Novel set of global descriptors, developed by her for amino acid sequence, also proved to be efficient in various types of DNA analysis. Teresa Head-Gordon 's research combines experimental, theoretical and computational approaches used toward the problems of protein folding and structure prediction. She is developing a global optimization approach to protein structure prediction that has been quite successful working directly on the potential energy surfaces of small homopolymers and peptides, and recently a-helical proteins. These optimization approaches will be combined with fold recognition algorithms to refine low-resolution fold predictions to higher level structural predictions as a means of structurally and functionally annotating whole genomes. These problems are computationally intensive, have been effective parallelized.
Indexing allows matches to be found in a large data set without exhaustively scanning the data.
Of the RNAs, only transfer and ribosomal RNAs are routinely included in genome annotation through their sequence and structural homology with other tRNAs and rRNAs. Holbrook's group is developing computational methods to locate novel functional RNA genes in genomic sequences. Artificial neural networks and hidden Markov models are useful in extracting key components from genome sequences in order to make predictions. Once all RNA genes and their products have been identified, post-genomic analysis includes structural and functional prediction. He and his group are exploiting the rapidly growing empirical data stored in sequence databases to make predictions of RNA function directly from sequence in analogy to the Prosite and Pfam databases of proteins.