A model based on minimotifs for classification of stable protein-protein complexes

Prediction of protein-protein interactions (PPIs) is an important problem in biology, since interactions play key role in most biological processes and functions in living cells. PPIs have been studied from many perspectives. Of these, an important problem is prediction of different complex types such as obligate vs. non obligate and transient vs. permanent, among others. We focus on prediction of obligate protein complexes, which are more stable and perform a specific function, as opposed to transient and non-obligate complexes which last for a short period of time. We have modeled the prediction problem using minimotifs, aka short-linear motifs, to extract information contained in the protein sequences to distinguish between obligate and non-obligate PPIs. Incorporating different classifiers such as the k-nearest neighbor (k-NN), the support vector machine (SVM) and linear dimensionality reduction (LDR) yields a very powerful scheme for prediction. On two well-known datasets, the model delivers classification accuracies as high as 99%. Analysis and cross-dataset validation show that the information contained in the training sequences is crucial for prediction and determination of stability in PPIs.