Exploring Intrinsic Disorder in Human Synucleins and Associated Proteins

Abstract

In this work, we explored the intrinsic disorder status of the three members of the synuclein family of proteins—α-, β-, and γ-synucleins—and showed that although all three human synucleins are highly disordered, the highest levels of disorder are observed in γ-synuclein. Our analysis of the peculiarities of the amino acid sequences and modeled 3D structures of the human synuclein family members revealed that the pathological mutations A30P, E46K, H50Q, A53T, and A53E associated with the early onset of Parkinson’s disease caused some increase in the local disorder propensity of human α-synuclein. A comparative sequence-based analysis of the synuclein proteins from various evolutionary distant species and evaluation of their levels of intrinsic disorder using a set of commonly used bioinformatics tools revealed that, irrespective of their origin, all members of the synuclein family analyzed in this study were predicted to be highly disordered proteins, indicating that their intrinsically disordered nature represents an evolutionary conserved and therefore functionally important feature. A detailed functional disorder analysis of the proteins in the interactomes of the human synuclein family members utilizing a set of commonly used disorder analysis tools showed that the human α-synuclein interactome has relatively higher levels of intrinsic disorder as compared with the interactomes of human β- and γ- synucleins and revealed that, relative to the β- and γ-synuclein interactomes, α-synuclein interactors are involved in a much broader spectrum of highly diversified functional pathways. Although proteins interacting with three human synucleins were characterized by highly diversified functionalities, this analysis also revealed that the interactors of three human synucleins were involved in three common functional pathways, such as the synaptic vesicle cycle, serotonergic synapse, and retrograde endocannabinoid signaling. Taken together, these observations highlight the critical importance of the intrinsic disorder of human synucleins and their interactors in various neuronal processes.

1. Introduction

The synuclein family of proteins, comprising α-, β-, and γ-synucleins, plays a critical role in synaptic regulation [1,2]. The proteins of the synuclein family are primarily expressed in vertebrate neuronal tissues, and in humans, they have been found to be associated with various neurodegenerative diseases, such as Parkinson’s disease (PD) [1,2]. All three family members were shown to be mostly disordered in the purified form in vitro [3,4,5,6,7,8,9], and the intrinsically disordered nature of α-synuclein was verified in cellulo [10,11,12,13,14,15,16]. However, at interaction with lipid membranes, the synuclein proteins can undergo disorder-to-order transitions and exhibit an α-helical lipid-bound structure, peculiarities of which have been well-studied due to the analysis of the pathological mutations causing toxicity related to the development of the early onset of PD [1,2].

Of the three synuclein proteins, α-synuclein has been the most studied due to its higher abundance in the brain and because of the discovery of its link to the pathogenesis of PD and later to the development of many other neurodegenerative diseases collectively known as synucleinopathies [17,18,19,20,21,22,23]. In fact, as of 31 March 2024, the Web of Science database contained 30,697 papers dedicated to this protein, a remarkable two-fold increase in comparison with the results of the analogous literature analysis reported in 2017 [1]. The researcher’s strong attention to this protein is determined by its important role in the pathogenesis of neurodegenerative diseases. Although α-synuclein has been originally found to be accumulated in the Lewy bodies (LBs) and Lewy neurites (LNs), which are specific pathological hallmarks in PD cases, later misbehavior of this protein has also been linked to multiple other neurodegenerative diseases, such as Alzheimer’s disease, Down’s syndrome [1], and many other synucleinopathies [17,18,19,20,21,22,23]. In fact, some of the other maladies associated with α-synuclein misbehavior include neurodegeneration with brain iron accumulation type 1 (NBIA1), pure autonomic failure, Down’s syndrome, amyotrophic lateral sclerosis-parkinsonism-dementia complex of Guam (Guam ALS/PDC), multiple system atrophy (MSA), and several LB disorders (that, in fact, might represent a clinical continuum [24]), such as sporadic and familial PD, dementia with Lewy bodies (DLB), diffuse Lewy body disease (DLBD), the Lewy body variant of Alzheimer’s disease (LBVAD), and PD dementia (PDD) [25,26,27,28,29,30,31,32,33].

