A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> The synthesis of a series of analogues of the monosaccharide alpha-glucosidase I inhibitor N-decyl-1-deoxynojirimycin (1) is described. With the incorporation of a single oxygen atom particularly at position seven in the N-decyl side chain, i.e. to give N-7-oxadecyl-dNM (4), the therapeutic ratio (alpha-glucosidase I inhibitory activity over toxicity in HepG2 cells) increases considerably. N-7-Oxadecyl-dNM inhibits purified porcine liver alpha-glucosidase I with an IC50 value of 0.28 microM. The position of the oxygen atom in the N-decyl side chain is of importance since N-3-oxadecyl-dNM is less active and, moreover, is toxic to HepG2 cells at 3 mM. Subsequently, the synthesis of a disaccharide inhibitor of alpha-glucosidase I is described. The aminodisaccharide ManNH2 alpha 1,2Glc (12) inhibits alpha-glucosidase I with an IC50 value of 15.7 microM. Two closely related monosaccharide derivatives of 12 did not inhibit the enzyme at low microM concentrations (no inhibition at 5 microM), showing the additional effect of binding of the aglycon fragment of the molecule to the active site of alpha-glucosidase I. Next, the N-alkyl-dNM derivatives were analysed for antiviral and immunomodulatory activity in-vitro. It is found that the most potent alpha-glucosidase I inhibitor from this study, N-7-oxadecyl-dNM (4) inhibits HIV-1 induced syncytia formation and lymphocyte proliferation in-vitro. Finally, compound 4 was also investigated in-vivo. N-7-Oxadecyl-dNM (4) reduced adjuvant-induced arthritis in rats making this compound a potential candidate for treating autoimmune diseases like rheumatoid arthritis. <s> BIB001 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks. <s> BIB002 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD. <s> BIB003 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions. <s> BIB004 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Motivation: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available. ::: ::: Results: First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings. ::: ::: Availability: The datasets used, the outcomes of the biclustering algorithms and the Bimax implementation for the reference model are available at http://www.tik.ee.ethz.ch/sop/bimax ::: ::: Contact: bleuler@tik.ee.ethz.ch ::: ::: Supplementary information: Supplementary data are available at http://www.tik.ee.ethz.ch/sop/bimax <s> BIB005 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> A screening process was applied to extracts made from Sutherlandia frutescens (L.) R. Br (Fabaceae) and Lobostemon trigonus (Boraginaceae) as identified by the Botany Department, University of Port Elizabeth to detect if any of the extracts inhibited the human immunodeficiency virus (HIV). For purposes of dereplication, sulphated polysaccharides were removed and bovine serum albumin (BSA) was included in the assays to adsorb non-specific tannins potentially present. In the reverse transcriptase (RT) assay, an aqueous extract of the Lobostemon leaves inhibited HIV-1 RT with an IC50 value of 49 microg/ml, while in the protease assay no inhibition was seen. In the alpha- and beta-glucosidase assays, no significant inhibition was seen with the inclusion of BSA, indicating tannin-based inhibitory effects on these two enzymes. The beta-glucuronidase inhibitory activity, however, was retained in the presence of BSA. The study shows that Sutherlandia extracts contain inhibitory compounds active against HIV target enzymes, while aqueous Lobostemon leaf extracts contain a potent HIV-1 RT inhibitor, thus showing a potential mechanistic action of these plants in aiding HIV-positive patients. <s> BIB006 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Transcription regulation networks control the expression of genes. The transcription networks of well-studied microorganisms appear to be made up of a small set of recurring regulation patterns, called network motifs. The same network motifs have recently been found in diverse organisms from bacteria to humans, suggesting that they serve as basic building blocks of transcription networks. Here I review network motifs and their functions, with an emphasis on experimental studies. Network motifs in other biological networks are also mentioned, including signalling and neuronal networks. <s> BIB007 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Brass AL, Dykxhoorn DM, Benita Y, et al. Science . 2008;319(5865):921–926 PURPOSE OF THE STUDY. The HIV genome encodes only 15 proteins and, therefore, must use multiple host-cell collaborators for successful replication and transmission. Required host-derived proteins include CD4 as the primary virus receptor and chemokine receptors as coreceptors. This study identified multiple other host proteins required for HIV activity. METHODS. Human cells known to be susceptible to HIV were exposed in vitro to HIV. Using small interfering RNAs able to inhibit each known gene in the human genome 1 at a time, the investigators tested whether HIV could establish an infection and copy itself. HIV dependence on >21 000 human genes was examined. RESULTS. More than 250 human genes were identified to be required for efficient HIV replication. Termed “HIV-dependency factors,” the products of these genes are known to participate in a broad array of cellular functions and implicate unsuspected pathways in the virus life cycle. CONCLUSIONS. The extraordinary dependence of HIV on human host proteins for efficient transmission and replication provides many