Speaker
Description
Proteins are important biomolecules of life. While many proteins can function independently, most proteins interact with other proteins to control and mediate their biological activities. Hence, studying protein-protein interactions (PPIs) is important to better understand biological functions. There are several biological factors that can influence the presence or absence of a PPI. In our work, we are specifically interested in studying how a PPI is affected by a biological phenomenon called alternative splicing (AS), where a single gene gives rise to multiple proteins. We aim to use deep learning methods to predict the likelihood of a PPI in the event of AS. In detail, we use (and later modify) an existing geometric deep learning approach to predict PPIs. Intuitively, given 3-dimensional structures of two proteins, the approach first represents the surface of each protein as a "cloud'' of points. Then, the approach computes surface curvatures and chemical characteristics as features of each point. Finally, the approach uses these features to train a neural network architecture and predict which parts of the two proteins are interacting. In our work, we further advance this existing approach to more accurately predict which parts of the two proteins interact. To do this, we use a convolutional neural network to extract advanced features for each protein and use those features to classify interacting versus non-interacting parts of a given pair of proteins. In our on-going work, we aim to use our novel approach to predict PPIs in the event of AS.
