2006 Annual Science Report
University of Hawaii, Manoa Reporting | JUL 2005 – JUN 2006
A Proteomic View of Adaptations to Extreme Environments
This project is undertaking to develop a concise, molecular level understanding of how biomolecular structure allows microorganisms to adapt to various extreme aqueous environments. Specifically, are there identifiable protein motifs that allow for protein stability, and therefore organism survivability, which are adapted to function in particular environments? First, we developed the Salt Bridge Statistical Analyzer (SBSA) program that runs primary protein sequences through a folding routine to predict the α-helix sequences. The helical sequences are then analyzed for the occurrence of both amino acid content and 32,800 motifs. After we had a working version of the program, we first determined that the number of observed motifs from predicted helices correlated very strongly with the occurrences observed from protein crystal structures (R values typically greater than 0.97). This indicated that the SBSA program could be used to reliably predict motif distributions for proteins that have not yet been studied using crystallography. The SBSA program was then used to study the full proteomes of 35 extremophilic and mesophilic organisms. We have examined amino acid and motif contents of these organisms in detail and discovered that each class of organism appears to have a unique set of motifs, a possible indication of environmental-specific adaptations. Based on this observation, we next sought an efficient means of comparing motifs distributions in large groups of organisms, are currently developing an “Envirogenic Tree” approach to this question. An Envirogenic Tree looks similar to a standard phylogenic tree, but shows environmentally related structural similarities. Ideally, an envirogenic tree would cluster organisms strictly as a function of the environment in which they grow in, but we have not quite achieved this yet. In our initial set of 35 organisms, we have constructed trees where ~90% of the components are grouped based on the environment (i.e. thermophiles clustered with thermophiles, mesophiles with mesophiles, and so on). Optimization of tree construction will give clues as to which motifs are the most responsible for environmental adaptation and will provide a new tool for researchers working on microbial genome analysis.