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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Explaining protein-protein interactions with knowledge graph-based semantic similarity.

Rita T Sousa1, Sara Silva1, Catia Pesquita1

  • 1LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal.

Computers in Biology and Medicine
|February 3, 2024
PubMed
Summary
This summary is machine-generated.

We introduce KGsim2vec, a novel explainable artificial intelligence method for biomedical research. This approach enhances machine learning model interpretability by using knowledge graph semantic similarity, improving predictions and identifying data biases.

Keywords:
Explainable artificial intelligenceKnowledge graphMachine learningProtein–protein interaction predictionSemantic similarity

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Area of Science:

  • Biomedical informatics
  • Artificial intelligence in science
  • Machine learning for drug discovery

Background:

  • Machine learning (ML) and artificial intelligence (AI) are increasingly used in biomedical applications like protein-protein interaction prediction.
  • Explainable AI (XAI) is crucial for scientific discovery, enabling understanding of ML mechanisms and data bias.
  • Knowledge graphs (KGs) represent domain knowledge but are often explored using non-explainable embeddings.

Purpose of the Study:

  • To develop an explainable method for representing entities in knowledge graphs for biomedical applications.
  • To enhance the interpretability and predictive performance of machine learning models in complex biological domains.
  • To provide an alternative to non-explainable knowledge graph embeddings.

Main Methods:

  • Proposed KGsim2vec, a novel method for generating explainable vector representations using aspect-oriented semantic similarity in knowledge graphs.
  • Utilized various machine learning models (decision trees, genetic programming, random forest, eXtreme gradient boosting) to predict entity relations.
  • Computed similarity across multiple semantic aspects within the knowledge graph.

Main Results:

  • Considering multiple semantic aspects in entity similarity representation improved both explainability and predictive performance.
  • KGsim2vec outperformed traditional black-box methods like knowledge graph embeddings and graph neural networks.
  • The developed models were capable of capturing biological phenomena and revealing data biases.

Conclusions:

  • KGsim2vec offers a more explainable and effective approach for biomedical applications compared to current embedding-based methods.
  • The method enhances scientific discovery by providing interpretable insights into biological relationships and data characteristics.
  • This work advances the integration of explainable AI with knowledge graphs for robust biomedical data analysis.