From Cooperative Self-Assembly to Water-Soluble Supramolecular Polymers Using Coarse-Grained Simulations

ACS Nano ◽  
2017 ◽  
Vol 11 (1) ◽  
pp. 1000-1011 ◽  
Author(s):  
Davide Bochicchio ◽  
Giovanni M. Pavan
Keyword(s):  
Self Assembly ◽  
Water Soluble ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Coarse Grained Simulations

scholarly journals Controlling the length of porphyrin supramolecular polymers via coupled equilibria and dilution-induced supramolecular polymerization

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Elisabeth Weyandt ◽  
Luigi Leanza ◽  
Riccardo Capelli ◽  
Giovanni M. Pavan ◽  
Ghislaine Vantomme ◽  
...  
Keyword(s):  
Complex Systems ◽  
Self Assembly ◽  
Chiral Discrimination ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Supramolecular System ◽  
Precise Control ◽  
Aggregate Morphology ◽  
The Individual ◽  
Supramolecular Polymerization

AbstractMulti-component systems often display convoluted behavior, pathway complexity and coupled equilibria. In recent years, several ways to control complex systems by manipulating the subtle balances of interaction energies between the individual components have been explored and thereby shifting the equilibrium between different aggregate states. Here we show the enantioselective chain-capping and dilution-induced supramolecular polymerization with a Zn2+-porphyrin-based supramolecular system when going from long, highly cooperative supramolecular polymers to short, disordered aggregates by adding a monotopic Mn3+-porphyrin monomer. When mixing the zinc and manganese centered monomers, the Mn3+-porphyrins act as chain-cappers for Zn2+-porphyrin supramolecular polymers, effectively hindering growth of the copolymer and reducing the length. Upon dilution, the interaction between chain-capper and monomers weakens as the equilibria shift and long supramolecular polymers form again. This dynamic modulation of aggregate morphology and length is achieved through enantioselectivity in the aggregation pathways and concentration-sensitive equilibria. All-atom and coarse-grained molecular simulations provide further insights into the mixing of the species and their exchange dynamics. Our combined experimental and theoretical approach allows for precise control of molecular self-assembly and chiral discrimination in complex systems.

scholarly journals Identifying and Tracking Defects in Dynamic Supramolecular Polymers

2019 ◽  
Author(s):  
Piero Gasparotto ◽  
Davide Bochicchio ◽  
Michele Ceriotti ◽  
Giovanni M. Pavan
Keyword(s):  
Self Assembly ◽  
Noncovalent Interactions ◽  
Molecular Simulations ◽  
Machine Learning Techniques ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Structure Stability ◽  
Supramolecular Polymer ◽  
Soft Systems ◽  
Local Defects

A central paradigm of self-assembly is to create ordered structures starting from molecular<br>monomers that spontaneously recognize and interact with each other via noncovalent interactions.<br>In the recent years, great efforts have been directed toward reaching the perfection in the<br>design of a variety of supramolecular polymers and materials with different architectures. The<br>resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers,<br>micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the<br>level of statistical ensembles to assess their average properties. However, molecular simulations<br>recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic<br>behavior and properties. The study of these defects poses considerable challenges, as the<br>flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes<br>a defect, and to characterize its stability and evolution. Here, we demonstrate the power<br>of unsupervised machine learning techniques to systematically identify and compare defects in<br>supramolecular polymer variants in different conditions, using as a benchmark 5°A-resolution<br>coarse-grained molecular simulations of a family of supramolecular polymers. We shot that this<br>approach allows a complete data-driven characterization of the internal structure and dynamics<br>of these complex assemblies and of the dynamic pathways for defects formation and resorption.<br>This provides a useful, generally applicable approach to unambiguously identify defects in<br>these dynamic self-assembled materials and to classify them based on their structure, stability<br>and dynamics.<br>

scholarly journals Shape selection and mis-assembly in viral capsid formation by elastic frustration

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Carlos I Mendoza ◽  
David Reguera
Keyword(s):  
Self Assembly ◽  
Viral Infections ◽  
Coarse Grained ◽  
Energetic Cost ◽  
Mechanical Resistance ◽  
Elastic Stresses ◽  
Shape Selection ◽  
Spherical Structures ◽  
Coarse Grained Simulations ◽  
Successful Replication

