Related Experiment Video
Updated: Sep 17, 2025
In Vitro Assay for Studying the Aggregation of Tau Protein and Drug Screening
Published on: November 20, 2018
A Multiscale Computational Study for the Identification of Novel Inhibitors Targeting Tau-Tubulin Kinase 1 (TTBK1) in
Prashant Kurkute1, Sumit Sonawane2, Kumar Pratyush2
1Shobhaben Pratapbhai Patel School of Pharmacy & Technology Management, SVKM's Narsee Monjee Institute of Management Studies, Mumbai, MH, India.
Introduction:
Excessive phosphorylation of tau protein by the tau-tubulin kinase 1 (TTBK1) enzyme is implicated in the pathogenesis of several neurodegenerative diseases. Based on a comprehensive literature review and availability of the co-crystal structure of TTBK1 in complex inhibitor (pdb id 4BTK), we designed a multiscale computational approach to identify novel hits from the ZINC13 chemical library.
Methods:
The High-Throughput Virtual Screening (HTVS) of the ZINC13 database (containing 13,195,609 molecules) was carried out against TTBK1 protein (PDB id 4BTK). Top-scoring molecules and reference molecules were further subjected to MD simulations, PCA analysis, DCCM assay, binding free energies calculations, and in-silico ADME calculations.
Results:
From a preliminary HTVS study, six molecules were identified based on their docking scores: ZINC37289024, ZINC89755080, ZINC20993115, ZINC72445968, ZINC28247630, and ZINC16638515, with the docking score of -10.186, -09.229, -09.045, -09.021, -08.920 and -08.821, respectively. In subsequent MD simulations studies, the protein backbone RMSD values were observed to be 1.978, 1.8178, 2.2309, 1.7933, 1.8837, 1.9461, and 1.8711 Å, respectively. Similarly, the protein backbone RMSF values were 0.9511, 1.0172, 1.2023, 1.0591, 1.0029, 1.9755, and 0.9200 Å, respectively. PCA, DCCM, and MMGBSA analysis indicated that these complexes were quite stable throughout the 100 ns MD simulations. In-silico ADME predictions of identified top six hits suggested that these top six hits possess favorable drug-like properties, supporting their potential as the lead candidates for therapeutic development.
Conclusion:
A multiscale molecular modelling approach was employed, and six top-scoring hits were identified as promising TTBK1 inhibitors. Analysis of the in-silico data suggested that ZINC37289024 would be the most promising clinical candidate for AD. However, further in-vitro and in-vivo experimental data would be needed for validation of these results.
