Improving the Protein-Protein Interaction Prediction Engine (PIPE) with Protein Physicochemical Properties

Protein-protein interactions (PPI) serve an important role in both protein and cell function. They are difficult and time consuming to determine experimentally and thus benefit from in silico prediction methods. This thesis improves a high throughput, sequence-based protein-protein interaction prediction method called the protein-protein interaction engine (PIPE). A Python implementation of the scoring of PIPE was developed. Subsequently, a sequence-based solvent accessibility approach was integrated with PIPE, improving PPI prediction recall by 0.9% at 90% precision. Finally, 166 different sequence-based physicochemical properties were generated using the ProtDCal software tool and were integrated with PIPE using the framework developed in this thesis. The best of these properties improved the recall of PIPE by 2% at 90% precision. This improvement was shown to be statistically significant and was confirmed on a larger test set including 10,000 protein pairs known to interact and 10,000 randomly selected pairs, assumed not to interact.