Docking-Based Screening of Natural Compounds as Inhibitors of ?-Synuclein Aggregation in Parkinson’s Disease: A Comprehensive Review

2.3 Oligomeric Toxins and Neurodegeneration Pathways

The critical yet unresolved issue regarding whether mature fibrils or prefibrillar oligomers act as the major toxic species of α-Syn has far-reaching consequences for developing novel therapeutic strategies(Cascella et al., 2021). Multiple lines of evidence from physical biochemistry, cellular biology, and animal models clearly demonstrate that soluble oligomeric assemblies serve as the principal pathological species(Bengoa-Vergniory et al., 2017. Prefibrillar oligomers (i) create circular membrane pores that destabilize electrochemical gradients and calcium regulation(Ono et al., 2023); (ii) hinder the UPS/ALP pathways and induce proteinopathy and proteostatic impairment(Danzer et al., 2007); (iii) provoke mitochondrial permeabilization and cytochrome c efflux leading to apoptosis(Peng et al., 2020); (iv) stimulate microglial activation of NLRP3 inflammasomes and neuroinflammatory mechanisms in astrocytes(Gibb et al., 2023); and (v) engage in prion-like transmission between interconnected neurons, leading to the Braak anatomical stage pattern(Braak et al., 2003; Brundin & Bhatt, 2018)..

2.4 Potential Drug Targets on α-Synuclein

Using computational pocket analysis, NMR chemical shift mapping, and cryo-EM structures, several binding sites were predicted for α-Syn that may prove useful for pharmaceutical development(Lashuel et al., 2013):

• NAC hydrophobic groove (positions 70-82): The principal region responsible for promoting aggregation with its hydrophobic pocket that can accommodate aromatic and lipophilic molecules. Ligand binding within the groove physically blocks the formation of intermolecular β-sheets, which is the most well-validated therapeutic site(Das et al., 2025; Martins et al., 2023).

• Inter-protofilament interface: Two-protofilament fibril structures obtained via Cryo-EM have an inter-protofilament interface characterized by a groove-like structure, which is stabilized by lysine 80-Glu83 salt bridges and hydrophobic contacts. Inter-protofilament interfaces can be disrupted using small molecules, leading either to fibril core destabilization or formation of non-toxic polymorphs(Guerrero-Ferreira et al., 2018; Li et al., 2018).

• Tyr39 aromatic patch: The exposed aromatic side chain of tyrosine 39 involved in π-stacking interactions between fibril-growing molecules. Polyphenols interacting with Tyr39 hinder the growth of α-Syn aggregates(Martins et al., 2023).

• C-terminal metals binding site: Metal ions such as Cu²? and Fe³? coordinated by His50, Asp121, and Glu123 trigger oxidative protein aggregation. Natural chelators binding the metals site indirectly inhibit protein aggregation(Arena et al., 2021; Camponeschi et al., 2013).

• N-terminal membrane-binding helix (residues 3-37): The region relevant for membrane-induced nucleation, where inhibitors hindering the membrane-induced α-Syn conformational changes hinder surface-catalyzed aggregate formation(Fonseca-Ornelas et al., 2014; Dvinskikh & Sandström, 2011).  

3. Natural Compound Classes as Aggregators of α-Synuclein

3.1 Polyphenols        

3.1.1 Epigallocatechin-3-Gallate (EGCG)

The most abundant catechin of green tea (Camellia sinensis), EGCG (C??H??O??; MW 458.37 Da; LogP 1.21), has been the focus of numerous researches investigating its inhibitory potential against α-Syn aggregation(Ehrnhoefer et al., 2008). The polyphenol structure of EGCG, with a benzopyran ring with attached galloyl group and catechol B-ring, comprises four phenolic rings, eight hydroxyl groups, and an ester bond that allows EGCG to interact through hydrogen bonds, π-π interactions, and redox properties. Structurally, EGCG binds to partially folded monomers and early oligomers of α-Synuclein, leading the aggregation process away from amyloid fibrils to non-toxic off-pathway aggregates(Ehrnhoefer et al., 2008; Bieschke et al., 2010).

