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

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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

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.

Mei (2013) uses homolog information when the features of a protein is unavailable. Applying machine learning methods and specially supervised learning for situations suffer from data scarcity is challenging. First, they rely on homologous proteins data to provide feature values like GO annotations and gene expression data. Being limited to well-studied pathogen systems like HIV-1 is the consequence of data dependency. (2012) two different methods are proposed including information transfer from other species and model-based imputation. However, for proteins with no available homolog, they have modeled gene expression value distribution. The first method is called RF which initiates the missing data to mean value and re-estimate it by choosing the nearest leaf node of the created forest. For instance, in Kshirsagar et al. They have compared the proposed Cross species imputation with other imputation techniques. This contributes a lot and downgrades the missing data significantly. Clear improvements are reported in comparison with the listed imputation methods. Recently, some solutions are proposed to overcome this limitation by offering substituted values for missing data. It should be noted that using solely statistical methods for estimating features like GO values will be hard due to high dimensionality. Pessimistic experiment, which uses only homolog features to train and test without incorporating any base proteins (called 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. They have designed various experiments to show the performance of substituting homolog features. Another intuitive method is choosing the average of the feature values and the last compared method is discarding any pair with missing value which leads to a reduced dataset.

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.

Introduction

However, it should be noted that making use of a lot of features without enriching training data may lead to over fitting in the model (Mei, 2014). Outperforming other features was the motivation for some studies to use GO features in PHI prediction (Mei, 2013, 2014) while features extracted from protein sequences, reported as not promising (Yu et al., 2010). Various studies utilize different sets of biological information through data integration to improve the prediction performance. Table Table22 summarizes the utilized features within different studies on PHI prediction, providing all the cataloged feature information is not always possible for all pathogen systems. Furthermore, various features claimed to have different predictive effects in PHI prediction.

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.

(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.

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).

References

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.

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.

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Alison Bai

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.

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