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

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.

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Filtering the set of potential interactions is the last step which is performed using the biological contexts of proteins and a network-level filter. The outcome of this process is decreasing the potential PHIs by about five orders of magnitude. Preliminary ideas presented in Davis et al. Therefore, unavailability of the spatial structural information would restrict the applicability of this method. Furthermore, they have only the ability to collect limited number of benchmark PPIs from literature to evaluate their prediction performance. A number of studies are based on structural similarities and use template PPIs to detect similar interacting pairs within host and pathogen proteins. The main drawback of this method is that finding high similarity between pathogen proteins and proteins with known structure is not guaranteed for all pathogen proteins. Their method starts with a set of host and pathogen proteins and then sequence matching procedures are used to determine the similarities between the host or pathogen proteins with known structure or known interaction protein partners. (2007) called comparative modeling and was based on their prior work (Davis et al., 2006). Sequence similarity score is only used when structure information is unavailable as a statistical potential assessment, to predict interacting partners.

Reliable experimental methods are time-consuming and expensive, making it unjustifiable to evaluate all possible PHIs. The methods which were successfully applied specifically for PHI prediction in the literature are categorized based on pathogen-host systems in Table Table11. Despite the critical need to improve the PHI knowledge, current progress is not adequate, suffering from scarcity of available experimental PHI data. In this paper, we concentrate on these computational studies, which are mandatory for enriching the available data and consequently increasing the pace of research in the field. At this point, computational approaches come to help by predicting putative PHIs. For instance, considering about 26,000 human proteins paired with a few thousands of pathogen proteins lead to millions of protein pairs to test experimentally. Scarce verified interactions are collected within a number of databases like HPIDB (Kumar and Nanduri, 2010), PATRIC (Wattam et al., 2014), PHISTO (Durmu Tekir et al., 2013), VirHostNet (Navratil et al., 2009), and VirusMentha (Calderone et al., 2014).


However, homology to known interactions is not sufficient for evaluating the biological evidence of the predicted results. Different filtering techniques should be considered for assessing the feasibility of the interactions under an in vivo condition and consequently decreasing the false positives. Simplicity and clear biological basis are the main advantages of these methods. The conserved interaction is called as Interolog. The simple method of identifying Interologs is as follows: Consider a template PPI pair (a, b) in a source species, find the homolog a in the host and the homolog b in the pathogen, conclude that (a, b) interact. The rationale behind this type of methods is the expectation of conserved interactions between a pair of proteins which have interacting homologs in another species.

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.

Then the data was searched for experimentally verified effectors or their homologs in another bacteria. The result is the possible interactions between Salmonella effectors and host proteins. They collect a list of Pfam domains and bacterial-human proteins which contains one of the listed domains. (2012) presents a method to predict and rank bacteria-human PPIs based on domain-domain interactions. The work in Arnold et al.

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).

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.

Ranking tabs

(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.

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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.

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.

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