Autodock Vina docking on PDB 6SST revealed that EGCG binds in the hydrophobic groove of the NAC region, with binding energy ranging from -8.4 to -10.2 kcal/mol, and MM-GBSA scoring from -40 to -55 kcal/mol. Important interactions observed included: several hydrogen bond interactions between the galloyl hydroxyl group and backbone NH of Val70, Thr72, and Ala76; π-π stacking interaction between the catechin B-ring and aliphatic side chain residues of Val74/Val76; and van der Waals interactions holding the galloyl residue to the hydrophobic spine of the β-sheet(Lorenzen et al., 2014). The major limitation for EGCG in clinical practice is its inability to cross the blood-brain barrier due to high TPSA (197 Ų) and molecular weight (>450 Da), requiring formulation techniques such as PLGA nanoparticles, liposome encapsulation, and prodrugs(Qing et al., 2009)..

3.1.2 Curcumin

Curcumin [(1E,6E)-1,7-bis(4-hydroxy-3-methoxyphenyl)hepta-1,6-diene-3,5-dione; M.W. 368.38 Da; logP 3.29], the main bioactive component in turmeric (Curcuma longa), exerts a direct inhibitory effect on α-Syn aggregation via several complementary pathways (Pandey et al., 2008).The extended π-electron system, which covers two methoxyphenol rings connected by a β-diketone group, facilitates the intercalation of curcumin into the hydrophobic NAC pocket and π-stacking with Tyr39 and Phe94(Ahmad & Lapidus, 2012).. According to docking studies, binding energies range from -8.0 to -9.5 kcal/mol, with several hydrogen bonds being formed between phenolic hydroxyl groups and the backbone of Thr75-Val82. Chelation of Cu²? and Fe³? ions at the C-terminal metal binding site by the β-diketone group is another indirect pathway of anti-aggregation activity(Pandey et al., 2008). Although curcumin exhibits excellent anti-aggregation activity in vitro (IC?? ~1-5 µM in thioflavin T tests), its low aqueous solubility (<0.1 mg/mL) and high Phase II gluc(Wang et al., 2024)..

3.1.3 Baicalein

Baicalein (5,6,7-trihydroxyflavone; MW 270.24 Da; LogP 2.06), a flavone aglycone isolated from the root of S. baicalensis Georgi, is one of the strongest and best characterized natural inhibitors of α-Syn fibril formation(Zhu et al., 2004).. Baicalein, which features a flat flavone scaffold with three hydroxyl substituents on C5, C6, and C7, forms a strong hydrogen-bonding network with Thr75, Val76, Thr81, and Ser87 within the NAC domain. The flat bicyclic ring system ensures deep insertion into the hydrophobic groove of the β-sheets. Binding energies calculated via docking analysis span values between −8.6 and −10.8 kcal/mol, whereas MM-GBSA predicted free binding energy values exceed −48 kcal/mol. What makes Baicalein unique is that, in low stoichiometry, it preferentially binds granular rather than amyloid fibrillar α-Syn oligomers and breaks down existing amyloid fibrils (demonstrated by AFM, TEM, and ThT experiments) (Zhu et al., 2004; Masuda et al., 2006). Baicalein is a CNS-penetrating compound that also inhibits α-Syn oligomerisation in living cells (Lu et al., 2011).

3.1.4 Resveratrol

Resveratrol (3,4’-dihydroxy-stilbene; M.Wt = 228.24 Da; logP = 2.82), a polyphenol present in grapes, red wines, and Polygonum cuspidatum, prevents α-Syn aggregation and disassembles fibrils at micromolar concentrations(Caruana et al., 2011).. Resveratrol’s structural features include its stilbene backbone, which offers a stiff and flat aromatic framework, enabling interactions like π-π stacking in the β-strand core of α-Syn fibrils. The presence of three hydroxyl groups in its structure enables hydrogen bonding with Ser87, Val82, and Thr75. Resveratrol docking has shown binding energy values between -7.5 and -9.2 kcal/mol. However, besides direct fibril inhibition, resveratrol has been shown to stimulate SIRT1 deacetylase activity and decrease α-Syn acetylation and aggregation, as well as promote autophagy by inhibiting mTOR signaling, clearing pre-existing aggregates (Wu et al., 2011; Albani et al., 2009).

