Despite undeniable therapeutic developments in infectiology, emerging infectious diseases continue to be a growing threat to public health, as seen by the current COVID- 19 pandemic caused by the novel virus severe acute respiratory syndrome coronavirus (SARS-CoV-2). This virus is classified as an obligate intracellular parasite that co-opts host cellular proteins, often through protein-protein interactions (PPIs), to ensure its replication. Therefore, this thesis aims to integrate high-throughput proteomic approaches with computational modelling to systematically characterize SARS-CoV-2-human networks for a detailed understanding of SARS-CoV-2 pathogenesis. The angiotensin-converting enzyme (ACE2) receptor of SARS-CoV-2 is displayed on many human cells, including the lungs and other organs. However, despite considerable knowledge explaining the SARS-CoV-2 infection mechanism, organ-specific SARS-CoV- 2-host protein interactions remain understudied. In Chapter 2, we carried out an organ/tissue-unbiased proteomic profiling approach of mapping SARS-CoV-2-human protein interactions using high-throughput mass spectrometry (MS)-based proteomic approaches. First, automated machine learning (ML)-based computational workflows with different algorithmic strategies were devised to generate high-quality tissue-specific and tissue-common SARS-CoV-2-human PPIs. Subsequent clustering of highly conserved networks using an optimized complex-based analysis framework uncovered several virally targeted protein complexes (VTCs), reflecting conserved mechanisms of replication. Finally, organ/tissue-specific interaction revealed that NSP3 protein evades host antiviral innate immune signaling by targeting IFIT5 for de-isgylation. Although host interactome is indirectly affected during viral infection, earlier studies have only focused on characterizing the properties of the viral proteins within the host-viral interactions. However, systematically exploring the host-viral interactions from the perspective of the host interactome is essential and should be included in PPI network for a better understanding of viral pathogenesis. In Chapter 3, we combined cofractionation mass spectrometry (CF-MS) with a novel deep learning-based framework, DeepiCE, to map physiologically relevant viral-host and host interactome. First, through comprehensive statistical validations, we demonstrated the remarkable performance of DeepiCE over the state-of-the-art method for network construction. DeepiCE was then applied to co-elution data from salivary samples of individuals infected with SARS-CoV- 2, which led to the generation of high-quality viral-host and host interactome maps highly relevant to SARS-CoV-2 infection. Subsequent clustering of resulting networks using a sophisticated two-stage clustering framework generated high-quality SARS-CoV-2 affected protein complexes, many of which were enriched for diverse cellular processes related to viral pathogenesis and provided new insights into SARS-CoV-2 infection from both the host and pathogen perspective. Despite arduous and time-consuming experimental efforts, PPIs for many pathogenic microbes with their human host are still unknown, limiting our understanding of the intricate interactions during infection and the identification of therapeutic targets. Since computational tools offer a promising alternative, in Chapter 4, we developed a R/Bioconductor package, HPiP software with a series of amino acid sequence property descriptors and an ensemble machine learning classifiers to predict the yet unmapped interactions between pathogen and host proteins. Using SARS-CoV-1 or the novel SARSCoV- 2 coronavirus-human PPI training sets as a case study, we show that HPiP achieves good performance with PPI predictions between SARS-CoV-2 and human proteins, which we confirmed experimentally using several quality control metrics. HPiP also exhibited strong performance in accurately predicting the previously reported PPIs when tested against the sequences of pathogenic bacteria, Mycobacterium tuberculosis and human proteins. Collectively, our fully documented HPiP software will hasten the exploration of PPIs for a systems-level understanding of many understudied pathogens and uncover molecular targets for repurposing existing drugs.