α-Synuclein aggregation leading to the formation of various oligomers, amorphous aggregates, and amyloid-like fibrils is one of the critical features of this protein, which can be affected by a variety of factors and mechanisms [1,34,35,36]. It was indicated that synucleinopathies represent the α-synuclein-related brain amyloidoses, as selectively vulnerable neurons and glia in different affected brain regions are characterized by the presence of common pathological intracellular inclusions containing α-synuclein, the formation of which correlates with the degeneration of the afflicted brain regions, leading to the onset and progression of the clinical symptoms of these diseases [17,18,23,26,33,37,38,39]. Accumulation of α-synuclein-containing inclusions was detected in the dorsal motor vagal and solitary nuclei, locus coeruleus, parabrachial nuclei, pedunculopontine, and raphe nuclei, periaqueductal gray, prepositus hypoglossal, substantia nigra, reticular formation, and ventral tegmental area, and demonstrated the presence of LN in brainstem fiber tracts and the existence of LBs and LNs in cranial nerve nuclei, premotor oculomotor, precerebellar, and vestibular brainstem nuclei [40,41,42]. Furthermore, the α-synuclein deposition-related pathological processes were shown to spread transneuronally along anatomical pathways [42], supporting the notion of prion-like propagation of the pathological spread within the affected brain during the disease progression (e.g., as described by Braak’s staging criteria for PD [43,44]).

Recent research has also suggested that α-synuclein can form polymorphic structures under certain conditions [1]. Moreover, both the monomeric and polymorphic forms of α-synuclein are amenable to various post-translational modifications (PTMs), providing means for a further increase in the structural and functional diversity of this protein. Furthermore, the capability of α-synuclein to form different high-molecular-weight assemblies was linked to the ability of this protein to trigger different synucleinopathies [45], as demonstrated by the direct observation of the induction of different synucleinopathies after injection of the different α-synuclein aggregated forms (oligomers, ribbons, and fibrils) in the rat brain [46].

Additionally, several pathological mutations of α-synuclein associated with the early onset of PD have been found to increase the aggregation potential of this protein in neurodegenerative diseases [47,48,49,50,51,52,53]. For example, mutation A53T has been found to accelerate fibril formation, thus increasing the chances of inconsistent interactions [1,47]. Another mutation is A30P, which is caused by the replacement of alanine at position 30 by proline. A30P has been found to reduce the binding of α-synuclein to vesicles [47]. Another mutation that has been well studied is E46K, where glutamic acid at position 46 is replaced with lysine [47]. This mutation increases the binding of α-synuclein to liposomes and shows similar effects as A53T. Histidine 50 to glutamine substitution (H50Q) represents another α-synuclein mutation associated with familial PD [54,55]. This mutation was predicted to perturb the same amphipathic α-helix as the previously described pathogenic mutations [55]. It was shown that H50Q was able to enhance the aggregation, secretion, and toxicity of α-synuclein, suggesting that this mutation may play a role in the extracellular toxicity of this protein [56].

Besides its astonishing multipathogeneity, α-synuclein has also been shown to present remarkable multifunctionality, exhibiting a wide range of highly diversified biological functions, ranging from control of the neuronal survival [57], regulation of the neuronal apoptotic response [58], and protection of neurons from various apoptotic stimuli [58], to metal binding [59,60,61,62] and interaction with pesticides and herbicides [63,64,65], to fatty acid binding [57] and interaction with plasma membranes leading to the formation of membrane channels or modification of membrane activity [66], to synaptic vesicle release and trafficking [57] and positive and negative regulation of neurotransmitter release [67], to association with mitochondria causing mitochondrial dysfunction [66], to regulation of various enzymes and transporters [57], to and to promiscuous interaction with hundreds of unrelated proteins and other binding partners [57,68,69,70]. To be able to possess its multifunctionality, the α-synuclein structure is expected to be pliable enough to accommodate such features, and indeed, it expresses itself in the form of an intrinsically disordered protein [1,34,35,36]. Such a diverse set of unrelated functions prompted interest among the researchers in exploring the various interactions of α-synuclein with other proteins and their roles in various degenerative diseases. An interesting question pertaining to the functionality of α-synuclein is the prevalence of intrinsic disorder in its interactome.

In contrast, β-synuclein has been understudied (actually, according to the Web of Science database, as of 31 March 2024, there are 463 papers dedicated to this protein) due to its relative scarcity in the neuronal tissues as compared with α-synuclein, which is estimated to account for up to 1% of the total protein in soluble cytosolic brain fractions [71]. However, β-synuclein is typically co-expressed with α-synuclein and acts as a molecular chaperone to inhibit α-synuclein aggregation [72]. Recent research has also linked β-synuclein to various neurodegenerative diseases, sparking interest in the functions of this protein [72]. β-Synuclein has been found to be critical in the reduction of α-synuclein aggregation-induced toxicity [36,72]. In addition, β-synuclein also regulates synaptic function and dopamine transmission through various structural changes [35].

