- C. Langmead, E. Xing, Hetunandan Kamichetty. 2011. Structured probabilistic models of proteins across spatial and fitness landscapes. Abstract: Proteins are dynamic molecules. They flex in space, adopting many different spatial configurations while performing their function and evolve over time, changing their amino acid composition in response to changing fitness landscapes. The thesis of this dissertation is that this inherent variability of proteins can be modeled by structurally sparse representations. These sparse models can then be used to efficiently reason about the properties of the protein by the means of algorithms that exploit their sparsity. This dissertation develops the first Probabilistic Graphical Model that models the entire protein across spatial configurations (GOBLIN). By compactly encoding and manipulating a probability distribution over an exponentially large space, and using statistical inference algorithms that exploit structural sparsity, GOBLIN is able to compute experimentally measurable properties of protein interactions quickly and accurately. 
