THE INTERACTIONS BETWEEN PROTEINS OF A LIVING CELL ARE IMPORTANT FOR ITS BIOLOGICAL FUNCTIONS AND DETERMINING THESE INTERACTIONS PROVIDE VALUABLE INFORMATION ABOUT HOW DO BIOLOGICAL SYSTEMS WORK. REGARDING THE IMPORTANCE OF THE PROTEIN-PROTEIN INTERACTIONS (PPI), IN ONE HAND SEVERAL EXPERIMENTAL TECHNIQUES HAVE BEEN DEVELOPED TO DETECT THE PPIS AND ON THE OTHER HAND, COMPUTATIONAL METHODS TRY TO PREDICT THESE INTERACTIONS VIA MUCH CHEAPER AND FASTER WAYS. THE SEQUENCE OF A PROTEIN IS ONE OF THE MOST AVAILABLE INFORMATION AND SO, IT HAS BEEN USED BY SEVERAL COMPUTATIONAL APPROACHES TO PREDICT THE PPIS. IN THIS STUDY, WE USED N-Gram ENCODING APPROACH TO TRANSFORM THE SEQUENCES INFORMATION OF PROTEINS INTO FEATURE VECTORS. AFTER CONCATENATING THE VECTORS OF ALL PROTEIN PAIRS, A RELAXED VARIABLE KERNEL DENSITY ESTIMATOR (RVKDE) IS USED AS A MACHINE LEARNING TOOL TO PREDICT THE INTERACTIONS. OUR RESULTS SHOW THAT AMONG N-Gram ENCODING METHODS, 2-Gram HAS SUPERIOR PERFORMANCE AND IMPROVES THE PREDICTION RESULTS.