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2002-2003
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Dr. Yiannis Kaznessis
Protein fold recognition based on detection of non-local interactions in
proteins
Date: February 17
Time: Noon to 1
Place: BSBE 4-101
Proteins are the molecular cornerstones of life. The determination of their
structure is deemed as a key step toward understanding the behavior of
biomolecules and initiating rational approaches for engineering molecular
solutions. Experimental efforts, such as x-ray crystallography and NMR
techniques are not currently efficient enough to allow for rapid structural
determination of the ever-increasing number of newly discovered sequences.
Hence, computational methodologies are becoming the sine qua non of protein
sequence/structure/function relationship research.
A prominent role in computational tools has been claimed by protein fold
recognition methods. As protein fold we define the arrangement of secondary
structural elements in 3D space and their topological connections.
In this work we have focused on the development of an algorithm that
incorporates non-local interactions between residues, significantly
improving existing protein fold recognition approaches. We have designed
pattern recognition techniques that assign scores/probabilities that a
target protein sequence belongs to a particular fold family. The feature
vectors for pattern recognition are an amalgamate of secondary structural
elements and intrachain distance constraints, exploiting the importance of
non-local interactions in the foldability, stability, and functionality of
protein molecules, as manifested in observed mutation patterns in classes of
homologous proteins.
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