1
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Wang Y, Li C, Zheng X. Markov State Models Reveal How Folding Kinetics Influence Absorption Spectra of Foldamers. J Chem Theory Comput 2024; 20:5396-5407. [PMID: 38900275 DOI: 10.1021/acs.jctc.4c00202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Self-assembly of platinum(II) complex foldamers is an essential approach to fabricate advanced luminescent materials. However, a comprehensive understanding of folding kinetics and their absorption spectra remains elusive. By constructing Markov state models (MSMs) from large-scale molecular dynamics simulations, we reveal that two largely similar dinuclear alknylplatinum(II) terpyridine foldamers, Pt-PEG and Pt-PE with slightly different bridges, exhibit distinctive folding kinetics. Particularly, Pt-PEG bears bridge-dominant, plane-dominant, and cooperative pathways, while Pt-PE only prefers the plane-dominant pathway. Such preference originates from their difference in intrabridge electrostatic interactions, leading to contrastive distributions of metastable states. We also found that the bridge-dominant pathway for Pt-PEG becomes more favorable when lowering the temperature. Interestingly, based on the comprehensive conformation ensembles from our MSMs, we reveal the conformation-dependent absorption spectra of Pt-PEG and Pt-PE. Our theoretical spectra not only align with experimental results but also reveal the contributions of diverse conformations to the overall absorption bands explicitly, facilitating the rational design of stimuli-responsive smart luminescent molecules.
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Affiliation(s)
- Yijia Wang
- Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, Key Laboratory of Medicinal Molecule Science and Pharmaceutics Engineering of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Chu Li
- Department of Chemistry, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Xiaoyan Zheng
- Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, Key Laboratory of Medicinal Molecule Science and Pharmaceutics Engineering of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China
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2
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Liu B, Xue M, Qiu Y, Konovalov KA, O’Connor MS, Huang X. GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics. J Chem Phys 2023; 159:094901. [PMID: 37655771 PMCID: PMC11005469 DOI: 10.1063/5.0158903] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/11/2023] [Indexed: 09/02/2023] Open
Abstract
Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers, and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenges. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, such as time-structure independent component analysis, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets, which combines graph neural networks with a variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory, which studies Markov processes without forcing detailed balance constraints, which addresses the out-of-equilibrium challenge in the self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely applied to molecular self-assembly.
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Affiliation(s)
- Bojun Liu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Mingyi Xue
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Yunrui Qiu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Kirill A. Konovalov
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Michael S. O’Connor
- Biophysics Graduate Program, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Xuhui Huang
- Author to whom correspondence should be addressed:
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3
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Qian Y, Evans D, Mishra B, Fu Y, Liu ZH, Guo S, Johnson ME. Temporal control by cofactors prevents kinetic trapping in retroviral Gag lattice assembly. Biophys J 2023; 122:3173-3190. [PMID: 37393432 PMCID: PMC10432227 DOI: 10.1016/j.bpj.2023.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/21/2023] [Accepted: 06/27/2023] [Indexed: 07/03/2023] Open
Abstract
For retroviruses like HIV to proliferate, they must form virions shaped by the self-assembly of Gag polyproteins into a rigid lattice. This immature Gag lattice has been structurally characterized and reconstituted in vitro, revealing the sensitivity of lattice assembly to multiple cofactors. Due to this sensitivity, the energetic criterion for forming stable lattices is unknown, as are their corresponding rates. Here, we use a reaction-diffusion model designed from the cryo-ET structure of the immature Gag lattice to map a phase diagram of assembly outcomes controlled by experimentally constrained rates and free energies, over experimentally relevant timescales. We find that productive assembly of complete lattices in bulk solution is extraordinarily difficult due to the large size of this ∼3700 monomer complex. Multiple Gag lattices nucleate before growth can complete, resulting in loss of free monomers and frequent kinetic trapping. We therefore derive a time-dependent protocol to titrate or "activate" the Gag monomers slowly within the solution volume, mimicking the biological roles of cofactors. This general strategy works remarkably well, yielding productive growth of self-assembled lattices for multiple interaction strengths and binding rates. By comparing to the in vitro assembly kinetics, we can estimate bounds on rates of Gag binding to Gag and the cellular cofactor IP6. Our results show that Gag binding to IP6 can provide the additional time delay necessary to support smooth growth of the immature lattice with relatively fast assembly kinetics, mostly avoiding kinetic traps. Our work provides a foundation for predicting and disrupting formation of the immature Gag lattice via targeting specific protein-protein binding interactions.
