UCSF

Studying the evolution of enzymes and their functions improves our ability to determine the functions of unknown enzymes and engineer enzymes to perform new functions. An enzyme's function is determined by its sequence and structure and we can trace the evolution of enzymes and their function by analyzing their sequences and structures. This dissertation describes work to extend these analyses of sequences and structures to use comparisons of enzyme functions in order to study enzyme evolution.

The first studies described in this dissertation use "traditional" sequence and structure analyses follow the evolution of an especially complex superfamily of enzymes. We found that within the protein family that we were studying, despite having a single function and a single evolutionary origin, no sequence or structural motifs unique to this family could be identified. We also found that sequence and structural determinants of specificity may lie outside of the active site. These results show that the correlation between sequence, structure, and function is not always straightforward and demonstrate the need for direct analyses of functions to study enzyme evolution.

In analogy to sequence and structure-based studies of enzyme evolution, we have examined a large number of enzyme superfamilies using a new computational analysis of patterns of substrate conservation. The patterns that we observe among substrates during enzyme evolution suggest more complex patterns of functional divergence than what has been proposed by previous theories of enzyme evolution. The method has been automated to facilitate large-scale annotation of enzymes discovered in sequencing and structural genomics projects. A data resource has been developed to share this data with researchers interested in improving predictions of enzyme function and in enzyme engineering.

The final study presented describes work to select templates for structural genomics efforts. The eventual goal is to increase the number of structures available to determine enzyme function and specificity using methods like comparative modeling, computational docking, and other experimental efforts.