new potential targets for antiretroviral therapy. REVIEWER COMMENTS. An example of targeting host proteins is the use of chemokine receptor 5 (CCR5) inhibitors. Many people with CCR5 deficiency are very resistant to HIV infection yet have limited if any clinical consequences. Maraviroc CCR5 inhibitor is approved for treatment for HIV infection. This study identified many more such potential targets. <s> BIB008 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Human Immunodeficiency Viruses (HIV-1 and HIV-2) rely upon host-encoded proteins to facilitate their replication. Here, we combined genome-wide siRNA analyses with interrogation of human interactome databases to assemble a host-pathogen biochemical network containing 213 confirmed host cellular factors and 11 HIV-1-encoded proteins. Protein complexes that regulate ubiquitin conjugation, proteolysis, DNA-damage response, and RNA splicing were identified as important modulators of early-stage HIV-1 infection. Additionally, over 40 new factors were shown to specifically influence the initiation and/or kinetics of HIV-1 DNA synthesis, including cytoskeletal regulatory proteins, modulators of posttranslational modification, and nucleic acid-binding proteins. Finally, 15 proteins with diverse functional roles, including nuclear transport, prostaglandin synthesis, ubiquitination, and transcription, were found to influence nuclear import or viral DNA integration. Taken together, the multiscale approach described here has uncovered multiprotein virus-host interactions that likely act in concert to facilitate the early steps of HIV-1 infection. <s> BIB009 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Human immunodeficiency virus (HIV)-1 depends on the host cell machinery to support its replication. To discover cellular factors associated with HIV-1 replication, we conducted a genome-scale siRNA screen, revealing more than 311 host factors, including 267 that were not previously linked to HIV. Surprisingly, there was little overlap between these genes and the HIV dependency factors described recently. However, an analysis of the genes identified in both screens revealed overlaps in several of the associated pathways or protein complexes, including the SP1/mediator complex and the NF-kappaB signaling pathway. cDNAs for a subset of the identified genes were used to rescue HIV replication following knockdown of the cellular mRNA providing strong evidence that the following six genes are previously uncharacterized host factors for HIV: AKT1, PRKAA1, CD97, NEIL3, BMP2K, and SERPINB6. This study highlights both the power and shortcomings of large scale loss-of-function screens in discovering host-pathogen interactions. <s> BIB010 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Background: In order to replicate, HIV, like all viruses, needs to invade a host cell and hijack it for its own use, a process that involves multiple protein interactions between virus and host. The HIV-1, Human Protein Interaction Database available at NCBI's website captures this information from the primary literature, containing over 2,500 unique interactions. We investigate the general properties and biological context of these interactions and, thus, explore the molecular specificity of the HIV-host perturbation. In particular, we investigate (i) whether HIV preferentially interacts with highly connected and 'central' proteins, (ii) known phenotypic properties of host proteins inferred from essentiality and disease-association data, and (iii) biological context (molecular function, processes and location) of the host proteins to identify attributes most strongly associated with specific HIV interactions. Results: After correcting for ascertainment bias in the literature, we demonstrate a significantly greater propensity for HIV to interact with highly connected and central host proteins. Unexpectedly, we find there are no associations between HIV interaction and inferred essentiality. Similarly, we find a tendency for HIV not to interact with proteins encoded by genes associated with disease. Crucially, we find that functional categories over-represented in HIV-host interactions are innately enriched for highly connected and central proteins in the host system. Conclusions: Our results imply that HIV's propensity to interact with highly connected and central proteins is a consequence of interactions with particular cellular functions, rather than being a direct effect of network topological properties. The lack of a propensity for interactions with phenotypically essential proteins suggests a selective pressure to minimise virulence in retroviral evolution. Thus, the specificity of HIV-host interactions is complex, and only superficially explained by network properties. <s> BIB011 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Since the 1980s, the rapid progression of the HIV/AIDS pandemic has prompted a major international research effort. As a result, the current knowledge on HIV biology, its evolution, and origins [1–5] exceeds that of many, if not all, other viruses. One of the most important areas of HIV research is the detailed understanding of HIV replication. As with all viruses, HIV must exploit the host’s cellular machinery and metabolism to copy its genetic material, synthesize viral proteins, and assemble new virions. The viral replication cycle is thus dependent on an intricate network of direct and indirect protein interactions: between the viral proteins, between the virus and the host, and ultimately between the various host