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Affiliation(s)
- Yian Qian
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Daniel Evans
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Bhavya Mishra
- Department of Physics, and Center for Cellular and Biomolecular Machines, University of California, Merced, California
| | - Yiben Fu
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Zixiu Hugh Liu
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Sikao Guo
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Margaret E Johnson
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland.
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4
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Qiu Y, O’Connor MS, Xue M, Liu B, Huang X. An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes. J Chem Theory Comput 2023; 19:4728-4742. [PMID: 37382437 PMCID: PMC11042546 DOI: 10.1021/acs.jctc.3c00318] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Conformational changes (i.e., dynamic transitions between pairs of conformational states) play important roles in many chemical and biological processes. Constructing the Markov state model (MSM) from extensive molecular dynamics (MD) simulations is an effective approach to dissect the mechanism of conformational changes. When combined with transition path theory (TPT), MSM can be applied to elucidate the ensemble of kinetic pathways connecting pairs of conformational states. However, the application of TPT to analyze complex conformational changes often results in a vast number of kinetic pathways with comparable fluxes. This obstacle is particularly pronounced in heterogeneous self-assembly and aggregation processes. The large number of kinetic pathways makes it challenging to comprehend the molecular mechanisms underlying conformational changes of interest. To address this challenge, we have developed a path classification algorithm named latent-space path clustering (LPC) that efficiently lumps parallel kinetic pathways into distinct metastable path channels, making them easier to comprehend. In our algorithm, MD conformations are first projected onto a low-dimensional space containing a small set of collective variables (CVs) by time-structure-based independent component analysis (tICA) with kinetic mapping. Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic pathways in the continuous CV space. Based on the trained VAE model, the TPT-generated ensemble of kinetic pathways can be embedded into a latent space, where the classification becomes clear. We show that LPC can efficiently and accurately identify the metastable path channels in three systems: a 2D potential, the aggregation of two hydrophobic particles in water, and the folding of the Fip35 WW domain. Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to four path channels. We expect that LPC can be widely applied to identify the dominant kinetic pathways underlying complex conformational changes.
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Affiliation(s)
- Yunrui Qiu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Michael S. O’Connor
- Biophysics Graduate Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Mingyi Xue
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Bojun Liu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Xuhui Huang
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Biophysics Graduate Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
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5
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Yang X, Lu ZY. Nanoparticle cluster formation mechanisms elucidated via Markov state modeling: Attraction range effects, aggregation pathways, and counterintuitive transition rates. J Chem Phys 2022; 156:214902. [DOI: 10.1063/5.0086110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Nanoparticle clusters are promising candidates for developing functional materials. However, it is still a challenging task to fabricate them in a predictable and controllable way, which requires investigation of the possible mechanisms underlying cluster formation at the nanoscale. By constructing Markov state models (MSMs) at the microstate level, we find that for highly dispersed particles to form a highly aggregated cluster, there are multiple coexisting pathways, which correspond to direct aggregation, or pathways that need to pass through partially aggregated, intermediate states. Varying the range of attraction between nanoparticles is found to significantly affect pathways. As the attraction range becomes narrower, compared to direct aggregation, some pathways that need to pass through partially aggregated intermediate states become more competitive. In addition, from MSMs constructed at the macrostate level, the aggregation rate is found to be counterintuitively lower with a lower free-energy barrier, which is also discussed.
