Title: Training large margin host-pathogen protein-protein interaction predictors,
Abstract: Detection of protein-protein interactions (PPIs) plays a vital role in
molecular biology. Particularly, infections are caused by the interactions of
host and pathogen proteins. It is important to identify host-pathogen
interactions (HPIs) to discover new drugs to counter infectious diseases.
Conventional wet lab PPI prediction techniques have limitations in terms of
large scale application and budget. Hence, computational approaches are
developed to predict PPIs. This study aims to develop large margin machine
learning models to predict interspecies PPIs with a special interest in
host-pathogen protein interactions (HPIs). Especially, we focus on seeking
answers to three queries that arise while developing an HPI predictor. 1) How
should we select negative samples? 2) What should be the size of negative
samples as compared to the positive samples? 3) What type of margin violation
penalty should be used to train the predictor? We compare two available methods
for negative sampling. Moreover, we propose a new method of assigning weights
to each training example in weighted SVM depending on the distance of the
negative examples from the positive examples. We have also developed a web
server for our HPI predictor called HoPItor (Host Pathogen Interaction
predicTOR) that can predict interactions between human and viral proteins. This
webserver can be accessed at the URL:
this http URL.