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

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

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).

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.

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.

On the opposite case, transfer and multitask learning methods are preferred. The computational methods primarily utilize sequence information, protein structure and known interactions. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions. 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.

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This has motivated some studies to overcome this problem by removing the need for negative data through using alternative methods (Mukhopadhyay et al., 2010, 2012, 2014; Mondal et al., 2012; Ray et al., 2012). Machine learning based methods which formulate PPI prediction as a classification task use both interacting and non-interacting protein pairs as positive and negative classes, respectively. Constructing negative class is not straightforward due to the fact that there is no experimentally verified non-interacting pair. They integrate bi-clustering with association rule mining, utilizing only positive samples to predict virus-human interactions.

(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.

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.

Data unavailability and scarcity refer to verified interacting PPIs, lack of verified non-interacting protein pairs and missing feature information for proteins. HIV-1 is the most distinguished pathogen which studied specifically using data-requiring machine learning methods. In this paper, we reviewed the studies which directly focused on computationally PHI prediction. Clearly some pathogen systems are well studied and targeted in more research regarding the availability of the required data. Knowledge transfer from related pathogen systems has shown to be an effective remedy, even for situations with no available interactions. Inter-species PPI predictions have gained more popularity in recent years. These methods enlighten a promising future direction for establishing computational methods which are augmented with additional transferred knowledge. Recent studies have found a new source of data to overcome these limitations. Computational methods may have important roles in paving the way for experimental PHI verifications by highlighting the high potential interactions and limiting the experimental scope which lead to expense reduction and probably the rapid knowledge development. Published approaches are categorized based on pathogen-host and the method they utilize. Therefore, the most important challenge for computationally prediction of PHIs, is the lack of available verified interactions and the relevant feature information in most of the pathogens systems.

(2014) introduces the stringent homology which does not rely only on intra-species template PPIs to discover interologs and make use of two different organisms as the source of template PPIs to predict PHIs. Zhou et al. They also claim that it is not only for the targeted host proteins which tend to be hub in their own PPI network and this is also true about targeting pathogen proteins.

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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.