3.1.5 Quercetin         

Quercetin (3,3',4',5,7-pentahydroxyflavone; MW 302.24 Da; LogP 1.54), abundant in onions, apples, capers, and tea, possesses both direct inhibition of aggregation within the α-Syn NAC region along with powerful abilities of metal chelation and antioxidation(Masuda et al., 2006). Binding to the α-Syn NAC region results in docking scores between −7.8 and [9,10]?9.4 kcal/mol, hydrogen bonding with Thr75, Val76, Gly84, and Ala85 residues, and π–π interactions between the B-ring of the flavone molecule and the fibril’s hydrophobic core(Wang et al., 2013). The 3-OH catechol group acts as a strong ligand for Cu²? and Fe³? at the C-terminal metal-binding site (log K ~12) and prevents oxidative aggregation(Altay et al., 2023).

3.2 Alkaloids 

3.2.1 Berberine

Berberine (MW 336.37 Da; LogP 1.30), a natural isoquinoline alkaloid derived from Berberis vulgaris, Coptis chinensis, and Hydrastis canadensis, is one of the most well-studied α-Syn fibril inhibitors both in computational modeling and experimental analysis(Huang et al., 2021). The planar tetracyclic isoquinoline core fits into the β-sheet backbone of α-Syn fibrils in terms of π-π stacking interactions and hydrophobic contacts with Val70, Val74, and Val82. In particular, the quaternary nitrogen atom forms an important electrostatic interaction with Glu83 (contributes ~1.8 kcal/mol to binding energy), while 9,10-dimethoxy substituents increase van der Waals interactions with Val76 and Ala78. Binding energy values of [18]?8.5 to ?10.1 kcal/mol have beenInhibition of MAO-B increases dopaminergic activity; AMPK activation promotes mitochondria regeneration; NF-κB inhibition reduces neuroinflammation; and modulation of gut microbiome can inhibit α-Syn transmission through gut-brain pathways (Wang et al., 2021).. Preclinical studies on α-Syn mouse transgenics reveal decreased Lewy body formation and enhanced motor function. Positive CNS ADMET properties (TPSA = 40.0 Ų; logBB = −0.2; Fabs oral = 36%) are favorable for further clinical investigation(Huang et al., 2021).

3.2.2 Sanguinarine and Coptisine

Sanguinarine (Molecular Weight: 332.35 g/mol) and coptisine (Molecular Weight: 320.32 g/mol), benzophenanthridine and isoquinoline alkaloids isolated from Sanguinaria canadensis and Coptis japonica respectively, display high affinity for α-Syn NAC region in docking simulation (?8.8 to ?10.5 kcal/mol). Planar arrangement of polycyclic aromatic groups of both molecules allows favorable π-π stacking inside β-sheet core of fibril, resulting in binding energy close to berberine. Nevertheless, high cytotoxicity of sanguinarine at higher doses (above 5 µM) calls for selectivity improvement(Yang et al., 2024)..

3.2.3 Piperine

Piperine (molecular weight 285.34 g/mol; logP 1.98), the main pungent alkaloid from black pepper (Piper nigrum), has been shown to have computational potential as a mild α-Syn aggregation inhibitor with a docking score of ?7.2 to ?8.4 kcal/mol(Caruana et al., 2011).. Besides acting directly on α-Syn aggregates, piperine is a bioavailability enhancer that can increase the bioavailability of curcumin by 2000% when administered in tandem with curcumin by inhibiting cytochrome P450 3A4 enzyme activity and P-glycoprotein efflux transporter activity(Shoba et al., 1998; Bhardwaj et al., 2002).

