In-Silico Prediction of Zingerone as A Potential Therapeutic Agent for Autistic Symptoms Via the CA2+ Signaling Pathway: A Molecular Docking and Molecular Simulation Approach

Autism Spectrum Disorder (ASD) is a heterogeneous neuro-developmental disorder with symptoms that predominantly onset between 12 to 24 months of age. The peculiarity of ASD is known from an early developmental window, showing persistent difficulties in social interaction and communication, stereotypical, restricted and repetitive behavioural patterns, and varying degrees of intellectual disability^[1]. The disorder is accompanied by other prominent comorbidities that tend to cluster into different subgroups that manifest as impairments outside the core characteristics of ASD^[2]. The overlapping tendencies of the core symptoms and the comorbidities explain the heterogeneity aspects of ASD.

Several proteins and genes are considered high risk in autism. Dysfunction in proteins associated with the intracellular calcium (Ca²⁺) signalling, such as parvalbumin (PVALB), a cytosolic Ca²⁺ buffering protein, resulted in enlarged cell body and mitochondrial size primarily in regions rich in PVALB⁺ neurons^[3]. Pvalb^–/– mice also exhibited three ASD-related core behavioural phenotypes, anomalous social reciprocity, communication impediment, and recurrent and stereotyped behaviour^[4]. An E183V mutation of the α subunit of calmodulin kinase II (CaMKIIα) resulted in the dysregulation of dendritic morphology and synaptic transmission, ultimately causing robust ASD-like behavioural phenotypes in mice^[5]. Additionally, altered expression of calmodulin kinase IV (CAMKIV) and dysfunction in its surface activators, such as CaV1.2, mGluR1, and mGluR5, are shown to be associated with autism^[6]. Aberrant cognitive function and its associated behavioural abnormality were also associated with decreased brain-derived neurotrophic factor (BDNF) expression in autistic patients^[7]. The hypothesis on the involvement of intracellular calcium signalling in autistic individuals and in pre-clinical ASD models is relatively new, with fewer studies.

Zingerone (ZNO) is a phenolic compound derived from 6-gingerol by reverse aldolization reaction found in thermally labile ginger. Drying, roasting or cooking ginger has been shown to increase the amount of ZNO extensively^{[8, 9]}. ZNO has been comprehensively studied for its anti-oxidant^[10-13], anti-inflammatory^[10,11,13], and anti-apoptotic properties^[13-16]. Such a widely appreciated molecule with diverse pharmaceutical properties had yet to be studied for its possible effects against ASD-associated proteins. Based on the above-mentioned considerations, we found it plausible to critically investigate the adsorption, distribution, metabolism, excretion (ADMET) and bioavailability of ZNO. Further, to understand the binding ability of ZNO with ASD related pathway through molecular docking analysis. For the first time, we report the binding ability of ZNO towards ASD related proteins and further validate the complex through molecular dynamics analysis.

MATERIALS AND METHODS

Swiss ADME analysis

SwissADME is a computer web-based tool that assesses the absorption, distribution, metabolism and excretion (ADME) of a potential therapeutic drug based on its in-house prolific methods such as the BOILED-Egg^[17], iLOGP^[18], and bioavailability radar^[19]. Briefly, the SMILES entry of ZNO^[20] was copied and pasted in the input box of SwissADME, and ‘run’ was clicked. The results obtained were then retrieved and interpreted^[19].

LogBBB analysis

LogBB_Pred is a machine learning web-based tool used to predict the blood–brain barrier (BBB) permeability (logBB) value^[21]. The SMILES entry of ZNO retrieved from PubChem was directly copied and pasted into the ‘INPUT SMILES’ entry and submitted. A compound predicated to have a logBB value less than or equal to -1 is considered ‘BBB permeable’.

Molecular Docking analysis

Molecular docking and binding site analysis were conducted using Autodock vina 1.1.2^[22]. Briefly, the 3D structure of ZNO was retrieved from PubChem. The crystal structures of the proteins were downloaded from AlphaFold Protein Structure Database^[23,24] based on their UniProt IDs (Table 1).

Molecular dynamics (MD) simulations

MD simulations were carried out using GROMACS^[30], one of the most widely used tools in simulation studies for assessing the relative firmness and structural stability of biomolecules and their complexes^[31]. The AMBER ff99SB-ILDN force field was employed to generate the protein topology^[32], while the ligand topology was created using ACPYPE^[33]. The two topologies were combined to form the protein-ligand complex, which was used for further MD simulations. A dodecahedron periodic boundary condition was applied with a box dimension of 1.0nm^[30]. The simulation box was solvated using the TIP3P water model^[34]. To neutralise the system, 0.15 M NaCl was added. Energy minimisation (EM) was performed using the steepest descent algorithm^[30], followed by equilibration for 1ns each under NVT conditions (constant no.of particles, volume, and temperature at 300 K) and NPT conditions (constant no. of particles, pressure at 1 bar, and temperature)^[30]. All simulation steps (EM, NVT, and NPT) utilised Molecular Dynamics Parameter files adapted from the protein-ligand MD simulation tutorial by Lemkul, 2024^[35]. The final production MD simulation was run for 100ns to analyse the stability of the protein-ligand complex. The root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessibility surface area (SASA), hydrogen bond (H-bond), and molecular mechanics/Poisson-Boltzmann surface area analysis (MMPBSA) were analysed after MD using the previously described literature^[36].

