Identification of 3D motifs based on sequences and structures for binding to CFI-400945, and deep screening-based
Maaged Abdullah1, Lalitha Guruprasad1
1School of Chemistry, University of Hyderabad, Hyderabad, India.
Abstract:
PLK-4 kinase plays an essential role in the cell cycle from regulating centriole duplication till cytokinesis and is therefore an attractive drug target in cancers such as breast, lung, and central nervous system tumors. CFI-400945 is an efficient PLK-4 inhibitor and inhibits other non-PLK family proteins at nanomolar concentrations. We have compared PLK-4 with other kinases to understand its similarity based on multiple sequence alignments from protein sequences of primary structures, outer and buried residues, and compact active site conservation based on three-dimensional motifs. These in-depth studies provide information on known interface targets and design of more selective inhibitors to PLK-4. Further, pharmacophore features based on CFI-400945 bound to PLK-4 were used for searching library of compounds that were screened using deep learning methods to bind PLK-4. The shortlisted molecules were docked into PLK-4 active site and were validated using molecular docking and molecular dynamics simulations studies. MM-PBSA calculations revealed the stability of hit molecules and PLK-4 complexes in comparison with CFI-400945 and the contribution to binding from key active site residues.
Insights
Polo-like kinase 4 (PLK-4) is a cancer drug target. Researchers compared PLK-4 to other kinases and used deep learning to discover new, selective PLK-4 inhibitors for cancer therapy.
Area of Science:
- Biochemistry
- Molecular Biology
- Drug Discovery
Background:
- Polo-like kinase 4 (PLK-4) is crucial for cell cycle regulation, including centriole duplication and cytokinesis.
- PLK-4 is a promising drug target for various cancers, such as breast, lung, and central nervous system tumors.
- CFI-400945 is a known PLK-4 inhibitor with potential off-target effects.
Purpose of the Study:
- To compare PLK-4 with other kinases to identify structural similarities and differences.
- To design more selective PLK-4 inhibitors.
- To discover novel compounds that bind to PLK-4 using computational methods.
Main Methods:
- Multiple sequence alignments of protein sequences and 3D structural motifs analysis.
- Pharmacophore modeling based on CFI-400945-PLK-4 complex.
- Deep learning for compound screening, molecular docking, and molecular dynamics simulations.
- Molecular mechanics with the Poisson-Boltzmann and surface area (MM-PBSA) calculations.
Main Results:
- Structural comparisons revealed conserved active site features in PLK-4.
- Deep learning and docking identified novel potential PLK-4 inhibitors.
- Molecular dynamics and MM-PBSA confirmed the stability and binding interactions of hit compounds.
Conclusions:
- Structural insights aid in designing selective PLK-4 inhibitors.
- Computational approaches successfully identified promising drug candidates for PLK-4.
- These findings contribute to the development of targeted cancer therapies.
