Kinetic network models to study molecular self-assembly in the wake of machine learning

Liu, Bojun; Qiu, Yunrui; Goonetilleke, Eshani Chrisana; Huang, Xuhui

doi:10.1557/s43577-022-00415-1

Cited by 16 publications

(19 citation statements)

References 70 publications

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“…KNMs are an approach used for computing the thermodynamic and kinetic properties of a kinetic process from an ensemble of MD simulations. [12,13,36,37] In KNMs, the configuration space is partitioned into a set of metastable states, and time is also coarse-grained into discrete units. With a proper selection of the time interval, the continuous dynamics can be simplified as Markovian transitions among different conformational states.…”

Section: Kinetic Network Models To Elucidate the Aiegen Aggregation K...mentioning

confidence: 99%

“…A general pipeline to build a KNM is summarized as follows (Figure 3) [13,37,38,40] : (i) conduct MD simulations initiated from different conformations and obtain an ensemble of MD simulation trajectories of the aggregation process [41] (Figure 3A); (ii) select geometric features [12] to describe the aggregate conformations, and further perform dimensionality reduction [42,43] using tICA [44] and VAMPnet/SRVnets [27,45,46] to find the slow collective variables (CVs) that can describe the kinetics of the aggregation processes; (iii) cluster the MD conformations into microstates (e.g., k-centers, [47,48] k-means, [49] etc.) according to the pairwise distances defined as Euclidian distances in the CV space (Figure 3B); (iv) lump microstates (usually hundreds to thousands) into a handful of metastable macrostates based on their kinetic connectivity using kinetic lumping algorithms, such as PCCA and PCCA+ [50][51][52] ; (v) build the KNM (Figure 3C), which contains a transition probability matrix (TPM).…”

Section: Kinetic Network Models To Elucidate the Aiegen Aggregation K...mentioning

confidence: 99%

“…[ 11 ] Kinetic network models (KNMs) built from an ensemble of MD simulations hold promise in addressing this challenge by providing statistical representations of AIEgen aggregate structures and elucidating the major aggregation pathways. [ 12 ] In this approach, the conformational space is partitioned into a set of metastable states, and time is also coarse‐grained into discrete units. With a proper selection of the time interval, the continuous dynamics can be simplified as Markovian transitions among different conformational states.…”

Section: Introductionmentioning

confidence: 99%

“…[ 11,13–23 ] In recent years, KNMs have been widely applied to study conformational dynamics of self‐assembly and aggregation. [ 12,24–26 ]…”

Section: Introductionmentioning

confidence: 99%

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Kinetic network models to elucidate aggregation dynamics of aggregation‐induced emission systems

Liu,

Kalin,

Liu

et al. 2023

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Aggregation‐induced emission (AIE) is a phenomenon where a molecule that is weakly or non‐luminescent in a diluted solution becomes highly emissive when aggregated. AIE luminogens (AIEgens) hold promise in diverse applications like bioimaging, chemical sensing, and optoelectronics. Investigation in AIE luminescence is also critical for understanding aggregation kinetics as the aggregation process is an essential component of AIE emission. Experimental investigation of AIEgen aggregation is challenging due to the fast timescale of the aggregation and the amorphous aggregate structures. Computer simulations such as molecular dynamics (MD) simulation provide a valuable approach to complement experiments with atomic‐level knowledge to study the fast dynamics of aggregation processes. However, individual simulations still struggle to systematically elucidate heterogeneous kinetics of the formation of amorphous AIEgen aggregates. Kinetic network models (KNMs), constructed from an ensemble of MD simulations, hold great potential in addressing this challenge. In these models, dynamic processes are modeled as a series of Markovian transitions occurring among metastable conformational states at discrete time intervals. In this perspective article, we first review previous studies to characterize the AIEgen aggregation kinetics and their limitations. We then introduce KNMs as a promising approach to elucidate the complex kinetics of aggregations to address these limitations. More importantly, we discuss our perspective on linking the output of KNMs to experimental observations of time‐resolved AIE luminescence. We expect that this approach can validate the computational predictions and provide great insights into the aggregation kinetics for AIEgen aggregates. These insights will facilitate the rational design of improved AIEgens in their applications in biology and materials sciences.

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Section: Kinetic Network Models To Elucidate the Aiegen Aggregation K...mentioning

confidence: 99%

Section: Kinetic Network Models To Elucidate the Aiegen Aggregation K...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…[ 11,13–23 ] In recent years, KNMs have been widely applied to study conformational dynamics of self‐assembly and aggregation. [ 12,24–26 ]…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Kinetic network models to elucidate aggregation dynamics of aggregation‐induced emission systems

Liu,

Kalin,

Liu

et al. 2023

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“…Transition path theory (TPT) − applied to a Markov state model (MSM) ,,,− holds great potential in obtaining the full ensemble of kinetic pathways from MD simulations. In this approach, the conformational space is partitioned into a set of metastable states, and time is coarse-grained into discrete units (called lag times).…”

Section: Introductionmentioning

confidence: 99%

An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes

Qiu

OʼConnor

Xue

et al. 2023

J. Chem. Theory Comput.

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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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Tailed phage machinery

Ibrahim,

Weadge,

Anany

2024

Microbial Genomics: Clinical, Pharmaceutical, and Industrial Applications

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Kinetic network models to study molecular self-assembly in the wake of machine learning

Cited by 16 publications

References 70 publications

Kinetic network models to elucidate aggregation dynamics of aggregation‐induced emission systems

Kinetic network models to elucidate aggregation dynamics of aggregation‐induced emission systems

An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes

Tailed phage machinery

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