Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor.
Laura C E ManoliuEliza C MartinAdina L MilacLaurentiu SpiridonPublished in: Molecules (Basel, Switzerland) (2021)
Alzheimer's disease is a neurodegenerative disorder incompatible with normal daily activity, affecting one in nine people. One of its potential targets is the apelin receptor (APJR), a G-protein coupled receptor, which presents considerably high expression levels in the central nervous system. In silico studies of APJR drug-like molecule binding are in small numbers while high throughput screenings (HTS) are already sufficiently many to devise efficient drug design strategies. This presents itself as an opportunity to optimize different steps in future large scale virtual screening endeavours. Here, we ran a first stage docking simulation against a library of 95 known binders and 3829 generated decoys in an effort to improve the rescoring stage. We then analyzed receptor binding site structure and ligands binding poses to describe their interactions. As a result, we devised a simple and straightforward virtual screening Stage II filtering score based on search space extension followed by a geometric estimation of the ligand-binding site fitness. Having this score, we used an ensemble of receptors generated by Hamiltonian Monte Carlo simulation and reported the results. The improvements shown herein prove that our ensemble docking protocol is suited for APJR and can be easily extrapolated to other GPCRs.
Keyphrases
- binding protein
- high throughput
- molecular dynamics
- monte carlo
- physical activity
- molecular dynamics simulations
- randomized controlled trial
- protein protein
- poor prognosis
- convolutional neural network
- current status
- cognitive decline
- dna binding
- emergency department
- adverse drug
- single cell
- machine learning
- virtual reality
- long non coding rna
- cerebrospinal fluid