Please check our team website at:
Sample of our projects:
Domain Mappings of Disease Mutations: http://bioinf.umbc.edu/dmdm
Domain Mapping of Disease Mutations (DMDM) is a database in which each disease mutation can be displayed by its gene, protein, or domain location. DMDM provides a unique domain-level view where all human coding mutations are mapped on the protein domain.
Thomas A. Peterson, Asa Adadey, Ivette Santana-Cruz, Yanan Sun, Andrew Winder, and Maricel G. Kann. DMDM: Domain Mapping of Disease Mutations. Bioinformatics, 26 (19), 2458-2459.
Extraction of Mutations from Biomedical Literature: http://bioinf.umbc.edu/emu
Extractor of Mutations (EMU) is an exciting project in which undergraduates from different fields work together on building a database of human mutations related to disease. At the core of the project is a text-mining method to extract mutations and a MetaMap/MeSH index searches to map diseases. See our EMU team at: http://bioinf.umbc.edu/lab_members.php (no programming experience required for pre-med, biology, biochemistry majors).
E. Doughty, A. Kertesz-Farkas, O. Bodenreider, G. Thompson, A. Adadey, T. Peterson, and M. G. Kann. (2010) Toward an automatic method for extracting cancer and other disease-related point mutations from biomedical literature 27 408-415.See Emily's interview featured at the UMBc undergraduate researchers site:
Protein Interactions and Diseases:
Understanding relationships among proteins is crucial to understand the molecular machinery of the cell. Computational tools to predict domain-domain interactions provide a detailed molecular view of the protein interactions and complements expensive and laborious experimental techniques to identify such interactions. The evolutionary distances of interacting proteins often display a higher level of similarity than those of non-interacting proteins. This finding indicates that interacting proteins are subject to common evolutionary constraints and constitute the basis of a method to predict protein interactions known as mirrortree. In a recent publication, we showed that binding neighborhoods of interacting proteins have, on average, higher co-evolutionary signal compared to the regions outside binding sites; although when the binding neighborhood was removed, the remaining domain sequence still contained some co-evolutionary signal. We have several projects focusing on the investigation of the role of compensatory mutations in protein co-evolution and which are shading light on the process of co-evolution of interacting proteins.
Kann, MG. Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Briefings in bioinformatics 11, 96-110 (2010).
Kann, MG, Shoemaker, BA, Panchenko, AR & Przytycka, TM. Correlated evolution of interacting proteins: looking behind the mirrortree. Journal of molecular biology 385, 91-98 (2009).
Kann, MG, Jothi, R, Cherukuri, PF & Przytycka, TM. Predicting protein domain interactions from coevolution of conserved regions. Proteins 67, 811-820 (2007).
Coming up: PINTT (Protein INteraction Text-mining Tool)
The goal of the PINTT project is to develop computational tools to extract protein interactions from literature. PINTT will be modeled after EMU: teams of undergraduate students from Biology, Computer Sciences and Bioinformatics will work together developing the software and methods to perform the task and creating manually curated databases to use as gold standard to benchmark the methods.PINTT is a collaboration with Dr. Graciela Gonzalez from Arizona State University (http://bmi.asu.edu/directory/869038). If you would like to join the PINTT project, please contact email@example.com