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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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 polypeptide...
Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Protein Organization01:24

Protein Organization

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.
The primary structure of a protein is its amino acid sequence.
Protein Organization01:24

Protein Organization

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.
The primary structure of a protein is its amino acid sequence.

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Related Experiment Video

Updated: May 25, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

From memorization to generalization: Why physics will improve machine learning -based prediction of protein

Ernest Glukhov1, Sandor Vajda2, Dima Kozakov3

  • 1Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.

Current Opinion in Structural Biology
|May 23, 2026
PubMed
Summary

Predicting protein-protein interactions (PPIs) is challenging. New physics-integrated machine learning models improve generalization by incorporating physical principles, moving beyond simple pattern memorization for more reliable predictions.

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: May 25, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Computational Biology
  • Structural Biology
  • Machine Learning

Background:

  • AlphaFold-like models excel at predicting single protein structures.
  • Reliable prediction of protein-protein interactions (PPIs), especially antibody-antigen docking, remains a significant challenge.
  • Current limitations often arise from data scarcity and reliance on pattern memorization.

Purpose of the Study:

  • To review emerging physics-integrated machine learning approaches for improving PPI prediction generalization.
  • To categorize strategies enhancing the physical plausibility of predictions.
  • To outline a path from memorization-based models to physically generalizable models.

Main Methods:

  • Enriching model inputs with physics-based sampling (e.g., molecular dynamics, fast Fourier transform ensembles).
  • Designing neural network architectures with strict geometric inductive biases (e.g., SE(3)-equivariance).
  • Constraining generative models using physical energy functions or potentials.

Main Results:

  • Hybrid strategies combining machine learning with physics principles show promise for overcoming data scarcity limitations.
  • These approaches aim to enforce physical plausibility throughout the prediction pipeline.
  • Improved generalization capabilities are observed for out-of-distribution targets compared to standard models.

Conclusions:

  • Physics-integrated machine learning offers a promising direction for advancing protein-protein interaction prediction.
  • Hybrid strategies represent a crucial step towards achieving true physical generalization in structural biology predictions.
  • This transition from memorization to physical generalization is key for tackling complex biological problems like antibody-antigen docking.