DMS Statistics Seminar

Speaker: Debswapna Bhattacharya, Assistant Professor, Department of Computer Science & Software Engineering

Title: Probabilistic Graphical Model for Protein Folding 

Abstract: Graphical models have emerged as one of the most powerful ways to capture the dynamics of natural systems in recent years. Protein folding is a grand puzzle of nature that remains to be solved even after more than 50 years of intense research. In this talk, I will first introduce the nature of protein molecule and present a computational abstraction of protein folding problem. Then, I will present my latest research of developing algorithms to simulate protein folding in silico by formulating probabilistic graphical model that integrates probability distributions from directional statistics, a field of statistics concerned with angles and orientations to model wind directions or astronomical observations, to represent and learn systematic patterns observed in naturally occurring proteins. Using the trained model as a proposal distribution and employing Markov chain Monte Carlo sampling, protein folding can be computationally replicated and predicted with high degree of accuracy and speed. The method overcomes the limitations of existing approaches and was ranked as one of the 19 most innovative methods in community-wide blind assessments, demonstrating great potential of graphical models in tackling protein folding problem in particular and other fundamental problems in life sciences in general.