A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task. <s> BIB001 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> A principal challenge currently facing biologists is how to connect the complete DNA sequence of an organism to its development and behaviour. Large-scale targeted-deletions have been successful in defining gene functions in the single-celled yeast Saccharomyces cerevisiae, but comparable analyses have yet to be performed in an animal. Here we describe the use of RNA interference to inhibit the function of ∼86% of the 19,427 predicted genes of C. elegans. We identified mutant phenotypes for 1,722 genes, about two-thirds of which were not previously associated with a phenotype. We find that genes of similar functions are clustered in distinct, multi-megabase regions of individual chromosomes; genes in these regions tend to share transcriptional profiles. Our resulting data set and reusable RNAi library of 16,757 bacterial clones will facilitate systematic analyses of the connections among gene sequence, chromosomal location and gene function in C. elegans. <s> BIB002 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> Most proteins do not evolve in isolation, but as components of complex genetic networks. Therefore, a protein's position in a network may indicate how central it is to cellular function and, hence, how constrained it is evolutionarily. To look for an effect of position on evolutionary rate, we examined the protein-protein interaction networks in three eukaryotes: yeast, worm, and fly. We find that the three networks have remarkably similar structure, such that the number of interactors per protein and the centrality of proteins in the networks have similar distributions. Proteins that have a more central position in all three networks, regardless of the number of direct interactors, evolve more slowly and are more likely to be essential for survival. Our results are thus consistent with a classic proposal of Fisher's that pleiotropy constrains evolution. <s> BIB003 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> Structural features found in biomolecular networks that are absent in random networks produced by simple algorithms can provide insight into the function and evolution of cell regulatory networks. Here we analyze “betweenness” of network nodes, a graph theoretical centrality measure, in the yeast protein interaction network. Proteins that have high betweenness, but low connectivity (degree), were found to be abundant in the yeast proteome. This finding is not explained by algorithms proposed to explain the scale-free property of protein interaction networks, where low-connectivity proteins also have low betweenness. These data suggest the existence of some modular organization of the network, and that the high-betweenness, low-connectivity proteins may act as important links between these modules. We found that proteins with high betweenness are more likely to be essential and that evolutionary age of proteins is positively correlated with betweenness. By comparing different models of genome evolution that generate scale-free networks, we show that rewiring of interactions via mutation is an important factor in the production of such proteins. The evolutionary and functional significance of these observations are discussed. <s> BIB004 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> The mouse genome database (MGD, http://www.informatics.jax.org/), the international community database for mouse, provides access to extensive integrated data on the genetics, genomics and biology of the laboratory mouse. The mouse is an excellent and unique animal surrogate for studying normal development and disease processes in humans. Thus, MGD's primary goals are to facilitate the use of mouse models for studying human disease and enable the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Core MGD data content includes gene characterization and functions, phenotype and disease model descriptions, DNA and protein sequence data, polymorphisms, gene mapping data and genome coordinates, and comparative gene data focused on mammals. Data are integrated from diverse sources, ranging from major resource centers to individual investigator laboratories and the scientific literature, using a combination of automated processes and expert human curation. MGD collaborates with the bioinformatics community on the development of data and semantic standards, and it incorporates key ontologies into the MGD annotation system, including the Gene Ontology (GO), the Mammalian Phenotype Ontology, and the Anatomical Dictionary for Mouse Development and the Adult Anatomy. MGD is the authoritative source for mouse nomenclature for genes, alleles, and mouse strains, and for GO annotations to mouse genes. MGD provides a unique platform for data mining and hypothesis generation where one can express complex queries simultaneously addressing phenotypic effects, biochemical function and process, sub-cellular location, expression, sequence, polymorphism and mapping data. Both web-based querying and computational access to data are provided. Recent improvements in MGD described here include the incorporation of single nucleotide polymorphism data and search tools, the addition of PIR gene superfamily classifications, phenotype data for NIH-acquired knockout mice, images for mouse phenotypic genotypes, new functional graph displays of GO annotations, and new orthology displays including sequence information and graphic displays. <s> BIB005 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> It has been a long-standing goal in systems biology to find relations between the topological properties and functional features of protein networks. However, most of the focus in network studies has been on highly connected proteins (“hubs”). As a complementary notion, it is possible to define bottlenecks as proteins with a high betweenness centrality (i.e., network nodes that have many “shortest paths” going through them, analogous to major bridges and tunnels on a highway map). Bottlenecks are, in fact, key connector proteins with surprising functional and dynamic properties. In particular, they are more likely to be essential proteins. In fact, in regulatory and other directed networks, betweenness (i.e., “bottleneck-ness”) is a much more significant indicator of essentiality than degree (i.e., “hub-ness”). Furthermore, bottlenecks correspond to the dynamic components of the interaction network—they are significantly less well coexpressed with their neighbors than nonbottlenecks, implying that expression dynamics is wired into the network topology. <s> BIB006 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> The ‘Human Immunodeficiency Virus Type 1 (HIV-1), Human Protein Interaction Database’, available through the National Library of Medicine at www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions, was