Bioinformatics analysis of genetic variation effects for protein engineering and biomedical application

Abstract

As sequencing technology has advanced, it has become crucial to predict and interpret the effects of genetic variations. Genetic variations can cause protein activity and function changes, impacting a cell's pathway activity or developing an organism's disease. Accurate simulation of the mutation effects on proteins, cells, and organisms has applications in (i) protein engineering by introducing specific amino acid substitutions to enhance or modulate protein activity and (ii) biomedical application by identifying disease-associated variants for disease diagnosis or prognosis. My graduate research focuses on developing computational methods to predict mutation effects through the sequence and 3D-structure analyses. In the first part of my research, I presented a method to find disease-associated variants by predicting mutation impacts using the co-evolution analysis of protein sequences. In detail, a scoring method was developed to measure the influence of an amino acid change on the co-evolutionary relationships. The method detected novel disease-associated variants at less-conserved sites and protein interaction interfaces which are challenging to identify using conventional methods. In the second part, I devised an enzyme engineering strategy that directs mutation strategy to improve enzyme activity by scanning the evolution of protein sequences. I hypothesized that amino acid pairs for various enzyme activities were encoded in the evolutionary history of protein sequences, whereas loss-of-function mutations were avoided since those are depleted during the evolution. The strategy was further experimentally validated by modulating the activities of three different enzymes of great interest in chemical production. In the last part, I developed a machine learning-based computational method to predict cancer recurrence within 5 years after surgery by analyzing epigenetic variations in bulk tumors from colorectal cancer patients. Specifically, the cancer recurrence was predicted based on the cellular deconvolution of bulk tumors into two distinct immune cell states: cancer-associated and noncancer-associated.