Identification of new small molecule allosteric SHP2 inhibitor through pharmacophore-based virtual screening, molecular docking, molecular dynamics simulation studies, synthesis and in vitro evaluation

Abstract

Src homology-2 (SH2) domain-containing phosphatase-2 (SHP2) is the first identified protooncogene and is a promising target for developing small molecule inhibitors as cancer chemotherapeutic agents. Pharmacophore-based virtual screening (PBVS) is a pharmacoinformatics methodology that employs physicochemical knowhow of the chemical space into the dynamic environs of computational technology to extract virtual molecular hits that are precise and promising for a drug target. In the current study, PBVS has been applied on Enamine^TM Advanced Collection of 551,907 molecules by using a pharmacophore model developed upon SHP099 by Molecular Operating Environment (MOE) software to identify potential small molecule allosteric SHP2 inhibitors. Obtained 37 hits were further filtered through DruLiTo software for drug-likeness and PAINS remover which yielded 35 hits. These were subjected to molecular docking studies against the tunnel allosteric site of SHP2 (PDB ID: 5EHR) to screen them according to their binding affinity for the enzyme. Top 5 molecules having highest binding affinity for 5EHR were passed through an ADMET prediction screening and the top 2 hits (ligands 111675 and 546656) with the most favourable ADMET profile were taken for post screening molecular docking and MD simulation studies. From the protein-ligand interaction pattern, conformational stability and energy parameters, ligand 111675 (SHP2 K_i = 0.118 µM) resulted as the most active molecule. Further, the synthesis and in vitro evaluation of the lead compound 111675 unveiled its potent inhibitory activity (IC₅₀ = 0.878 ± 0.008 µM) against SHP2.

Communicated by Ramaswamy H. Sarma

Keywords:

• SHP2
• pharmacophore query
• ligand-based virtual screening
• molecular docking
• molecular dynamics simulation
• synthesis

Acknowledgments

RM and SK are thankful to the Indian Institute of Technology (BHU) Varanasi, for providing a Teaching Assistantship. The support and the resources provided by ‘PARAM Shivay Facility’ under the National Supercomputing Mission, Government of India at the Indian Institute of Technology (BHU), Varanasi and the Central Instrument Facility (CIF), IIT (BHU) and Analytical Laboratory, Department of Chemistry, BHU are gratefully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Ministry of Education (erstwhile MHRD), Government of India, New Delhi (Grant number: STARS 1/583).