You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions 
Chapter Title:  Category-2: structural similarity and motif sequence-based approach 
1. Abstract of Cited Paper (BIB001):  Summary: InterPreTS (Interaction Prediction through Tertiary Structure) is a web-based version of our method fo rp redicting protein–protein interactions (Aloy and Russell, 2002, Proc. Natl Acad. Sci. USA, 99, 5896– 5901). Given a pair of query sequences, we first search for homologues in a database of interacting domains (DBID) of known three-dimensional complex structures. Pairs of sequences homologous to a known interacting pair are scored for how well they preserve the atomic contacts at the interaction interface. InterPreTS includes a useful interface for visualising molecular details of any predicted interaction. Availability: http://www.russell.embl.de/interprets. Contact: aloy@embl.de 
2. Abstract of Cited Paper (BIB002):  Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (http://salilab.org/pibase). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domain-porcine alpha-amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE (http://salilab.org/modbase). 
3. Abstract of Cited Paper (BIB003):  Background ::: The hepatitis C virus (HCV) non-structural 5A protein (NS5A) contains a highly conserved C-terminal polyproline motif with the consensus sequence Pro-X-X-Pro-X-Arg that is able to interact with the Src-homology 3 (SH3) domains of a variety of cellular proteins. 
4. Abstract of Cited Paper (BIB004):  Short functional peptide motifs cooperate in many molecular functions including protein interactions, protein trafficking, and posttranslational modifications. Viruses exploit these motifs as a principal mechanism for hijacking cells and many motifs are necessary for the viral life-cycle. A virus can accommodate many short motifs in its small genome size providing a plethora of ways for the virus to acquire host molecular machinery. Host enzymes that act on motifs such as kinases, proteases, and lipidation enzymes, as well as protein interaction domains, are commonly mutated in human disease, suggesting that the short peptide motif targets of these enzymes may also be mutated in disease; however, this is not observed. How can we explain why viruses have evolved to be so dependent on motifs, yet these motifs, in general do not seem to be as necessary for human viability? We propose that short motifs are used at the system level. This system architecture allows viruses to exploit a motif, whereas the viability of the host is not affected by mutation of a single motif. 
5. Abstract of Cited Paper (BIB005):  BackgroundIn the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes.ResultsWe developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence.ConclusionsUnderstanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention.

Literature Review:
In Doolittle et al. BIB006 , the significant amount of protein structural information available in the Protein Data Bank are used for predicting PPIs between HIV-1 and human proteins. The use of protein structure information integrated with the documented PPIs is established to be an alternative way for the prediction of possible protein interactions BIB002 BIB001 BIB003 . The motivation of using the structural information is that given a set of proteins with known structure and interaction partner, the proteins that show similar structure with these given proteins are likely to interact with those interaction partners. In BIB006 , this argument is validated in context of HIV-1 and human proteins. Based on the protein structural similarity, an interaction map between human and HIV-1 is generated. For this, interactions between 'HIV-similar' (i.e. structurally similar to some HIV protein) human proteins and 'targets' (i.e. the human proteins interacted with 'HIV-similar') are identified and an association among the interactions between HIV-1 protein and the 'target' proteins is established. The predicted interactions are filtered Review on analysis and prediction of HIV-1-human PPI | 843 based on functional information from previous studies to make a first set of predictions. For further reducing the list of likely interactions, these results are refined by requiring both the HIV-1 protein and the target human protein to be present in the same location within the cell, based on GO CC annotation. This produced a final prediction set including fewer predictions with higher confidence. In Evans et al.  , the prediction task is guided by the association between short ELMs  conserved in HIV-1 proteins and the human protein CDs known to interact with these ELMs. The functional roles of interactions conceded by ELMs and their CDs are addressed in several literature  BIB004 BIB005 . The availability of motif/domain pairs associated with the HIV-1 and human PPIs is extensively used here for discovering new interactions. The total prediction set was built from the union of two sets of human proteins H1 and H2. H1 contains the human proteins that have direct interactions with one or more HIV-1 proteins through a human CD and virus ELM. H2 consists of the human proteins that act as a competitor of HIV-1 protein to interact with a H1 protein via the conserved ELM. Therefore, if H1 has a CD, then it may use this CD to interact with an ELM present on both H2 and HIV-1 proteins.