Martinoid: the peptoid martini force field

• Published in: Physical chemistry chemical physics : PCCP 2024-02, Vol.26 (6), p.4939-4953
• Main Authors: Swanson, Hamish W. A, van Teijlingen, Alexander, Lau, King Hang Aaron, Tuttle, Tell
• Format: Article
• Language: English
• Subjects: Molecular dynamics; Parameterization; Self-assembly; Simulation
• ISSN: 1463-9076; 1463-9084
• DOI: 10.1039/d3cp05907c

Many exciting innovations have been made in the development of assembling peptoid materials. Typically, these have utilised large oligomeric sequences, though elsewhere the study of peptide self-assembly has yielded numerous examples of assemblers below 6-8 residues in length, evidencing that minimal peptoid assemblers are not only feasible but expected. A productive means of discovering such materials is through the application of in silico screening methods, which often benefit from the use of coarse-grained molecular dynamics (CG-MD) simulations. At the current level of development, CG models for peptoids are insufficient and we have been motivated to develop a Martini forcefield compatible peptoid model. A dual bottom-up and top-down parameterisation approach has been adopted, in keeping with the Martini parameterisation methodology, targeting the reproduction of atomistic MD dynamics and trends in experimentally obtained log  D 7.4 partition coefficients, respectively. This work has yielded valuable insights into the practicalities of parameterising peptoid monomers. Additionally, we demonstrate that our model can reproduce the experimental observations of two very different peptoid assembly systems, namely peptoid nanosheets and minimal tripeptoid assembly. Further we can simulate the peptoid helix secondary structure relevant for antimicrobial sequences. To be of maximum usefulness to the peptoid research community, we have developed freely available code to generate all requisite simulation files for the application of this model with Gromacs MD software. Martinoid is an expansive forcefield for coarse grained simulation of peptoids. It successfully reproduces various peptoid assembly results. A python tool to generate CG input files is also made available.