A Bai / P Hourigan (@1.11) vs R Bains / A Poulos (@6.0)

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A Bai / P Hourigan will win
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A Bai / P Hourigan – R Bains / A Poulos Match Prediction | 03-10-2019 02:35

Interactions between pathogen and host proteins allow pathogenic microorganisms to manipulate host mechanisms in order to use host capabilities and to escape from host immune responses (Dyer et al., 2010). Many studies concerning identification of protein interactions and their associated networks were published (Aloy and Russell, 2003). Inter-species interactions may take many forms; in this survey, however, we focus on PPIs between pathogens and their hosts. Therefore, a complete understanding of infection mechanisms through PHIs is crucial for the development of new and more effective therapeutics. Most of the previous studies were primarily focused on determining protein-protein interactions (PPIs) within a single organism (intra-species PPI prediction), while the prediction of PPIs between different organisms (inter-species PPI prediction) has recently emerged. Pathogen-host interaction (PHI) prediction is worthwhile to enlighten the infection mechanisms in the scarcity of experimentally-verified PHI data.

Some studies validate their results by measuring the shared interactions with other published materials (Mukhopadhyay et al., 2012, 2014; Segura-Cabrera et al., 2013). The lack of gold standard PHI data and the complexity of PHI mechanisms lead to a hard assessment phase, in a way that predicted interactions are rarely supported by a biological basis. Here we focus on computational metrics which are widely used in publications to evaluate the accuracy of their results, which are shown in Table Table66.

(2007) due to applying different techniques and datasets for same pathogen-host system. The assumption is that when two orthologous groups are shared between more than two species, there will be a potential Interolog between those orthologous groups. The notable point is negligible intersection of the predicted interactions with those of the reported predictions in Dyer et al. Another research uses high confidence intra-species PPIs to detect Interologs using ortholog information (Lee et al., 2008). The potential interactions are filtered using gene ontology annotations followed by pathogen sequence filtering based on the presence or absence of translocational signals to refine the predictions.

Pessimistic experiment, which uses only homology features for train and test without incorporating any base proteins (called as target in the article) has promising results, indicating that using homolog information is an effective substitute for the target information to tackle the problem of data unavailability. Mei (2013) uses homolog information (features) when the features of a protein is unavailable. They have designed different experiments to show the performance of substituting homology features. Homolog knowledge can be used indirectly as a remedy for data scarcity and data unavailability by homolog knowledge transfer.

They apply the same method for developing an interaction network between Dengue virus and its hosts (Doolittle and Gomez, 2011). Human proteins which have high structural similarity to a HIV protein are identified and their known interacting partners are determined as targets. Table Table44 summarizes the conducted research for predicting PHIs based on structural data. Again, with a similar idea those proteins with comparable structures share interaction partners. Another research developed a map of interactions between HIV-1 and human proteins based on protein structural similarity (Doolittle and Gomez, 2010). A comparison of known crystal structures is performed to measure structural similarity between host and pathogen proteins. The work suffers from the lack of assessment data in a way that, very limited number of used benchmark PPIs are specific to the viral pathogen. These predicted results refined by two filtering steps using data from the recent RNAi screens and cellular co-localization information. The assumption is that HIV proteins have the same interactions as their human peers.

They predict PPIs using PreDIN (Kim et al., 2002) and PreSPI (Han et al., 2004) algorithms based on domain information. They presented XooNET which provides about 3500 possible interaction pairs as well as the graphical visualizations of the interaction networks. (2007) which makes use of domain information from InterProScan (Quevillon et al., 2005). A similar knowledge source is chosen in Kim et al. A study for prediction of interacting proteins of rice and Xanthomonas oryzae pathovar oryzae (Xoo) also uses domain information (Kim et al., 2008).

A homology detection method using template PPI databases, DIP (Salwinski et al., 2004) and iPfam (Finn et al., 2014), is published in Krishnadev and Srinivasan (2008) to predict PHI pairs. Searching the sequences of host and pathogen proteins within two template databases are conducted to find a superset of all interactions which are physically and structurally compatible. The authors have applied the same procedure for different pathogens in their subsequent works (Tyagi et al., 2009; Krishnadev and Srinivasan, 2011). These potential interactions are refined within two additional filtering steps, to detect biologically feasible interactions including integration of expression and sub-cellular localization data.

Conformal prediction is used in Nouretdinov et al. Their approach also allows the user to determine confidence level for prediction. This method evaluates the conformance of new pairs with interacting pairs using a method called non-conformity measure (NCM) which shows distinction measure of an example regarding others. (2012) and the results are compared with those of Tastan et al. (2009) to assess the predictions.

The idea of exploiting domains as building blocks of proteins for predicting PPIs is well-studied for single organisms (Wojcik and Schchter, 2001; Pagel et al., 2004) regarding the fact that domains are the mediators of interactions. However, small list of interactions are presented and their biological relevance are not strongly evaluated. (2007) is one of the pioneer published research for predicting PHIs. To apply this idea to a pathogen-host system, they identify domains in every host and pathogen proteins and compute the interaction probability for each pair of host and pathogen proteins that contain at least one domain. The approach presented in Dyer et al. To predict interactions between host and pathogen proteins, they present an algorithm that integrates protein domain profiles with interactions between proteins from the same organism. For every pair of functional domains (d, e) which is present in protein pair (g, h) respectively, the probability of interacting (g, h) is assessed using Bayesian statistics.


They emphasize the importance of constructing a high-resolution, 3D structural view of pathogen-host and within-host PPI networks to discover new principles of PHIs through their review paper in Franzosa et al. (2012). The method starts with extracting human interacting pairs from PDB and followed by mapping virus proteins to them by sequence similarity. Applicability of the method is limited to human-human and virus-human PPIs for which 3D structural models are available. Authors in Franzosa and Xia (2011) claim to significantly reduce the rate of false positives by presenting virus-human structural interaction network, in which, each PPI is associated with a high confidence 3D structural model.

Not all pathogen systems are appropriate for applying the mentioned domain based approaches, since domains and the related information are not available for all pathogens. (2009) concentrates on protein interactions based on short eukaryotic linear motifs (ELMs) for HIV-1 proteins interacting with human protein counter domains (CDs). They do not accept the idea of having relatively weak link among motif/domain bindings and the actual virus-host PPIs which is presented in Tastan et al. They predict two kinds of interactions for each virus protein, including direct human protein targets (called H1) which bind to virus via a human CD and a virus ELM and the second type includes indirect interactions in which, host proteins that their normal interactions with H1 proteins are potentially disrupted by competition with an HIV-1 protein. For instance, information on domains and the related statistics are not available for a considerable number of the HIV-1 proteins. Table Table55 summarizes the conducted research for predicting PHIs based on domain and motif knowledge. Regarding this limitation, the work in Evans et al. (2009).