Ranjan V. Mannige

Peptoid nanosheets exhibit a new secondary-structure motif

A promising route to the synthesis of protein-mimetic materials that are capable of complex functions, such as molecular recognition and catalysis, is provided by sequence-defined peptoid polymers—structural relatives of biologically occurring polypeptides. Peptoids, which are relatively non-toxic and resistant to degradation, can fold into defined structures through a combination of sequence-dependent interactions. 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. Here we use molecular-dynamics simulations, together with scattering and microscopy data, to determine the atomic-resolution structure of the recently discovered peptoid nanosheet, an ordered supramolecular assembly that extends macroscopically in only two dimensions. Our simulations show that nanosheets are structurally and dynamically heterogeneous, can be formed only from peptoids of certain lengths, and are potentially porous to water and ions. Moreover, their formation is enabled by the peptoids’ adoption of a secondary structure that is not seen in the natural world. This structure, a zigzag pattern that we call a Σ(‘sigma’)-strand, results from the ability of adjacent backbone monomers to adopt opposed rotational states, thereby allowing the backbone to remain linear and untwisted. Linear backbones tiled in a brick-like way form an extended two-dimensional nanostructure, the Σ-sheet. The binary rotational-state motif of the Σ-strand is not seen in regular protein structures, which are usually built from one type of rotational state. We also show that the concept of building regular structures from multiple rotational states can be generalized beyond the peptoid nanosheet system.

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

Peptoids are positional isomers of peptides: peptoid sidechains are attached to backbone nitrogens rather than α‐carbons. Peptoids constitute a class of sequence‐specific polymers resistant to biological degradation and potentially as diverse, structurally and functionally, as proteins. While molecular simulation of proteins is commonplace, relatively few tools are available for peptoid simulation. Here, we present a first‐generation atomistic forcefield for peptoids. Our forcefield is based on the peptide forcefield CHARMM22, with key parameters tuned to match both experimental data and quantum mechanical calculations for two model peptoids (dimethylacetamide and a sarcosine dipeptoid). We used this forcefield to demonstrate that solvation of a dipeptoid substantially modifies the conformations it can access. We also simulated a crystal structure of a peptoid homotrimer, H‐(N‐2‐phenylethyl glycine)3‐OH, and we show that experimentally observed structural and dynamical features of the crystal are accurately described by our forcefield. The forcefield presented here provides a starting point for future development of peptoid‐specific simulation methods within CHARMM.

Heterogeneity of functional groups in a metal–organic framework displays magic number ratios

Significance Arranging multiple component types in a controlled manner within solid frameworks may one day allow us to combine the heterogeneity characteristic of biological materials with the robustness characteristic of synthetic ones. Here, we report the synthesis of a metal–organic framework whose two component types are incorporated within it in a manner that is robust with respect to changes of solution composition. Calculations done on a model system suggest that this robustness occurs because component types become kinetically trapped during framework growth. Our calculations also show how different controlled arrangements of component types might be obtained by varying their noncovalent interactions, or by varying the topology of the covalent framework within which they are housed. Multiple organic functionalities can now be apportioned into nanoscale domains within a metal-coordinated framework, posing the following question: how do we control the resulting combination of “heterogeneity and order”? Here, we report the creation of a metal–organic framework, MOF-2000, whose two component types are incorporated in a 2:1 ratio, even when the ratio of component types in the starting solution is varied by an order of magnitude. Statistical mechanical modeling suggests that this robust 2:1 ratio has a nonequilibrium origin, resulting from kinetic trapping of component types during framework growth. Our simulations show how other “magic number” ratios of components can be obtained by modulating the topology of a framework and the noncovalent interactions between component types, a finding that may aid the rational design of functional multicomponent materials.

Modeling sequence-specific polymers using anisotropic coarse-grained sites allows quantitative comparison with experiment.

Certain sequences of peptoid polymers (synthetic analogs of peptides) assemble into bilayer nanosheets via a nonequilibrium assembly pathway of adsorption, compression, and collapse at an air-water interface. As with other large-scale dynamic processes in biology and materials science, understanding the details of this supramolecular assembly process requires a modeling approach that captures behavior on a wide range of length and time scales, from those on which individual side chains fluctuate to those on which assemblies of polymers evolve. Here, we demonstrate that a new coarse-grained modeling approach is accurate and computationally efficient enough to do so. Our approach uses only a minimal number of coarse-grained sites but retains independently fluctuating orientational degrees of freedom for each site. These orientational degrees of freedom allow us to accurately parametrize both bonded and nonbonded interactions and to generate all-atom configurations with sufficient accuracy to perform atomic scattering calculations and to interface with all-atom simulations. We have used this approach to reproduce all available experimental X-ray scattering data (for stacked nanosheets and for peptoids adsorbed at air-water interfaces and in solution), in order to resolve the microscopic, real-space structures responsible for these Fourier-space features. By interfacing with all-atom simulations, we have also laid the foundation for future multiscale simulations of sequence-specific polymers that communicate in both directions across scales.

