Peroxidases are a large family of enzymes that can generate reactive oxygen species involved in many cellular processes including aging, gene expression and signal transduction. Post-translational modifications (PTMs) of proteins play important roles in the cellular processes of peroxidases. Rare mutations involved in PTMs can alter molecular interaction sites and generate deleterious outcomes. However, the genetic mechanism of PTMs affected by rare mutations in peroxidases remains unknown. High throughput technologies generate large datasets for PTM sites and damaging mutations, which makes this discovering process possible. This project will develop novel computational tools to identify PTM sites and to predict the effects of mutations to peroxidases. The bioinformatics tools and databases developed in this program will be expected to have broader applications in broad biology research and machine learning community. In addition to the new computational tools and biological knowledge, this project will encourage the participation of underrepresented minority students to STEM research. The creation of the Machine Learning course section and Big Data club will help students from HBCUs develop solid foundations for genetics and bioinformatics. The outreach activities, which include the involvement of students in research training activities, workshops and seminars, will help minority students develop essential skills in Big Data analytics.
The project will investigate the effects of rare mutations and PTM sites in peroxidases using bioinformatics tools and experimental approaches. Novel bioinformatics approaches will be developed to predict mutation effects and PTM sites, and to analyze the effects of peroxidase rare mutations involved in PTMs in Human and Drosophila through three objectives: 1) Tool Development: new biological sequence features and machine learning algorithms will be used to develop bioinformatics tools for predicting protein sumoylation sites and the effects of missense mutations on protein stability; 2) Bioinformatics Analysis: sequence-based machine learning predictors will be designed to discover the rare damaging mutations, predict the effects of mutations on protein stability, and identify the mutations involved in PTMs, with a structure-based energy calculation to quantify the energy changes to assess the effects of mutations on peroxidase structure and function; and 3) Experimental Validation: biological impacts of PTM-related mutations in curly suppressor (Cysu), a heme peroxidase involved in the wing expansion and the maturation process in Drosophila, will be assessed. Genome editing tools will be used to generate mutant flies and to test neuronal functions of homologues to inspect their contributions to the phenotypes of Cysu transgenic flies. The new bioinformatics and experimental methods used in this study will provide useful information for characterizing the functional effects of rare mutations involved in PTMs in peroxidases. Results of this project, including research papers and computational tools, will be made available at http://tengbioinformatics.com/.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