proteins that constitute the subverted cellular systems. When we also take into account the host’s immune response and intrinsic antiviral factors, there are clearly a large number of host–pathogen relationships that are important to our full understanding of HIV biology. However, until recently [6], this valuable information has remained ‘locked’ in the published literature, making it time-consuming to study by individual researchers and inaccessible to computational analysis, thus hindering the progress of research. Here we highlight new developments in the area of host–pathogen systems biology that in our opinion will provide helpful insights to the HIVresearch community. <s> BIB012 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <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> BIB013 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> BackgroundThe National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics.ResultsOur analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems.ConclusionsHIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another. <s> BIB014 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Human immunodeficiency virus type 1 (HIV-1) Tat is a multifunctional protein that contributes to several pathological symptoms of HIV-1 infection as well as playing a critical role in virus replication. Tat is a robust transactivating protein that induces a variety of effects by altering the expression levels of cellular and viral genes. The functions of Tat are therefore primarily related to its role in modulation of gene expression. In this review the functions of HIV-1 Tat that have been well documented, as well as a number of novel functions that have been proposed for this protein, are discussed. Since some of the functions of Tat vary in different cell types in a concentration-dependent manner and because Tat sometimes exerts the same activity through different pathways, study of this protein has at times yielded conflicting and controversial results. Due to its pivotal role in viral replication and in disease pathogenesis, Tat and the cellular pathways targeted by Tat are potential targets for new anti-HIV drugs. <s> BIB015 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Human immunodeficiency virus type 1 (HIV-1) exploits a diverse array of host cell functions in order to replicate. This is mediated through a network of virus-host interactions. A variety of recent studies have catalogued this information. In particular the HIV-1, Human Protein Interaction Database (HHPID) has provided a unique depth of protein interaction detail. However, as a map of HIV-1 infection, the HHPID is problematic, as it contains curation error and redundancy; in addition, it is based on a heterogeneous set of experimental methods. Based on identifying shared patterns of HIV-host interaction, we have developed a novel methodology to delimit the core set of host-cellular functions and their associated perturbation from the HHPID. Initially, using biclustering, we identify 279 significant sets of host proteins that undergo the same types of interaction. The functional cohesiveness of these protein sets was validated using a human protein-protein interaction network, gene ontology annotation and sequence similarity. Next, using a distance measure, we group host protein sets and identify 37 distinct higher-level subsystems. We further demonstrate the biological significance of these subsystems by cross-referencing with global siRNA screens that have been used to detect host factors necessary for HIV-1 replication, and investigate the seemingly small intersect between these data sets. Our results highlight significant host-cell subsystems that are perturbed during the course of HIV-1 infection. Moreover, we characterise the patterns of interaction that contribute to these perturbations. Thus, our work disentangles the complex set of HIV-1-host protein interactions in the HHPID, reconciles these with siRNA screens and provides an accessible and interpretable map of infection. <s> BIB016 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other. <s> BIB017 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> We provide the first formalization true to the best of our knowledge to the problem of finding bicliques in a directed graph. The problem is addressed employing a two-stage approach based on an existing biclustering algorithm. This novel problem is useful in several biological applications of which we focus only on analyzing the viral-host protein interaction graphs. Strong and significant bicliques of HIV-1 and human proteins are derived using the proposed methodology, which provides insights into some novel regulatory functionalities in case of the acute immunodeficiency syndrome in human. <s> BIB018 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> Cellular restriction factors help to defend humans against human immunodeficiency virus (HIV). HIV accessory proteins hijack at least three different Cullin-RING ubiquitin ligases, which must be activated by the small ubiquitin-like protein NEDD8, in order <s> BIB019 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Interaction types guide regulation of host factor by HIV-1 <s> In this work, we model the problem of mining quasi-bicliques from weighted viral-host protein-protein interaction network as a biclustering problem for identifying strong interaction modules. In this regard, a multiobjective genetic algorithm-based biclustering technique is proposed that simultaneously optimizes three objective