Unraveling the Role of Charge Patterning in the Micellar Structure of Sequence-Defined Amphiphilic Peptoid Oligomers by Molecular Dynamics Simulations

Abstract

Electrostatic interactions play a significant role in regulating biological systems and have received increasing attention due to their usefulness in designing advanced stimulus-responsive materials. Polypeptoids are highly tunable N-substituted peptidomimetic polymers that lack backbone hydrogen bonding and chirality. Therefore, polypeptoids are suitable systems to study the effect of noncovalent interactions of substituents without complications of backbone intramolecular and intermolecular hydrogen bonding. In this study, all-atom molecular dynamics (MD) simulations were performed on micelles formed by a series of sequence-defined ionic polypeptoid block copolymers consisting of a hydrophobic segment and a hydrophilic segment in an aqueous solution. By combining the results from MD simulations and experimental small-angle neutron scattering data, further insights were gained into the internal structure of the formed polypeptoid micelles, which is not always directly accessible from experiments. In addition, information was gained into the physics of the noncovalent interactions responsible for the self-assembly of weakly charged polypeptoids in an aqueous solution. While the aggregation number is governed by electrostatic repulsion of the negatively charged carboxylate (COO – ) substituents on the polypeptoid chain within the micelle, MD simulations indicate that the position of the charge on singly charged chains mediates the shape of the micelle through the charge–dipole interactions between the COO – substituent and the surrounding water. Therefore, the polypeptoid micelles formed from the single-charged series offer the possibility for tailorable micelle shapes. In contrast, the polypeptoid micelles formed from the triple-charged series are characterized by more pronounced electrostatic repulsion that competes with more significant charge–sodium interactions, making it difficult to predict the shape of the micelles. This work has helped further develop design principles for the shape and structure of self-assembled micelles by controlling the position of charged moieties on the backbone of polypeptoid block copolymers.

Development of a Systematic and Extensible Force Field for Peptoids (STEPs)

“…Even though polarization effects are not directly included, the optimized parameters and structures could serve as the basis for such an effort. As a basis for developing a new specialist peptoid force field, GAFF2 was chosen as it has been shown to perform well for peptoids and for the wide variety of atom types included in the existing parameter set . The functional form of the force field is given in eq 1.…”

“…It is worth noting that multiple force fields are available in the protein space, but that field is significantly more developed and studied. Despite this higher bar to entry, peptoid simulation papers include at least 19 GAFF-based, ,,− 13 MFTOID-, ,− 11 CHARMM-, or CGenFF-based, − 3 PEPDROID-based, − and other computational studies. − One particularly noteworthy effort was the development of a peptoid rotamer library containing over 50 side chains in the structural prediction tool ROSETTA based on CHARMM peptide parameters. , …”

“…Our goal with this work is to begin to unify these efforts by parameterizing a peptoid-specific force field on an initial set of 70 different side chains, maintain a public database, and publish a consistent method for adding parameterized sidechains to that database. We have chosen the GAFF2 force field as the base for our modifications due to the breadth of tools available to create and simulate peptoid structures within the Amber suite and its demonstrated success in modeling peptoid backbones. ,, Following the pioneering effort by Renfrew et al in ROSETTA, we build on the 52 sidechains in their original rotamer library as well as the amino acid analogs . Our model is trained on a large ensemble of structures for each monomer and compared in detail for the canonical test cases, sarcosine, and Nspe, for their performance in reproducing Ramachandran-like plots from DFT optimization.…”

Determining ion activity coefficients in ion-exchange membranes with machine learning and molecular dynamics

“…28 Increase in computational power has boosted the growth of physics-based tools like molecular dynamics (MD). For example, MD simulation has been used to study polymer phase behavior 29,30 and estimate properties such as thermal conductivity, 31,32 water diffusion constant, 14 and diffusion coefficient. 33 MD simulations provide valuable information about the solvation and spatial distribution of counterions and ionic groups along the polymer backbone, which helps to explain the measured conductivity and ionic activity in experiments.…”

Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations

“…31 An increase in computational power has boosted the growth of physics-based tools like MD. For example, MD simulation has been used to study polymer phase behavior 32,33 and estimate properties such as thermal conductivity, 34,35 water diffusion constant, 13 and diffusion coefficient. 36 MD simulations provide valuable information about the solvation and spatial distribution of counterions and ionic groups along the polymer backbone, which helps to explain the measured conductivity and ionic activity in experiments.…”