Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities

Yasuda, Ikki; Endo, Katsuhiro; Yamamoto, Eiji; Hirano, Yoshinori; Yasuoka, Kenji

doi:10.1038/s42003-022-03416-7

Cited by 16 publications

(23 citation statements)

References 63 publications

(96 reference statements)

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“…Future studies with other proteins such as PDE4 should be interesting. A novel method for predicting ligand–protein binding affinity from conformational fluctuations of BRD4 protein by machine learning is reported [ 36 ]. It is not the magnitude of the RMSF that is important, but the residue where variation occurs.…”

Section: Discussionmentioning

confidence: 99%

Molecular Dynamics Simulation of the Complex of PDE5 and Evodiamine

Kobayashi

Nakajima

Noguchi

et al. 2023

Life

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Alzheimer’s disease is an irreversible neurological disorder for which there are no effective small molecule therapeutics. A phosphodiesterase 5 (PDE5) inhibitor is a candidate medicine for the treatment of Alzheimer’s disease. Rutaecarpine, an indole alkaloid found in Euodiae Fructus, has inhibitory activity for PDE5. Euodiae Fructus contains more evodiamine than rutaecarpine. Therefore, we performed molecular dynamics simulations of the complex of PDE5 and evodiamine. The results showed that the PDE5 and (−)-evodiamine complexes were placed inside the reaction center compared to the case of PDE5 and (+)-evodiamine complex. The binding of (−)-evodiamine to PDE5 increased the root-mean-square deviation and radius of gyration of PDE5. In the PDE5 with (−)-evodiamine complex, the value of the root-mean-square fluctuation of the M-loop, which is thought to be important for activity, increased. This result suggests that (−)-evodiamine may have inhibitory activity.

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Section: Discussionmentioning

confidence: 99%

Molecular Dynamics Simulation of the Complex of PDE5 and Evodiamine

Kobayashi

Nakajima

Noguchi

et al. 2023

Life

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“…An ML-assisted analysis developed by Endo co-workers , was applied to the NEMD simulation data (Figure ). Because the size of the MD data is too large to be suitable for deep neural networks (DNNs), a local dynamics ensemble (LDE) was used to represent molecular movements.…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, a principal component (PC) analysis was applied to remove the translation and rotation of the coordinate system, i.e. to find the axes of PC1 and PC2, that show the largest variances . This procedure reduces the k -dimensional vectors into two-dimensional vectors.…”

Section: Methodsmentioning

confidence: 99%

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Combining Molecular Dynamics and Machine Learning to Analyze Shear Thinning for Alkane and Globular Lubricants in the Low Shear Regime

Yasuda

Kobayashi

Endo

et al. 2023

ACS Appl. Mater. Interfaces

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Lubricants with desirable frictional properties are important in achieving an energy-saving society. Lubricants at the interfaces of mechanical components are confined under high shear rates and pressures and behave quite differently from the bulk material. Computational approaches such as nonequilibrium molecular dynamics (NEMD) simulations have been performed to probe the molecular behavior of lubricants. However, the lowshear-velocity regions of the materials have rarely been simulated owing to the expensive calculations necessary to do so, and the molecular dynamics under shear velocities comparable with that in the experiments are not clearly understood. In this study, we performed NEMD simulations of extremely confined lubricants, i.e., two molecular layers for four types of lubricants confined in mica walls, under shear velocities from 0.001 to 1 m/s. While we confirmed shear thinning, the velocity profiles could not show the flow behavior when the shear velocity was much slower than thermal fluctuations. Therefore, we used an unsupervised machine learning approach to detect molecular movements that contribute to shear thinning. First, we extracted the simple features of molecular movements from large amounts of MD data, which were found to correlate with the effective viscosity. Subsequently, the extracted features were interpreted by examining the trajectories contributing to these features. The magnitude of diffusion corresponded to the viscosity, and the location of slips that varied depending on the spherical and chain lubricants was irrelevant. Finally, we attempted to apply a modified Stokes−Einstein relation at equilibrium to the nonequilibrium and confined systems. While systems with low shear rates obeyed the relation sufficiently, large deviations were observed under large shear rates.

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“…A lower free binding energy suggests a more e cient protein-ligand relationship and a higher a nity between protein and ligand. [33] Interactions between ligands and Bax proteins have been explored to test their binding a nity using Auto dock Vina PyRx docking tools. The interaction between the two docking complexes was contrasted with each other based on their binding strength, which in turn depends on the interaction of hydrogen bonding and other bonding between proteins and ligands.…”

Section: Anticancer Activities Of Schiff Base and Mn(ii) Complexmentioning

confidence: 99%

Eco-friendly Synthesis of Schiff base Mn(II) and Cu(II) Complexes: Antimicrobial, Antifungal, Molecular Docking and Anticancer Studies

Veeravel¹,

Rajasekar²,

Chakkaravarthy

et al. 2023

Preprint

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The Schiff base ligand, (E-N-((E)-3-phenylallylidene) aniline was prepared by from cinnamaldehyde with aniline via green route synthetic method. Reactions of the ligand with Mn(II) and Cu(II) precursors generate new complexes. The structures of the ligand and the new metal complexes were elucidated using various physicochemical measurements (FT-IR, NMR, Mass, UV-Vis, and elemental analyses). Further, the in vitro antibacterial and antifungal studies divulge that the Cu(II) complex exhibited potent activity over the ligand and Mn complex. In contrast the new manganese complex outperformed the nickel complex as well as the ligand in the cleavage of PUC57 DNA by the involvement of free radicals. Furthermore the in vitro cytotoxicity of the compounds against oral cancer cell line has been examined. The molecular docking studies revealed that the Mn(II) complex interact with SER-15 through hydrogen bonding and it is closely associated with Bax protein with three hydrogen bond interactions through the amino acids residues GLN-32 and ASP-33. Ultimately, the most frequently used parameters for the prediction of drug-likeness were calculated for the new Schiff base compounds.

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Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities

Cited by 16 publications

References 63 publications

Molecular Dynamics Simulation of the Complex of PDE5 and Evodiamine

Molecular Dynamics Simulation of the Complex of PDE5 and Evodiamine

Combining Molecular Dynamics and Machine Learning to Analyze Shear Thinning for Alkane and Globular Lubricants in the Low Shear Regime

Eco-friendly Synthesis of Schiff base Mn(II) and Cu(II) Complexes: Antimicrobial, Antifungal, Molecular Docking and Anticancer Studies

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