functions to obtain dense biclusters having high mean interaction strengths. The performance of the proposed technique has been compared with that of other existing biclustering methods on an artificial data. Subsequently, the proposed biclustering method is applied on the records of biologically validated and predicted interactions between a set of HIV-1 proteins and a set of human proteins to identify strong interaction modules. For this, the entire interaction information is realized as a bipartite graph. We have further investigated the biological significance of the obtained biclusters. The human proteins involved in the strong interaction module have been found to share common biological properties and they are identified as the gateways of viral infection leading to various diseases. These human proteins can be potential drug targets for developing anti-HIV drugs. <s> BIB020
In section 2 we have seen that the HHPID data set includes 68 unique types of interactions between HIV-1 and host proteins and depending on the regulation type we can subdivide the whole bipartite network into three subnetworks. Similar type of classification of interaction types depending on direction of regulation can be observed in BIB011 and in BIB014 . In BIB011 , the interactions are classified into three polar categories ('positive', 'negative' and 'neutral'). Positive category involves nine interaction types, viz., 'activated by', 'activates', 'enhanced by', 'enhances', 'stabilizes', 'stimulated by', 'stimulates', 'upregulated by' and 'upregulates', whereas negative category consists of 13 types of interactions, viz., 'cleavage induced by', 'cleaved by', 'cleaves', 'competes with', 'degraded by', 'degrades', 'disrupts', 'downregulated by', 'downregulates', 'inactivates', 'induces cleavage of', 'inhibited by' and 'inhibits'. The remaining 25 interaction types are denoted as 'neutral'. To investigate the perturbation of multiple host processes via HIV-1 genes, an analysis is performed to show the involvement of those three classes of interactions in the 10 over-represented biological processes in HIV-1 interactions identified in BIB012 . It is noticed that for most of the over-represented biological processes' GO terms, the majority of interactions are mainly 'positive' in nature, whereas for immune response, majority of interactions are more 'negative' in nature. This is obvious because 'negative' set contains interactions directed from human to HIV-1 proteins and have an adverse effect on HIV-1 proteins. In BIB014 , almost similar classification of the interaction types is noticed. Here interaction types are classified under two categories: regulatory (e.g. up-and downregulated, regulated by) and activation/inhibition (activates, inhibits, inhibited by), depending on the functional level of interactions. Undirected interactions like 'binds', 'interacts with' are not considered. Two directed distinct subnetworks are built by considering regulatory interactions in one network and activation/inhibition interactions in the other. A connectivity analysis has been performed for each HIV-1 protein and human protein on both networks. As expected, HIV-1 protein Tat shows high connectivity with host proteins, as it acts as the central transactivator in promoting viral transcription, which affects disease progression by interacting with neighbouring cells after being released to the intercellular medium BIB015 . Gp120 has almost the same number of interactions because it is essential in facilitating entry in different cells. On the other hand, the structural proteins P1, P6 and Nucleocapsid as well as unspliced Pol have only a small number of interactions. For performing similar analysis for each human protein they have enriched their HIV-1-human PPI network with interactions from human protein interaction databases BIND BIB003 , BioGRID (http://thebiogrid.org/) and HPRD (http://www.hprd.org/). The interactions between HDFs (HIV dependency factor), which is a class of human proteins that are essential for HIV-1 replication, are considered here. They have constructed two networks: local, which is built by considering the interactions between HIV-1 interacting human proteins or HDFs BIB008 BIB009 BIB010 BIB017 , and global, which is constructed by considering the interactions between HDFs and non-HDF proteins. These two networks are then analysed by measuring the degree distributions of HDFs by considering only HDF-HDF interactions and HDFs-HIV protein interactions separately. Besides noting the degree distributions, these two networks are also analysed with respect to the three network centrality metrics: degree, betweenness and eigenvector centrality. The eigenvector centrality of a protein describes how well it is connected to other well-connected proteins. They have compared the local HDF network with total human protein interaction network using a one-sided Kolmogorov-Smirnov test  and showed that the measured degrees, eigenvector centralities and betweenness scores in the local and global network are not from the same distribution. This suggests that the local network is significantly more central than the global network with respect to the three metrics. Association of small substructure and strong interaction module with significant host cell subsystems The biological networks have specifically been found to consist of small recurring patterns, so-called network motifs BIB004 BIB002 BIB007  . These building blocks have been extensively used to study the structure and dynamic behaviour of networks. In