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Jain RK, Hall CK, Santiso EE. Using Enhanced Sampling Simulations to Study the Conformational Space of Chiral Aromatic Peptoid Monomers. J Chem Theory Comput 2023; 19:9457-9467. [PMID: 37937823 DOI: 10.1021/acs.jctc.3c00803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
Peptoids, or N-substituted glycines, are peptide-like materials that form a wide variety of secondary structures owing to their enhanced flexibility and a diverse collection of possible side chains. Compared to that of peptides, peptoids have a substantially more complex conformational landscape. This is mainly due to the ability of the peptoid amide bond to exist in both cis- and trans-conformations. This makes conventional molecular dynamics simulations and even some enhanced sampling approaches unable to sample the complete energy landscapes. In this article, we present an extension to the CGenFF-NTOID peptoid atomistic forcefield by adding parameters for four side chains to the previously available collection. We employ explicit solvent well-tempered metadynamics simulations to optimize our forcefield parameters and parallel bias metadynamics to study the cis-trans isomerism for SN1-phenylethyl (s1pe) and SN1-naphthylethyl (s1ne) peptoid monomers, the free energy minima generated from which are validated with available experimental data. In the absence of experimental data, we supported our atomistic simulations with ab initio calculations. This work represents an important step toward the computational design of peptoid-based materials.
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Affiliation(s)
- Rakshit Kumar Jain
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Carol K Hall
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Erik E Santiso
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
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2
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Harris BS, Bejagam KK, Baer MD. Development of a Systematic and Extensible Force Field for Peptoids (STEPs). J Phys Chem B 2023; 127:6573-6584. [PMID: 37462325 DOI: 10.1021/acs.jpcb.3c01424] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Peptoids (N-substituted glycines) are a class of biomimetic polymers that have attracted significant attention due to their accessible synthesis and enzymatic and thermal stability relative to their naturally occurring counterparts (polypeptides). While these polymers provide the promise of more robust functional materials via hierarchical approaches, they present a new challenge for computational structure prediction for material design. The reliability of calculations hinges on the accuracy of interactions represented in the force field used to model peptoids. For proteins, structure prediction based on sequence and de novo design has made dramatic progress in recent years; however, these models are not readily transferable for peptoids. Current efforts to develop and implement peptoid-specific force fields are spread out, leading to replicated efforts and a fragmented collection of parameterized sidechains. Here, we developed a peptoid-specific force field containing 70 different side chains, using GAFF2 as starting point. The new model is validated based on the generation of Ramachandran-like plots from DFT optimization compared against force field reproduced potential energy and free energy surfaces as well as the reproduction of equilibrium cis/trans values for some residues experimentally known to form helical structures. Equilibrium cis/trans distributions (Kct) are estimated for all parameterized residues to identify which residues have an intrinsic propensity for cis or trans states in the monomeric state.
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Affiliation(s)
- Bradley S Harris
- Physical Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99352, United States
| | - Karteek K Bejagam
- Physical Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99352, United States
| | - Marcel D Baer
- Physical Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99352, United States
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3
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Shao L, Ma J, Prelesnik JL, Zhou Y, Nguyen M, Zhao M, Jenekhe SA, Kalinin SV, Ferguson AL, Pfaendtner J, Mundy CJ, De Yoreo JJ, Baneyx F, Chen CL. Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction. Chem Rev 2022; 122:17397-17478. [PMID: 36260695 DOI: 10.1021/acs.chemrev.2c00220] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.
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Affiliation(s)
- Li Shao
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jinrong Ma
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Jesse L Prelesnik
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Yicheng Zhou
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mary Nguyen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Samson A Jenekhe
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - François Baneyx
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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4
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Raubenolt BA, Rick SW. Simulation studies of polypeptoids using replica exchange with dynamical scaling and dihedral biasing. J Comput Chem 2022; 43:1229-1236. [PMID: 35543334 DOI: 10.1002/jcc.26887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/15/2022] [Accepted: 04/22/2022] [Indexed: 11/06/2022]
Abstract
Polypeptoids differ from polypeptides in that the amide bond can more frequently adopt both cis and trans conformations. The transition between the two conformations requires overcoming a large energy barrier, making it difficult for conventional molecular simulations to adequately visit the cis and trans structures. A replica-exchange method is presented that allows for easy rotations of the amide bond and also an efficient linking to a high temperature replica. The method allows for just three replicas (one at the temperature and Hamiltonian of interest, a second high temperature replica with a biased dihedral potential, and a third connecting them) to overcome the amide bond sampling problem and also enhance sampling for other coordinates. The results indicate that for short peptoid oligomers, the conformations can range from all cis to all trans with an average cis/trans ratio that depends on side chain and potential model.
