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Fukunishi, Yoshifumi; Sugihara, Yuusuke; Mikami, Yoshiaki; Sakai, Kohta; Kusudo, Hiroshi; Nakamura, Haruki

doi:10.5571/synth.2.60

Cited by 9 publications

(10 citation statements)

References 10 publications

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“…Docking optimization and validation was performed by redocking of the cocrystallized conformation of cognate ligand into the PI3K binding site. Consequently, RMSD (root mean square deviation) of the best‐docked conformation of the native ligand from the experimental one was used for validation of the docking study . The best‐docked and experimental conformation of wortmannin correlated quite well with an RMSD of 1.73Ǻ and binding free energy of −8.96 Kcal/mol (Table ).…”

Section: Resultsmentioning

confidence: 99%

Discovery of neurotrophic agents based on hydroxycinnamic acid scaffold

et al. 2016

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The number of people affected by neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease is rapidly increasing owing to the global increase in life expectancy. Small molecules with neurotrophic effects have great potential for management of these neurological disorders. In this study, different (C1-C12) alkyl ester derivatives of hydroxycinnamic acids (HCAs) were synthesized (a total of 30 compounds). The neurotrophic capacity of the test compounds was examined by measuring promotion of survival in serum-deprived conditions and enhancement of nerve growth factor (NGF)-induced neurite outgrowth in PC12 neuronal cells. p-Coumaric, ferulic, and sinapic acids and their esters did not alter cell survival, while caffeic acid and all its alkyl esters, especially decyl and dodecyl caffeate, significantly promoted neuronal survival at 25 μm. Methyl, ethyl, propyl, and butyl caffeate esters also significantly enhanced NGF-induced neurite outgrowth, among which the most effective ones were propyl and butyl esters, which at 5 μm led to 25- and 22-fold increases in the number of neurites, respectively. The findings of the docking study suggested phosphatidylinositol 3-kinase (PI3K) as the potential molecular target. In conclusion, our findings demonstrate that alkyl esters of caffeic acid can be useful as scaffolds for the discovery of therapeutic agents for neurodegenerative diseases.

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Section: Resultsmentioning

confidence: 99%

Discovery of neurotrophic agents based on hydroxycinnamic acid scaffold

et al. 2016

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“…We studied MMP2, MMP3, MMP7, MMP9, and MMP13 by the docking‐score QSAR method and the naïve protein−compound docking simulations. We applied the naïve protein−ligand docking calculation by Sievgene . Figure A shows the correlation between the experimental and the calculated protein−compound binding free energies of MMP2 by Sievgene.…”

Section: Resultsmentioning

confidence: 99%

“…To calculate the protein−compound docking scores s i b , we used our own program, Sievgene,, a protein−ligand flexible docking program for in silico drug screening. Sievgene is a part of the myPresto system, which is available online (http://presto.protein.osaka‐u.ac.jp/myPresto4/) and is free for academic use.…”

Section: Methodsmentioning

confidence: 99%

“…The protein−compound docking scores s i b were calculated by the protein−compound docking program Sievgene . This ligand‐flexible program reconstructed about 50% of the receptor−compound complexes in PDB (132 in total) with an accuracy of less than 2 Å root mean square deviation (RMSD) in a self‐docking test . The computational setup in the present study was exactly the same as that in the previous study.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Protein−compound Binding Energies from Known Activity Data: Docking‐score‐based Method and its Applications

Fukunishi

Yamashita²,

Mäshimo

et al. 2018

Molecular Informatics

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We used protein−compound docking simulations to develop a structure‐based quantitative structure−activity relationship (QSAR) model. The prediction model used docking scores as descriptors. The binding free energy was approximated by a weighted average of docking scores for multiple proteins. This approximation was based on a pharmacophore model of receptor pockets and compounds. The weights of the docking scores were restricted to small values to avoid unrealistic weights by a regularization term. Additional outlier elimination improved the results. We applied this method to two groups of targets. The first target was the kinase family. The cross‐validation results of 107 kinase proteins showed that the RMSE of predicted binding free energies was 1.1 kcal/mol. The second target was the matrix metalloproteinase (MMP) family, which has been difficult for docking programs. MMPs require metal‐binding groups in their inhibitor structures in many cases. A quantum effect contributes to the metal−ligand interaction. Despite this difficulty, the present method worked well for the MMPs. This method showed that the RMSE of predicted binding free energies was 1.1 kcal/mol. In comparison, with the original docking method the RMSE was 1.7 kcal/mol. The results suggest that the present QSAR model should be applied to general target proteins.

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“…In the drug development process, accurate prediction of the binding configuration between a target protein and a drug candidate is essential for optimizing the molecular structure of promising lead compounds. Protein‐ligand docking based on molecular mechanics (MM) such as DOCK, Auto Dock, rDock, GOLD, and myPresto/sievgene, which perform shape fitting between a rigid body protein and a flexible compound, are widely used for virtual drug screening and have often contributed to the discovery of weak protein binders . However, this type of simulation approach has several issues.…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Protein‐Ligand Binding Mode Prediction Using a Molecular Dynamics‐Based Pocket Generation Approach

Araki

Iwata

et al. 2018

J Comput Chem

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Protein‐drug binding mode prediction from the apo‐protein structure is challenging because drug binding often induces significant protein conformational changes. Here, the authors report a computational workflow that incorporates a novel pocket generation method. First, the closed protein pocket is expanded by repeatedly filling virtual atoms during molecular dynamics (MD) simulations. Second, after ligand docking toward the prepared pocket structures, binding mode candidates are ranked by MD/Molecular Mechanics Poisson‐Boltzmann Surface Area. The authors validated our workflow using CDK2 kinase, which has an especially‐closed ATP‐binding pocket in the apo‐form, and several inhibitors. The crystallographic pose coincided with the top‐ranked docking pose for 59% (34/58) of the compounds and was within the top five‐ranked ones for 88% (51/58), while those estimated by a conventional prediction protocol were 9% (5/58) and 50% (29/58), respectively. Our study demonstrates that the prediction accuracy is significantly improved by preceding pocket expansion, leading to generation of conformationally‐diverse binding mode candidates. © 2018 Wiley Periodicals, Inc.

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Cited by 9 publications

References 10 publications

Discovery of neurotrophic agents based on hydroxycinnamic acid scaffold

Discovery of neurotrophic agents based on hydroxycinnamic acid scaffold

Prediction of Protein−compound Binding Energies from Known Activity Data: Docking‐score‐based Method and its Applications

Improving the Accuracy of Protein‐Ligand Binding Mode Prediction Using a Molecular Dynamics‐Based Pocket Generation Approach

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