch Repair01:20

Mismatch Repair

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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Updated: Mar 10, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via

Juntao Deng1, Miao Gu1, Pengyan Zhang1

  • 1Department of Automation, Tsinghua University, Shuangqing Road 30, Haidian District, Beijing, 100084, China.

Briefings in Bioinformatics
|March 8, 2026
PubMed
Summary
This summary is machine-generated.

Predicting how mutations affect protein-protein interactions (PPIs) is crucial for understanding diseases. New deep learning models, MutPPI and MutPPI-plus, accurately predict these stability changes (ΔΔG), aiding protein engineering.

Keywords:
binding free energy changedata augmentationdeep learningmutationprotein–protein interaction

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Protein-protein interactions (PPIs) are fundamental to cellular processes.
  • Dysregulation of PPIs is implicated in numerous diseases.
  • Accurate prediction of mutation effects on PPI stability (ΔΔG) is vital for disease mechanism elucidation and protein engineering.

Purpose of the Study:

  • To develop and validate advanced computational models for predicting the impact of mutations on protein-protein interaction stability.
  • To enhance predictive accuracy by integrating structural, evolutionary, and multimodal data.
  • To introduce a novel data augmentation strategy for improved model generalization.

Main Methods:

  • Development of MutPPI, a graph-based deep learning model utilizing GIN-GAT for feature extraction from protein complex structures.
  • Integration of evolutionary information from protein language models to create MutPPI-plus.
  • Implementation of a mutation-path-based data augmentation strategy to enrich input modalities.
  • Performance evaluation on benchmark datasets including single-point (S645) and multi-point mutation datasets (SM_ZEMu, SM595, SM1124).

Main Results:

  • MutPPI demonstrated superior performance over 12 existing methods on the S645 dataset.
  • MutPPI-plus, incorporating evolutionary information, achieved enhanced predictive accuracy.
  • The mutation-path data augmentation strategy improved the generalization of both models.
  • MutPPI-plus with data augmentation achieved state-of-the-art results on multiple datasets, significantly outperforming DDMut-PPI.

Conclusions:

  • The developed multimodal deep learning models (MutPPI and MutPPI-plus) offer a versatile and accurate computational tool for predicting ΔΔG.
  • The physically informed data augmentation method enhances model generalization and predictive power.
  • These advancements facilitate rational protein design and deepen the understanding of mutation effects in disease contexts.