γ-Synuclein is expressed primarily in the peripheral nervous system, in contrast to α- and β-synucleins [73]. Similar to β-synuclein, γ-synuclein has been relatively understudied due to its lesser abundance as compared with the other members of the synuclein family (as of 31 March 2024, there are 498 papers dedicated to this protein in the Web of Science database). γ-Synuclein has been found to be linked to breast and ovarian cancer [73]. However, specific γ-synuclein mutations have also been found in various neurodegenerative diseases, such as Alzheimer’s, raising speculation regarding their role in the detection and potential treatment of such diseases.

One of the basic premises of modern protein science is the recognition and acceptance of the existence of intrinsically disordered proteins (IDPs) and hybrid proteins with intrinsically disordered regions (IDRs) [74,75,76,77,78], which are abundantly present in nature [75]. These biologically active proteins that do not have unique 3D structures as a whole or in part exist as dynamic conformational ensembles [77,79,80,81,82,83,84], which, at the global level, can be collapsed-disordered (molten globule-like), partially collapsed-disordered (pre-molten globule-like), or extended-disordered (coil-like) [85,86]. In a more general view, IDPs are characterized by a highly dynamic, complex, and mosaic structure with multi-level spatiotemporal heterogeneity, where different parts of a protein can be ordered or disordered to a different degree [87,88]. Since ordered and differently disordered protein regions might have well-defined and specific functions, the spatiotemporal heterogeneity of IDPs/IDRs defines their multifunctionality [89]. Therefore, IDPs/IDRs represent structurally and functionally heterogeneous complex systems that operate within the framework of the protein structure-function continuum model [89,90,91,92,93]. The functional repertoire of IDPs, which are typically engaged in recognition, regulation, signaling, and control of various biological pathways and processes [94,95,96], complements the functions of ordered proteins [97,98,99,100]. The structural flexibility of IDPs/IDRs also determines the variety of ways that can be used to regulate and control their functions [87,101,102,103], with one of the important regulatory means being a variety of post-translational modifications (PTMs) [104,105]. Furthermore, structural pliability and the capability of IDPs/IDRs to be involved in weak multivalent interactions define the broad involvement of these proteins in the biological liquid–liquid phase separation (LLPS) that forms the molecular mechanism of the biogenesis of various membrane-less organelles (MLO) and biomolecular condensates [89,106,107,108]. Finally, many IDPs are involved in various human diseases [57,84,94,97,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127].

The intrinsically disordered nature of the synuclein family of proteins and their link to various cellular structures and processes observed in the norm and neurodegenerative diseases prompted research into the synuclein family. The functional and structural diversity of these proteins introduces various challenges in the determination of the complete function of the synuclein family. Moreover, the interactions of these proteins with other proteins, which may or may not be intrinsically disordered, introduce additional challenges in the study of neurodegenerative disease. In this work, we explore and compare the sequence and structure of the human synuclein family with those of species from other classes. We attempt to determine the similarity of the synuclein family across species to aid in establishing the function of the proteins. Further, we also conduct a detailed disorder analysis of the proteins of the human synuclein family. Due to the wide variety of interacting proteins in the interactomes of the synuclein family, we performed a detailed disorder analysis of the interacting proteins exhibiting the highest disorder.

