Virtual screening and molecular dynamics study of natural products against Rab10 for the treatment of Alzheimer’s disease

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder associated with aging. Various enzymatic targets have been and are still being studied in an attempt to discover new drugs for the treatment of AD; however, Rab GTPases are still relatively unexplored. These enzymes regulate cellular processes by alternating of GDP and GTP nucleotides. In vitro studies have shown that the knockdown of Rab10 reduces the production of Aβ40 and Aβ42 peptides, making it a promising target for the treatment of AD. In order to identify potential Rab10 inhibitors, the structure-based virtual screening (SBVS) was used considering a subset of 80763 natural products obtained from ZINC15 database. Tertiary structure of Rab10 was obtained from the Protein Data Bank and the Autodock Vina program was used in the SBVS to filter potential bioactive substances against this enzyme. The SBVS protocol was validated by redocking the co-crystallized GNP and the binding energies of the GDP and GTP were used as controls in the pharmacodynamic analysis. Thus, it was possible to select 45 compounds with binding energy less or equal −11 kcal.mol⁻¹. ADME/T properties of these compounds were evaluated by the SwissADME program, where it was possible to identify 6 promising molecules. The resulting complexes were subjected to molecular dynamics simulations to analyze the pharmacodynamics over time. The results suggest that the compound ZINC4090657 (derived from quinolizidine) and the compounds ZINC4000106 and ZINC0630250 (derived from coumarin) have favorable pharmacological characteristics for the inhibition of Rab10, with ZINC4090657 being the most promising one.

Communicated by Ramaswamy H. Sarma

Keywords:

• Small GTPases
• in silico
• dementia
• drug discovery
• bioinformatics

Acknowledgements

The authors acknowledge the Superintendence of Technology and Information at USP for the High-Performance Computing (HPC) Computing Resources and Ricardo O. S. Soares for the ‘readHBmap’ python script.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work has been supported by Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES) – Financing Code 001, and the National Council for Scientific and Technological Development – Brazil (CNPq), process #141224/2017-7.