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Affiliation(s)
- Bryan A Raubenolt
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana, USA
| | - Steven W Rick
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana, USA
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5
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Zhao M. Hierarchical assemblies of polypeptoids for rational design of advanced functional nanomaterials. Biopolymers 2021; 112:e23469. [PMID: 34406644 DOI: 10.1002/bip.23469] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/29/2021] [Accepted: 07/30/2021] [Indexed: 12/19/2022]
Abstract
Polypeptoids (poly-N-substituent glycines) are a class of highly tailorable peptidomimetic polymers. Polypeptoids have identical backbones as polypeptides (poly-C-substituent glycines), but sidechains of polypeptoids are appended to backbone nitrogen rather than α-carbon of polypeptides. As a result, peptoid backbone lacks of chirality and hydrogen bond donors. This unique structure gives polypeptoids a combined merit of both high stability as synthetic polymers and biocompatibility as biopolymers. In addition, peptoid sequences can be engineered precisely to assemble specific crystalline patterns such as spheres, fibers, ribbons, tubes, and sheets, which shows promising potentials of polypeptoids for different applications such as antimicrobials, catalysts, drug delivery, and templating inorganic materials. In this review, we summarize recent investigations into hierarchical self-assembly pathways and molecular structures of peptoid crystals that are of interest as templates for fabricating functional materials for potential biomedical, biochemical, and bioengineering applications. This review provides a summary of recent experimental and computational studies of polypeptoid assembly in solution and solid-liquid interfaces, current achievements in the field, and discusses future challenges and opportunities for the rational design of self-assembled polypeptoid nanomaterials.
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Affiliation(s)
- Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois, USA
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6
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Ma Z, Wang S, Kim M, Liu K, Chen CL, Pan W. Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics. SOFT MATTER 2021; 17:5864-5877. [PMID: 34096961 DOI: 10.1039/d1sm00364j] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.
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Affiliation(s)
- Zhan Ma
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
| | - Shu Wang
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
| | - Minhee Kim
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kaibo Liu
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Wenxiao Pan
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
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7
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Zhao M, Sampath J, Alamdari S, Shen G, Chen CL, Mundy CJ, Pfaendtner J, Ferguson AL. MARTINI-Compatible Coarse-Grained Model for the Mesoscale Simulation of Peptoids. J Phys Chem B 2020; 124:7745-7764. [DOI: 10.1021/acs.jpcb.0c04567] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Janani Sampath
- Physical Science Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sarah Alamdari
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Gillian Shen
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Chun-Long Chen
- Physical Science Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J. Mundy
- Physical Science Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Jim Pfaendtner
- Physical Science Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Andrew L. Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
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Reese HR, Shanahan CC, Proulx C, Menegatti S. Peptide science: A "rule model" for new generations of peptidomimetics. Acta Biomater 2020; 102:35-74. [PMID: 31698048 DOI: 10.1016/j.actbio.2019.10.045] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 10/17/2019] [Accepted: 10/30/2019] [Indexed: 02/07/2023]
Abstract
Peptides have been heavily investigated for their biocompatible and bioactive properties. Though a wide array of functionalities can be introduced by varying the amino acid sequence or by structural constraints, properties such as proteolytic stability, catalytic activity, and phase behavior in solution are difficult or impossible to impart upon naturally occurring α-L-peptides. To this end, sequence-controlled peptidomimetics exhibit new folds, morphologies, and chemical modifications that create new structures and functions. The study of these new classes of polymers, especially α-peptoids, has been highly influenced by the analysis, computational, and design techniques developed for peptides. This review examines techniques to determine primary, secondary, and tertiary structure of peptides, and how they have been adapted to investigate peptoid structure. Computational models developed for peptides have been modified to predict the morphologies of peptoids and have increased in accuracy in recent years. The combination of in vitro and in silico techniques have led to secondary and tertiary structure design principles that mirror those for peptides. We then examine several important developments in peptoid applications inspired by peptides such as pharmaceuticals, catalysis, and protein-binding. A brief survey of alternative backbone structures and research investigating these peptidomimetics shows how the advancement of peptide and peptoid science has influenced the growth of numerous fields of study. As peptide, peptoid, and other peptidomimetic studies continue to advance, we will expect to see higher throughput structural analyses, greater computational accuracy and functionality, and wider application space that can improve human health, solve environmental challenges, and meet industrial needs. STATEMENT OF SIGNIFICANCE: Many historical, chemical, and functional relations draw a thread connecting peptides to their recent cognates, the "peptidomimetics". This review presents a comprehensive survey of this field by highlighting the width and relevance of these familial connections. In the first section, we examine the experimental and computational techniques originally developed for peptides and their morphing into a broader analytical and predictive toolbox. The second section presents an excursus of the structures and properties of prominent peptidomimetics, and how the expansion of the chemical and structural diversity has returned new exciting properties. The third section presents an overview of technological applications and new families of peptidomimetics. As the field grows, new compounds emerge with clear potential in medicine and advanced manufacturing.
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9
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Islam NN, Sharma A, Gyawali G, Kumar R, Rick SW. Coarse-Grained Models for Constant pH Simulations of Carboxylic Acids. J Chem Theory Comput 2019; 15:4623-4631. [DOI: 10.1021/acs.jctc.9b00159] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Naeyma N. Islam
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Arjun Sharma
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Gaurav Gyawali
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Revati Kumar
- Department of Chemistry, Louisiana State University, Baton Rouge, Louisiana 70808, United States
| | - Steven W. Rick
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
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