Building motif lexicons

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DESCRIPTION (provided by applicant): With the complete sequencing of numerous genomes and the annotation of proteomes, one of the next major challenges in biology is to understand the functions and integration of the encoded proteins (a NIH Roadmap area of emphasis). Deciphering protein function is a very time consuming, expensive process, as reflected by the disproportionately low percentage of proteins with well-established functions. One approach for extrapolating established functions to new proteins is to predict short motifs. Short motifs target proteins for post-translational modification, trafficking to cellular compartments, and binding to other proteins or molecules. Our cross-disciplinary team has built Minimotif Miner (MnM), a short motif database and platform- independent web-tool that identifies motif consensus sequences in protein queries and thus potential new protein functions (http://mnm.engr.uconn.edu/). MnM can also be used to develop new hypotheses of how specific mutations cause human disease and to identify putative targets for the development of therapeutic drugs, antibiotics, insecticides, and antiviral agents. Despite the utility of MnM and other motif resources, prediction of functional motifs still has two major limitations, which we address in this proposal. 1) To reduce the false-positive prediction of motifs, we have created a new language that allows us to consider the 3- dimensional structural conservation of motifs. For each motif, we will build specific motif definitions by combining experimental data with data from motif structures in the Protein Data Bank. We will also determine the sequence permutations that can form the observed motif structure by using molecular dynamic simulations. 2) To build a more comprehensive motif database we will use artificial intelligence to mine PubMed. The expert system will use automated literature screening, document summarization, and motif identification efficiency score to extract the majority of known motifs from PubMed. Addressing these limitations will vastly increase the utility of short motif prediction.