The successful assembly of a closed protein shell (or capsid) is a key step in the replication of viruses and in the production of artificial viral cages for bio/nanotechnological applications. During self-assembly, the favorable binding energy competes with the energetic cost of the growing edge and the elastic stresses generated due to the curvature of the capsid. As a result, incomplete structures such as open caps, cylindrical or ribbon-shaped shells may emerge, preventing the successful replication of viruses. Using elasticity theory and coarse-grained simulations, we analyze the conditions required for these processes to occur and their significance for empty virus self-assembly. We find that the outcome of the assembly can be recast into a universal phase diagram showing that viruses with high mechanical resistance cannot be self-assembled directly as spherical structures. The results of our study justify the need of a maturation step and suggest promising routes to hinder viral infections by inducing mis-assembly.

scholarly journals The influence of arm composition on the self-assembly of low-functionality telechelic star polymers in dilute solutions

2020 ◽  
Author(s):  
Esmaeel Moghimi ◽  
Iurii Chubak ◽  
Dimitra Founta ◽  
Konstantinos Ntetsikas ◽  
George Polymeropoulos ◽  
...  
Keyword(s):  
Self Assembly ◽  
Dynamic Properties ◽  
Star Polymers ◽  
Coarse Grained ◽  
Dilute Solutions ◽  
Solvent Quality ◽  
Physical Experiments ◽  
Coarse Grained Simulations ◽  
Two Populations

Abstract We combine synthesis, physical experiments, and computer simulations to investigate self-assembly patterns of low-functionality telechelic star polymers (TSPs) in dilute solutions. In particular, in this work, we focus on the effect of the arm composition and length on the static and dynamic properties of TSPs, whose terminal blocks are subject to worsening solvent quality upon reducing the temperature. We find two populations, single stars and clusters, that emerge upon worsening the solvent quality of the outer block. For both types of populations, their spatial extent decreases with temperature, with the specific details (such as temperature at which the minimal size is reached) depending on the coupling between inter- and intra-molecular associations as well as their strength. The experimental results are in very good qualitative agreement with coarse-grained simulations, which offer insights into the mechanism of thermoresponsive behavior of this class of materials.

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

2019 ◽  
Author(s):  
Piero Gasparotto ◽  
Davide Bochicchio ◽  
Michele Ceriotti ◽  
Giovanni M. Pavan
Keyword(s):  
Self Assembly ◽  
Noncovalent Interactions ◽  
Molecular Simulations ◽  
Machine Learning Techniques ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Structure Stability ◽  
Supramolecular Polymer ◽  
Soft Systems ◽  
Local Defects

A central paradigm of self-assembly is to create ordered structures starting from molecular<br>monomers that spontaneously recognize and interact with each other via noncovalent interactions.<br>In the recent years, great efforts have been directed toward reaching the perfection in the<br>design of a variety of supramolecular polymers and materials with different architectures. The<br>resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers,<br>micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the<br>level of statistical ensembles to assess their average properties. However, molecular simulations<br>recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic<br>behavior and properties. The study of these defects poses considerable challenges, as the<br>flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes<br>a defect, and to characterize its stability and evolution. Here, we demonstrate the power<br>of unsupervised machine learning techniques to systematically identify and compare defects in<br>supramolecular polymer variants in different conditions, using as a benchmark 5°A-resolution<br>coarse-grained molecular simulations of a family of supramolecular polymers. We shot that this<br>approach allows a complete data-driven characterization of the internal structure and dynamics<br>of these complex assemblies and of the dynamic pathways for defects formation and resorption.<br>This provides a useful, generally applicable approach to unambiguously identify defects in<br>these dynamic self-assembled materials and to classify them based on their structure, stability<br>and dynamics.<br>

scholarly journals Coarse-grained simulation reveals key features of HIV-1 capsid self-assembly

2016 ◽  
Author(s):  
John M A Grime ◽  
James F Dama ◽  
Barbie K Ganser-Pornillos ◽  
Cora L Woodward ◽  
Grant J Jensen ◽  
...  
Keyword(s):  
Self Assembly ◽  
Coarse Grained ◽  
Capsid Assembly ◽  
Molecular Crowding ◽  
Local Conditions ◽  
Conformational Variability ◽  
Multi Stage ◽  
Capsid Shell ◽  
Coarse Grained Simulations ◽  
Hiv 1

