Improving Molecular-Level Protein Docking and Interpreting System-Level Cancer Mechanisms through Machine Learning
Abstract
Protein-protein interactions (PPIs) are crucial to cellular function, yet researchers still have
much to discover about their mechanisms. At the molecular level, two new computational tools
are proposed in this study to facilitate protein docking: 1. A regression model for predicting
both the direction and extent of protein conformational change, especially the extent in this study,
provides a new approach for structural ensemble generation and conformational sampling. 2. A
classifier for assessing whether a protein pair is suitable for rigidity docking provides a method for
performing a sanity check before uniformly applying rigidity assumption in protein docking.
At the intra-cellular system level, PPIs participate intensively in the propagation of mutational
effects of cancer, which is well-known as a complex disease often derived from "driver genes" containing
pathogenic mutations. Here I propose a new machine learning framework with biologically
meaningful features to identify driver genes with the help of PPI network topology. Further interpretation
of the machine learning model can help us understand cancer mechanisms by explaining
the reason why cancer would prefer to attack those positions in network.
Citation
Chen, Haoran (2018). Improving Molecular-Level Protein Docking and Interpreting System-Level Cancer Mechanisms through Machine Learning. Master's thesis, Texas A & M University. Available electronically from https : / /hdl .handle .net /1969 .1 /174024.