In silico two-hybrid system for the selection of physically interacting protein pairs.
Deciphering the interaction links between proteins has become one of the main tasks of experimental and bioinformatic methodologies. Reconstruction of complex networks of interactions in simple cellular systems by integrating predicted interaction networks with available experimental data is becoming one of the most demanding needs in the postgenomic era. On the basis of the study of correlated mutations in multiple sequence alignments, we propose a new method (in silico two-hybrid, i2h) that directly addresses the detection of physically interacting protein pairs and identifies the most likely sequence regions involved in the interactions. We have applied the system to several test sets, showing that it can discriminate between true and false interactions in a significant number of cases. We have also analyzed a large collection of E. coli protein pairs as a first step toward the virtual reconstruction of its complete interaction network.
Pazos
Florencio
F
Protein Design Group, CNB-CSIC, Madrid, Spain.
Valencia
Alfonso
A
D000818:Animals; D001426:Bacterial Proteins / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D029968:Escherichia coli Proteins / Q000737:chemistry / Q000378:metabolism; D019143:Evolution, Molecular; D009154:Mutation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D016415:Sequence Alignment; D020798:Two-Hybrid System Techniques
D001426:Bacterial Proteins; D029968:Escherichia coli Proteins; D011506:Proteins