BIB014 , four types of network motifs were identified in the two subnetworks (regulatory and activation/inhibition) as mentioned in the last section. These significant patterns were identified comparing with 1000 randomized networks, which were created by randomly rewiring the original networks. The significant patterns are shown in Figure 9 . The three-node feedback loop motif (shown in Figure 9a Review on analysis and prediction of HIV-1-human PPI | 837 consists of one HIV-1 protein that regulates/signals a human protein, which in turn regulates/signals the HIV-1 protein. The interactions among proteins signify the nature of feedback pattern in these motifs. For example, two upregulations or two downregulations result in a positive feedback, whereas a negative feedback will be the result of one up-and one downregulation. When two HIV-1 proteins regulate/activate/ inhibit one human protein, then co-regulation or co-activation/ inhibition patterns are generated (shown in Figure 9c ). The two interactions can be of the same type (e.g. either upregulation or inhibition). The three node 'clique' pattern (shown in Figure 9d ) consisting of two human proteins and one HIV-1 protein is also identified where the interactions among the proteins have different patterns. A feed-forward type BIB004 BIB002 BIB007  (or self-regulatory) motif occurs when two connected HDFs indirectly interact via an HIV-1 protein. An exhaustive analysis of these interaction patterns along with different interaction types may result in understanding of dynamics and structure of significant host cell subsystems that are perturbed during the course of HIV-1 infection. In Macpherson et al. BIB016 , a nice characterization of interaction patterns is observed that is used to investigate host cellular subsystems and their association in the perturbation of host cellular functions. In this work, they have identified some higher-level biological subsystems and were able to infer the biological importance of these subsystems in terms of HIV-1 replication, host cell perturbation and regulation of the immune response. For identifying key host functions that are entirely involved in HIV-1 infection and replication, they defined a set of host proteins that take part in a common set, or 'profile', of HIV-1 interactions. For this they have used BiMax BIB005 biclustering technique to group the host proteins that encounter similar perturbation during HIV-1 infection. In this work, they retrieved 1434 host proteins and 3939 interactions associated with the specific interaction types from HHPID. A binary interaction matrix containing 1434 rows, 1292 columns and 3939 non-zero values was created corresponding to human proteins, all types of HIV-1 interactions and all HIV-1-human PPIs, respectively. Biclustering of this matrix yielded 1306 biclusters, among which 279 biclusters were identified as significant (P 0.001) by Monte Carlo simulation. These biclusters were enriched with important sets of human proteins that experienced similar perturbations during HIV-1 infection. The biological significance of host protein sets and their associated interaction profiles defined by the biclusters is assessed according to three measures: PPI network clustering, semantic similarity in terms of shared GO annotation and sequence similarity. They integrated the human proteins from significant biclusters with human PPI network. By this, 38 biclusters were found in which the proteins shared a greater number of interactions, 24 biclusters were found where the proteins form a bigger largest connected component and 38 biclusters were found to be enriched with proteins that have a smaller average shortest path length than that expected by random chance (P 0.05). These signify that HIV-1 proteins have a strong inclination to interact with host proteins in a similar manner, which share interactions with one another. These also identify the presence of multiple HIV-1 interactions with host protein complexes or other closely associated host network modules. The host proteins in significant biclusters are more similar in terms of their GO annotation than that expected by random chance in all ontologies: molecular function, cellular component (CC) and biological process. These results indicate that HIV-1 interacts in a similar pattern with proteins that have similar GO annotation. It is also observed that the human proteins within the significant biclusters are more similar in their protein sequence than that expected by random chance (P << 0:001). Among all the biclusters, 101 significant biclusters were identified in which human proteins were more similar in their sequences than that expected by random chance. The results show that paralogous groups of host proteins have more inclination to be involved in the same combinations of regulatory and physical HIV-1 interaction. It is observed that several biclusters are enriched with multiple host proteins that show similar interaction profiles among them. By combining these biclusters according to shared information, an overview of HIV-1 interactions with a given set of host proteins is identified. Some distinguishable patterns of cytokine regulation by HIV-1, such as the largely stimulatory effects of gp120, Tat and Nef; upregulation of TNF-alpha and interleukins 1 and 6; the repressive action of Vpr and gp160; and downregulation of interleukin 2 and interferon-c are identified. Two HIV-1-host PPI networks are also created in which the human host is depicted as a series of cellular subsystems and the