This thesis evaluates and extends the state-of-the-art in sequence-based binary protein-protein interaction (PPI) prediction for bacterial species. Accurately predicting PPIs for bacteria enables researchers to quickly identify targets for developing antimicrobial drugs and expand interactome knowledge for bacteria. E. coli is used here as a model organism for bacteria. A systematic and unbiased evaluation of four classifiers, SPRINT, DPPI, DEEPFE, and PIPR is conducted on new E. coli datasets. Classifier enhancement is accomplished using a stacked reciprocal perspective (RP) classifier, a technique recently developed by the cuBIC lab. Cross-validation results improve by 16.6% for the area under precision-recall (auPR) curve compared to the best base classifier, which increases to 262.5% when considering a 1:100 positive-to-negative sample imbalance. The results of this thesis also indicate the need for new benchmark datasets, more bacterial PPI data, and consistent evaluation protocols to be followed for new PPI predictions.