The maturation of HIV-1 viral particles is essential for viral infectivity. During maturation, many copies of the capsid protein (CA) self-assemble into a capsid shell to enclose the viral RNA. The mechanistic details of the initiation and early stages of capsid assembly remain to be delineated. We present coarse-grained simulations of capsid assembly under various conditions, considering not only capsid lattice self-assembly but also the potential disassembly of capsid upon delivery to the cytoplasm of a target cell. The effects of CA concentration, molecular crowding, and the conformational variability of CA are described, with results indicating that capsid nucleation and growth is a multi-stage process requiring well-defined metastable intermediates. Generation of the mature capsid lattice is sensitive to local conditions, with relatively subtle changes in CA concentration and molecular crowding influencing self-assembly and the ensemble of structural morphologies.

scholarly journals Identifying and Tracking Defects in Dynamic Supramolecular Polymers

2019 ◽  
Author(s):  
Piero Gasparotto ◽  
Davide Bochicchio ◽  
Michele Ceriotti ◽  
Giovanni M. Pavan
Keyword(s):  
Self Assembly ◽  
Noncovalent Interactions ◽  
Molecular Simulations ◽  
Machine Learning Techniques ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Structure Stability ◽  
Supramolecular Polymer ◽  
Soft Systems ◽  
Local Defects

A central paradigm of self-assembly is to create ordered structures starting from molecular<br>monomers that spontaneously recognize and interact with each other via noncovalent interactions.<br>In the recent years, great efforts have been directed toward reaching the perfection in the<br>design of a variety of supramolecular polymers and materials with different architectures. The<br>resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers,<br>micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the<br>level of statistical ensembles to assess their average properties. However, molecular simulations<br>recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic<br>behavior and properties. The study of these defects poses considerable challenges, as the<br>flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes<br>a defect, and to characterize its stability and evolution. Here, we demonstrate the power<br>of unsupervised machine learning techniques to systematically identify and compare defects in<br>supramolecular polymer variants in different conditions, using as a benchmark 5°A-resolution<br>coarse-grained molecular simulations of a family of supramolecular polymers. We shot that this<br>approach allows a complete data-driven characterization of the internal structure and dynamics<br>of these complex assemblies and of the dynamic pathways for defects formation and resorption.<br>This provides a useful, generally applicable approach to unambiguously identify defects in<br>these dynamic self-assembled materials and to classify them based on their structure, stability<br>and dynamics.<br>

scholarly journals Computational and experimental approaches to controlling bacterial microcompartment assembly

2021 ◽  
Author(s):  
Yaohua Li ◽  
Nolan W. Kennedy ◽  
Siyu Li ◽  
Carolyn E. Mills ◽  
Danielle Tullman-Ercek ◽  
...  
Keyword(s):  
Self Assembly ◽  
Bacterial Species ◽  
Coarse Grained ◽  
Bacterial Phyla ◽  
Energy Applications ◽  
Bacterial Microcompartments ◽  
Bacterial Microcompartment ◽  
Experimental Approaches ◽  
Amino Acid Mutations ◽  
Coarse Grained Simulations

AbstractBacterial microcompartments compartmentalize the enzymes that aid chemical and energy production in many bacterial species. These protein organelles are found in various bacterial phyla and are postulated to help many of these organisms survive in hostile environments such as the gut of their hosts. Metabolic engineers are interested in repurposing these organelles for non-native functions. Here, we use computational, theoretical and experimental approaches to determine mechanisms that effectively control microcompartment self-assembly. As a model system, we find via multiscale modeling and mutagenesis studies, the interactions responsible for the binding of hexamer-forming proteins propanediol utilization bacterial microcompartments from Salmonella and establish conditions that form various morphologies. We determine how the changes in the microcompartment hexamer protein preferred angles and interaction strengths can modify the assembled morphologies including the naturally occurring polyhedral microcompartment shape, as well as other extended shapes or quasi-closed shapes. We demonstrate experimentally that such altered strengths and angles are achieved via amino acid mutations. A thermodynamic model that agrees with the coarse-grained simulations provides guidelines to design microcompartments. These findings yield insight in controlled protein assembly and provide principles for assembling microcompartments for biochemical or energy applications as nanoreactors.

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