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Title: Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution

Abstract

Molecular self-assembly is a powerful tool in materials design, wherein noncovalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable, and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly—biomineralizing peptides, hierarchicalmore » peptoids, and large protein assemblies—and will aid in the development of a new synthesis science for achieving precise molecular control in materials design.« less

Authors:
 [1];  [2]; ORCiD logo [1]
+ Show Author Affiliations
  1. Univ. of Washington, Seattle, WA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
Publication Date:
Research Org.:
Univ. of Washington, Seattle, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1781117
Grant/Contract Number:  
SC0019288
Resource Type:
Accepted Manuscript
Journal Name:
Chemistry of Materials
Additional Journal Information:
Journal Volume: 32; Journal Issue: 19; Journal ID: ISSN 0897-4756
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Self organization; Interfaces; Peptides and proteins; Protein structure; Biomineralization

Citation Formats

Sampath, Janani, Alamdari, Sarah, and Pfaendtner, Jim. Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution. United States: N. p., 2020. Web. doi:10.1021/acs.chemmater.0c01891.
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Sampath, Janani, Alamdari, Sarah, & Pfaendtner, Jim. Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution. United States. https://doi.org/10.1021/acs.chemmater.0c01891
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Sampath, Janani, Alamdari, Sarah, and Pfaendtner, Jim. Mon . "Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution". United States. https://doi.org/10.1021/acs.chemmater.0c01891. https://www.osti.gov/servlets/purl/1781117.
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@article{osti_1781117,
title = {Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution},
author = {Sampath, Janani and Alamdari, Sarah and Pfaendtner, Jim},
abstractNote = {Molecular self-assembly is a powerful tool in materials design, wherein noncovalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable, and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly—biomineralizing peptides, hierarchical peptoids, and large protein assemblies—and will aid in the development of a new synthesis science for achieving precise molecular control in materials design.},
doi = {10.1021/acs.chemmater.0c01891},
journal = {Chemistry of Materials},
number = 19,
volume = 32,
place = {United States},
year = {Mon Aug 17 00:00:00 EDT 2020},
month = {Mon Aug 17 00:00:00 EDT 2020}
}
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Computer Simulation of Protein Materials at Multiple Length Scales: From Single Proteins to Protein Assemblies
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Hierarchical Three-Dimensional Microbattery Electrodes Combining Bottom-Up Self-Assembly and Top-Down Micromachining
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A Solid-State Deuterium NMR and Sum-Frequency Generation Study of the Side-Chain Dynamics of Peptides Adsorbed onto Surfaces
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Understanding Adsorption-Desorption Dynamics of BMP-2 on Hydroxyapatite (001) Surface
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DNA Self-assembly for Nanomedicine
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Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures
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Designable and dynamic single-walled stiff nanotubes assembled from sequence-defined peptoids
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Simulation of Crystallization of Biominerals
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Tuning crystallization pathways through sequence engineering of biomimetic polymers
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Biophysical experiments and biomolecular simulations: A perfect match?
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Discovery Through the Computational Microscope
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Length scales in glass-forming liquids and related systems: a review
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Probing the Self-Assembly Mechanism of Diphenylalanine-Based Peptide Nanovesicles and Nanotubes
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Shaken, Not Stirred: Collapsing a Peptoid Monolayer To Produce Free-Floating, Stable Nanosheets
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Silica Morphogenesis by Lysine-Leucine Peptides with Hydrophobic Periodicity
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Supercharging enables organized assembly of synthetic biomolecules
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Dynamic DNA devices and assemblies formed by shape-complementary, non-base pairing 3D components
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Examining the Accuracy of Density Functional Theory for Predicting the Thermodynamics of Water Incorporation into Minerals: The Hydrates of Calcium Carbonate
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Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method
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Biotechnological mass production of DNA origami
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Biointerface Structural Effects on the Properties and Applications of Bioinspired Peptide-Based Nanomaterials
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Protein therapeutics: a summary and pharmacological classification
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4.6Å Cryo-EM reconstruction of tobacco mosaic virus from images recorded at 300keV on a 4k×4k CCD camera
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Ab initio modeling of protein/biomaterial interactions: competitive adsorption between glycine and water onto hydroxyapatite surfaces
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The effects of multiple probes on the hybridization of target DNA on surfaces
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A Collective Variable for the Efficient Exploration of Protein Beta-Sheet Structures: Application to SH3 and GB1
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Sequence Programmable Peptoid Polymers for Diverse Materials Applications
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Exhaustively Sampling Peptide Adsorption with Metadynamics
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Sequence–Structure–Binding Relationships Reveal Adhesion Behavior of the Car9 Solid-Binding Peptide: An Integrated Experimental and Simulation Study
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Design of self-assembly dipeptide hydrogels and machine learning via their chemical features
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A Bias-Exchange Approach to Protein Folding
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Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics
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Specificity and Biomineralization Activities of Ti-Binding Peptide-1 (TBP-1)
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Quantitative self-assembly prediction yields targeted nanomedicines
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