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In the world of proteins, form defines function.
Based on interactions between their constituent amino acids, proteins form
specific conformations, folding and twisting into distinct, chemically directed
shapes. The resulting structure dictates the proteins’ actions; thus accurate
modeling of structure is vital to understanding functionality.
Peptoids, the synthetic cousins of proteins, follow
similar design rules. Less vulnerable to chemical or metabolic breakdown than
proteins, peptoids are promising for diagnostics, pharmaceuticals, and as a
platform to build bioinspired nanomaterials, as scientists can build and
manipulate peptoids with great precision. But to design peptoids for a specific
function, scientists need to first untangle the complex relationship between a
peptoid’s composition and its function-defining folded structure.
Past efforts to predict protein structure have met
with limited success, but now a scientific team led by Glenn Butterfoss, and
Barney Yoo, research scientists at New York University, in collaboration with
investigators from the U.S. Department of Energy’s Lawrence Berkeley
National Laboratory (Berkeley Lab), Stony Brook University, and Temple
University have demonstrated that a computer modeling approach similar to one
used to predict protein structures can accurately predict peptoid
conformation as well.
The authors describe this accomplishment in a paper
in the Proceedings of the National Academy of Sciences (PNAS)
titled, “De novo structure prediction and experimental
characterization of folded peptoid oligomers,” coauthored by Jonathan Jaworski,
Ilya Chorny, Ken Dill, Ronald Zuckermann, Richard Bonneau, Kent Kirshenbaum,
and Vincent Voelz.
“Natural selection has engineered protein
sequences that can self-assemble into molecular machines with
specific functions. Why can’t we do the same with biologically inspired
synthetic materials?” Voelz, principal investigator with Temple
University, explains.
With this mission in mind, the collaborative team
of scientists developed the project two years ago (2010) at the 7th Peptoid
Summit, a conference devoted to peptoid research hosted by Berkeley Lab’s
Molecular Foundry.
“The research was carried out by a remarkable,
interdisciplinary team of scientists,” says Kent Kirshenbaum of NYU. “Some of
the team have worked together on this truly difficult problem for almost 20
years. The researchers include both experimentalists and theorists who have
been able to guide one another in discovering how these peptoid molecules
fold.”
Together, they proposed a ‘blind structure
prediction’ challenge. This self-assessment technique, responsible for the
enormous progress in the world of protein structure modeling, allows scientists
to test the fidelity of their computational models by predicting the
three-dimensional structure of a known molecule and then comparing their
proposed structure to the X-ray crystallography results.
An analogous, combined experimental-computational
method was employed by the peptoid team in an effort to advance the
computational design of peptoid structure. X-ray crystal structures for three
peptoid molecules, two small and linear and one larger and cyclical, were
simultaneously determined, but not disclosed to the theoretical modelers. The
experimentalists then used a combination of two simulation techniques, Replica
Exchange Molecular Dynamics (REMD) simulation and Quantum Mechanical refinement
(QM). REMD can efficiently predict the preferred general conformations, and the
QM calculations further refine the conformational prediction. In combination,
these two calculations accurately define the physical structures of molecules.
The proposed structural predictions of
the peptoid molecules did exceedingly well at calculating the actual
folded conformations. The first two blind predictions were calculated for two
linear, small N-alkyl and N-aryl peptoid trimers. Of these,
the N-aryl peptoid trimer was the best blind prediction, matching the
crystal described conformation to within 0.2 Å. The N-alkyl trimer
prediction matched less well with the crystal results because of its increased
flexibility.
The greater challenge facing the group was
structural prediction of the larger, cyclic peptoid nonamer. Six different
possible conformations were considered for the final, submitted prediction and
the top pick proved to agree best with the crystallography results to an
accuracy of 1.0 Å.
This success suggests that reliable structure
prediction for complex three-dimensional folds is within reach, an enormous
step forward on the path to reliable and efficient computational peptoid
design.
“This will hopefully break open the field of
peptoid structure prediction and design, an area we desperately need to guide
our more well-developed synthetic efforts,” says Ron Zuckermann, co-author and
director of the Biological Nanostructures Facility at Berkeley Lab’s Molecular
Foundry.
“It is an exciting time for peptoid research,” says
author Glenn Butterfoss, research scientist with NYU. “The community of labs
working on these molecules is growing, and both the diversity and creativity of
recent studies is quite astonishing. We hope our work here, aimed toward
understanding the structural behavior of peptoids in three dimensional space,
serves as a building block for future efforts to design peptoid molecules with
practical functions.”
