This paper presents a framework for predicting protein-protein interactions 
(PPI) that integrates structure-based information with other functional 
annotations, e.g. GO, co-expression and co-localization, etc., Given two protein 
sequences, the structure-based interaction prediction technique threads these 
two sequences to all the protein complexes in the PDB and then chooses the best 
potential match. Based on this match, structural information is incorporated 
into logistic regression to evaluate the probability of these two proteins 
interacting. This paper also describes a random forest classifier which can 
effectively combine the structure-based prediction results and other functional 
annotations together to predict protein interactions. Experimental results 
indicate that the predictive power of the structure-based method is better than 
many other information sources. Also, combining the structure-based method with 
other information sources allows us to achieve a better performance than when 
structure information is not used. We also tested our method on a set of 
approximately 1000 yeast genes and, interestingly, the predicted interaction 
network is a scale-free network. Our method predicted some potential 
interactions involving yeast homologs of human disease-related proteins.
SUPPLEMENTARY INFORMATION: http://theory.csail.mit.edu/struct2net