ALL-ATOM AND COARSE-GRAINED PLATFORM TO SIMULATE PEPTOIDS

Date of Award

8-22-2025

Date Published

September 2025

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Biomedical and Chemical Engineering

Advisor(s)

Shikha Nangia

Keywords

Coarse-grained;Computational Simulation;Martini 3;Peptoid;Reverse mapping

Abstract

Peptoids, or N-substituted glycines, are synthetic peptidomimetic oligomers that exhibit exceptional proteolytic stability, conformational flexibility, and tunable bioactivity, thereby expanding their utility in applications ranging from antimicrobial therapeutics to molecular self-assembly and biomaterials engineering. To investigate the influence of sequence composition on structural stability and aggregation propensity, a multiscale simulation platform is developed to systematically explore the conformational and assembly behavior of seven-residue peptoid oligomers constructed from three chemically distinct building blocks: NLY, a lysine-mimetic residue bearing a cationic ammonium side chain; BEN, a chiral benzyl residue introducing aromaticity and conformational rigidity; and BBR, a bromobenzyl residue capable of halogen bonding and electronic modulation. A custom Python pipeline was developed to automate the derivation of CG bonded parameters from AA data, enabling iterative refinement and validation of CG models against atomistic benchmarks. The platform comprises three core components: (1) all-atom (AA) system construction and simulation with CHARMM General force field, (2) coarse-grained (CG) model development and simulation with MARTINI 3 force field, and (3) reverse-mapping from CG to AA. Additionally, automated simulations tools were developed to establish the systems, run the simulations and analyze the results. In total, the study evaluated 2,187 unique peptoid sequences, each subjected to extensive equilibrium and self-assembly simulations. Furthermore, an efficient and template-free reverse mapping algorithm was designed to reconstruct CHARMM-compatible atomistic structures directly from CG simulation outputs, bridging CG predictions with detailed structural validation. High-throughput simulations of twenty-molecule assemblies allowed identification of sequences that preferentially form low-order aggregates (monomers to tetramers), which are favorable for solution stability and downstream functionalization. The optimized CG parameters, validated across homopolymer and mixed-sequence systems, were shown to accurately recapitulate key features of peptoid conformational landscapes and self-assembly dynamics. Overall, this work presents a robust and extensible modeling framework for peptoids, integrating atomistic accuracy with coarse-grained efficiency. It provides foundational methodologies for future design, simulation, and experimental realization of functional peptoid-based nanostructures and materials.

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