Chemoinformatics and machine learning techniques to identify novel inhibitors of the lemur tyrosine kinase-3 receptor involved in breast cancer

TL;DR

This study uses chemoinformatics and machine learning to find new inhibitors for the LMTK3 receptor, which may help in treating breast cancer.

Contribution

The study introduces novel inhibitors for LMTK3 and provides insights into their binding mechanisms through molecular simulations.

Findings

Top three compounds showed best binding affinities during docking simulations.

Protein dynamics analysis revealed structural changes in complexes with novel inhibitors.

Binding free energy analysis indicated higher stability in the top1 complex compared to the control.

Abstract

Breast cancer is still the largest cause of cancer death in women, and around 70% of primary breast cancer patients are estrogen receptor (ER)-positive, which is the most frequent kind of breast cancer. The lemur tyrosine kinase-3 (LMTK3) receptor has been linked to estrogen responsiveness in breast cancer. However, the function of LMTK3 in reaction to cytotoxic chemotherapy has yet to be studied. Breast cancer therapy research remains tricky due to a paucity of structural investigations on LMTK3. We performed structural investigations on LMTK3 using molecular docking and molecular dynamics (MD) simulations of the LMTK3 receptor in complex with the top three inhibitor molecules along with a control inhibitor. Analysis revealed the top three compounds show the best binding affinities during docking simulations. Interactive analysis of hydrogen bonds inferred hotspot residues Tyr163, Asn138, Asp133, Tyr56, Glu52, Ser132, Asp313, and Asp151. Some other residues in the 5-Å region determined strong alkyl bonds and conventional hydrogen bond linkages. Furthermore, protein dynamics analysis revealed significant modifications among the top complexes and the control system. There was a transition from a loop to a-helix conformation in the protein–top1 complex, and in contrast, in complexes top2 and top3, the formation of a stabilizing sheet in the C chain was observed, which limited significant mobility and increased complex stability. Significant structural alterations were observed in the protein–top complexes, including a shorter helix region and the creation of some loop regions in comparison to the control system. Interestingly, binding free energies, including MMGB/PBSA WaterSwap analysis estimation, reveals that the top1 complex system was more stable than other systems, especially in comparison to the control inhibitor complex system. These results suggest a the plausible mode of action for the novel inhibitors. Therefore, the current investigation contributes to understanding the mechanism of action, serving as a basis for future experimental studies.

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