A Bai / P Hourigan (@1.11) vs R Bains / A Poulos (@6.0)
03-10-2019

Our Prediction:

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

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.

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.

Table Table33 summarizes the published research for predicting PHIs based on homology information. For instance, the number of interologs within bacterial PPIs are not dignificant (Kshirsagar et al., 2013b) demonstrating that we cannot rely only on homolog information for every situation without being cautious about data availability. Clearly, it is reasonable to predict more genomic and proteomic data will be available in the future and consequently more accurate homologs are identified paving the way of studying less-known pathogens. The most important obstacle for using homology based methods is scarcity of available homolog information.

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.

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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.

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).

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.

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).

Introduction

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.

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