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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 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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DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.

Yunzhuo Zhou1,2, YooChan Myung1,2, Carlos H M Rodrigues1

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DDMut-PPI, a new deep learning model, accurately predicts how mutations affect protein-protein interactions (PPIs). This tool enhances understanding of disease mechanisms and aids in developing new therapeutics by analyzing binding free energy changes.

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Area of Science:

  • Computational biology
  • Biochemistry
  • Genomics

Background:

  • Protein-protein interactions (PPIs) are crucial for cellular processes and disease pathogenesis.
  • Predicting mutation effects on PPIs is vital for drug discovery but challenging for current computational methods.
  • Existing tools often face limitations in balancing prediction accuracy and computational efficiency.

Purpose of the Study:

  • To develop an advanced deep learning model, DDMut-PPI, for accurate prediction of binding free energy changes in PPIs due to mutations.
  • To improve the efficiency and precision of predicting the impact of single and multiple point mutations on protein interaction stability.
  • To provide a valuable computational tool for researchers investigating the molecular basis of PPIs and associated diseases.

Main Methods:

  • Developed DDMut-PPI, a deep learning model utilizing a Siamese network architecture and graph convolutional networks.
  • Employed residue-specific embeddings from ProtT5 protein language model as node features.
  • Integrated molecular interaction data as edge features, combining evolutionary and spatial information of protein interfaces.

Main Results:

  • DDMut-PPI achieved a high prediction accuracy with a Pearson correlation of up to 0.75 (RMSE: 1.33 kcal/mol).
  • The model demonstrated robust performance in predicting both stabilizing and destabilizing mutations.
  • DDMut-PPI outperformed existing state-of-the-art methods in predicting changes in PPI binding free energy.

Conclusions:

  • DDMut-PPI represents a significant advancement in predicting mutation effects on protein-protein interactions.
  • The model offers a powerful and efficient tool for researchers in structural biology, drug discovery, and disease mechanism studies.
  • DDMut-PPI is accessible via a web server and API, facilitating broader research applications.