Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis

Chong, Wei Lim; Chupradit, Koollawat; Chin, Sek Peng; Khoo, Mai Mai; Khor, Sook Mei; Tayapiwatana, Chatchai; Nimmanpipug, Piyarat; Thongkum, Weeraya; Lee, Vannajan Sanghiran

doi:10.3390/molecules26185696

Cited by 14 publications

(8 citation statements)

References 55 publications

(62 reference statements)

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“…Consequently, future investigations could shed light on this aspect. Experimental techniques such as dynamic light scattering, nanoparticle tracking analysis, and microscopy methods are commonly employed to measure and analyze changes in particle size when proteins are subjected to UA‐ILs (Chong et al., 2021; Huang, Zhang et al., 2020).…”

Section: Effect Of Ultrasound‐assisted Ionic Liquids On the Physicoch...mentioning

confidence: 99%

How does ultrasound‐assisted ionic liquid treatment affect protein? A comprehensive review of their potential mechanisms, safety evaluation, and physicochemical and functional properties

Zhang,

Boateng,

2023

Comp Rev Food Sci Food Safe

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Proteins are essential to human health with enormous food applications. Despite their advantages, plant and animal proteins often exhibit limited molecular flexibility and poor solubility due to hydrogen bonds, hydrophobic interactions, and ionic interactions within their molecular structures. Thus, there is an urgent need to modify the rigid structure of proteins to enhance their stability and functional properties. Ultrasound‐assisted ionic liquid (UA‐IL) treatment for developing compound modification and producing proteins with excellent functional properties has received interest. However, no review specifically addresses the interactions between UA‐ILs and proteins. Hence, this review focused on recent research advancements concerning the effects and potential reaction mechanisms of UA‐ILs on the physicochemical properties (including particle size; primary, secondary, and tertiary structure; and surface morphology) as well as the functionality (such as solubility, emulsifying properties, and foaming ability) of proteins. Moreover, the safety evaluation of modified proteins was also discussed from various perspectives, such as acute and chronic toxicity, genotoxicity, cytotoxicity, and environmental and microbial toxicity. This review demonstrated that UA‐IL treatment‐induced protein structural changes significantly impact the functional characteristics of proteins. This treatment approach efficiently promotes protein structure stretching and spatial rearrangement through cavitation, thermal effects, and ionic interactions. As a result, the functional properties of modified proteins exhibited an obvious enhancement, thereby bringing more opportunities to utilize modified protein products in the food industry. Potential future directions for protein modification using UA‐ILs were also proposed.

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Section: Effect Of Ultrasound‐assisted Ionic Liquids On the Physicoch...mentioning

confidence: 99%

How does ultrasound‐assisted ionic liquid treatment affect protein? A comprehensive review of their potential mechanisms, safety evaluation, and physicochemical and functional properties

Zhang,

Boateng,

2023

Comp Rev Food Sci Food Safe

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show abstract

“…The experimental methods to achieve the qualitative and quantitative data are via the interaction studies like gel shift assays, calorimetry, fluorescence-based techniques, nuclear magnetic resonance spectroscopy (NMR), etc., along with in silico methods like molecular dynamics. − Experimental methods (cryoelectron microscopy, X-ray crystallography, NMR, etc.) to determine the structure of PNA complexes are reliable in order to quantify interaction specificity.…”

Section: Introductionmentioning

confidence: 99%

DeePNAP: A Deep Learning Method to Predict Protein–Nucleic Acid Binding Affinity from Their Sequences

Pandey,

Behara,

Sharma

et al. 2024

J. Chem. Inf. Model.

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Predicting the protein–nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting K D and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface:

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“…The current methods to achieve this qualitative and quantitative data are via interaction studies like gel shift assays, calorimetry, fluorescence-based techniques, nuclear magnetic resonance spectroscopy (NMR), etc. along with in-silico methods like molecular dynamics [11][12][13][14] . The threedimensional (3D) structure elucidation of PNA complexes experimentally is a reliable method for identifying binding residues that result in interaction specificity.…”

Section: Introductionmentioning

confidence: 99%

DeePNAP: A deep learning method to predict protein-nucleic acids binding affinity from sequence

Pandey,

Behara,

Sharma

et al. 2023

Preprint

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Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs, while also relying on both sequence and structural information of the PNA complexes for both train/test and also as inputs. As PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to a small training dataset. Additionally, a majority of the tools predict a single parameter such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model trained on a vast and heterogeneous dataset with 14,401 entries (from both eukaryotes and prokaryotes) of ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from the sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting theKDand ΔΔG implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface:http://14.139.174.41:8080/

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Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis

Cited by 14 publications

References 55 publications

How does ultrasound‐assisted ionic liquid treatment affect protein? A comprehensive review of their potential mechanisms, safety evaluation, and physicochemical and functional properties

How does ultrasound‐assisted ionic liquid treatment affect protein? A comprehensive review of their potential mechanisms, safety evaluation, and physicochemical and functional properties

DeePNAP: A Deep Learning Method to Predict Protein–Nucleic Acid Binding Affinity from Their Sequences

DeePNAP: A deep learning method to predict protein-nucleic acids binding affinity from sequence

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