Broadening the scope of WEE1 inhibitors: identifying novel drug candidates via computational approaches and drug repurposing

Abstract

The protein kinase Wee1 plays a vital role in the G2/M cell cycle checkpoint activation, triggered by double-stranded DNA disruptions. It fulfills this task by phosphorylating and consequently deactivating the cyclin B linked to Cdk1/Cdc2 at the Tyr15 residue, leading to a G2 cell cycle halt and subsequent delay of mitosis post DNA damage. Despite advancements, only the Wee1 inhibitor MK1775 has made it to Phase II clinical trials, presenting a challenge in innovative chemical structure development for small molecule discovery. To navigate this challenge, we employed an e-pharmacophore model of the MK1775-WEE1 complex (PDB ID: 5V5Y), using in silico screening of FDA-approved drugs. We chose six drugs for analog creation, guided by docking scores, key residue interactions, and ligand occupancy. Utilizing the ‘DrugSpaceX’ database, we generated 2,776 analogues via expert-defined transformations. Our findings identified DE90612 as the top-ranked analogue, followed by DE363106, DE489678, DE395383, DE90548, DE689343, DE395019, and DE538066. These analogues introduced unique structures not found in other databases. A t-SNE structurally diversified distribution map unveiled promising transformations linked to Temozolomide for WEE1 inhibitor development. Simulations of the WEE1-DE90612 complex (a Temozolomide analogue) for 200 nanoseconds demonstrated stability, with DE90612 forging robust bonds with active site residues and sustaining vital contacts at ASN376 and CYS379. These results underscore DE90612's potential inhibitory properties at the WEE1 binding site, warranting additional in vitro and in vivo exploration for its anticancer activity. Our approach outlines a promising pathway for creating diverse WEE1 inhibitors with suitable biological properties for potential oncology therapeutics.

Communicated by Ramaswamy H. Sarma

Keywords:

• WEE1 kinase
• small molecule inhibitors
• e-pharmacophore model
• t-SNE distribution map
• analogue generation
• MD simulation

Acknowledgments

Authors thank Sri Ramachandra Institute of Higher Education and Research (Deemed to be University) for providing the infrastructure facility. We also thank PSGCP and R. C. Patel Institute for Schrodinger and simulation support, respectively.

Authors’ contributions

Jaikanth Chandrasekaran: conceptualization, investigation, methodology, visualization, writing manuscript. Yogeetha Sivakumaresan, Keerthika Shankar, Melphiya Dickson, Shruthi Laya Saravana Kumar, Lalitha Ramanathan: investigation, visualization, writing manuscript. Iqrar Ahmad, Harun Patel: performed MD simulation.

Disclosure statement

Authors declare no competing interest in any form.

Data availability

The data that support the findings of this study are available in the Supporting Information Material of this paper.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.