Improving the Understanding of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein Interaction Network

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Inferring Virus-Host relationship between HPV and its host Homo sapiens using protein interaction network

Human papilloma virus (HPV) is a serious threat to human life globally with over 100 genotypes including cancer causing high risk HPVs. Study on protein interaction maps of pathogens with their host is a recent trend in ‘omics’ era and has been practiced by researchers to find novel drug targets. In current study, we construct an integrated protein interaction map of HPV with its host human in Cytoscape and analyze it further by using various bioinformatics tools. We found out 2988 interactions between 12 HPV and 2061 human proteins among which we identified MYLK, CDK7, CDK1, CDK2, JAK1 and 6 other human proteins associated with multiple viral oncoproteins. The functional enrichment analysis of these top-notch key genes is performed using KEGG pathway and Gene Ontology analysis, which reveals that the gene set is enriched in cell cycle a crucial cellular process, and the second most important pathway in which the gene set is involved is viral carcinogenesis. Among the viral proteins, E7 has the highest number of associations in the network followed by E6, E2 and E5. We found out a group of genes which is not targeted by the existing drugs available for HPV infections. It can be concluded that the molecules found in this study could be potential targets and could be used by scientists in their drug design studies.

Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory

Leptospirosis is the most emerging zoonotic disease of epidemic potential caused by pathogenic species of Leptospira. The bacterium invades the host system and causes the disease by interacting with the host proteins. Analyzing these pathogen-host protein interactions (PHPIs) may provide deeper insight into the disease pathogenesis. For this analysis, inter-species as well as intra-species protein interactions networks of Leptospira interrogans and human were constructed and investigated. The topological analyses of these networks showed lesser connectivity in inter-species network than intra-species, indicating the perturbed nature of the inter-species network. Hence, it can be one of the reasons behind the disease development. A total of 35 out of 586 PHPIs were identified as key interactions based on their sub-cellular localization. Two outer membrane proteins (GpsA and MetXA) and two periplasmic proteins (Flab and GlyA) participating in PHPIs were found conserved in all pathogenic, intermediate and saprophytic spp. of Leptospira. Furthermore, the bacterial membrane proteins involved in PHPIs were found playing major roles in disruption of the immune systems and metabolic processes within host and thereby causing infectious disease. Thus, the present results signify that the membrane proteins participating in such interactions hold potential to serve as effective immunotherapeutic candidates for vaccine development.

An improved method for predicting interactions between virus and human proteins

The interaction of virus proteins with host proteins plays a key role in viral infection and consequent pathogenesis. Many computational methods have been proposed to predict protein-protein interactions (PPIs), but most of the computational methods are intended for PPIs within a species rather than PPIs across different species such as virus-host PPIs. We developed a method that represents key features of virus and human proteins of variable length into a feature vector of fixed length. The key features include the relative frequency of amino acid triplets (RFAT), the frequency difference of amino acid triplets (FDAT) between virus and host proteins, and amino acid composition (AC). We constructed several support vector machine (SVM) models to evaluate our method and to compare our method with others on PPIs between human and two types of viruses: human papillomaviruses (HPV) and hepatitis C virus (HCV). Comparison of our method to others with same datasets of HPV-human PPIs and HCV-human PPIs showed that the performance of our method is significantly higher than others in all performance measures. Using the SVM model with gene ontology (GO) annotations of proteins, we predicted new HPV-human PPIs. We believe our approach will be useful in predicting heterogeneous PPIs.

Computational approaches for prediction of pathogen-host protein-protein interactions

Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions.