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Peptoid structure prediction paper in PNAS

posted Aug 20, 2012, 7:34 PM by Vincent Voelz   [ updated Aug 21, 2012, 12:06 PM ]
We are pleased to announce that our new paper has just been published in PNAS:  "De novo structure prediction and experimental characterization of folded peptoid oligomers" by Glenn L. Butterfoss, Barney Yoo, Jonathan N. Jaworskic, Ilya Chorny, Ken A. Dill, Ronald N. Zuckermann, Richard Bonneau, Kent Kirshenbaum, and Vincent A. Voelz.

This paper is a major milestone in efforts to predict the foldameric structures of molecules called peptoids, and already there has been some good press:


Peptoids are synthetic polymers that mimic the chemical structures of proteins.  Like proteins, they also have the ability to self-assemble into unique three-dimensional structures, making them an exciting platform for molecular design, with potential applications ranging from nanomaterials to biotherapeutics.

While it's easy to synthesize chemically diverse arrays of peptoid sequences, the greater challenge has been to design sequences that code for specific structures and functions.  Now, a multi-disciplinary team including researchers from Temple University, Lawrence Berkeley National Lab, and New York University and has made major progress in using computational models to predict the structures of peptoids.  This achievement means that reliable and efficient computational design of peptoids may soon be available.
 
A key aspect of this work was the blind prediction of three peptoid structures that were later obtained by X-ray crystallography, including the largest cyclic peptoid molecular ever characterized.  Blind prediction tests have been extremely useful in improving algorithms for protein structure prediction, and it is hoped that similar tests for peptoids will continue to improve prediction accuracy.

The computational researchers in this unique collaboration (Voelz and Butterfoss) were able to demonstrate that a hierarchical approach of molecular simulation and quantum mechanical studies could accurately predict peptoid conformation.  

Also published in the study are two new peptoid X-ray crystal structures, including the largest cyclic peptoid yet characterized.   Solving new strutcures are key to understanding how the specific sequence of peptoid groups controls the shape of folded peptoids, as well as being critical to refining computational models of peptoids.


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