Protein phenotypes from genomic information: 3D, mutation, and design
Abstract: Attributes of living systems are constrained in evolution. An alternative to the analysis of conserved attributes ('characters') is analysis of functional interactions ('couplings') that cause conservation. For proteins, the evolutionary sequence record can be exploited to provide exquisitely accurate information about 3D structures and functional sites. Recent progress is based on high throughput sequencing as an experimental technology and global probability models under the maximum entropy principle as a key theoretical tool. I will describe how these advances are used in accurate prediction of 3D interactions, complexes, protein plasticity, designing proteins for synthetic biology and therapeutics - and extrapolate to the study of the effects of human genetic variation.
There is a now major opportunity to link genomic information to phenotype and apply this to concrete engineering and health problems, such as disease likelihood, the emergence of drug resistance. My lab will concentrate on four interrelated areas at different scales of biology that address the challenge to infer causality in biological information.
Debora has a PhD in computational biology and a track record of developing novel algorithms and statistics to successfully address unsolved biological problems. She has a passion for statistics and is driven by a desire to understand and interpret human genetic variation.