3.3 Terpenoids

3.3.1 Ursolic Acid and Oleanolic Acid

Ursolic acid (UA; molecular weight 456.71 g/mol; logP 5.88) and oleanolic acid (OA; molecular weight 456.71 g/mol; logP 5.74), pentacyclic triterpenoids found in apple peel, rosemary (Rosmarinus officinalis), and loquat (Eriobotrya japonica), are inhibitors of α-Syn fibrillation via hydrophobic intercalation into the NAC groove (Isacchi et al., 2022).. The rigid pentacyclic structure of UA and OA occupies a larger hydrophobic volume than polyphenols, leading to substantial van der Waals interactions with Val70, Ala69, Ile88, Val82, and Ala78 (AutoDock Vina scores: ?8. In vitro ThT assays provide evidence for dose-dependent inhibition (IC??: UA ~12 µM; OA ~18 µM). Insolubility issues require nanoscale formulation to ensure proper in vivo bioavailability(Isacchi et al., 2022)..

3.3.2 Withanolide A

Withanolide A (MW 470.64 Da; LogP 2.42) is a lactone isolated from the Withania somnifera (Ashwagandha) plant. It is one of the strongest naturally occurring comounds that act as α-Syn ligands, based on docking calculations of [9]?11.2 kcal/mol (Akhoon et al., 2016). and an MM-GBSA value of −54 kcal/mol. This tetracyclic steroidal compound forms multiple hydrophobic interactions within the entire NAC groove (Ala69, Val70, Val74, Val76, Thr81) while its lactone carbonyl forms essential hydrogen bonding with Thr75. The promising ADMET properties (TPSA 96.8 Ų; logBB slightly positive; MW <500 Da) along with the clinically confirmed CNS effects of W. somnifera make withanolides very promising for further development (Akhoon et al., 2016; Manral & Saravanan, 2020).

3.4 Marine Natural Products

3.4.1 Eckol and Dieckol (Phlorotannins)

 The natural polyphenols eckol and dieckol, isolated from the edible brown algae species Ecklonia cava, are some of the most potent known α-Syn binders, as discovered through virtual screening. The compound dieckol (Molecular weight 742.56 Da; LogP 1.2), a dimeric phlorotannin, scores ?10.5-11.4 kcal/mol in binding studies and −50 to −58 kcal/mol for MM-GBSA, due to its extended polyphenolic core capable of forming extensive hydrogen bonding networks (Thr72, Val74, Ala76, Thr81, Ala85) and multiple aromatic ring interactions(Cha et al.). Dieckol's superior computational CNS ADMET profile, alongside strong antioxidant and neuro-inflammatory properties demonstrated using SH-SY5Y cell line models, makes phlorotannins promising leads for anti-Parkinsonian drug development (Cha et al., 2016; Kim et al., 2019).

3.4.2 Fucoxanthin

Fucoxanthin (Molecular weight 658.91 Da; LogP 5.21), a carotenoid found in brown algae (Undaria pinnatifida, Laminaria japonica), shows excellent docking affinity (Scores: ?8.4 to ?9.6 kcal/mol) with the NAC-region(Zhang et al., 2022).. The polyene chain of Fucoxanthin fits into the hydrophobic pocket through van der Waals interaction, whereas acetate and hydroxyl moieties establish H-bonds with residues Thr75 and Ser87. Fucoxanthin’s anti-inflammatory, antioxidant, and neuroprotection mechanisms in cellular PD models support its potential direct anti-aggregation mechanism in addition to its computational efficacy(Pruccoli et al., 2024).

