Mechanism of Vitamin D Receptor Ligand-Binding Domain Regulation Studied by gREST Simulations

Tremendous Fidelity of Vitamin D3 in Age-related Neurological Disorders

Vitamin D3 (VD) is a secosteroid hormone and shows a pleiotropic effect in brain-related disorders where it regulates redox imbalance, inflammation, apoptosis, energy production, and growth factor synthesis. Vitamin D3's active metabolic form, 1,25-dihydroxy Vitamin D3 (1,25(OH)2D3 or calcitriol), is a known regulator of several genes involved in neuroplasticity, neuroprotection, neurotropism, and neuroinflammation. Multiple studies suggest that VD deficiency can be proposed as a risk factor for the development of several age-related neurological disorders. The evidence for low serum levels of 25-hydroxy Vitamin D3 (25(OH)D3 or calcidiol), the major circulating form of VD, is associated with an increased risk of Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), dementia, and cognitive impairment. Despite decades of evidence on low VD association with neurological disorders, the precise molecular mechanism behind its beneficial effect remains controversial. Here, we will be delving into the neurobiological importance of VD and discuss its benefits in different neuropsychiatric disorders. The focus will be on AD, PD, and HD as they share some common clinical, pathological, and epidemiological features. The central focus will be on the different attributes of VD in the aspect of its anti-oxidative, anti-inflammatory, anti-apoptotic, anti-cholinesterase activity, and psychotropic effect in different neurodegenerative diseases.

Deciphering the importance of MD descriptors in designing Vitamin D Receptor agonists and antagonists using machine learning

The Vitamin D Receptor (VDR) ligand-binding domain undergoes conformation change upon the binding of VDR agonists/antagonists. Helix 12 ((H)12) is one of the important helices at VDR ligand binding and its conformational changes are controlled by the binding of agonists and antagonists molecules. Various molecular modeling studies are available to explain the agonistic and antagonistic activity of vitamin D analogs. In this work, for the first time, we attempted to generate a machine learning model with fingerprints, 2D, 3D and MD descriptors that are specific to Vitamin D analogs and VDR. Initially, 2D and 3D descriptors and fingerprints of 1003 vitamin D analogs were calculated using CDK and RDKit. The machine learning model was generated using descriptors and fingerprints. Further, 80 Vitamin D analogs (40 VDR agonists + 40 VDR antagonists) were docked in the VDR active site. 50ns MD simulation was performed for each protein-ligand complex. Different MD descriptors such as Solvent Accessible Surface Area (SASA), radius of gyration, PC1 and PC2 were calculated and considered along with CDK and RDKit descriptors as features for machine learning calculations. A few other descriptors that are related to VDR conformational changes such as conformation of the (H)12, the angle at kink were considered for machine learning model generation. It was observed that the descriptors calculated from VDR conformational changes i) were able to distinguish between agonists and antagonists ii) provide key and comprehensive information about the unique binding characteristics of agonists and antagonists iii) provide a strong basis for the machine learning model generation. Overall, this study attempts the utilization of descriptors that are specific to a protein conformation will be helpful for the generation of an efficient machine learning model.