RESULTS

In-silico SwissADME prediction of ZNO and BBB permeability analysis

SwissADME was utilised to understand the ADMET profile of ZNO as a potential drug candidate (Figure 1).  The molecular weight of ZNO was 194.23g/mol, which lay in the optimal range of the Lipinski rule of five (MW < 500Da). The ‘fraction Csp3’ refers to the proportion of carbon atoms in a molecule that are sp³ hybridised. A fractional Csp3 value is expected to be at least 0.25. ZNO was reported to have a fractional Csp3 value of 0.36, considerably higher than the 0.25 suggested value. Molecular polar surface area (PSA) is a descriptor shown to correlate well with passive molecular transport through membranes, and therefore, allows prediction of transport properties of drugs. ZNO was reported to have a TPSA of 46.53Å, indicative of having optimal polar properties.

Figure 1: SwissADME analysis of ZNO

Figure 2: Saturation graph of ZNO in SwissADME

The predictions for passive human gastrointestinal absorption (HIA) and BBB permeation can both be assessed based on the Boiled egg model (Figure 3). The model is based on the drug acting as a substrate or a non-substrate of permeability glycoproteins (PGP). One of the major roles of PGP is to protect the brain from xenobiotics. From the boiled egg graphics, it can be visually noted that ZNO was predicted to be present inside the yolk (yellow), and indicated as PGP-. This could mean that ZNO is a non-substrate of PGP, signifying the drug may not be flushed out of the brain membrane rapidly, predicting the possible ability of ZNO to cross the BBB and reach the targeted site without inhibition.

Figure 3: Boil-egg model of ZNO from SwissADME analysis

Drug-likeness was established from structural or physiochemical inspections of compounds advanced enough to be considered oral-drug candidates. The Swiss ADME uses 5 rules for its assessment to consider a compound drug like the Lipinski (Pfizer) filter, the Ghose (Amgen), Veber (GSK), Egan (Pharmacia), and Muegge (Bayer) methods. The Abbot bioavailability score is similar but seeks to predict the probability of a compound to have at least 10% oral bioavailability in rats or measurable Caco-2 permeability. Based on the Abbot bioavailability score, ZNO was predicted to have 55% oral bioavailability.

LogBB_Pred was used to predict the BBB permeability (logBB value) of ZNO. Generally, compounds with a predicted logBB over the cutoff of -1.0 are categorised as BBB-permeable. ZNO was expected to have a LogBB value of -0.16203 and was considered BBB permeable (Figure 4).

Figure 4: Log BB score of ZNO, indicating BBB permeability

                 The ligand ZNO was docked with Ca²⁺ signalling protein targets associated with ASD. The binding affinity of all ligand-protein complexes was expressed as kcal/mol (Table 2).

Table 2: Binding energies of the ZNO-protein complexes

The ligand-protein complex of ZNO-BDNF (Figure 5) had a binding energy score of -5.3kcal/mol. The complex was stabilised by ASN (A: 189), forming a carbon-hydrogen bond, a form of weak interaction, and further stabilised by pi-pi stacking between the aromatic rings of ZNO and TYR (A: 193).

Figure 5: a) represents the 2D image of ZNO-BDNF complex visualised using Biovia Discovery Studio, version 2025, and b) represents the 3D image of ZNO-BDNF complex visualised using PyMol, 3.1

Figure 6: a) represents the 2D image of ZNO-PRVA complex visualised using Biovia Discovery Studio, version 2025, and b) represents the 3D image of ZNO-PRVA complex visualised using PyMol, 3.1

Figure 7: a) represents 2D image of the ZNO-PP2BA complex visualised using Biovia Discovery Studio, version 2025, and B) represents the 3D image of ZNO-PP2BA complex visualised using PyMol, 3.1

Figure 8: a) represents the 2D image of the ZNO-CALM1 complex visualised using Biovia Discovery Studio, version 2025, and b) represents the 3D image of the ZNO-CALM1 complex visualised using PyMol, 3.1

Figure 9: a) represents the 2D image of the ZNO-KCC2A complex visualised using Biovia Discovery Studio, version 2025 and B) represents the 3D image of the ZNO-KCC2A complex visualised using PyMol, 3.1

Figure 10: a) represents the 2D image of ZNO-KCC4 complex visualised using Biovia Discovery Studio, version 2025, and b) represents the 3D image of the ZNO-KCC4 complex visualised using PyMol, 3.1

Figure 11: a) represents the 2D image of the ZNO-CAC1C complex visualised using Biovia Discovery Studio, version 2025, and B) represents the 3D image of the ZNO-CAC1C complex visualised using PyMol, 3.1

Figure 12: a) represents the 2D image of the ZNO-CALB1 complex visualised using Biovia Discovery Studio, version 2025, and B) represents the 3D image of the ZNO-CALB1 complex visualised using PyMol, 3.1