Computer Analysis Of Low-complexity Amino Acid And Nucle

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The goal of this project is to define and analyze, using computational methods, segments of protein and nucleotide sequences showing compositional bias (including low-complexity regions or domains) and to understand their structural, functional and evolutionary significance, and their pathology. In protein sequences, these regions comprise a large proportion of the genome encoded amino acids (approximately 25% in most eukaryotes,and most of the translated protein sequences contain at least one such region). They may contain homopolymeric tracts or mosaics of a few amino acids, or repeated patterns, frequently subtle, including those typical of many non-globular domains. New mathematical definitions and algorithms are continuing to be developed to identify regions of compositional bias, and to discover and analyze properties of these regions relevant to their structures, interactions, biological functions, and evolution. Sequences from very AT-rich or GC-rich genomes, which include those of several important infectious disease organisms, raise special problems and opportunities for these studies. Interspersed low-complexity sequences are particularly abundant in many eukaryotic proteins crucial in morphogenesis and embryonic development, RNA processing, transcriptional regulation, signal transduction and aspects of cellular and extracellular structural integrity. Experimental data indicate that low complexity segments of proteins are generally non-globular, intrinsically unstructured, or conformationally mobile. However, knowledge of the molecular structures and dynamics of these domains is still very limited because they are generally relatively intractable to investigation by crystallography and NMR, and they account for less than 1% of the residues in current structural databases. Hence, mathematically rigorous sequence analysis provides a primary methodology for gaining insights into their biology, and for raising questions to be investigated expermentally. These methods are also valuable, for both nucleotide and amino acid sequences, in detecting and eliminating some artifacts in sequence database searches and alignment analysis.