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

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.

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.

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.


(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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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).

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Each task is formulated as predicting PHI data between each pathogen and its host. Their goal is to predict intra-species pathogen PPIs as target with the aid of human PPIs as source network through defining a similarity matrix to act as a bridge between them. (2013b) to integrate knowledge from different pathogen-host systems to increase the prediction power of the combined model. Another study conducts three different individual classifiers on three GO features (molecular functions, cellular localization, and biological processes) on available protein features and at the same time three classifiers on alternative homolog features to exploit transfer learning. Another multitask formulation is used in Kshirsagar et al. A combination of supervised and semi-supervised approaches is proposed by Qi et al. For PPI prediction, a method was proposed in Xu et al. To define similarity between tasks and transfer shared knowledge, they assume that similar pathogens tend to target same biological process in human. They applied relatively same idea using a multi instance AdaBoost method to transfer homolog feature as the second instance of proteins (Mei, 2014; Mei and Zhu, 2014). Multitask learning uses commonalities among different domains and learn problem simultaneously between them within a shared task formulation, which leads to better performance rather conducting learning task on individual domain. To implement this idea, optimization problem is conducted and dissimilarities are penalized in the objective function. (2010) which uses collective matrix factorization originally proposed by Singh and Gordon (2008) to transfer knowledge from a relatively dense PPI network called source for predicting new PPIs in a sparse target PPI network. An ensemble classifier produces final result using weighting probability outputs of individual classifiers (Mei, 2013). In other words, commonality hypothesis is introduced that assumes pathway membership of human proteins in positive PHIs should be similar between different tasks. Semi-supervised task on partially positive labels is conducted to improve the supervised classification which trains multi-layer perceptron using labeled data. (2013a) for the cases where no known interaction is available by exploiting precisely chosen instances from a source task. A review paper, Xu and Yang (2011) presents some of the studies utilizing this idea in bioinformatics. (2010) through multitask learning. One of the promising remedies to tackle the problem of data scarcity is eliciting and transferring data from related domains to desired formulation. They use transfer learning in Kshirsagar et al.

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.

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Computational methods for predicting PHIs exploit known protein and domain interactions, and information on sequence of proteins. Network topology measures can complement these data. For instance, targeting hubs and bottleneck proteins in human PPI network by pathogen proteins is a well-accepted idea (Dyer et al., 2008; Durmu Tekir et al., 2012; Schleker and Trilling, 2013; Zheng et al., 2014), though, they are not the sole targeted proteins (Chen et al., 2012). Classic machine learning methods are valuable remedy for cases where enough data for training are available. However, valuable efforts have recently been performed to apply these techniques for situations suffer from scarcity of known interaction data using machine learning based methods as transfer and multitask learning (Xu et al., 2010; Kshirsagar et al., 2013a,b). Considering the relative availability of interaction data for HIV-Human system, notable number of studies are dedicated to this pathogen. Some other viral and bacterial pathogens are investigated and human is the main target as the host for investigation.


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.