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Affiliation(s)
- Xi Yang
- Institute of Theoretical Chemistry, State Key Laboratory of Supramolecular Structure and Materials, Jilin University, Changchun 130021, China
| | - Zhong-Yuan Lu
- Institute of Theoretical Chemistry, State Key Laboratory of Supramolecular Structure and Materials, Jilin University, Changchun 130021, China
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6
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Ji L, Li Y, Wang J, Ning A, Zhang N, Liang S, He J, Zhang T, Qu Z, Gao J. Community Reaction Network Reduction for Constructing a Coarse-Grained Representation of Combustion Reaction Mechanisms. J Chem Inf Model 2022; 62:2352-2364. [PMID: 35442657 DOI: 10.1021/acs.jcim.2c00240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A community-reaction network reduction (CNR) approach is presented for mechanism reduction on the basis of a network-based community detection technique, a concept related to pre-equilibrium in chemical kinetics. In this method, the detailed combustion mechanism is first transformed into a weighted network, in which communities of species that have dense inner connections under the critical ignition conditions are identified. By analyzing the community partitions in different regions, we determine the effective functional groups and driving processes. Then, a skeletal model for the overall mechanism is deduced according to the network centrality data, including transition pathway identification and reaction-path flux. The CNR method is illustrated on the hydrogen autoignition system which has been extensively investigated, and a new reduced mechanism involving seven processes is proposed. Dynamics simulations employing the present CNR model show that the computed ignition time and distribution of major species on a wide range of temperature and pressure conditions are in accord with the experiments and results from other methods.
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Affiliation(s)
- Lin Ji
- Department of Chemistry, Capital Normal University, Beijing 100048, China
| | - Yue Li
- Department of Chemistry, Capital Normal University, Beijing 100048, China
| | - Jie Wang
- Department of Chemistry, Capital Normal University, Beijing 100048, China
| | - An Ning
- Department of Chemistry, Capital Normal University, Beijing 100048, China
| | - Naixin Zhang
- Department of Chemistry, Capital Normal University, Beijing 100048, China
| | - Shengyao Liang
- Department of Chemistry, Capital Normal University, Beijing 100048, China
| | - Jiyun He
- Department of Chemistry, Capital Normal University, Beijing 100048, China
| | - Tianyu Zhang
- Department of Chemistry, Capital Normal University, Beijing 100048, China
| | - Zexing Qu
- Laboratory of Theoretical and Computational Chemistry, Jilin University, Changchun 130015, China
| | - Jiali Gao
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China.,Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
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7
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Wang H, Zheng X. Theoretical Study of Macrocyclic Host Molecules: From Supramolecular Recognition to Self-Assembly. Phys Chem Chem Phys 2022; 24:19011-19028. [DOI: 10.1039/d2cp02152h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Supramolecular chemistry focuses on molecular recognition and self-assembly of various building blocks through weak non-covalent interactions, including anion-π, hydrogen bond (HB), hydrophobic interactions, van der Waals (vdW) interactions, etc, which...
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8
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Zhu L, Jiang H, Cao S, Unarta IC, Gao X, Huang X. Critical role of backbone coordination in the mRNA recognition by RNA induced silencing complex. Commun Biol 2021; 4:1345. [PMID: 34848812 PMCID: PMC8632932 DOI: 10.1038/s42003-021-02822-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 10/26/2021] [Indexed: 01/02/2023] Open
Abstract
Despite its functional importance, the molecular mechanism underlying target mRNA recognition by Argonaute (Ago) remains largely elusive. Based on extensive all-atom molecular dynamics simulations, we constructed quasi-Markov State Model (qMSM) to reveal the dynamics during recognition at position 6-7 in the seed region of human Argonaute 2 (hAgo2). Interestingly, we found that the slowest mode of motion therein is not the gRNA-target base-pairing, but the coordination of the target phosphate groups with a set of positively charged residues of hAgo2. Moreover, the ability of Helix-7 to approach the PIWI and MID domains was found to reduce the effective volume accessible to the target mRNA and therefore facilitate both the backbone coordination and base-pair formation. Further mutant simulations revealed that alanine mutation of the D358 residue on Helix-7 enhanced a trap state to slow down the loading of target mRNA. Similar trap state was also observed when wobble pairs were introduced in g6 and g7, indicating the role of Helix-7 in suppressing non-canonical base-paring. Our study pointed to a general mechanism for mRNA recognition by eukaryotic Agos and demonstrated the promise of qMSM in investigating complex conformational changes of biomolecular systems.
