UCSF

Models of protein structure at atomic resolution are fundamental to many problems in biology. Increasingly, we are interested in computing models of protein structure based on noisy, sparse, and heterogeneous data; demanding a rigorous approach to modeling. Bayesian inference is one such approach. In Chapter 1, we formalize modeling as a search for all models consistent with the input information. In Chapter 2, we develop a computational framework for computing models from heterogeneous biophysical software. In Chapter 3, we develop a modeling method to compute a model of mTORC2 in complex with a native substrate, Akt1. In Chapter 4, we develop a modeling method to compute multi-state models from X-ray crystallography.