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Related Concept Videos

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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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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 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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Related Experiment Video

Updated: Dec 24, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.

Swagata Pahari1, Gen Li1, Adithya Krishna Murthy1

  • 1Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA.

International Journal of Molecular Sciences
|April 11, 2020
PubMed
Summary

SAAMBE-3D is a new, fast machine learning tool that accurately predicts how amino acid mutations affect protein-protein interactions, aiding disease research and genome-wide studies.

Keywords:
disruptive and non-disruptive mutationmachine learningprotein–protein bindingstabilizing and destabilizing mutation

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Protein-protein interactions (PPIs) are vital for cellular function.
  • Mutations altering PPIs can lead to diseases.
  • Accurate prediction of mutation effects on PPIs is crucial for disease understanding and genomic studies.

Purpose of the Study:

  • Introduce SAAMBE-3D, an advanced machine learning method for predicting mutation effects on PPIs.
  • Provide a fast and accurate computational tool for researchers.

Main Methods:

  • Developed SAAMBE-3D, a machine learning-based approach.
  • Benchmarked performance against the SKEMPI v2.0 database using five-fold validation.
  • Tested on homo- and hetero-dimer datasets from Cornell University via five-fold cross-validation.

Main Results:

  • Achieved high accuracy with Pearson correlation coefficients of 0.78-0.82.
  • Outperformed existing algorithms in blind tests.
  • Demonstrated exceptional performance with AUC of 1.0 and 0.96 on dimer datasets.
  • Prediction time is less than a second.

Conclusions:

  • SAAMBE-3D is an accurate and rapid tool for assessing mutation impacts on PPIs.
  • The software is available as a web server and standalone code for broad accessibility.
  • Facilitates genome-wide studies and research into disease mechanisms driven by altered PPIs.