3.5 Other Significant Natural Classes

Other NCs, in addition to those mentioned above, showing substantial computational efficacy against α-Syn protein aggregation include: oleuropein (Molecular weight 540.5 Da; docking scores: ?8.3 to ?9.6 kcal/mol), a secoiridoid glycoside extracted from Olea europaea leaves(Caruana et al., 2011);; rosmarinic acid (Molecular weight 360.3 Da; ?7.9 to ?9.1 kcal/mol), a caffeic acid ester found in Rosmarinus officinalis(Ono et al., 2012); ginsenosides Rg3 and Rb1 (Molecular weight 785–958 Da; ?8.0 to found in Panax ginseng, which inhibit α-Syn fibrillation and disaggregate preformed fibrils (Ardah et al., 2015). These compounds collectively represent promising scaffolds for further optimization and development as anti-aggregation therapeutics targeting α-Syn in Parkinson's disease (Kabra et al., 2019; Yang et al., 2024).

4. Computational Method for Screening Natural Compounds Against α-Synuclein

4.1 Target Protein Structure Preparation

The accuracy and validity of the docking results are significantly affected by the quality of the receptor protein used. In the case of α-Syn, it is preferable to use the higher resolution structure obtained by cryo-EM fibrils (PDB: 6SST, 6H6B, 7Y7A), rather than the previously available structures with lower resolution (Guerrero-Ferreira et al., 2018; Guerrero-Ferreira et al., 2019). Important steps during the preparation process involve: Protonation at physiological pH (7.4) using PROPKA 3.1 or Protein Prep Wizard of the Maestro software (Schrödinger) (Olsson et al., 2011); Homology modeling of residues lacking using MODELLER 10 or Swiss-Model; Removal of crystallographic issues while retaining the important water molecules; Energy minimization through OPLS4 or ff19SB force fields to eliminate potential steric conflicts; Structural quality verification through MolProbity and Ramachandran analysis (goal: >95% in favorable regions) (Chen et al., 2010; Williams et al., 2018). Due to the IDP nature of α-Syn, ensemble docking with an appropriate number of conformers derived from pre-simulation MD trajectories (50–100 ns) or members of the NMR ensemble gives better results (Fusco et al., 2014).

4.2 Natural Compounds Library Curation and Preparation

Specific natural compound (NC) databases used for α-Syn virtual screening include diverse resources offering structural and biological annotation. The COCONUT database aggregates over 400,000 NCs with structural diversity filters (Sorokina et al., 2021); SuperNatural III contains approximately 325,000 curated NCs with predicted ADMET and drug-like parameters (Günther et al., 2022). Additional specialized resources include UNPD (>230,000 structurally defined NCs), MarinLit and Marine Natural Products databases focused on marine bioactivity, and FooDB/PhytoHub for food-sourced bioactive compounds (Sorokina et al., 2021). Ligand preparation involves the following steps: 3D structure generation using SMILES from RDKit, Open Babel, or CORINA; tautomer and protonation state generation using LigPrep (Schrödinger) or MolVS; conformer ensemble generation using ConfGen or Omega; assignment of partial atomic charges using the RESP method at HF/6-31G* level (or AM1-BCC for ultra-high-throughput workflows); and filtration for PAINS (Pan Assay Interference Compounds) and aggregator properties using FAF-Drugs4 (Baell & Holloway, 2010).

4.3 Docking Platforms and Screening Cascade

A tiered virtual screening cascade balances throughput with accuracy across successive filtering stages:

BBB permeation is an absolute necessity for the activity of α-Syn aggregation inhibitors that operate inside the dopaminergic neurons of the SNpc. CNS-specific ADMET criteria have higher threshold values than their peripheral counterparts: MW <450 Da (ideally); logP 1-3; TPSA <90 Ų; HBD ≤3; HBA ≤7; no more than 8 rotatable bonds; and CNS MPO ≥4.0. Numerous high-molecular-weight natural compounds (EGCG, ginsenosides, oleuropein, dieckol) fail to meet at least one CNS criterion, thus requiring nanoscale delivery systems, prodrug strategies, or fragment engineering to reach therapeutic concentrations in the brain (Javed et al., 2019; Alam et al., 2021). The modified version of the ‘beyond the rule of five’ model recognizes that NCs often violate Lipinski’s rule but exert their therapeutic effects via active transport, high affinity, and metabolic activation.[ Baicalein, berberine, resveratrol, and piperine have shown to possess the best CNS drug-likeness properties, which meet all Lipinski’s Ro5 rules, favorable BBB permeation, adequate oral bioavailability, and CNS MPO values ≥4.8 (Javed et al., 2019; Penthala et al., 2022). Curcumin and quercetin fall into an intermediary category where a concerted effort at nanoformulation or molecular modification will enable clinically significant CNS levels. Glycosides with high MW (ginsenosides, oleuropein) and catechins (EGCG) are the hardest nut to crack from a CNS delivery standpoint.