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Affiliation(s)
- Lizhe Zhu
- Warshel Institute for Computational Biology, School of Life and Health Sciences, The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, 518172, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Hanlun Jiang
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Department of Biochemistry, Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Siqin Cao
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Ilona Christy Unarta
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xin Gao
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
| | - Xuhui Huang
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
- Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
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9
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Li C, Liu Z, Goonetilleke EC, Huang X. Temperature-dependent kinetic pathways of heterogeneous ice nucleation competing between classical and non-classical nucleation. Nat Commun 2021; 12:4954. [PMID: 34400646 PMCID: PMC8367957 DOI: 10.1038/s41467-021-25267-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 07/26/2021] [Indexed: 12/04/2022] Open
Abstract
Ice nucleation on the surface plays a vital role in diverse areas, ranging from physics and cryobiology to atmospheric science. Compared to ice nucleation in the bulk, the water-surface interactions present in heterogeneous ice nucleation complicate the nucleation process, making heterogeneous ice nucleation less comprehended, especially the relationship between the kinetics and the structures of the critical ice nucleus. Here we combine Markov State Models and transition path theory to elucidate the ensemble pathways of heterogeneous ice nucleation. Our Markov State Models reveal that the classical one-step and non-classical two-step nucleation pathways can surprisingly co-exist with comparable fluxes at T = 230 K. Interestingly, we find that the disordered mixing of rhombic and hexagonal ice leads to a favorable configurational entropy that stabilizes the critical nucleus, facilitating the non-classical pathway. In contrast, the favorable energetics promotes the formation of hexagonal ice, resulting in the classical pathway. Furthermore, we discover that, at elevated temperatures, the nucleation process prefers to proceed via the classical pathway, as opposed to the non-classical pathway, since the potential energy contributions override the configurational entropy compensation. This study provides insights into the mechanisms of heterogeneous ice nucleation and sheds light on the rational designs to control crystallization processes.
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Affiliation(s)
- Chu Li
- Department of Chemistry, Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Zhuo Liu
- Department of Chemistry, Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Eshani C Goonetilleke
- Department of Chemistry, Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Xuhui Huang
- Department of Chemistry, Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Kowloon, Hong Kong.
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10
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Ye X, Khomami B. Self-assembly of linear diblock copolymers in selective solvents: from single micelles to particles with tri-continuous inner structures. SOFT MATTER 2020; 16:6056-6062. [PMID: 32496492 DOI: 10.1039/d0sm00402b] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Large-scale dissipative particle dynamics (DPD) simulations have been performed to investigate the self-assembly of over 20 000 linear diblock copolymer chains in a selective solvent. Specifically, we found that the transition from spherical to cylindrical vesicles and in turn to disk-like and onion-like vesicles, and finally to tri-continuous spherical particles is mainly due to the increase in the aggregation number. In addition, the structures with large aggregation numbers are formed through the fusion of smaller aggregates and the length of the corona block of the block copolymer plays a critical role in the resulting morphology. Furthermore, our simulations indicate that the very larger amount of polymer in our simulation is the key to the observation of a state of dynamic equilibrium between free chains and aggregates in solution, as well as the formation of more complex structures from linear diblock copolymers in selective solvents. Overall, this study paves the way for future coordinated experimental/computational studies on the formation of nanoparticles with complex morphologies from diblock copolymers, an area of great scientific and industrial interest.
