Zeolites Structure, Influenza Evolution and Vaccine Effectiveness, with Prediction of Emerging Flu Strain Clusters
Deem, Micheal W
Doctor of Philosophy
In my thesis, I have devided the main contents into three areas. The first area is to solve problems related to zeolite structures. Known zeolite structures have relatively few seven-membered rings. Previous quantum mechanical calculations suggest there is no particular energy penalty for seven-membered rings. Predicted zeolite structures sampled from all possible symmetries also do not explain why there are so few observed seven-membered rings. In chapter 2, I analyze the ring size distributions of predicted structures as a function of energy and density. I show that predicted structures with low density, in the range where known zeolites exist, have relatively few seven-membered rings. It appears that the constraint of proximity to the low-density edge of predicted structures is what leads to a low probability of seven-membered rings. These results suggest that low-density predicted structures are similar to known zeolites and of greatest interest as new synthetic targets. The second part concerns about the evolution of Influenza viruses and discusses a way to prediction vaccine effectiveness. Influenza A is a serious disease that causes significant morbidity and mortality, and vaccines against the seasonal influenza disease are of variable effectiveness. In chapter 3, I discuss the use of the pepitope method to predict the dominant influenza strain and the expected vaccine effectiveness in the coming flu season. I illustrate how the effectiveness of the 2014/2015 A/Texas/50/2012 [clade 3C.1] vaccine against the A/California/02/2014 [clade 3C.3a] strain that emerged in the population can be estimated via pepitope. In addition, I show by a multidimensional scaling analysis of data collected through 2014, the emergence of a new A/New Mexico/11/2014-like cluster [clade 3C.2a] that is immunologically distinct from the A/California/02/2014-like strains. The last chapter discusses detecting the emergence of new flu strains. Early detection of incipient dominant influenza strains is one of the key steps in the design and manufacture of an effective annual influenza vaccine. In chapter 4, I summarize the state of the predictive art and report the most current results for pandemic H3N2 flu vaccine design. A 2006 model of dimensional reduction (compaction) of viral mutational complexity derives two-dimensional Cartesian mutational maps (2DMM) that exhibit an emergent dominant strain as a small and distinct cluster of as few as 10 strains. I show that recent extensions of this model can detect incipient strains one year or more in advance of their dominance in the human population. My structural interpretation of my unexpectedly rich 2DMM involves sialic acid, and is based on nearly 6000 strains in a series of recent 3-year time windows. Vaccine effectiveness is predicted best by analyzing dominant mutational epitopes.