Alex Berlaga, Kaylyn Torkelson, Aniruddha Seal, Jim Pfaendtner, Andrew L. Ferguson
{"title":"A Modular and Extensible CHARMM-Compatible Model for All-Atom Simulation of Polypeptoids","authors":"Alex Berlaga, Kaylyn Torkelson, Aniruddha Seal, Jim Pfaendtner, Andrew L. Ferguson","doi":"arxiv-2409.06103","DOIUrl":null,"url":null,"abstract":"Peptoids (N-substituted glycines) are a class of sequence-defined synthetic\npeptidomimetic polymers with applications including drug delivery, catalysis,\nand biomimicry. Classical molecular simulations have been used to predict and\nunderstand the conformational dynamics of single peptoid chains and their\nself-assembly into diverse morphologies including sheets, tubes, spheres, and\nfibrils. The CGenFF-NTOID model based on the CHARMM General ForceField has\ndemonstrated success in enabling accurate all-atom molecular modeling of the\nstructure and thermodynamic behavior of peptoids. Extension of this force field\nto new peptoid side chain chemistries has historically required\nparameterization of new side chain bonded interactions against ab initio and/or\nexperimental data. This fitting protocol improves the accuracy of the force\nfield but is also burdensome and time consuming, and precludes modular\nextensibility of the model to arbitrary peptoid sequences. In this work, we\ndevelop and demonstrate a Modular Side Chain CGenFF-NTOID (MoSiC-CGenFF-NTOID)\nas an extension of CGenFF-NTOID employing a modular decomposition of the\npeptoid backbone and side chain parameterizations wherein arbitrary side chain\nchemistries within the large family of substituted methyl groups (i.e., -CH3,\n-CH2R, -CHRR' -CRR'R'') are directly ported from CGenFF without any additional\nreparameterization. We validate this approach against ab initio calculations\nand experimental data to to develop a MoSiC-CGenFF-NTOID model for all 20\nnatural amino acid side chains along with 13 commonly-used synthetic side\nchains, and present an extensible paradigm to efficiently determine whether a\nnovel side chain can be directly incorporated into the model or whether\nrefitting of the CGenFF parameters is warranted. We make the model freely\navailable to the community along with a tool to perform automated initial\nstructure generation.","PeriodicalId":501146,"journal":{"name":"arXiv - PHYS - Soft Condensed Matter","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Soft Condensed Matter","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Peptoids (N-substituted glycines) are a class of sequence-defined synthetic
peptidomimetic polymers with applications including drug delivery, catalysis,
and biomimicry. Classical molecular simulations have been used to predict and
understand the conformational dynamics of single peptoid chains and their
self-assembly into diverse morphologies including sheets, tubes, spheres, and
fibrils. The CGenFF-NTOID model based on the CHARMM General ForceField has
demonstrated success in enabling accurate all-atom molecular modeling of the
structure and thermodynamic behavior of peptoids. Extension of this force field
to new peptoid side chain chemistries has historically required
parameterization of new side chain bonded interactions against ab initio and/or
experimental data. This fitting protocol improves the accuracy of the force
field but is also burdensome and time consuming, and precludes modular
extensibility of the model to arbitrary peptoid sequences. In this work, we
develop and demonstrate a Modular Side Chain CGenFF-NTOID (MoSiC-CGenFF-NTOID)
as an extension of CGenFF-NTOID employing a modular decomposition of the
peptoid backbone and side chain parameterizations wherein arbitrary side chain
chemistries within the large family of substituted methyl groups (i.e., -CH3,
-CH2R, -CHRR' -CRR'R'') are directly ported from CGenFF without any additional
reparameterization. We validate this approach against ab initio calculations
and experimental data to to develop a MoSiC-CGenFF-NTOID model for all 20
natural amino acid side chains along with 13 commonly-used synthetic side
chains, and present an extensible paradigm to efficiently determine whether a
novel side chain can be directly incorporated into the model or whether
refitting of the CGenFF parameters is warranted. We make the model freely
available to the community along with a tool to perform automated initial
structure generation.