De novo structure prediction and experimental characterization of folded peptoid oligomers

Abstract

Peptoid molecules are biomimetic oligomers that can fold into unique three-dimensional structures. As part of an effort to advance computational design of folded oligomers, we present blind-structure predictions for three peptoid sequences using a combination of Replica Exchange Molecular Dynamics (REMD) simulation and Quantum Mechanical refinement. We correctly predicted the structure of a N-aryl peptoid trimer to within 0.2 Å rmsd-backbone and a cyclic peptoid nonamer to an accuracy of 1.0 Å rmsd-backbone. X-ray crystallographic structures are presented for a linear N-alkyl peptoid trimer and for the cyclic peptoid nonamer. The peptoid macrocycle structure features a combination of cis and trans backbone amides, significant nonplanarity of the amide bonds, and a unique "basket" arrangement of (S)-N(1-phenylethyl) side chains encompassing a bound ethanol molecule. REMD simulations of the peptoid trimers reveal that well folded peptoids can exhibit funnel-like conformational free energy landscapes similar to those for ordered polypeptides. These results indicate that physical modeling can successfully perform de novo structure prediction for small peptoid molecules.foldamer | molecular simulation F oldamers are synthetic polymers that-like proteins-have the ability to self-assemble into unique folded structures (1). Examples of foldamer systems include β-peptides, γ-peptides, azapeptides, oligoureas, arylamides, oligohydrazides, polyphenylacetylenes, and peptoids, among others (2, 3). Of these, peptoids offer an attractive platform for designing functionalized, conformationally ordered molecular architectures ( Fig. 1): they can be readily synthesized to incorporate chemically diverse side chains (4, 5), are resistant to proteolysis (6), and can retain structure and function in nonaqueous solvents. Peptoids have found capacity for diverse applications such as antimicrobials (7), drug delivery platforms (8), therapeutics (9), enantioselective catalysts (10), and nanostructured materials (11,12).Unlike peptides, peptoids lack the ability to form backbone hydrogen bonds and can readily populate both cis and trans backbone amide states. Thus, new strategies may be required to enable the rational design of ordered peptoid structures. For instance, even though the peptoid backbone is achiral, bulky chiral side chain groups such as 1-phenylethyl or 1-naphthylethyl can be used to induce stereocontrolled cis-amide helical structures resembling polyproline I (13-15). Such helices have been used to form tertiary assemblies (11, 16), and have been incorporated into enzymes with minimal loss of function (17). Alternatively, peptoid N-aryl side chains have been used to induce trans-amide helices that can mimic polyproline II structure (18,19). It remains to be determined how these local rules can be used to control the global three-dimensional structure of peptoid macromolecules (20).In order to design peptoids for applications, we need a way to predict their native structures from their sequences. Successes in designing prot...

Development and use of an atomistic CHARMM-based forcefield for peptoid simulation

“…Using this forcefield, we carried out all‐atom simulations of a peptoid in vacuum and in explicit water, demonstrating that solvating a sarcosine dipeptoid changes its accessible backbone conformations. We also simulated a crystal structure of a peptoid homotrimer (bearing N ‐2‐phenylethyl sidechains), a molecule previously shown to be not well described by peptide‐based forcefields . The structure and dynamics of the crystal seen in simulations agree qualitatively with properties observed experimentally .…”

Peptoid nanosheets exhibit a new secondary-structure motif

“…Peptoids, which are relatively non-toxic and resistant to degradation 3 , can fold into defined structures through a combination of sequencedependent interactions [3][4][5][6][7][8] . However, the range of possible structures that are accessible to peptoids and other biological mimetics is unknown, and our ability to design protein-like architectures from these polymer classes is limited 9 .…”

Accelerating molecular simulations of proteins using Bayesian inference on weak information

“…Physical simulations offer important advantages in the long run, providing a principled and transferrable basis for understanding properties; the capability to go beyond just native structures alone to dynamics, binding, folding, and mechanisms; applicability where databases are limited, including membrane proteins or other foldable polymers, such as peptoids (4); and extensibility to other temperatures, solvents, and binding conditions, for example. A proper physical model requires a plausible physical energy function that can accurately predict native structures (validation); that applies across many different proteins (transferrable); that satisfies Boltzmann's law (physical); that scales up to sufficiently large proteins (practical); and, when predicting folding, that begins from the fully unfolded state (to avoid inadvertent biases).…”