Integrative machine learning methods for prediction of protein-protein interactions and analysis of the dynamics of interactomes

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Proteins are large molecules that constitute the body of cellular machinery of any living organism or biological system, playing important roles in fundamental and essential biological processes such as cellular morphology and physiology, DNA synthesis, transcription, translation, splicing and many others. Proteins, however, do not act in isolation but perform their functions by interacting with molecules such as DNA, RNA, and other proteins. The interactome aims to study the main aspects of protein interactions in a living system. The interactome is rather dynamic as the interactions and ultimately functions are manifested in a temporal and spatial manner. To understand the complex cellular mechanisms involved in a biological system, it is necessary to study the nature and specificity of these interactions and the dynamics involved in it at the molecular level, for which prediction of protein-protein interactions (PPIs) has played a significant role. Although prediction of PPIs has been studied from many different perspectives in solving different problems, the main aspects that are studied include: sites of interfaces (where), arrangement of proteins in a complex (how – aka docking), type of protein complex (what), molecular interaction event (if), and temporal and spatial trends (dynamics). This proposal focuses mostly on prediction of types, interaction events and temporal aspects of PPIs, more specifically, on devising machine learning approaches for prediction and analysis of PPIs from high-throughput data, understanding the dynamic aspects of these interactions and their relationships with genomic and transcriptional features. The main goals of this research are to: (1) develop new machine learning approaches for prediction of high-throughput PPIs, which include structural and sequence-based information; (2) analyze and elucidate the main properties of the dynamics associated with high-throughput PPIs; (3) integrate transcriptomics data from next generation sequencing techniques with interactomics in applications for detecting biomarkers and understanding the transcriptogenomics mechanisms involved in prostate and breast cancer. Predicting interactions and unraveling the important properties and dynamic aspects of PPIs will help understand cellular and molecular mechanisms, improving disease diagnosis and treatment, and drug development. This will provide valuable information for researchers in biology, biochemistry and life sciences. In addition, integrative approaches for transcriptomics are novel in applications such as prostate cancer, which is one of the applicant's collaborative projects. This will bring a much better insight than the current approaches that associate biomarkers (chimeric events or splicing) with genes. However, isoforms or forms of proteins that interact with other proteins via domains or short motifs are part of interaction networks which are also dynamic. The advantage of integrating transcriptomics and interactomics to produce more robust biomarkers will benefit disease screening, diagnosis, treatment and follow-up. One of the primary goals in cancer studies is detection at early stages, and in many cases, cancer patients are over-treated. Prostate and breast cancer are among the most common types of cancer in Canada, and have been among the main causes of cancer death over the past ten years. Using machine learning techniques for prediction and discovery of reliable biomarkers can lead to improvements in cancer diagnosis, especially in early detection. By means of multi-disciplinary collaborations, the findings in interactomics and integration with transcriptoimcs will benefit the development of novel therapeutic strategies and lower recurrence in cancer.