created to catalog all interactions between HIV-1 and human proteins published in the peer-reviewed literature. The database serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. To facilitate this discovery approach, the following information for each HIV-1 human protein interaction is provided and can be retrieved without restriction by web-based downloads and ftp protocols: Reference Sequence (RefSeq) protein accession numbers, Entrez Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. Currently, 2589 unique HIV-1 to human protein interactions and 5135 brief descriptions of the interactions, with a total of 14 312 PMID references to the original articles reporting the interactions, are stored in this growing database. In addition, all protein–protein interactions documented in the database are integrated into Entrez Gene records and listed in the ‘HIV-1 protein interactions’ section of Entrez Gene reports. The database is also tightly linked to other databases through Entrez Gene, enabling users to search for an abundance of information related to HIV pathogenesis and replication. <s> BIB007 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <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> Host factors essential for HIV replication <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> Host factors essential for HIV replication <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> Host factors essential for HIV replication <s> The essentiality of a gene or protein is important for understanding the minimal requirements for cellular survival and development. Numerous computational methodologies have been proposed to detect essential proteins from large protein-protein interactions (PPI) datasets. However, only a handful of overlapping essential proteins exists between them. This suggests that the methods may be complementary and an integration scheme which exploits the differences should better detect essential proteins. We introduce a novel algorithm, UniScore, which combines predictions produced by existing methods. Experimental results on four Saccharomyces cerevisiae PPI datasets showed that UniScore consistently produced significantly better predictions and substantially outperforming SVM which is one of the most popular and advanced classification technique. In addition, previously hard-to-detect low-connectivity essential proteins have also been identified by UniScore. <s> BIB011 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> One-to-one orthologous genes of relatively closely related species are widely assumed to have similar functions and cause similar phenotypes when deleted from the genome. Although this assumption is the foundation of comparative genomics and the basis for the use of model organisms to study human biology and disease, its validity is known only from anecdotes rather than from systematic examination. Comparing documented phenotypes of null mutations in humans and mice, we find that >20% of human essential genes have nonessential mouse orthologs. These changes of gene essentiality appear to be associated with adaptive evolution at the protein-sequence, but not gene-expression, level. Proteins localized to the vacuole, a cellular compartment for waste management, are highly enriched among essentiality-changing genes. It is probable that the evolution of the prolonged life history in humans required enhanced waste management for proper cellular function until the time of reproduction, which rendered these vacuole proteins essential and generated selective pressures for their improvement. If our gene sample represents the entire genome, our results would mean frequent changes of phenotypic effects of one-to-one orthologous genes even between relatively closely related species, a possibility that should be considered in comparative genomic studies and in making cross-species inferences of gene function and phenotypic effect. <s> BIB012 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <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> BIB013 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <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> BIB014 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> BackgroundIdentification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value.ResultsIn this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized α-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins.ConclusionsWe demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method. <s> BIB015 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> Identifying essential proteins is important for understanding the minimal requirements for cellular survival and development. Numerous computational methods have been proposed to identify essential proteins from protein-protein interaction (PPI) network. However most of methods only use the PPI network topology information. HartGT indicated that essentiality is a product of the protein complex rather than the individual protein. Based on these, we propose a new method ECC to identify essential proteins by integration of subgraph centrality (SC) of PPI network and protein complexes information. We apply ECC method and six centrality methods on the yeast PPI network. The experimental results show that the performance of ECC is much better than that of six centrality methods, which means that the prediction of essential proteins based on both network topology and protein complexes set is much better than that only based on network topology. Moreover, ECC has a significant improvement in prediction of low-connectivity essential proteins. <s> BIB016 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Host factors essential for HIV replication <s> Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. The rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on the location of single protein, but ignored the relevance between interactions and protein essentiality. In this paper, a new centrality measure for identifying essential proteins based on edge clustering coefficient, named as NC, is proposed. Different from previous centrality measures, NC considers both the centrality of a node and the relationship between it and its neighbors. For each interaction in the network, we calculate its edge clustering coefficient. A node's essentiality is determined by the sum of the edge clustering coefficients of interactions connecting it and its neighbors. The new centrality measure NC takes into account the modular nature of protein essentiality. NC is applied to three different types of yeast protein-protein interaction networks, which are obtained from the DIP database, the MIPS database and the BioGRID database, respectively. The experimental results on the three different networks show that the number of essential proteins discovered by NC universally exceeds that discovered by the six other centrality measures: DC, BC, CC, SC, EC, and IC. Moreover, the essential proteins discovered by NC show significant cluster effect. <s> BIB017
