Research
Genomic Science Leadership Initiative
This is not published. Co-organized a 3-day workshop introducing participants to the connection and importance of the natural world to molecular biology and genomics. Participants gain hands-on laboratory and computer experience to explore the presence of bacteria (through 16S rRNA) in Colorado and New Mexico environmental samples affected by the Gold King Mine Spill. This experience introduces the latest DNA technologies and current molecular and computational biology methods. Participants learn about science career paths and leadership philosophy/practices.
click here for an overview of the 2018 workshop
click here for an overview of the 2017 workshop
Using protein active site predictors to classify protein superfamilies
As a result of high-throughput protein structure initiatives, over 14,400 protein structures have been solved by structural genomics (SG) centers and participating research groups. While the totality of SG data represents a tremendous contribution to genomics and structural biology, reliable functional information for these proteins is generally lacking. Better functional predictions for SG proteins will add substantial value to the structural information already obtained. Our method described herein, Graph Representation of Active Sites for Prediction of Function (GRASP-Func), predicts quickly and accurately the biochemical function of proteins by representing residues at the predicted local active site as graphs rather than in Cartesian coordinates. We compare the GRASP-Func method to our previously reported method, structurally aligned local sites of activity (SALSA), using the ribulose phosphate binding barrel (RPBB), 6-hairpin glycosidase (6-HG), and Concanavalin A-like Lectins/Glucanase (CAL/G) superfamilies as test cases. In each of the superfa- milies, SALSA and the much faster method GRASP-Func yield similar correct classification of pre- viously characterized proteins, providing a validated benchmark for the new method. In addition, we analyzed SG proteins using our SALSA and GRASP-Func methods to predict function. Forty- one SG proteins in the RPBB superfamily, nine SG proteins in the 6-HG superfamily, and one SG protein in the CAL/G superfamily were successfully classified into one of the functional families in their respective superfamily by both methods. This improved, faster, validated computational method can yield more reliable predictions of function that can be used for a wide variety of appli- cations by the community.