7. Structure-Activity Relationship of Natural Compounds Scaffold 

7.1 Structural Characteristics Determining the Polyphenolic Scaffold

Based on systematic studies of docking between α-Syn and polyphenols, the following principles can be highlighted which determine their activity in inhibition of aggregation of the protein: (i) planar configuration is a key factor, since analogs of flavones and flavonols where conjugation is broken are docked 2-3 times less effectively than the respective compounds; (ii) the number and configuration of the hydroxyl groups define the density of the hydrogen bonding networks with optimal configuration including 3-6 OH phenolic groups; (iii) in addition to hydrophobic effect, galloyl group of EGCG contributes three additional hydrogen bond acceptors; (iv) catechol type with bis-ortho-dihydroxy B-rings is preferable both due to direct effect and capacity to chelate copper ions; (v) saturation of the C ring leads to decrease in planarity and aromaticity and therefore decreases binding up to 3.0 kcal/mol; (vi) methoxy substitutions increase lipophilicity and metabolic stability while preserving the binding ability (Caruana et al., 2011; Ardah et al., 2014; Meng et al., 2010).

7.2 Determinants of Alkaloid Scaffold:

 Binding AffinityIn the case of isoquinoline alkaloids, there is a relationship between binding affinity and (i) the extent of aromaticity of the ring system—complete aromatic structures like berberine have a binding advantage of 1.5–2.5 kcal/mol compared to their less aromatic counterparts canadine due to planarity requirements; (ii) N-methylation and quaternization, which enhance positive charges and electrostatic interactions with Glu83; (iii) methoxylation at C9/C10, which enhances hydrophobic interactions with Val74 and Val76; and (iv) scaffold volume (Hussain et al., 2022; Habtemariam, 2020).

7.3 Determinants of Terpenoid Framework

The pentacyclic triterpenoids illustrate that: (i) a C28 carboxylate group is necessary for electrostatic interactions between the lysine residues Lys80 and Lys96 (esterification decreases the affinity by approximately 2 kcal/mol); (ii) a C2α-hydroxyl group contributes to increased binding through hydrogen bonding with Thr72; (iii) an unsaturated A-ring structure (2-oxo or 3-oxo compounds) improves binding through enhanced π-interaction potential; (iv) the molecule volume of 350-500 ų fits perfectly into the hydrophobic pocket of NAC; and (v) the relative (Akhoon et al., 2016; Wongtrakul et al., 2021)

8. Experimental Validations of Computational Predictions

8.1 In vitro biophysical assays

Quantification of inhibition in Thioflavin T (ThT) Fluorescence Kinetics assays is the main in vitro technique used for validation of computational predictions of anti-aggregant activity. For high-affinity docking hits (predicting binding energy <?8.5 kcal/mol), experimental IC?? values range from 1-50 µM in ThT assays in compound families tested. A correlation (r² = 0.65-0.78) between the strength of docking scores and ThT IC?? values for curated compounds has been shown, validating the prediction capability of docking with known shortcomings of scoring algorithms (Pujols et al., 2018; Linsenmeier et al., 2022). Additional in vitro biophysical assays include: morphological studies using atomic force microscopy (AFM) and transmission electron microscopy (TEM) to characterize aggregates formed in the presence of NCs; dot blot with specific antibodies to determine the type of inhibitory aggregate formed (A11 oligomer selective; OC fibril selective); oligomeric species formation using size exclusion chromatography-multiparameter light scattering (SEC-MALS); and direct binding affinity using surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC), giving a comparison for KD, ΔH, and ΔS binding energies.

