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In silico study to identify novel NEK7 inhibitors from natural sources by a combination strategy

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Abstract

Cancer poses a significant global health challenge and significantly contributes to mortality. NEK7, related to the NIMA protein kinase family, plays a crucial role in spindle assembly and cell division. The dysregulation of NEK7 is closely linked to the onset and progression of various cancers, especially colon and breast cancer, making it a promising target for cancer therapy. Nevertheless, the shortage of high-quality NEK7 inhibitors highlights the need for new therapeutic strategies. In this study, we utilized a multidisciplinary approach, including virtual screening, molecular docking, pharmacokinetics, molecular dynamics simulations (MDs), and MM/PBSA calculations, to evaluate natural compounds as NEK7 inhibitors comprehensively. Through various docking strategies, we identified three natural compounds: (−)-balanol, digallic acid, and scutellarin. Molecular docking revealed significant interactions at residues such as GLU112 and ALA114, with docking scores of −15.054, −13.059, and −11.547 kcal/mol, respectively, highlighting their potential as NEK7 inhibitors. MDs confirmed the stability of these compounds at the NEK7-binding site. Hydrogen bond analysis during simulations revealed consistent interactions, supporting their strong binding capacity. MM/PBSA analysis identified other crucial amino acids contributing to binding affinity, including ILE20, VAL28, ILE75, LEU93, ALA94, LYS143, PHE148, LEU160, and THR161, crucial for stabilizing the complex. This research demonstrated that these compounds exceeded dabrafenib in binding energy, according to MM/PBSA calculations, underscoring their effectiveness as NEK7 inhibitors. ADME/T predictions showed lower oral toxicity for these compounds, suggesting their potential for further development. This study highlights the promise of these natural compounds as bases for creating more potent derivatives with significant biological activities, paving the way for future experimental validation.

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Abbreviations

ADME/T:

Absorption, distribution, metabolism, excretion, and toxicity

CL:

Clearance

DCCM:

Dynamic cross-correlation map

DFT:

Density functional theory

DSSP:

Definition secondary structure of protein

FEL:

Free energy landscape

FMO:

Frontier molecular orbitals

H-bond:

Hydrogen bond

HOMO:

Highest occupied molecular orbital

LUMO:

Lowest unoccupied molecular orbital

MDCK:

Madin–Darby canine kidney

MDs:

Molecular dynamics simulations

MEP:

Molecular electrostatic potential

MM/PBSA:

Molecular mechanics Poisson–Boltzmann surface area

NEK:

NIMA-related kinase

PCA:

Principal component analysis

PDB:

Protein database

PKC:

Protein kinase C

PPB:

Plasma protein binding

Rg:

Radius of gyration

RMSD:

Root-mean-square deviation

RMSF:

Root-mean-square fluctuation

SBVS:

Structure-based virtual screening

SMILES:

Simplified Molecular Input Line Entry System

T 1/2 :

Half-life

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Authors and Affiliations

  1. State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China

    Heng Zhang, Chenhong Lu, Qilong Yao & Qingcai Jiao

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  1. Heng Zhang
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  2. Chenhong Lu
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  3. Qilong Yao
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  4. Qingcai Jiao
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Contributions

HZ and CL conceptualized the study and designed the research methodology. HZ and QY performed the virtual screening, molecular docking, and ADME/T predictions. CL and QJ handled molecular dynamics (MD) simulations, molecular mechanics Poisson-Boltzmann surface area (MM/PBSA)-based binding free energy calculations, and DFT calculations. HZ and CL analyzed the results and wrote the initial draft of the manuscript. QJ prepared the figures and tables. QY provided critical revisions to the manuscript for important intellectual content. All authors participated in the review, editing process, and approved the final manuscript for publication.

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Correspondence to Qingcai Jiao.

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Zhang, H., Lu, C., Yao, Q. et al. In silico study to identify novel NEK7 inhibitors from natural sources by a combination strategy. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10838-4

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