2. Results and Discussion

2.1. Intrinsic Disorder Status of Members of Human Synuclein Family

The amino acid sequences of all the synucleins analyzed in this study are listed in Supplementary Table S1. Figure 1, Figure 2, Figure 3 and Figure 4 represent the results of the intrinsic disorder-centric analysis of human α-, β-, and γ-synucleins, which consist of 140, 134, and 127 amino acids, respectively. It was emphasized that among the characteristic features of human synucleins is the presence of acidic stretches within their C-terminal regions, whereas within their 87 N-terminal residues, they possess a degenerative KTKEGV repeat that defines the hydrophobic variability of their sequences with a periodicity of 11 amino acids, which is characteristic of the amphipathic helices [128]. To illustrate the sequence similarity of the members of the human synuclein family, Figure 1A represents the results of the multiple sequence alignment of these proteins. Although human α- and β-synucleins share 78% identical residues, including conserved C-termini containing three identically placed tyrosine residues, β-synuclein lacks 11 residues (residues 73–83) within its middle region [19]. There is 60% sequence similarity between human α- and γ-synucleins, with γ-synuclein lacking the tyrosine-rich C-terminal signature of α- and β-synucleins [19]. The results of multiple sequence alignment were combined with the outputs of the PONDR^® VSL2-based per-residue disorder analysis to generate the aligned disorder profiles of human synucleins. Figure 1B shows that all three proteins are mostly disordered. To better illustrate differences in the disorder propensity of these proteins, we generated their “difference disorder spectra” by subtracting the human α-synuclein per-residue disorder propensities from the corresponding data for the β- and γ-synucleins (see Figure 1C). The use of this approach highlights the local differences in the disorder propensity, as positive peaks in the resulting plots show regions in β- and γ-synucleins with an increased local disorder propensity relative to the human α-synuclein. On the other hand, negative peaks correspond to regions with decreased disorder propensity. Therefore, Figure 1C clearly indicates that β-synuclein is moderately less disordered than α-synuclein (with the noticeable exception of the 35 residues in its C-terminal region), whereas γ-synuclein is noticeably more disordered than both other synucleins almost over its entire length (with the exception of the 25 N-terminal residues).

The analysis of these figures provides compelling evidence of the highly disordered nature of all three members of the human synuclein family. Originally, the interest of the researchers in human α-synuclein was promoted by finding a relation between the aggregation of this protein and the pathogenesis of Parkinson’s disease (PD), which is recognized as the most common aging-related movement disorder and the second most common neurodegenerative disease after Alzheimer’s disease (AD). It is estimated that ~1.5 million Americans are affected by PD. Sporadic (or idiopathic) forms of this disease account for about 95% of PD patients [129,130]. The probability of sporadic PD development increases with age, with only a small percentage of patients diagnosed before the age of 50 [131]. The prevalence of PD is much greater among those who are at least 65 years old [132]. Approximately 1% of the population at 65–70 years of age is affected by PD, whereas the number of PD patients increases to 4–5% in 85-year-olds [133]. In addition to the sporadic form, multiple familial forms of PD are associated with mutations in a number of genes. These hereditary forms account for ~4% of PD patients who develop early-onset disease before the age of 50 [134,135]. The pathological hallmarks of PD are the presence of cytosolic filamentous inclusions known as Lewy bodies (LBs) and Lewy neurites (LNs) in surviving dopaminergic neurons within the substantia nigra [8,9]. These inclusions that contain aggregated forms of α-synuclein can also be found in other parts of the brain [136] and are associated with the pathogenesis of various synucleinopathies [25,26,27,28,29,30,31,32,33], characterized by the presence of the common pathologic inclusions composed of aggregated α-synuclein, which are deposited in selectively vulnerable neurons and glia [17,18,23,38]. Finding α-synuclein in LBs and LNs [32,37], as well as the existence of the specific missense mutations in the SNCA gene, corresponding to the A30P, E46K, and A53T substitutions in the α-synuclein protein in autosomal dominant early-onset forms of PD [137,138,139], and a link of other early-onset PD forms to the hyper-expression of wild type α-synuclein due to the gene duplication/triplication [140,141,142] strongly implicated α-synuclein in the PD pathogenesis.

The α-synuclein sequence is assumed to contain three functional regions: the N-terminal region (residues 1–60) contains four 11-amino acid imperfect repeats with a conserved motif (KTKEGV, residues 10–15, 21–26, 32–37, and 43–48); the central region (residues 61–95) that contains three additional repeats (residues 58–63, 69–74, and 80–85) and is known as a highly amyloidogenic non-Aβ component of AD plagues (NAC) region that was found in amyloid plaques associated with AD [118]; and the highly charged C-terminal region (residues 96–140) which is involved in protein–protein interactions. Note that the N-terminal and central regions comprise a lipid-binding domain. A detailed experimental analysis of purified α-synuclein in vitro provided strong evidence of the highly disordered nature of this protein [3,4,6,143]. However, it was also indicated that the structure of α-synuclein does not represent a random coil but is characterized by the presence of transient long-range contacts within the protein [9,144,145,146].

