Using molecular docking and molecular dynamics to investigate protein-ligand interactions

Morris, Connor; Corte, Dennis Della

doi:10.1142/s0217984921300027

Cited by 87 publications

(41 citation statements)

References 76 publications

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“…Molecular docking has become an important technology in computer-aided drug research [ 23 ]. This method uses docking to explore the interactions between small molecules and binding pockets of proteins to predict binding patterns and affinity [ 24 ]. In this study, the three ligands were docked with α-glucosidase, and the results are shown in Figure 6 .…”

Section: Resultsmentioning

confidence: 99%

Fishing of α-Glucosidase’s Ligands from Aloe vera by α-Glucosidase Functionalized Magnetic Nanoparticles

Yuan

Wan

et al. 2021

Molecules

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α-Glucosidase was immobilized on magnetic nanoparticles (MNPs) for selective solid-phase extraction of the enzyme’s ligands present in Aloe vera, which is a medicinal plant used for the treatment of various diseases and possesses anti-diabetic activity. One new compound, aloeacone (2), together with two known compounds, aloenin aglycone (1) and aloin A (3), were fished out as the enzyme’s ligands. The structure of 2 was determined by HR-MS and comprehensive NMR techniques. Compound 3 exhibited a weak inhibitory effect on α-glucosidase, while compounds 1 and 2 were found to possess activation effects on the enzyme for the first time. It is interesting that both an inhibitor and agonists of α-glucosidase were fished out in one experiment.

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Section: Resultsmentioning

confidence: 99%

Fishing of α-Glucosidase’s Ligands from Aloe vera by α-Glucosidase Functionalized Magnetic Nanoparticles

Yuan

Wan

et al. 2021

Molecules

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“…MD is crucial to the discovery of new drug [ 60 ] and is a powerful method for studying protein-ligand interactions [ 61 ]. In contrast to experimental methods alone, MD simulations can address diseases caused by protein misfolding and virtual screening; it also identifies the stability of protein-ligand complexes and ligand binding kinetics [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

“…We found that ligand has three hydrogen bonds with protein; a study [ 63 ] had shown that hydrogen bonding has an advantage over shapeless forces if it can form hydrogen bonds. In this study, MD simulation revealed that the structure of TP53-quercetin was very stable when the simulation temperature was 300 K. It is important to explore the application of new drug by molecular docking and MD simulations [ 61 ]; however, it is still necessary to perform in vitro experiment to verify the prediction.…”

Section: Discussionmentioning

confidence: 99%

“…From a holistic aspect, QHSSD could inhibit the cell proliferation and angiogenesis and yield anti-inflammatory and antioxidant effects through regulating TP53, VEGFA, EGFR, TNF, and NOS3. MD is crucial to the discovery of new drug [60] and is a powerful method for studying protein-ligand interactions [61]. In contrast to experimental methods alone, MD simulations can address diseases caused by protein misfolding and virtual screening; it also identifies the stability of protein-ligand complexes and ligand binding kinetics [62].…”

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confidence: 99%

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Network Pharmacology and Molecular Docking-Based Prediction of the Mechanism of Qianghuo Shengshi Decoction against Rheumatoid Arthritis

Zeng

Jiang³

et al. 2021

BioMed Research International

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Qianghuo Shengshi decoction (QHSSD) is a classical Chinese medicine formula, which is used in clinical practice for the treatment of rheumatoid arthritis (RA) in China. However, the pharmacological mechanism of QHSSD on RA has remained unclear by now. We collected and screened active compounds and its potential targets by the pharmacology platform of Chinese herbal medicines. In addition, the therapeutic targets of RA were obtained and selected from databases. Network construction analyzed that 128 active compounds may act on 87 candidate targets and identified a total of 18 hub targets. GO annotation and KEGG enrichment investigated that the action mechanism underlying the treatment of RA by QHSSD might be involved in cell proliferation, angiogenesis, anti-inflammation, and antioxidation. Finally, molecular docking verification showed that TP53, VEGFA, TNF, EGFR, and NOS3 may be related to the RA treatment and molecular dynamics simulation showed the stability of protein-ligand interactions. In this work, QHSSD might exert therapeutic effect through a multicomponent, multitarget, and multipathway in RA from a holistic aspect, which provides basis for its mechanism of action and subsequent experiments.

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“…Protein structures can either be obtained from experiments, homology modeling, or computational structure prediction. Accurate structures can be used for the rational design of biosensors [1], prediction of small-molecule docking [2], enzyme design [3], or simulation studies to explore protein dynamics. [4] Recent progress in the field of computational structure prediction includes end-2-end deep learning models Alphafold2 [5] and RoseTTAfold [7] that are able to predict highly accurate protein structures from multiple sequence alignments.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets

Stern

Hedelius

Fisher

et al. 2021

Preprint

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The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures, however, it has a prohibitively long inference time for applications that require the folding of hundreds of sequences. The prediction of protein structure annotations, such as amino acid distances, can be achieved at a higher speed with existing tools, such as the ProSPr network. Here, we report on important updates to the ProSPr network, its performance on the recent Critical Assessment of Structure Prediction (CASP14) competition, and an evaluation of its accuracy dependency on multiple sequence alignment depth. We also provide a detailed description of the architecture and the training process, accompanied by reusable code. This work is anticipated to provide a solid foundation for the further development of protein distance prediction tools.

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Using molecular docking and molecular dynamics to investigate protein-ligand interactions

Cited by 87 publications

References 76 publications

Fishing of α-Glucosidase’s Ligands from Aloe vera by α-Glucosidase Functionalized Magnetic Nanoparticles

Fishing of α-Glucosidase’s Ligands from Aloe vera by α-Glucosidase Functionalized Magnetic Nanoparticles

Network Pharmacology and Molecular Docking-Based Prediction of the Mechanism of Qianghuo Shengshi Decoction against Rheumatoid Arthritis

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets

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