Training large margin host-pathogen protein-protein interaction predictors

TL;DR

This paper develops large margin machine learning models, including a weighted SVM, to predict host-pathogen protein interactions, addressing key challenges in negative sample selection and model training, and provides a web server for practical use.

Contribution

It introduces a novel weighted SVM approach for HPI prediction and compares negative sampling methods, advancing computational tools for infectious disease research.

Findings

Weighted SVM improves prediction accuracy.

Negative sampling method impacts model performance.

Web server HoPItor facilitates practical HPI predictions.

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: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor.