The study of Protein–Protein Interactions (PPIs) has a crucial role in biology, medicine and the pharmaceutical industry. PPIs can be investigated from two aspects: The interaction partners of a specific protein and the amino acid residues participating in a given PPI. Information about a protein’s interaction partners allows scientists to construct protein interaction networks, such as signaling pathways, which in turn facilitate the understanding of many biological and clinical observations.
Following are the list of tools commonly used to PPIs predictions:
Protein-Protein Interaction Sites
A consensus neural network method for predicting protein-protein interaction sites
A server to predict interacting protein pairs and interacting sites by homology modeling of complex structures
Prediction of protein interfaces using an empirical model
Prediction of interaction hotspots from sequence
Automated decision-tree approach to predicting protein-protein interaction hot spots
A meta server for predicting protein-protein interaction sites. meta-PPISP is built on three individual web servers: cons-PPISP, PINUP, and Promate
Identification of optimal surface patches with the lowest docking desolvation energy values
Protein binding site prediction with an empirical scoring function
Other Sites (DNA, RNA, Metals)
Web server for predicting soft metal binding sites in proteins
A knowledge-based method for the prediction of DNA-protein interactions
Given the structure of a protein known to bind DNA, the method predicts residues that contact DNA using neural network method
Predicts DNA binding proteins for proteins with known 3D structure.
