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The Levinson Emerging Scholars Program
Adrian Laurenzi - Biology and Computer Science
Adrian Laurenzi first became interested in the intersection of computation and biology when he was a senior in high school. He worked on a project in at a lab at the University of Arizona to understand a plant pathway used synthesized secondary metabolites. While working at the U of A he devised a computational method that helped to identify and isolate one of the enzymes in the pathway. Almost immediately after entering UW as a freshman Adrian joined Ram Samudrala's computational biology group to pursue his interest in computational biology. As a member of Ram's group Adrian has worked on a number of independent projects concerned primarily with drug discovery. By integrating software developed in Ram's group Adrian developed a computational method to identify protein targets in the malaria parasite for which an inhibitory compound would produce minimal side effects in humans. More recently Adrian has been working to apply and adapt protein structure prediction software to expressed sequence tag (EST) databases in order to improve our ability to predict the functionality of novel genes within the databases. This could accelerate the discovery of novel genes and gene networks and has important implications in medicine and drug development. In the future Adrian plans to continue his work in biomedical computation by developing open source software as an independent consultant or academic scientist. Adrian feels that creating open source software is the most effective way to make an impact as a scientist because his software will help enable the discoveries of a potentially large number of other scientists. He strongly believes in creating software that is free and open so that it is available for other scientists to use and build upon.
Mentor: Ram Samudrala, Microbiology
Project Title: Optimization of protein structure prediction software for EST data
Abstract: Large databases of expressed sequence tags (ESTs) are available containing the expressed genes from a tremendous variety of organisms. Many projects such as the Gene Index Project at Harvard University are underway, databasing the expressed genes from a tremendous variety of organisms. There are over 60 million ESTs in GenBank representing well over half of all GenBank entries. To make efficient use of EST data computational techniques have been developed to analyze and organize EST databases. ESTs have been useful in discovering new genes, understanding gene expression and regulation, and constructing genome maps, all of which have important implications in medicine. However, the utility of EST data relies upon our ability to make accurate annotations that describe the functionality of the ESTs in the source organism. Presently most approaches used to annotate ESTs rely on sequence-based comparison methods such as BLAST. This is limiting because the function of a protein is dependent upon its tertiary (3-D) structure. Therefore, the ability to reliably predict the functionality of an EST could be improved if we were able to accurately predict the structure of the proteins that ESTs encode. We have demonstrated that ProtinfoAB and Rosetta3.1 can reliably predict the structures of parts of proteins encoded by sequences that contain approximately 75% or more of the full-length protein sequence suggesting these methods would be useful in annotating at least a subset of sequences from an EST database. We propose to optimize prediction of partial structures of proteins encoded by ESTs by combining ab initio and template-based protein structure prediction methods: ProtinfoAB and ProtinfoCM. Optimization of structure prediction methods for EST data will enhance our ability to predict the functionality of ESTs enabling more informed bench experiments and expediting the discovery of new genes with potential utility in medicine.