Research advances of molecular docking and molecular dynamic simulation in recognizing interaction between muscle proteins and exogenous additives

Bai, Genpeng; Pan, Yiling; Zhang, Yuemei; Li, Yang; Wang, Jinpeng; Wang, Ying; Teng, Wendi; Jin, Guofeng; Geng, Fang; Cao, Jinxuan

doi:10.1016/j.foodchem.2023.136836

Cited by 35 publications

(16 citation statements)

References 82 publications

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“…In the field of food science, this technique is also powerful in understanding the binding mechanisms between enzymes and substrates and screening food ingredients for potential therapeutic properties. ,− For instance, He et al used Autodock to illustrate the interaction mechanisms between trilobatin and α-glucosidase. They found that hydrogen bonding acted as the dominant force governing the interaction between trilobatin and residues (Ser240, Ser241, Asp242, Arg315, Asp352, Glu411, and Arg442) near the active site cavity of α-glucosidase, with an average distance of less than 2.9 Å.…”

Section: Strategies To Develop Rice Bran Lipase Inhibitorsmentioning

confidence: 99%

Developing Lipase Inhibitor as a Novel Approach to Address the Rice Bran Rancidity Issue─A Critical Review

Li,

Liu,

Cai

et al. 2024

J. Agric. Food Chem.

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Rice bran is a valuable byproduct from the food processing industry, which contains abundant protein, essential unsaturated fatty acids, and numerous bioactive compounds. However, its susceptibility to rancidity greatly restricts its wide utilization. Many strategies have been proposed to delay the rancidity of rice bran, but most of them have their respective limitations. Here, we proposed that developing rice ban lipase peptide inhibitors represents an alternative and promising prescription for impeding the rancidity of rice bran, in contrast to the conventional stabilization approaches for rice bran. For this reason, the rancidity mechanisms of rice bran and the research progress of rice bran lipases were discussed. In addition, the feasibility of utilizing in silico screening and phage display, two state-of-the-art technologies, in the design of the related peptide inhibitors was also highlighted. This knowledge is expected to provide a theoretical basis for opening a new avenue for stabilizing rice bran.

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Section: Strategies To Develop Rice Bran Lipase Inhibitorsmentioning

confidence: 99%

Developing Lipase Inhibitor as a Novel Approach to Address the Rice Bran Rancidity Issue─A Critical Review

Li,

Liu,

Cai

et al. 2024

J. Agric. Food Chem.

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“…Bai et al. (2023) pointed out that the interactions between food proteins and ligands would influence many characteristics, including the color, texture, and flavor of meat products. Accordingly, by conducting molecular dynamics simulation to get detailed interaction information, these characteristics can be monitored.…”

Section: Applications Of Molecular Simulation To Food Protein–ligand ...mentioning

confidence: 99%

“…Molecular dynamics simulation can show the molecular interaction mechanism in detail, which provides a method to improve food quality. Bai et al (2023) pointed out that the interactions between food proteins and ligands would influence many characteristics, including the color, texture, and flavor of meat products.…”

Section: Mechanism Exploration Of Molecular Interactions and Characte...mentioning

confidence: 99%

“…In this context, molecular simulation has attracted considerable attention. It is a computer‐aided method for modeling molecular structure and interaction behavior at the atomic level (Bai et al., 2023). It reflects various physicochemical properties of binding systems through theoretical calculations, including binding affinity, interaction forces, and key residues (Farhadian et al., 2019; Geng et al., 2023; Hu, Dai, et al., 2023).…”

Section: Introductionmentioning

confidence: 99%

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Molecular simulation for food protein–ligand interactions: A comprehensive review on principles, current applications, and emerging trends

Jin,

Wei

2023

Comp Rev Food Sci Food Safe

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In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein–ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein–ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein–ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme‐substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein–ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein–ligand interaction simulation. Overall, the use of molecular simulation to study food protein–ligand interactions has a promising prospect.

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“…Molecular dynamics (MD) is a powerful computational technique to understand and predict the behavior of biological systems, such as nucleic acids, proteins, lipid membranes, and many others. − The all-atom MD explicitly models all atoms in the simulated system. However, since the computational cost scales with the number of atoms, the method is often inapplicable to biologically relevant time scales and system sizes.…”

Section: Introductionmentioning

confidence: 99%

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Coste,

Slejko,

Zavadlav

et al. 2023

J. Chem. Theory Comput.

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Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.

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Research advances of molecular docking and molecular dynamic simulation in recognizing interaction between muscle proteins and exogenous additives

Cited by 35 publications

References 82 publications

Developing Lipase Inhibitor as a Novel Approach to Address the Rice Bran Rancidity Issue─A Critical Review

Developing Lipase Inhibitor as a Novel Approach to Address the Rice Bran Rancidity Issue─A Critical Review

Molecular simulation for food protein–ligand interactions: A comprehensive review on principles, current applications, and emerging trends

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

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