ITR: Collaborative Research - Computational Learning and Discovery in Biological Sequence, Structure and Function Mapping

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EIA-0225656Reddy, RajCarnegie Mellon UniversityTitle: Computational Learning and Discovery in Biological Sequence, Structure and Function MappingComputer scientists, together with biological chemists will collaborate using statistical and computational tools and methods that the computer scientists have been developing for dealing with human language to better understand the function of proteins. Proteins are major players in the functioning of human and all other living cells. As in languages, where sequences of letters determine patterns of words and sentences, sequences of amino acids in proteins determine protein structure, dynamics and function. Such sequences and their constituents can be thought of as syllables or words that have particular properties. Given these sequences, scientists want to be able to predict their geometrical structure and dynamics, and hence their function. A deeper understanding of the relationship between these is required so that the information hidden in the DNA sequences of genes can be used to develop drugs to fight disease. In particular, there is great societal demand to understand and treat degenerative diseases, many of which are based on defective triggers for protein shape and interactions. Work toward these goals requires deep knowledge both in computer science and in biological chemistry, and must therefore be collaborative in nature. Carnegie Mellon computer scientists will therefore be partnering with colleagues with expertise in Biological Chemistry at the University of Pittsburgh, the Massachusetts Institute of Technology (MIT), Boston University and the National Research Council of Canada. Industry collaborators include Mathworks, Inc., and medical bioinformatics company, Medstory, Inc. Using tools like statistical language modeling, machine learning methods and high-level language processing for understanding how proteins work inside cells is a relatively new field called computational biolinguistics. At this point, the researchers have been able to detect protein fragment signatures from pathogens by application of statistical language modeling technologies to genome sequences, promising novel strategies in identifying and targeting such pathogens. .