Dynamic New World: Refining Our View of Protein Structure, Function and Evolution

Proteins are crucial to the functioning of all lifeforms. Traditional understanding posits that a single protein occupies a single structure (“fold”), which performs a single function. This view is radically challenged with the recognition that high structural dynamism—the capacity to be extra “floppy”—is more prevalent in functional proteins than previously assumed. As reviewed here, this dynamic take on proteins affects our understanding of protein “structure”, function, and evolution, and even gives us a glimpse into protein origination. Specifically, this review will discuss historical developments concerning protein structure, and important new relationships between dynamism and aspects of protein sequence, structure, binding modes, binding promiscuity, evolvability, and origination. Along the way, suggestions will be provided for how key parts of textbook definitions—that so far have excluded membership to intrinsically disordered proteins (IDPs)—could be modified to accommodate our more dynamic understanding of proteins.

An exhaustive survey of regular peptide conformations using a new metric for backbone handedness (h)

The Ramachandran plot is important to structural biology as it describes a peptide backbone in the context of its dominant degrees of freedom—the backbone dihedral angles φ and ψ (Ramachandran, Ramakrishnan & Sasisekharan, 1963). Since its introduction, the Ramachandran plot has been a crucial tool to characterize protein backbone features. However, the conformation or twist of a backbone as a function of φ and ψ has not been completely described for both cis and trans backbones. Additionally, little intuitive understanding is available about a peptide’s conformation simply from knowing the φ and ψ values of a peptide (e.g., is the regular peptide defined by φ = ψ = − 100° left-handed or right-handed?). This report provides a new metric for backbone handedness (h) based on interpreting a peptide backbone as a helix with axial displacement d and angular displacement θ, both of which are derived from a peptide backbone’s internal coordinates, especially dihedral angles φ, ψ and ω. In particular, h equals sin(θ)d∕|d|, with range [−1, 1] and negative (or positive) values indicating left(or right)-handedness. The metric h is used to characterize the handedness of every region of the Ramachandran plot for both cis (ω = 0°) and trans (ω = 180°) backbones, which provides the first exhaustive survey of twist handedness in Ramachandran (φ, ψ) space. These maps fill in the ‘dead space’ within the Ramachandran plot, which are regions that are not commonly accessed by structured proteins, but which may be accessible to intrinsically disordered proteins, short peptide fragments, and protein mimics such as peptoids. Finally, building on the work of (Zacharias & Knapp, 2013), this report presents a new plot based on d and θ that serves as a universal and intuitive alternative to the Ramachandran plot. The universality arises from the fact that the co-inhabitants of such a plot include every possible peptide backbone including cis and trans backbones. The intuitiveness arises from the fact that d and θ provide, at a glance, numerous aspects of the backbone including compactness, handedness, and planarity.

Origination of the Protein Fold Repertoire from Oily Pluripotent Peptides

While the repertoire of protein folds that exists today underlies most of life’s capabilities, our mechanistic picture of protein fold origination is incomplete. This paper discusses a hypothetical mechanism for the emergence of the protein fold repertoire from highly dynamic and collapsed peptides, exemplified by peptides with high oil content or hydrophobicity. These peptides are called pluripotent to emphasize their capacity to evolve into numerous folds transiently available to them. As evidence, the paper will discuss previous simulation work on the superior fold evolvability of oily peptides, trace (“fossil”) evidence within proteomes seen today, and a general relationship between protein dynamism and evolvability. Aside from implications on the origination of protein folds, the hypothesis implies that the vanishing utility of a random peptide in protein origination may be relatively exaggerated, as some random peptides with a certain composition (e.g., oily) may fare better than others. In later sections, the hypothesis is discussed in the context of existing discussions regarding the spontaneous origination of biomolecules.

The BackMAP Python module: how a simpler Ramachandran number can simplify the life of a protein simulator

Protein backbones occupy diverse conformations, but compact metrics to describe such conformations and transitions between them have been missing. This report re-introduces the Ramachandran number (ℛ) as a residue-level structural metric that could simply the life of anyone contending with large numbers of protein backbone conformations (e.g., ensembles from NMR and trajectories from simulations). Previously, the Ramachandran number (ℛ) was introduced using a complicated closed form, which made the Ramachandran number difficult to implement. This report discusses a much simpler closed form of ℛ that makes it much easier to calculate, thereby making it easy to implement. Additionally, this report discusses how ℛ dramatically reduces the dimensionality of the protein backbone, thereby making it ideal for simultaneously interrogating large numbers of protein structures. For example, 200 distinct conformations can easily be described in one graphic using ℛ (rather than 200 distinct Ramachandran plots). Finally, a new Python-based backbone analysis tool—BackMAP—is introduced, which reiterates how ℛ can be used as a simple and succinct descriptor of protein backbones and their dynamics.