Related Experiment Video
Updated: May 8, 2026
08:38
Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
Published on: March 3, 2015
Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms
Eli Rodgers-Melnick1, Mark Culp, Stephen P DiFazio
1Department of Biology, West Virginia University, Morgantown, West Virginia, 26506, USA. stephen.difazio@mail.wvu.edu.
BMC Genomics
|September 11, 2013
Summary
We developed ENTS, a computational method to predict protein-protein interactions (PPIs) using only primary sequence data. This tool efficiently predicts whole-genome PPIs for eukaryotes, aiding in genome annotation and biological network discovery.
Area of Science:
- Computational Biology
- Bioinformatics
- Systems Biology
Background:
- Protein-protein interactions (PPIs) are crucial for understanding biological networks and emergent phenotypes.
- Experimental PPI identification is laborious, error-prone, and often lacks sufficient functional data for new organisms.
- Current computational PPI prediction methods can be conservative or data-intensive.
Purpose of the Study:
- To develop and validate a novel computational technique for predicting protein-protein interactions (PPIs) using only primary sequence data.
- To enable large-scale, whole-genome PPI prediction for eukaryotic organisms, including those with limited functional data.
- To provide a tool for de novo genome annotation and biological network exploration.
Main Methods:
- A random-forest based classification technique, termed ENTS (Efficient Network Topology Search), was developed.
- Input features included pairwise combinations of conserved domains and predicted subcellular localization of proteins.
- The method was applied to predict interactomes for *Populus trichocarpa*, *Saccharomyces cerevisiae*, *Homo sapiens*, *Mus musculus*, and *Arabidopsis thaliana*.
Main Results:
- ENTS efficiently predicts PPIs on a whole-genome scale for any eukaryotic organism.
- Predicted interactomes for multiple species, including the first for *Populus trichocarpa*, were generated.
- ENTS performance was comparable or superior to existing PPI predictors, including those using functional data.
- Predicted interactions demonstrated biological meaningfulness through functional annotation similarity and gene co-expression enrichment.
- Biological insights were gained, including metabolic pathway groupings, disease associations, and evolutionary dynamics of duplicated genes.
Conclusions:
- The ENTS classifier is a valuable tool for de novo genome sequence annotation.
- It provides initial insights into regulatory and metabolic network topology.
- ENTS reveals relationships not evident from traditional homology-based annotations.
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02:26Protein 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,...
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,...
02:26Protein 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,...
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,...
02:04Protein-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...
01:49Conserved 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 analyses the...
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 analyses the...
02:54Evolutionary Relationships through Genome Comparisons
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...