8.2 Cellular and Animal Models Validation

After successful validation at both computation and biochemical levels, the hits undergo validation by translational approach. Common cellular assays utilized for validation include SH-SY5Y human neuroblastoma cells with transient or stable expression of α-Syn (either wild type or A53T mutant); iPSC-derived human dopaminergic neurons from Parkinson’s disease patient harboring mutations in the SNCA locus; primary cultures of rat mesencephalic neuronal-glial co-cultures treated with preformed fibrils (PFFs); and Caenorhabditis elegans expressing α-Syn::GFP reporter in body-wall muscles (Lu et al., 2011; Jiang et al., 2010). For in vivo validation, mouse models injected with α-Syn PFFs (via striatum or substantia nigra), Drosophila melanogaster carrying transgenes encoding human α-Syn protein (expressed in dopaminergic neurons) used as a rapid screening platform to assess efficacy and underlying mechanism of action, and rat AAV-induced α-Syn overexpression serve as relevant platforms. The most solid evidences on agreement between computational and experimental approaches involve: baicalein and berberine (anti-aggregation effect validated in vitro, cells and animal experiments and matching predictions for docking analysis) (Zhu et al., 2004; Teng et al., 2016); EGCG and curcumin (significant number of in vitro and cellular studies validating NAC region binding mode) (Bieschke et al., 2010; Pandey et al., 2008); withanolides (promising computational predictions not yet validated experimentally) (Akhoon et al., 2016).

9. Challenges, limitations, and future directions  

9.1 Basic Challenges to Drug Discovery of an Intrinsically Disordered Protein

The intrinsic disorder of α-Syn raises several fundamental challenges for drug discovery that set it apart from traditional structured enzymes or receptors: (i) there is no stable or well-defined binding pocket in monomeric α-Syn, hence requiring ensembles of conformations to be generated rather than optimizing towards one unique conformation; (ii) the existence of more than one form of fibril (rod-form, twister, MSA-like, and DLB-like) each possessing their own druggable surfaces implies that drugs should ideally bind to common motifs found in all polymorphic forms (Pirhaghi et al., 2025; Guerrero-Ferreira et al., 2019); (iii) early stages of oligomer formation that cause the highest level of toxicity still lack knowledge regarding their structures, thus preventing structure-based design of oligomer-specific inhibitors; (iv) PTMs (phosphorylation at Ser129, oxidation of Methionine residues, and truncation of C-terminal residues) observed in disease states alter the folding of the protein and binding site geometry.

9.2 Limitations of Existing Computational Methods

The existing docking pipelines have inherent limitations, which include: (i) conventional scoring functions trained on globular proteins and their ligands provide poor performance on the more open and solvent-exposed NAC interface (Oikawa & Takada, 2024; Boulaamane et al., 2023); (ii) insufficient experimental co-crystals or cryo-EM structures of natural compounds with α-Syn pose a limitation in using machine learning-based approaches, lacking adequate training and testing data; (iii) rigid docking procedures involving individual fibrils do not adequately reflect the dynamic nature of IDPs during early-stage aggregation; (iv) ADMET models based on drug compound libraries fail to cover all possible chemical diversity in complex natural compounds (glycosides, polyketides, terpenoid saponins); and (v) benchmarking positive control data sets for α-Syn aggregation inhibitors are considerably smaller and poorly curated compared to those of well-characterized drug targets.

9.3 Emerging Technologies and Future Directions

The course of development in this area can be described as follows based on technological progress and strategies:

•Cryo-EM co-structure determination: New approaches for determining the co-structure of natural compounds-α-Syn fibrils using cryo-EM will deliver atomic resolution binding mode information, equivalent to that obtained by protein-ligand crystallography. Recently, some research groups have successfully determined sub-3 Å resolution co-structures of amyloid targeting compounds, and using this method for NC lead molecules will revolutionize the accuracy of optimization.

