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Protein-protein Interfaces

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 Networks

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,...

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Updated: Jun 9, 2026

SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells

SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells

Published on: March 3, 2017

Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins.

Yanjun Qi¹, Oznur Tastan, Jaime G Carbonell

¹NEC Labs America, Princeton, NJ 08540, USA. qyj@cs.cmu.edu

Bioinformatics (Oxford, England)

|September 9, 2010

Summary

This summary is machine-generated.

This study introduces a novel semi-supervised multi-task framework to enhance protein-protein interaction (PPI) prediction. The method effectively utilizes partially labeled data, improving upon existing state-of-the-art approaches for HIV-1 and human protein interactions.

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Last Updated: Jun 9, 2026

SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells

SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells

Published on: March 3, 2017

Peptide-based Identification of Functional Motifs and their Binding Partners

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

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

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

Published on: January 26, 2024

Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Protein-protein interactions (PPIs) are fundamental to biological processes.
Supervised learning for PPI prediction is limited by the scarcity of labeled interacting protein pairs.
Partially labeled data, representing potential interactions without definitive experimental evidence, is abundant.

Purpose of the Study:

To develop a semi-supervised multi-task framework for improved PPI prediction.
To leverage both labeled and partially labeled protein pair data.
To enhance the identification of interactions between HIV-1 and human proteins.

Main Methods:

A multi-task learning framework combining a supervised classification task and a semi-supervised auxiliary task.
Utilizing a multi-layer perceptron network for PPI prediction.
Exploring three semi-supervision approaches: classification, ranking, and embedding with partially labeled data.

Main Results:

The proposed framework effectively utilizes partially labeled data for PPI prediction.
The method demonstrated improved performance over state-of-the-art techniques for HIV-1 and human protein interactions.
Semi-supervised multi-task learning enhances the prediction accuracy by incorporating auxiliary information.

Conclusions:

Semi-supervised multi-task learning offers a powerful approach to overcome data limitations in PPI prediction.
The framework successfully improves the identification of interacting protein pairs, particularly in complex systems like HIV-1-human interactions.
This work highlights the potential of leveraging partially labeled data in biological network analysis.