interactions of HIV-1 proteins with these subsystems are investigated to find some recognizable patterns of interaction in these networks. Using a neighbour joining method, identified biclusters are combined. A tree is constructed using a distance measure based on the overlaps between host proteins constituting the biclusters. The tree is partitioned into different sections according to the over-representation of the GO terms of the host proteins in the combined biclusters. Thirty-seven biological subsystems are identified to be associated with the created partitions. Associating the biclusters in this way with 37 biological subsystems, strong associations between biclusters and specific subsystems are identified. It will be interesting to see the association between biclusters that consist of human proteins having the same regulation type with HIV-1 and the aforesaid 37 biological subsystems. For this purpose we use biclustering technique to define biclusters in each of the three subnetwork ('positive', 'bidirected' and 'negative') identified in HIV-human PPI network by considering direction of regulation of each interaction. A total of 110 biclusters (69 from subnetwork 1, 21 from subnetwork 2 and 20 from subnetwork 3) are identified using Bimax BIB006 algorithm. Figure 10 shows overlap of subsystems involved in the biclusters of three subnetworks, viz., positive, bidirected and negative. It appears from the figure that most of the subsystems do not show propensity to be involved in some specific interaction types. Only six subsystems, viz., 'Glutamate receptor activity', 'protein phosphatase type 2A complex', 'DNA integration', 'lactate dehydrogenase activity', 'adenylate cyclase activity', 'cytoskeleton', four subsystems, viz., 'ubiquitin', 'heat shock 70kDa protein', 'glucosidase activity', 'mRNA transport' and one subsystem, viz., 'DNA helicase activity' show some sort of inclination to be involved with specific regulation types like 'positive', 'negative' and 'bidirected', respectively. To present a distilled representation of the relationship between over-represented biological subsystems mediated through regulation types, we have constructed three bipartite networks specific to each of the three regulation types. Figure 11a has total 69 nodes, each of which corresponds to one bicluster and 27 nodes representing the biological subsystems associated with those biclusters. The small yellow nodes represent biclusters, whereas red nodes stand for the subsystems. To know to what extent each subsystem is associated with each bicluster, we vary the node size of the subsystems proportionally to the associated proteins with all the biclusters. The edges in this network represent bicluster-subsystem association that alienates the subsystems in a regulation-specific manner. The edge width is proportional to the proportion of proteins in the biclusters associated with the subsystems. It can be observed that most of the subsystems solely associated with negative regulation have a clear role in immune response in the host cell. For example, it is experimentally verified that synthesis of glucosidase show antiviral and immunosuppressive activity of HIV-1 by inhibiting HIV-1-induced syncytia formation and lymphocyte proliferation BIB006 BIB001 . In BIB019 , it is also verified that NEDD8 (a small ubiquitin-like protein) affects the restoration of Vif-mediated degradation of the host restriction factor APOBEC3G (A3G), and this presents a novel antiretroviral therapeutic approach and enhances the ability of the immune system to combat HIV. Recently, Maulik et al. BIB020 proposed a multi-objective biclustering technique for extracting strong interaction modules in a weighted HIV-1-human PPI network. A strong interaction module consists of a set of viral and host proteins, which are highly interacting with each other. The human proteins involved in those strong interaction modules are likely to be considered as important gateways for HIV-1 proteins and may be regarded as potential drug targets. In this work, the HIV-1-human protein interaction network is realized as a bipartite graph where two sets of nodes represent HIV-1 proteins and human proteins, respectively, and edges denote the interactions. The network is weighted by interaction strength predicted by Tastan et al. BIB013 . Each 'quasi-biclique' (dense bipartite subgraph) represents a strong interaction module in this weighted bipartite network. The main issue in this work is that it relaxes the stricter Review on analysis and prediction of HIV-1-human PPI | 839 condition for forming bicliques BIB018 by introducing 'quasibiclique' that are considered as almost bicliques in HIV-1-human bipartite protein interaction network. Searching is carried out over three different objectives for finding the quasibicliques in HIV-1-human bipartite protein interaction network. The resulting strong interaction modules (or 'quasi-bicliques') are then analysed to investigate the involvement of dense subnetworks during HIV-1 infection cycle. Some important human proteins that are responsible for early and late stages of HIV-1 infection were found in the extracted 'quasi bicliques'. For example, at least one occurrence of kinase protein is observed in all the identified quasi bicliques. Beside the Kinase family proteins the bicliques are also enriched with CD4, chemokine receptor CXCR4, AP2B1 like proteins. Gene Ontological analysis over the human proteins identified in the 'quasi-bicliques' reveal several biologically homogeneous strong interaction modules.