• De novo molecule generation with GNN/transformer models: Using graph neural networks (GNN) and transformers to generate novel scaffolds combining the characteristics of natural products with enhanced α-Syn binding can achieve unprecedented optimization in this area.

• MD sampling for aggregation kinetics: The use of MARTINI coarse-grain MD and metadynamics simulation on µs-to-ms time scales will quantify the NC effects on full aggregation kinetics, which cannot be observed using atomistic MD, providing mechanistic insight into the process of nucleation or elongation inhibition.

• Proteomics-guided polypharmacology profiling: Thermal proteome profiling (TPP) and activity-based protein profiling (ABPP) of NC-treated neuronal cells will allow researchers to obtain the whole spectrum of target engagement sites, thus validating the computational prediction of polypharmacological mechanisms.

• Brain targeting nanoparticle formulation: Transferrin receptor targeting PLGA nanoparticles, exosome-like nanovectors, and nasal-to-brain transport technologies are poised to solve the permeability issues of the BBB associated with TPSA-containing natural products such as EGCG, phlorotannins, and ginsenosides for CNS drug action (Alam et al., 2021; Bhatt et al., 2025).

• Fragment-Based Natural Product Drug Design (FBNPD): Fragmenting complex natural products to identify their smallest active core fragments and further grow the fragments using information from cryo-EM binding and computational scoring is essential for CNS-directed natural product drug discovery.

• Taking the fastest regulatory route to α-Syn disease treatment via drug repurposing: Clinical-safe NCs already have an existing track record of success in treating metabolic syndromes (berberine), anti-inflammatory conditions (curcumin Theracurmin®), and cardiovascular disorders (resveratrol).

CONCLUSION

The present review highlights the scientific rationale and burgeoning body of evidence supporting natural compounds as potential α-synuclein aggregation inhibitors in Parkinson’s disease, which have been prioritized using molecular docking and other computational methods. The unparalleled pharmacological diversity, structural variety, biological evolution to optimize target recognition, and safety records of natural compounds make them ideal candidates for drug discovery efforts to develop disease-modifying PD agents. Among the various natural compound families considered, the polyphenolic group is by far the most widely studied class with respect to computational and experimental validation as inhibitors, showing multi-pharmacophore interactions with the hydrophobic pocket of the NAC domain through aromatic π-stacking, hydrogen bonding, and indirect chelation mechanisms. Alkaloids such as berberine inhibit NAC-region aggregation while also exhibiting pleotropic neuroprotection via MAO-B inhibition, AMPK agonism, and neuroinflammatory modulation. Terpenoids like withanolide A and ursolic acid, as well as marine-derived compounds like dieckol and fucoxanthin, stand out as high-affinity molecules where computational studies far exceed experimental studies. From a methodology standpoint, the incorporation of ensemble docking with multiple cryo-EM fibril polymorphs, MM-GBSA re-scoring, e-pharmacophores, and MD simulation stands as the current cutting edge approach for prioritization of natural compound anti-aggregation leads. The strict application of ADMET filtering of CNS relevance, selecting baicalein, berberine, resveratrol, and piperine as the most translationally relevant candidates, provides a rational framework for prioritizing natural compound development for experimental and clinical applications.   Going forward will require coordinated effort on many fronts, including cryo-EM co-structure determination of NC-α-Syn complexes to ensure the structural basis for optimization; comprehensive ThT, AFM, SPR, and ITC validation of computational leads at highest priority; iPSC and in vivo modeling for biological and therapeutic efficacy; and eventually, well-designed clinical trials in early PD for the most promising natural compounds. The confluence of structure-based drug design, artificial intelligence, CNS drug delivery by nanotechnology, and ever deepening molecular knowledge of synucleinopathies presents a unique opportunity to convert computationally guided leads to an effective disease-modifying treatment for Parkinson’s disease.

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