Proteins are evolved molecular machines capable of self-assembly and reliable functioning in a fluctuating environment.  They form complex interaction and regulatory networks from which intricate yet robust cellular behavior emerges.  Understanding how these remarkable properties of proteins arise as the result of evolution is a central challenge in molecular and systems biology, and can lead to better strategies for protein design and synthetic biology.  In this talk, I will show how bioinformatics analysis of genome-wide measurements for diverse protein properties can help delineate major evolutionary factors responsible for shaping today`s proteins, focusing on the eukaryotic model organism Saccharomyces cerevisiae (baker`s yeast). First, I will present an analysis of structural determinants of protein evolution at the residue level.  I will show that protein evolution is constrained by the biophysics of protein folding, the mutational robustness of proteins, and the biophysics and function of protein-protein interactions.  Second, I will present an analysis of protein evolution within protein-protein interaction and transcriptional regulatory networks.  I will show that transcription factor (TF) hubs with many interaction partners or regulators evolve qualitatively differently from generic protein hubs.  This striking difference reveals the hierarchical nature of cellular networks, and suggests that components within the same global network can be governed by distinct evolutionary principles.

Bio:

Yu (Brandon) Xia is an Assistant Professor in the Bioinformatics Graduate Program and the Department of Chemistry at Boston University, with a secondary appointment in the Department of Biomedical Engineering.  He received his B.S. in Chemistry (major) and Computer Science (minor) from Peking University.  He gained his Ph.D. in Chemistry from Stanford University, working with Michael Levitt on computational structural biology.  Following that he was a Jane Coffin Childs postdoctoral fellow at Yale University, working with Mark Gerstein on protein bioinformatics.  His research focuses on computational structural and systems biology, in particular the prediction and analysis of protein structures and networks.