Georgia Institute of TechnologyCenter for the study of systems biology
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Research Interests

  • Bioinformatics
  • Protein function prediction
  • Elucidation of metabolic pathways
  • Development and implementation of algorithms for large-scale computer simulations of proteins

One of the goals of genome sequencing projects is to develop tools for comparing and interpreting the resulting genomic information. In particular, one would like to be able to identify protein function from sequence. Our group is developing a series of tools to achieve this objective based on the sequence-structure-function paradigm. To accomplish this objective, we are developing algorithms that can predict protein structure from sequence. Included are both ab initio folding tools as well as threading methods. Our ab initio folding approaches appear capable of predicting low resolution structures for a substantial fraction of small, single domain proteins. If a limited amount of experimental data is provided, substantially larger systems can be handled. Furthermore, we have shown that such low resolution models can be used to identify active sites in proteins, and thereby we can employ structural information to predict protein function. This suggests a means for the large scale functional screening of genomic sequence databases based on the prediction of structure from sequence, then on the identification of functional active sites in the predicted structure. This opens up the possibility of screening entire genomes to identify protein having a specified biochemical activity. Finally, such proteins are analyzed in the context of their role in various metabolic pathways.

In memory of Adrian K. Arakaki