Prediction and analysis of protein structure

This thesis, which contains an introduction and four manuscripts, summarises my efforts during my the past four years to understand proteins, their structure and dynamics. The first manuscript presents a protocol that refines models as part of a protein structure prediction pipeline. To achieve this, we used spatial information from determined structures and sequence information from multiple alignments. The protocol was used to improve the quality of rough models containing only one point per residue. In the second manuscript we investigated protein fold space. We compared models with known fold to determined structures and found that out models contained many folds that were not seen in the present pool of structures in the PDB. Comparison of structural features revealed no reason why the model folds could not exist. We investigated how well geometric comparison methods distinguished fold in the third manuscript. We presented a novel measure of topological similarity and showed that geometric methods have trouble distinguishing fold differences between both models and PDB structures. In the last manuscript we showed that the architecture is the most important factor for dynamics as measured by normal modes. Protein fold has some effect and cannot be discarded completely, but larger differences in fold does not necessarily correspond to larger differences in flexibility if the architecture is the same

Elk group sizes and covariates

Elk group sizes and covariate

Appendix B. Extended infectiousness.

Extended infectiousness

Appendix C. Supporting video files of model dynamics.

Supporting video files of model dynamics

Appendix A. Supporting figures of model structure and output.

Supporting figures of model structure and output

Map of study area and number of brucellosis-affected livestock herds (1990–2014) per hunt district (HD).

<p>HDs in the study area overlap with the designated surveillance area (DSA) for brucellosis in livestock. YNP = Yellowstone National Park; GTNP = Grand Teton National Park. Inset: Map of contiguous United States and study area.</p

Shifting brucellosis risk in livestock coincides with spreading seroprevalence in elk

<div><p>Tracking and preventing the spillover of disease from wildlife to livestock can be difficult when rare outbreaks occur across large landscapes. In these cases, broad scale ecological studies could help identify risk factors and patterns of risk to inform management and reduce incidence of disease. Between 2002 and 2014, 21 livestock herds in the Greater Yellowstone Area (GYA) were affected by brucellosis, a bacterial disease caused by <i>Brucella abortus</i>, while no affected herds were detected between 1990 and 2001. Using a Bayesian analysis, we examined several ecological covariates that may be associated with affected livestock herds across the region. We showed that livestock risk has been increasing over time and expanding outward from the historical nexus of brucellosis in wild elk on Wyoming’s feeding grounds where elk are supplementally fed during the winter. Although elk were the presumed source of cattle infections, occurrences of affected livestock herds were only weakly associated with the density of seropositive elk across the GYA. However, the shift in livestock risk did coincide with recent increases in brucellosis seroprevalence in unfed elk populations. As increasing brucellosis in unfed elk likely stemmed from high levels of the disease in fed elk, disease-related costs of feeding elk have probably been incurred across the entire GYA, rather than solely around the feeding grounds. Our results suggest that focused disease mitigation in areas where seroprevalence in unfed elk is high could reduce the spillover of brucellosis to livestock. We also highlight the need to better understand the epidemiology of spillover events with detailed histories of disease testing, calving, and movement of infected livestock. Finally, we recommend using case-control studies to investigate local factors important to livestock risk.</p></div

Parameter estimates (log odds) for models of brucellosis-affected hunt districts (HD).

<p>Parameter estimates (log odds) for models of brucellosis-affected hunt districts (HD).</p

Interacting effects on the probability of a brucellosis-affected hunt district (HD).

<p>(A) elk brucellosis seroprevalence × fed, and (B) year × fed. Note y axes differ among panels. Black and blue lines (mean) and shaded areas (95% credible intervals) describe the estimated relationships for fed and unfed elk, respectively. Probability of an Affected HD = probability of an HD having brucellosis-affected livestock detected. Fed = elk feeding ground present.</p

Estimated and empirical brucellosis seroprevalence in elk per elk hunt district (HD).

<p>Maps depict estimated brucellosis seroprevalence in elk in 1990 and 2014 for HDs in the study area. YNP = Yellowstone National Park; GTNP = Grand Teton National Park. Inset: Map of contiguous United States and study area. Small panels on the right depict modeled (grey) and empirical (blue) seroprevalence (y axis; range from 0 to 1) over time (x axis; range from 1990 to 2014) from a subset of the study area. Arrows show which elk hunt district corresponds to each panel. Blue lines are 95% binomial confidence intervals, and grey shaded regions are 95% credible intervals.</p