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Affiliation(s)
- Xianggui Ye
- Materials Research and Innovation Laboratory (MRAIL), Sustainable Energy Education and Research Center (SEERC), Department of Chemical and Biomolecular Engineering, The University of Tennessee Knoxville, TN 37996, USA.
| | - Bamin Khomami
- Materials Research and Innovation Laboratory (MRAIL), Sustainable Energy Education and Research Center (SEERC), Department of Chemical and Biomolecular Engineering, The University of Tennessee Knoxville, TN 37996, USA.
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11
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Mantha S, Qi S, Schmid F. Bottom-up Construction of Dynamic Density Functional Theories for Inhomogeneous Polymer Systems from Microscopic Simulations. Macromolecules 2020. [DOI: 10.1021/acs.macromol.0c00130] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Sriteja Mantha
- Institut für Physik, Johannes Gutenberg Universität Mainz, Staudingerweg 9, 55128 Mainz, Germany
| | - Shuanhu Qi
- Key Laboratory of Bio-inspired Smart Interfacial Science and Technology of Ministry of Education, School of Chemistry, Beihang University, Beijing 100191, China
| | - Friederike Schmid
- Institut für Physik, Johannes Gutenberg Universität Mainz, Staudingerweg 9, 55128 Mainz, Germany
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12
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Sengupta U, Carballo-Pacheco M, Strodel B. Automated Markov state models for molecular dynamics simulations of aggregation and self-assembly. J Chem Phys 2019; 150:115101. [DOI: 10.1063/1.5083915] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Affiliation(s)
- Ushnish Sengupta
- Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Jülich, 52425 Jülich, Germany
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| | - Martín Carballo-Pacheco
- Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Jülich, 52425 Jülich, Germany
- AICES Graduate School, RWTH Aachen University, Schinkelstraße 2, 52062 Aachen, Germany
- School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom
| | - Birgit Strodel
- Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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13
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Peng JH, Wang W, Yu YQ, Gu HL, Huang X. Clustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems. CHINESE J CHEM PHYS 2018. [DOI: 10.1063/1674-0068/31/cjcp1806147] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Jun-hui Peng
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Wei Wang
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Ye-qing Yu
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Han-lin Gu
- Department of Mathematics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Xuhui Huang
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
- State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
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14
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Zeng X, Li ZW, Zheng X, Zhu L, Sun ZY, Lu ZY, Huang X. Improving the productivity of monodisperse polyhedral cages by the rational design of kinetic self-assembly pathways. Phys Chem Chem Phys 2018; 20:10030-10037. [PMID: 29620122 DOI: 10.1039/c8cp00522b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Hollow polyhedral cages hold great potential for application in nanotechnological and biomedical fields. Understanding the formation mechanism of these self-assembled structures could provide guidance for the rational design of the desired polyhedral cages. Here, by constructing kinetic network models from extensive coarse-grained molecular dynamics simulations, we elucidated the formation mechanism of the dodecahedral cage, which is formed by the self-assembly of patchy particles. We found that the dodecahedral cage is formed through increasing the aggregate size followed by structure rearrangement. Based on this mechanistic understanding, we improved the productivity of the dodecahedral cage through the rational design of the patch arrangement of patchy particles, which promotes the structural rearrangement process. Our results demonstrate that it should be a feasible strategy to achieve the rational design of the desired nanostructures via the kinetic analysis. We anticipate that this methodology could be extended to other self-assembly systems for the fabrication of functional nanomaterials.
