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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...

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,...

Conserved Binding Sites

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Protein Complex Assembly

Protein Complex Assembly

Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types. Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...

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Related Experiment Video

Updated: Dec 30, 2025

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method.

Xiaodi Yang¹, Shiping Yang², Qinmengge Li³

¹State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.

Computational and Structural Biotechnology Journal

|January 24, 2020

Summary

This study introduces a computational method using doc2vec and Random Forest to predict human-virus protein-protein interactions (PPIs). The approach offers high accuracy, aiding in understanding viral infections and accelerating research.

Keywords:

AC, Auto Covariance ACC, Accuracy AUC, area under the ROC curve AUPRC, area under the PR curve Adaboost, Adaptive Boosting CT, Conjoint Triad Doc2vec Embedding Human-virus interaction LD, Local Descriptor MCC, Matthews correlation coefficient ML, machine learning MLP, Multiple Layer Perceptron MS, mass spectroscopy Machine learning PPIs, protein-protein interactions PR, Precision-Recall Prediction Protein-protein interaction RBF, radial basis function RF, Random Forest ROC, Receiver Operating Characteristic SGD, stochastic gradient descent SVM, Support Vector Machine Y2H, yeast two-hybrid

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Area of Science:

Computational biology
Virology
Bioinformatics

Background:

Identifying human-virus protein-protein interactions (PPIs) is crucial for understanding viral infection mechanisms.
Experimental determination of PPIs is laborious and time-consuming, necessitating computational approaches.
Computational methods provide hypotheses for large-scale interactome analysis.

Purpose of the Study:

To develop an accurate computational framework for predicting human-virus PPIs.
To leverage unsupervised sequence embedding for protein feature representation.
To offer a freely accessible web server for predicting host-pathogen PPIs.

Main Methods:

Applied doc2vec, an unsupervised sequence embedding technique, to generate low-dimensional feature vectors for protein sequences.
Trained a Random Forest (RF) classifier using a comprehensive dataset of known human-virus PPIs.
Compared the proposed method against existing human-virus PPI prediction techniques.

Main Results:

Achieved excellent predictive accuracy, outperforming various machine learning algorithms and sequence encoding schemes.
Demonstrated competitive and promising performance compared to three established human-virus PPI prediction methods.
The doc2vec encoding effectively captures contextual information relevant to protein-protein interactions.

Conclusions:

The proposed computational framework, utilizing doc2vec and RF, is a powerful tool for predicting human-virus PPIs.
This method accelerates the exploration of host-pathogen interactions and provides insights into viral mechanisms.
The accessible web server facilitates further research in host-pathogen interactome studies.