An Effective Approach to Identifying and Categorizing Evolving 3D Structural Motifs in Protein Folding Data
Oral Defence Date:
Professor Yang, Professor Murphy, and Professor Singh
Molecular Dynamics-based simulations have been employed to study the protein folding process, in which a protein acquires its functional three-dimensional structure. This has resulted in a large number of protein folding trajectories, each consisting of a series of 3D structures of the protein under study. As a result, it becomes increasing important to effectively analyze such data to facilitate a deeper understanding of the protein folding mechanism. In this thesis, we focus on identifying important three-dimensional structural motifs in the folding data. To achieve this, we have proposed a multi-step algorithm that is not only computationally efficient but also captures the evolving nature of the folding process. Empirical evaluation demonstrates that such motifs are effective at characterizing a protein’s structural evolution in its folding process. Furthermore, such motifs can also be utilized to address important issues such as detecting important protein folding events.
Protein folding trajectory analysis; three-dimensional structural motif identification