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Affiliation(s)
- Xiangze Zeng
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
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15
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Zeng X, Zhu L, Zheng X, Cecchini M, Huang X. Harnessing complexity in molecular self-assembly using computer simulations. Phys Chem Chem Phys 2018; 20:6767-6776. [PMID: 29479585 DOI: 10.1039/c7cp06181a] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In molecular self-assembly, hundreds of thousands of freely-diffusing molecules associate to form ordered and functional architectures in the absence of an actuator. This intriguing phenomenon plays a critical role in biology and has become a powerful tool for the fabrication of advanced nanomaterials. Due to the limited spatial and temporal resolutions of current experimental techniques, computer simulations offer a complementary strategy to explore self-assembly with atomic resolution. Here, we review recent computational studies focusing on both thermodynamic and kinetic aspects. As we shall see, thermodynamic approaches based on modeling and statistical mechanics offer initial guidelines to design nanostructures with modest computational effort. Computationally more intensive analyses based on molecular dynamics simulations and kinetic network models (KNMs) reach beyond it, opening the door to the rational design of self-assembly pathways. Current limitations of these methodologies are discussed. We anticipate that the synergistic use of thermodynamic and kinetic analyses based on computer simulations will provide an important contribution to the de novo design of self-assembly.
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Affiliation(s)
- Xiangze Zeng
- Department of Chemistry, Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
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Meng L, Sheong FK, Zeng X, Zhu L, Huang X. Path lumping: An efficient algorithm to identify metastable path channels for conformational dynamics of multi-body systems. J Chem Phys 2017; 147:044112. [DOI: 10.1063/1.4995558] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Affiliation(s)
- Luming Meng
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xiangze Zeng
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Lizhe Zhu
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xuhui Huang
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
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Zheng X, Zhu L, Zeng X, Meng L, Zhang L, Wang D, Huang X. Kinetics-Controlled Amphiphile Self-Assembly Processes. J Phys Chem Lett 2017; 8:1798-1803. [PMID: 28365997 DOI: 10.1021/acs.jpclett.7b00160] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Amphiphile self-assembly is an essential bottom-up approach of fabricating advanced functional materials. Self-assembled materials with desired structures are often obtained through thermodynamic control. Here, we demonstrate that the selection of kinetic pathways can lead to drastically different self-assembled structures, underlining the significance of kinetic control in self-assembly. By constructing kinetic network models from large-scale molecular dynamics simulations, we show that two largely similar amphiphiles, 1-[11-oxo-11-(pyren-1-ylmethoxy)-undecyl]pyridinium bromide (PYR) and 1-(11-((5a1,8a-dihydropyren-1-yl)methylamino)-11-oxoundecyl)pyridinium bromide (PYN), prefer distinct kinetic assembly pathways. While PYR prefers an incremental growth mechanism and forms a nanotube, PYN favors a hopping growth pathway leading to a vesicle. Such preference was found to originate from the subtle difference in the distributions of hydrophobic and hydrophilic groups in their chemical structures, which leads to different rates of the adhesion process among the aggregating micelles. Our results are in good agreement with experimental results, and accentuate the role of kinetics in the rational design of amphiphile self-assembly.
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Affiliation(s)
- Xiaoyan Zheng
- Department of Chemistry, Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction, Hong Kong University of Science and Technology , Clear Water Bay, Kowloon, Hong Kong
- HKUST-Shenzhen Research Institute , Nanshan, Shenzhen 518057, People's Republic of China
| | - Lizhe Zhu
- Department of Chemistry, Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction, Hong Kong University of Science and Technology , Clear Water Bay, Kowloon, Hong Kong
| | - Xiangze Zeng
- Department of Chemistry, Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction, Hong Kong University of Science and Technology , Clear Water Bay, Kowloon, Hong Kong
| | - Luming Meng
- Department of Chemistry, Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction, Hong Kong University of Science and Technology , Clear Water Bay, Kowloon, Hong Kong
| | - Lu Zhang
- Department of Chemistry, Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction, Hong Kong University of Science and Technology , Clear Water Bay, Kowloon, Hong Kong
| | - Dong Wang
- Key Laboratory of Organic OptoElectronics and Molecular Engineering, Department of Chemistry, Tsinghua University , Beijing 100084, People's Repulic of China
| | - Xuhui Huang
- Department of Chemistry, Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction, Hong Kong University of Science and Technology , Clear Water Bay, Kowloon, Hong Kong
- HKUST-Shenzhen Research Institute , Nanshan, Shenzhen 518057, People's Republic of China
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