Transporters catalyze entry and exit of molecules into and out of cells and organelles. They achieve cellular homeostasis, are responsible for multidrug resistance in pathogens and tumors, and when defective, cause dozens of important human genetic diseases. Our laboratory maintains, updates and improves the Transporter Classification Database, TCDB, which houses the Transporter Classification (TC) system, adopted officially by the International Union of Biochemistry and Molecular Biology (IUBMB). TCDB is the internationally acclaimed, carefully annotated, universal standard for classifying and providing information about transporters and transport-related proteins in all major domains of life. It presents sequence, biochemical, physiological, pathological, structural and evolutionary data about these proteins and the transport systems they comprise. It uses a successful system of classification based on transporter class, subclass, family, subfamily, and individual transporter. In this competitive renewal of GM0077402, we propose to broaden and deepen our efforts to expand, update, automate and interlink TCDB. We will generate new data concerning transport proteins, design new machine learning approaches for data, and introduce procedures for making functional predictions of uncharacterized transporters. This last effort will derive reliable new biological knowledge from a variety of sources, including phylogeny, motif, domain, operon and regulon analyses.
Our Specific Aims are as follows: 1. To develop software for automatic text mining and information extraction. 2. To conduct bioinformatic analyses and molecular biological experiments for TC knowledge expansion. 3. To interconnect TCDB bidirectionally with other relevant databases, thereby creating a """"""""network"""""""" of knowledge from current """"""""island"""""""" databases. 4. To use multiple approaches to derive reliable functional predictions as guides for future research. 5. To utilize a newly formed TCDB advisory board and establish a plan for modernization and sustainability. These goals are top priorities for rendering TCDB increasingly useful to the scientific community.
TCDB is a database providing the worldwide scientific community with systematized information about proteins that catalyze transmembrane transport of salts, nutrients, toxins, drugs and macromolecules. It is the only IUBMB approved system for classifying transport proteins. Funding of this proposal will allow the maintenance and further development of TCDB, interlinking with related databases, expansion of machine learning approaches for information acquisition, and introduction of approaches for predicting the functions of uncharacterized proteins.
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