Structural Motifs as Determinants of Function

The quest to reveal the relationship between structure and function in proteins is of primary importance. While decades of experimental and theoretical research have shown that tertiary structure is a strong determinant, even stronger than sequence, of function, we do not quite understand this relationship from a quantitative point of view. Revealing this relationship would mean that we can predict the biological function and cellular role of a protein from knowledge of its tertiary structure. This would be a great step forward, as it would complete our understanding of how the millions of proteins we have already decoded contribute to the basic biology of organisms, and how this information can be used in a therapeutic setting to treat diseased cells.

Ongoing research in our lab is focusing on finding structural motifs that can be effective determinants of biological function. A preliminary study by two undergraduate students during Summer 2011 focused on compiling libraries of known structural motifs in proteins. Details of that study, which focused on the promise of using structural motifs for assembly of novel protein conformations, can be found in Bryn's DREU Journal . Recent work by us is focusing on machine learning techniques to model the relationship between structure and function in proteins. This work has appeared in: Kevin Molloy, Jennifer M. Van, Daniel Barbara, and Amarda Shehu. "Higher-order Representations for Automated Organization of Protein Structure Space." IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), New Orleans, LA, 2013. A journal version of this article is expected to appear soon.

This is Kevin's presentation at ICCABS 2013 on this work.

On this Project:

  • Kevin Molloy

    Jennifer Van (Undergraduate student)

    Daniel Barbara

    Amarda Shehu