In agreement with experimental data, Figure 2A,B show that human α-synuclein is predicted to be highly disordered by most computational tools utilized in this study. Furthermore, Figure 2B shows that the C-terminal region of this protein contains two molecular recognition features (MoRFs, which are disordered regions that can undergo binding-induced folding at interaction with specific partners) (residues 87–94 and 111–140), and the entire protein is heavily decorated by multiple PTMs (which are commonly located within intrinsically disordered regions, IDRs), clearly indicating the crucial functional role of its intrinsic disorder. Figure 2C shows that human α-synuclein is characterized by a high liquid–liquid phase separation (LLPS) potential. Its probability of spontaneous liquid–liquid phase separation (p_LLPS) value of 0.6249 exceeds the threshold of 0.6, indicating that the α-synuclein can act as a droplet-driver capable of undergoing LLPS spontaneously [147]. Furthermore, the C-terminal region of this protein contains a long droplet-promoting region (DPR, residues 101–140), which also includes an aggregation hotspot (residues 115–123), which is defined as a region that is capable of promoting the conversion of the liquid-like condensed state into a solid-like amyloid state [148]. These predicted LLPS potentials of human α-synuclein are in line with the experimentally demonstrated capability of this protein to undergo LLPS [149,150,151,152,153].

Curiously, Figure 2D shows that human α-synuclein is expected to contain multiple regions with context-dependent interactions (residues 3–13, 15–75, 77–92, 94–105, and 115–123), i.e., regions exhibiting ordered or disordered binding modes depending on the cellular context (environment, sub-cellular localization, partners, and PTMs). These regions are capable of engaging in a multiplicity of binding modes in a cellular context-dependent manner [154]. The data shown in Figure 2B,D indicate that human α-synuclein is predisposed to be a promiscuous binder, as its almost entire sequence can act as a potential binding platform. In line with this conjecture, Figure 5A shows that α-synuclein can be engaged in interaction with 356 proteins, forming a very dense protein–protein interaction network, 357 members of which are connected by 7316 interactions. This network is characterized by an average node degree of 41 and an average local clustering coefficient of 0.639. Since the expected number of edges in a random set of proteins of the same size and degree distribution drawn from the genome is 2946, this α-synuclein-centric network has significantly more interactions than what would be expected (its PPI enrichment p-value is <1.0 × 10⁻¹⁶). The five most enriched biological processes, molecular functions, and cellular components (as per Gene Ontology annotations) of the members of this network, as well as the most enriched local STRING network clusters and KEGG pathways, are listed in

Table 1. Functional enrichment of the networks centered at human α-, β-, and γ-synucleins.

