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

Ewes in non-pneumonia (healthy) years: impact of covariates on the relative risk of dying (of causes other than pneumonia) outside of pneumonia epidemics.

<p>SE = Standard error.</p>*<p>Model was inestimable, since all pneumonia-year mortalities among individuals with lags of 2 or more occurred among residents; the translocation coefficient could not be calculated.</p

Cox proportional hazards model-estimated relative risks of mortality of ewes, rams, and lambs after accounting for differences in sex and age.

<p>Risk of dying of pneumonia relative to naïve individuals given: an indicator for any previous pneumonia exposure (<i>Indicator</i>), the number of years since the last exposure event (<i>Lag</i> of 1, 2 or > = 3), increasing number of exposure events (<i>Count</i>), and translocation status (<i>Trans</i>). Bottom panel: model-estimated risk of dying for lambs during outbreaks of pneumonia given ewe covariates. Coefficient estimates from univariate models are in grey and coefficient estimates from multivariate models are in black. Risk values are drawn from proportional hazards model coefficient estimates, with the point estimate denoted by the box or vertical line, and horizontal lines extending to the 95% confidence limits.</p

Study area and pneumonia history calculation.

<p>A. Study area: we report data from 388 radio-collared adult bighorn sheep and 753 ewe-lamb pairs within 12 of these 16 populations in Hells Canyon (WA = Washington, ID = Idaho, OR = Oregon; see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061919#pone.0061919.s001" target="_blank">Fig. S1</a> for populations' names and pneumonia histories). B. How individual pneumonia histories were constructed. The bighorn sheep in panel B was collared (II) at age 6 and died (III) at age 11 (in the middle of the biological year). Age was estimated at capture (II) or by incisor cementum analysis after death (III). Based on its population's pneumonia history (red indicates years with pneumonia mortality, green indicates years when pneumonia was not detected; see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061919#pone.0061919.s001" target="_blank">Fig. S1</a>), this animal experienced 8 pneumonia exposures (<i>Count</i> = 8). The time since last exposure (<i>Lag</i>) was 0 when it died.</p

Potential relationships between past exposure of bighorn sheep to pneumonia and mortality during subsequent pneumonia epidemics.

<p>Potential relationships between past exposure of bighorn sheep to pneumonia and mortality during subsequent pneumonia epidemics.</p

Rams: results from Cox proportional hazards model of ram hazard of dying from pneumonia given covariates over the age-based timescale.

<p>SE = Standard error.</p

Number of animals included in the analysis.

*<p>lambs born to radio-collared ewes with a known fate by October 1.</p

Ewes: results from Cox proportional hazards model of ewe relative risk of dying from pneumonia given covariates over the age-based timescale.

<p>SE = Standard error.</p

Relationship between cumulative past exposure and risk of dying.

<p>a. Ewe risk of dying from pneumonia as a function of the number (<i>count</i>) of previous exposures (relative to unexposed ewes). A relative risk of 1 on the y-axis represents no effect of <i>count</i> on the risk of dying of pneumonia. The shaded area represents the 95% confidence bounds on the hazard ratio associated with continuously increasing number of previous exposures (in a model fit on an age-scale that included a fixed effect for translocation status). The error bars bound the 95% confidence range for the uniquely estimated hazard ratios associated with each value of <i>count</i>. The line connects the estimated median risk of dying relative to the risk for previously unexposed ewes. <i>Count</i> values above six are grouped within the <i>count</i> = 6 category. b. Lambs' risk of dying in a pneumonia epidemic given the number of times the lamb's mother was exposed. The shaded area represents the 95% confidence bounds on the hazard ratio associated with continuously increasing number of previous exposures (<i>count</i>) of the ewe (in a model fit on lamb age-scale that included a fixed effect for ewe's translocation status). The error bars bound the 95% confidence range for the uniquely estimated hazard ratios associated with each number of exposures for ewes. The line connects the estimated median risk of lamb mortality. c. A conceptual diagram illustrating how individual frailty may drive the apparent relationship in part a. Frailties decrease as number of exposures (or age) increase because the weak (at the upper tail of the distribution) die first, and cannot be observed in later years, leaving an increasing proportion of strong individuals.</p

Lambs: results from Cox proportional hazards models of lamb hazard of dying given dam (ewe) covariates in all years (pneumonia and healthy years; top), years without pneumonia (pneumonia years excluded; middle) and years with pneumonia (healthy years excluded; bottom).

<p>SE = Standard error; SD = standard deviation; PN Year = years with outbreak of pneumonia in lambs.</p