Knowledge of protein-protein interactions (PPIs) is necessary to understand system-level aspects of organisms including that of psychiatric processes. The PPI network (interactome) acts as a vehicle for several types of biomedical research. It can be used to understand the disease mechanisms, drug targets and side effects, and genetic causes for disease. There are 1,014 human genes associated with 'brain';for 448 of these genes not even a single PPI is known today. While it is useful to discover which pairs of proteins interact, it is also exceptionally challenging as more than 99.9% of protein pairs do not interact. The objective of this work is to carry out systematically designed computational work to discover the human mental health and inflammation (MHAIN) interactome. The MHAIN interactome refers to the network of PPIs where at least one of the two proteins is involved in either brain or inflammation. Many challenges will be addressed in discovering new protein-protein interactions towards building the interactome. Well-established algorithms from diverse computational fields, such as machine learning and signal processing will be applied to achieve the proposed goals. Further, pathways of influence of neuropsychological processes and inflammatory processes on each other will be mined from the interactome. Selected interactions will be validated by wet-lab experiments. The approaches that will be employed for predicting the MHAIN interactome are proactive-learning (to obtain the labels and features of proteins that would provide the maximum impact), transfer-learning (to transfer the knowledge of protein features and interactions from one species to another), multi-sensor fusion (to intelligently integrate output from different predictors). The predicted interactome would accelerate discovery of the biology and treatment of mental health related diseases, by serving as a central resource for biomedical research on mental health. For every protein of interest, verifiable hypothesis of its interactions will be generated that reduce the search space of interactions of that protein from about 25,000 to a few possibilities. New PPIs of proteins involved in major depressive disorder, psychosis in patients with Alzherimer's disease, and systemic inflammation (which is intricately connected to psychiatric diseases), will be put in biomedical context by co- investigators who specialize in these areas. The interactome will identify """"""""hub-proteins"""""""" that are central to many pathways. It will provide hypothesis of """"""""functional connectivity"""""""" of new proteins (namely, those of which nothing is currently known). Further, many diseases are found to be untreatable by suppressing a single pathway, and often require manipulating multiple pathways, and the MHAIN interactome can provide the overlap points of multiple pathways that may be used for treatment of diseases. The MHAIN interactome, which would be the outcome of the proposed research, can thus have a broad impact on our understanding of molecular basis of mental health. 1

Public Health Relevance

The proposed research discovers interactions among proteins that are involved in mental health and inflammation. The knowledge of such interactions would generate hypotheses about the role of these proteins in biological functional pathways and would contribute towards the understanding of the biology of diseases and towards development of mechanisms of treatment.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
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Freund, Michelle
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University of Pittsburgh
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Roth, Adam; Subramanian, Sandeep; Ganapathiraju, Madhavi K (2018) Towards Extracting Supporting Information About Predicted Protein-Protein Interactions. IEEE/ACM Trans Comput Biol Bioinform 15:1239-1246
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