A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Conclusion <s> Human immunodeficiency virus-1 (HIV-1) in acquired immune deficiency syndrome (AIDS) relies on human host cell proteins in virtually every aspect of its life cycle. Knowledge of the set of interacting human and viral proteins would greatly contribute to our understanding of the mechanisms of infection and subsequently to the design of new therapeutic approaches. This work is the first attempt to predict the global set of interactions between HIV-1 and human host cellular proteins. We propose a supervised learning framework, where multiple information data sources are utilized, including cooccurrence of functional motifs and their interaction domains and protein classes, gene ontology annotations, posttranslational modifications, tissue distributions and gene expression profiles, topological properties of the human protein in the interaction network and the similarity of HIV-1 proteins to human proteins’ known binding partners. We trained and tested a Random Forest (RF) classifier with this extensive feature set. The model’s predictions achieved an average Mean Average Precision (MAP) score of 23%. Among the predicted interactions was for example the pair, HIV-1 protein tat and human vitamin D receptor. This interaction had recently been independently validated experimentally. The rank-ordered lists of predicted interacting pairs are a rich source for generating biological hypotheses. Amongst the novel predictions, transcription regulator activity, immune system process and macromolecular complex were the top most significant molecular function, process and cellular compartments, respectively. Supplementary material is available at URL www.cs.cmu.edu/~oznur/hiv/hivPPI.html <s> BIB001 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Conclusion <s> Motivation: Protein–protein interactions (PPIs) are critical for virtually every biological function. Recently, researchers suggested to use supervised learning for the task of classifying pairs of proteins as interacting or not. However, its performance is largely restricted by the availability of truly interacting proteins (labeled). Meanwhile, there exists a considerable amount of protein pairs where an association appears between two partners, but not enough experimental evidence to support it as a direct interaction (partially labeled). ::: ::: Results: We propose a semi-supervised multi-task framework for predicting PPIs from not only labeled, but also partially labeled reference sets. The basic idea is to perform multi-task learning on a supervised classification task and a semi-supervised auxiliary task. The supervised classifier trains a multi-layer perceptron network for PPI predictions from labeled examples. The semi-supervised auxiliary task shares network layers of the supervised classifier and trains with partially labeled examples. Semi-supervision could be utilized in multiple ways. We tried three approaches in this article, (i) classification (to distinguish partial positives with negatives); (ii) ranking (to rate partial positive more likely than negatives); (iii) embedding (to make data clusters get similar labels). We applied this framework to improve the identification of interacting pairs between HIV-1 and human proteins. Our method improved upon the state-of-the-art method for this task indicating the benefits of semi-supervised multi-task learning using auxiliary information. ::: ::: Availability: http://www.cs.cmu.edu/~qyj/HIVsemi ::: ::: Contact: qyj@cs.cmu.edu <s> BIB002 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Conclusion <s> Background ::: Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein–protein interactions (PPIs), they have primarily focused on intra-species protein–protein interactions. <s> BIB003
We provide an overview of computational methods on the analysis of HIV-1-human PPI network and the prediction of novel PPIs between HIV-1 and human proteins. We have extensively surveyed a number of works, from both analysis and prediction aspects. The mostly used database, which has been extensively used for these studies, is the HHPID database. Some of the studies refined this curated data set before using it in their methods. For example, Qi et al. BIB002 provided an expert's view of the interaction data set for selecting the positive and negative samples before applying classification task. Similar type of rectification of this data set is noticed in several other studies. However, the availability of this data set of HIV-interacting human Review on analysis and prediction of HIV-1-human PPI | 847 proteins raises the possibility of identifying putative novel drug targets by in silico methods. The approaches designed for network analysis used different computational and statistical methods and some topological properties of network to uncover the inherent relationship between HIV-1 and human proteins. Among all the works we have surveyed here, biclustering technique is extensively used in network analysis as well as for PPI prediction. In the network analysis field, we find a gradual change in the analysis technique. Former works are fully devoted to the analysis of topological properties of the HIV-1-human PPI network. Recently analysis of bicliques or biclusters extracted from the HIV-1-human PPI network sheds light on the modular structures of the network. Several techniques we have reviewed here pertaining to network analysis, have taken this approach. The techniques for interaction prediction are also regarded as important resources for discovering interesting relationships between HIV-1 and human proteins. Although the works in this category primarily predict some novel interactions between HIV-1 and human proteins, the methods used for this purpose make use of divergent techniques. Classification-based methods are widely used for prediction. For example, Dyer et al. BIB003 made use of SVM-based classifier, whereas RF is used in Tastan et al. BIB001 and Qi et al. BIB002 . However, all the classification techniques suffer from insufficiency of negative examples. To compensate for this problem, different techniques are used. However, as their predicted interactions have little overlap with experimentally verified HDF sets, it is hard to believe that these interactions surely exist in reality. However, the conformal approach we have studied here also gives confidence to a new interaction based on the feature set derived by Tastan et al. As this feature set is rich enough, the interactions with a high P-value are reliable with some confidence. It also may be noted that the predictions in Doolittle et al. are associated with low P-values but high overlap with HDFs. This contrasts with the results of Tastan et al., indicating that the two methods are probably predicting two different subsets of true PPIs. One of the aims of reviewing all these methodologies and examining their predicted interaction sets is to highlight the common ones, which are supported by most of the techniques. Though this number is small, but there is enough reason to believe these interactions to be real as most of the methodologies agree on them. In this article, we completely focus on the computational effort that has been made for analysing HIV-1-human PPI network and predicting interactions between HIV-1 proteins and human proteins. A brief discussion on some experimental approaches for HIV-1-human PPI identification is provided in the supplementary file (experimental-technique.docx). Similar type of review may be done on other type of viruses and pathogens. If sufficient information is available on a viral-host interaction network, these can be analysed accordingly. Although the works we reviewed here suffer from several problems and deficiencies, there are reasons to believe that these in silico approaches will soon be of immense importance for understanding infectious diseases in general and in particular for the development of drugs and vaccines to counter viral replication or the pathogenic effects of infection. There are more to discover from the systems biology perspective that focus on data integration from different sources and complex interplay between virus and host proteins in the virus-host PPI network. A detailed understanding of this intricate network and a precise study of its properties will yield novel insights into the development of new therapeutics and potentially new intervention strategies.