In Pinney et al. BIB013 an attempt was made to uncover different new developments in HIV research that provide a helpful insight into the viral perturbation in the host proteome. For this purpose, they have used the existing knowledge of HIV-host interactions documented in HHPID BIB007 . The HHPID data set is visualized here as a detailed map of viral perturbation of the host proteome in the context of gene ontological annotations of human proteins in this data set. The over-represented biological process terms of the human proteins in this data set reveal how the various parts of the viral replication cycle are delegated to different genes and the way in which HIV-1 gene products cooperate to target specific parts of the human cellular system. A minimum spanning tree (MST) is constructed by calculating the semantic distance BIB001 between each pair of HIV-1 interacting gene products. As a consequence the gene products that are close to each other in the MST are more likely to be involved in common biological process. Pinney et al. BIB013 found little overlap between HIV-dependency factor (HDF) (which is basically a class of human proteins that are essential for HIV replication) identified by Brass et al. BIB008 , Konig et al. BIB009 and Zhou et al. BIB010 and HIV-1 interacting human proteins reported in HHPID (shown in Figure 5 ). This discrepancy between HDF data sets and HHPID data is not surprising because the experimental screening procedure for HDF detection mostly relies on the cell line, HIV strain used and particular experimental protocol. However, 131 human proteins are believed to interact with HIV-1, as supported by at least two studies; these have a better chance of being a host factor or an essential protein. Essential or lethal proteins are those, deletion of which will result in lethality or infertility, i.e. the organism cannot survive without these proteins  BIB002 . Lethality of proteins is linked with different unique topological characteristics of proteins in the human PPI network BIB015 BIB016 BIB011 BIB017 . It is demonstrated that the proteins with high degree and high betweenness centrality have a tendency of being essential    BIB003 BIB004 BIB006 . In general 'centrality-lethality' rule states that deletion of a hub protein is more likely to be lethal than deletion of a non-hub protein  . The underlying base of the 'centrality-lethality' rule is set up by some interactions between proteins that are indispensable for the survival or reproduction of an organism. For examining the involvement of essential proteins with the HIV-interacting ones, Dickerson et al. BIB014 study the relationship between protein essentiality and degree/betweenness centrality. For this, they have considered mouse genome knockout data from BIB005 . Here a human gene is considered as 'essential' if a knockout of its mouse ortholog has a lethal impact BIB012 . They surprisingly observed that the HIV-1 interacting proteins turn out to be nonessential according to the definition of essentiality in BIB012 . The possible reason behind this may be that the essential proteins may follow poor correlation with degree and betweenness centrality. It is interesting to see whether the HIV-1 interacting proteins play important roles in human biological processes or have certain functionalities that make them essential. For this, we have adopted a rank aggregation scheme to rank all the proteins that are predicted to interact with HIV-1, and are supported by at least two of the four studies (shown in Figure 5 ). We have used five topological metrics, viz., density, betweenness centrality, clustering coefficient, eigenvector centrality and average density of neighbourhood nodes, to rank all the proteins. A weighted rank aggregation technique  is used to build an ordered list of proteins. Figure 6 shows the result of applying the rank aggregation algorithm. It appears from the figure that most of the proteins that are predicted to interact with HIV-1 and are supported by at least three studies get better rank than the others. Table 1 shows 11 proteins interacting with HIV-1 and are supported by at least three literature studies. Columns show the ranked list of these proteins corresponding to each metric. Four proteins, viz., 'CD4', 'TCEB3', 'CHST1' and 'DDX3X' got aggregated rank above 50. The conserved RNA helicase DDX3X has rank 106, which indicates that it is not so much essential as others in the list. DDX3X plays a major role in nuclear export of viral RNA transcripts into cellular cytoplasm. It is known to inherit antiviral immune mechanisms, which shows its importance in viral replication as well as antiviral activity. Interestingly in  it is reported that knockdown of DDX3X drastically deteriorates HIV-1 replication in host cells. CD4 is a glycoprotein found on the surface of immune cells such as T helper cells and is essential for building up the human immune system. Although CD4 was placed in the 78th position in the optimal ranking, it plays a key role in transferring signal to other types of immune cells. Similarly, CXCR4, which acts as an important co-receptor for HIV-1 entrance in the host cell, got 35th position in the optimal ranking list. It is notable that CXCR4 appears in the first position in the ranking list corresponding to the degree or connectivity. All the four studies support the interaction with protein RELA. We have performed a significance test to investigate whether the ranking of HIV-1 interacting proteins is significantly better than a random set of proteins. For this purpose, Figure 5 . Overlap between host factors identified in three systematic screening studies BIB008 BIB009 BIB010 and HIV interacting human proteins reported in HHPID data set. A colour version of this figure is available at BIB online: http://bib. oxfordjournals.org. we first rank all 129 HIV-1 interacting proteins supported by at least two studies in the corpus of all human proteins. Ranking is performed by considering the five topological metrics, used earlier. We also collected 129 human proteins randomly from the whole human protein set and performed ranking based on these five topological metrics. We have carried out this procedure 300 times and got 300 ranked lists of random proteins. These are then compared with the ranking of HIV-1 interacting proteins by using Wilcoxon Rank Sum test. The resulting P-value is significantly low (8.6733e-10), which indicates that the ranking of HIV-1 interacting proteins is significantly better than a random set of proteins. Ranking of proteins apparently induces essentiality, possibly because of its affinity to get strongly correlated with unique topological characteristics. So, the HIV-1 interacting proteins, which got low ranks, are highly important for facilitating HIV-1 infection.