Abstract
Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions.
Highlights
Many studies concerning identification of protein interactions and their associated networks were published (Aloy and Russell, 2003)
Homology detection method using template protein-protein interactions (PPIs) databases, DIP, and iPfam Interologs were inferred from ortholog information obtained from high confidence databases Homology detection method using template PPI databases, DIP, and iPfam Homology detection method using template PPI databases, DIP, and iPfam Introduce stringent homology which uses inter species template PPI Conserved pathogen-host interaction (PHI) network is generated using interacting proteins of the common conserved inter-species bacterial PPI Obtain host-pathogen interactome using sequence and interacting domain similarity to known PPIs Interolog and Domain based approaches are used to predict PHIs The ortholog information for the four species are integrated from different databases and interspecies PPI network is constructed followed by dynamic modeling of regulatory responses leads to identifying interactions
The work in Evans et al (2009) concentrates on protein interactions based on short eukaryotic linear motifs (ELMs) for HIV-1 proteins interacting with human protein counter domains (CDs)
Summary
Many studies concerning identification of protein interactions and their associated networks were published (Aloy and Russell, 2003). Pathogen-host interaction (PHI) prediction is worthwhile to enlighten the infection mechanisms in the scarcity of experimentally-verified PHI data. Scarce verified interactions are collected within a number of databases like HPIDB (Kumar and Nanduri, 2010), PATRIC (Wattam et al, 2014), PHISTO (Durmus Tekir et al, 2013), VirHostNet (Navratil et al, 2009), and VirusMentha (Calderone et al, 2014) At this point, computational approaches come to help by predicting putative PHIs. In this paper, we concentrate on these computational studies, which are mandatory for enriching the available data and increasing the pace of research in the field.
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