A new sequence based encoding for prediction of host–pathogen protein interactions. (February 2019) - Kösesoy, İrfan and Gök, Murat and Öz, Cemil
Graphical abstract: Highlights: We proposed a novel and robust sequence based feature extraction method to predict pathogen–host interactions. We have applied our method (LBE) and other well known sequence based methods to the Bacillus Anthracis and Yersinia Pestis data sets. We have achieved to increase the accuracy of pathogen–host interaction prediction by using our conjecture that the location of amino acids can be used as a feature to differentiate proteins. Based on the experimental results, one can conclude that our method is more successful than other encoding methods, used in this study, with decision tree (RF and j48) and instance based (kNN) classifiers. Abstract: Pathogen–host interactions are very important to figure out the infection process at the molecular level, where pathogen proteins physically bind to human proteins to manipulate critical biological processes in the host cell. Data scarcity and data unavailability are two major problems for computational approaches in the prediction of pathogen–host interactions. Developing a computational method to predict pathogen–host interactions with high accuracy, based on protein sequences alone, is of great importance because it can eliminate these problems. In this study, we propose a novel and robust sequence based feature extraction method, named Location Based Encoding, to predict pathogen–host interactions with machine learning based algorithms. In this context, we use Bacillus Anthracis and Yersinia Pestis data sets as the pathogen organisms and human proteins as the host model to compare our method with sequence based protein encoding methods, which are widely used in the literature, namely amino acid composition, amino acid pair, and conjoint triad. We use these encoding methods with decision trees (Random Forest, j48), statistical (Bayesian Networks, Naive Bayes), and instance based (kNN) classifiers to predict pathogen–host interactions. We conduct different experiments to evaluate the effectiveness of our method. We obtain the best results among all the experiments with RF classifier in terms of F1, accuracy, MCC, and AUC.