SAXS-guided unbiased coarse-grained Monte Carlo simulation for identification of self-assembly nanostructures and dimensions

Pahari, Silabrata; Liu, Shuhao; Lee, Chi H.; Akbulut, Mustafa; Kwon, Joseph

doi:10.1039/d2sm00601d

Cited by 9 publications

(5 citation statements)

References 60 publications

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“…Determining the correct solution from the set of these multiple solutions is challenging. − In our study, solution 3 matches the results highlighted in Figure and is the correct solution for spatiotemporal diffusivity. It is clear from Figures d–f and b that solution 2 is not physically relevant.…”

Section: Resultsmentioning

confidence: 99%

Unveiling Latent Chemical Mechanisms: Hybrid Modeling for Estimating Spatiotemporally Varying Parameters in Moving Boundary Problems

Pahari,

Shah,

Sang-Il Kwon

2024

Ind. Eng. Chem. Res.

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Hybrid modeling has gained substantial recognition due to its capacity to seamlessly integrate machine learning methodologies while preserving the fundamental physical principles inherent in a model. Although these hybrid models have predominantly been applied to temporal processes governed by ordinary differential equations, the intricacies of various real-world systems, characterized by a diverse array of physical processes, extend far beyond this domain. This study extensively investigates the application of hybrid modeling techniques within the context of a complex biological system regulated by the reaction−diffusion equation, a specific type of partial differential equation. The primary objective is to tackle the challenges associated with latent chemical mechanisms that are concealed from direct observation. The proposed approach introduces a hybrid modeling framework that synergizes neural networks with mathematical methods to estimate parameters exhibiting spatiotemporal variations within a category of problems involving dynamic boundaries. Model training is executed through a back-propagation algorithm, adept at efficiently updating these parameters while ensuring numerical stability. Subsequently, the hybrid model is employed in the context of reaction−diffusion models, yielding results that validate its proficiency in precisely estimating variable diffusivity across both space and time as well as temporal fluctuations in cell proliferation rates and cell carrying density. The mean squared error of the final output predictions by the hybrid model is 9.4 × 10 −6 as compared to that of a simple first-principles model which is 3.12 × 10 −4 .

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Section: Resultsmentioning

confidence: 99%

Unveiling Latent Chemical Mechanisms: Hybrid Modeling for Estimating Spatiotemporally Varying Parameters in Moving Boundary Problems

Pahari,

Shah,

Sang-Il Kwon

2024

Ind. Eng. Chem. Res.

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“…This process evaluates configurations for acceptability depending on their energy and the system's temperature. [53] The field of supramolecular chemistry has been greatly impacted by MC simulations. [54] They make it possible to comprehend non-covalent host-guest complex intermediates at a level of depth that is not achievable by experimentation.…”

Section: Simulationmentioning

confidence: 99%

“…Mapping the thermodynamic landscape of self‐assembly systems is made possible by the random sampling that MC simulations execute on a given energy landscape. This process evaluates configurations for acceptability depending on their energy and the system's temperature [53] …”

Section: Theoretical Approaches To Understanding Intermediate States ...mentioning

confidence: 99%

Transient and elusive intermediate states in self‐assembly processes: An overview

Zhang,

Hu,

Xing

et al. 2024

Responsive Materials

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The transient and elusive intermediate states are the keys in self‐assembly processes, which are common phenomena shaping the structure, properties, and functionalities of assembled materials across many scientific domains. However, the understanding about the intermediate states of self‐assembly process is always challenging and limited. In this review, we focus on these states by combining theoretical and experimental approaches. By examining a wide variety of self‐assembly systems that span from biological to metal–organic nanostructures, this review uncovers the wealth of intermediate states of self‐assembled materials. In addition to combining the current knowledge, it will identify challenges and provide a new insight into the opportunities for future research.

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“…One prolific example of ensemble fitting is the combination of SAXS data with various docking and modeling algorithms. This SAXS-assisted method has been implemented and applied to various research areas, including protein–protein interactions [ 237 , 238 , 239 , 240 , 241 , 242 , 243 ], high-order structures [ 213 , 214 , 244 , 245 , 246 , 247 ], protein dynamics [ 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 ], RNA dynamics [ 258 , 259 , 260 , 261 ], and the study of IDPs [ 6 , 262 , 263 , 264 , 265 ].…”

Section: Theoretical and Computational Biophysical Techniquesmentioning

confidence: 99%

Biophysical and Integrative Characterization of Protein Intrinsic Disorder as a Prime Target for Drug Discovery

et al. 2023

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Protein intrinsic disorder is increasingly recognized for its biological and disease-driven functions. However, it represents significant challenges for biophysical studies due to its high conformational flexibility. In addressing these challenges, we highlight the complementary and distinct capabilities of a range of experimental and computational methods and further describe integrative strategies available for combining these techniques. Integrative biophysics methods provide valuable insights into the sequence–structure–function relationship of disordered proteins, setting the stage for protein intrinsic disorder to become a promising target for drug discovery. Finally, we briefly summarize recent advances in the development of new small molecule inhibitors targeting the disordered N-terminal domains of three vital transcription factors.

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SAXS-guided unbiased coarse-grained Monte Carlo simulation for identification of self-assembly nanostructures and dimensions

Abstract: Recent studies show that solvated amphiphiles can form nanostructured self-assemblies called dynamic binary complexes (DBCs) in the presence of ions. Since the nanostructures of DBCs are directly related to their...

Cited by 9 publications

References 60 publications

Unveiling Latent Chemical Mechanisms: Hybrid Modeling for Estimating Spatiotemporally Varying Parameters in Moving Boundary Problems

Unveiling Latent Chemical Mechanisms: Hybrid Modeling for Estimating Spatiotemporally Varying Parameters in Moving Boundary Problems

Transient and elusive intermediate states in self‐assembly processes: An overview

Biophysical and Integrative Characterization of Protein Intrinsic Disorder as a Prime Target for Drug Discovery

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