Protein	ID	Description	Order of Magnitude of the p-Value
α-synuclein	Biological Process (Gene Ontology)
	GO:0006120	Mitochondrial electron transport, NADH to ubiquinone	−44
	GO:0007005	Mitochondrion organization	−44
	GO:0042776	Proton motive force-driven mitochondrial ATP synthesis	−43
	GO:0006810	Transport	−41
	GO:0051179	Localization	−41
	Molecular Function (Gene Ontology)
	GO:0008137	NADH dehydrogenase (ubiquinone) activity	−46
	GO:0019899	Enzyme binding	−36
	GO:0009055	Electron transfer activity	−35
	GO:0015399	Primary active transmembrane transporter activity	−31
	GO:0005515	Protein binding	−29
	Cellular Component (Gene Ontology)
	GO:0005747	Mitochondrial respiratory chain complex I	−49
	GO:0005737	Cytoplasm	−47
	GO:0098803	Respiratory chain complex	−42
	GO:0070469	Respirasome	−42
	GO:0005746	Mitochondrial respirasome	−42
	Local Network Cluster (STRING)
	CL:11079	NADH dehydrogenase (ubiquinone) activity	−46
	CL:11077	Respiratory chain complex	−42
	CL:11070	Respiratory chain complex, and Complex I biogenesis	−41
	CL:11080	NADH dehydrogenase (ubiquinone) activity	−39
	CL:11066	Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins, and respiratory chain complex IV	−37
	KEGG Pathways
	hsa05012	Parkinson’s disease	−124
	hsa05014	Amyotrophic lateral sclerosis	−99
	hsa05010	Alzheimer’s disease	−97
	hsa05020	Prion disease	−95
	hsa05016	Huntington’s disease	−87
β-synuclein	Biological Process (Gene Ontology)
	GO:0099003	Vesicle-mediated transport in synapse	−23
	GO:0001505	Regulation of neurotransmitter levels	−22
	GO:0007268	Chemical synaptic transmission	−22
	GO:0099504	Synaptic vesicle cycle	−21
	GO:0006836	Neurotransmitter transport	−21
	Molecular Function (Gene Ontology)
	GO:1903136	Cuprous ion binding	−6
	GO:0000149	SNARE binding	−6
	GO:0005507	Copper ion binding	−5
	GO:0019899	Enzyme binding	−5
	GO:0015318	Inorganic molecular entity transmembrane transporter activity	−4
	Cellular Component (Gene Ontology)
	GO:0043005	Neuron projection	−38
	GO:0098793	Presynapse	−37
	GO:0045202	Synapse	−35
	GO:0030424	Axon	−33
	GO:0036477	Somatodendritic compartment	−31
	Local Network Cluster (STRING)
	CL:14440	Mixed, including early-onset Parkinson’s disease, and C-terminal of Roc (COR) domain	−13
	CL:23285	Neurotransmitter transport, and RIMS-binding protein, third SH3 domain	−12
	CL:23286	Mixed, including synaptic vesicle pathway, and Cytoskeleton of presynaptic active zone	−10
	CL:14443	Early-onset Parkinson’s disease	−9
	CL:23287	Mixed, including presynaptic active zone cytoplasmic component, and Clathrin-sculpted vesicle	−9
	KEGG Pathways
	hsa04721	Synaptic vesicle cycle	−8
	hsa05012	Parkinson’s disease	−6
	hsa05033	Nicotine addiction	−5
	hsa04726	Serotonergic synapse	−2
	hsa04911	Insulin secretion	−2
γ-synuclein	Biological Process (Gene Ontology)
	GO:0006836	Neurotransmitter transport	−5
	GO:0001505	Regulation of neurotransmitter levels	−5
	GO:0050885	Neuromuscular process controlling balance	−4
	GO:0099504	Synaptic vesicle cycle	−4
	GO:0007399	Nervous system development	−4
	Molecular Function (Gene Ontology)
	GO:0005326	Neurotransmitter transmembrane transporter activity	−4
	GO:1903136	Cuprous ion binding	−3
	GO:0017075	Syntaxin-1 binding	−2
	GO:0015370	Solute:sodium symporter activity	−2
	GO:0015108	Chloride transmembrane transporter activity	−2
	Cellular Component (Gene Ontology)
	GO:0043005	Neuron projection	−12
	GO:0120025	Plasma membrane bounded cell projection	−11
	GO:0030424	Axon	−10
	GO:0150034	Distal axon	−9
	GO:0098793	Presynapse	−9
	Local Network Cluster (STRING)
	CL:23829	Mixed, including antibiotic biosynthesis monooxygenase, and Synuclein	−3
	CL:20559	Mixed, including habenula development, and Regulation of retinal ganglion cell axon guidance	−3
	CL:23287	Mixed, including presynaptic active zone cytoplasmic component, and clathrin-sculpted vesicle	−2
	CL:23831	Mixed, including synuclein, and negative regulation of myoblast fusion	−2
	CL:23313	Mixed, including autosomal dominant nonsyndromic deafness 25, and ureter cancer	−2
	KEGG Pathways
	hsa04721	Synaptic vesicle cycle	−5
	hsa04723	Retrograde endocannabinoid signaling	−4
	hsa04726	Serotonergic synapse	−3
	hsa04724	Glutamatergic synapse	−3
	hsa04912	GnRH signaling pathway	−2
α-synuclein + β-synuclein + γ-synuclein	Biological Process (Gene Ontology)
	GO:0051179	Localization	−44
	GO:0006810	Transport	−42
	GO:0051234	Establishment of localization	−42
	GO:0007005	Mitochondrion organization	−40
	GO:0006120	Mitochondrial electron transport, NADH to ubiquinone	−40
	Molecular Function (Gene Ontology)
	GO:0008137	NADH dehydrogenase (ubiquinone) activity	−41
	GO:0019899	Enzyme binding	−38
	GO:0005515	Protein binding	−34
	GO:0009055	Electron transfer activity	−31
	GO:0015399	Primary active transmembrane transporter activity	−26
	Cellular Component (Gene Ontology)
	GO:0005737	Cytoplasm	−51
	GO:0031982	Vesicle	−45
	GO:0005747	Mitochondrial respiratory chain complex I	−44
	GO:0043005	Neuron projection	−42
	GO:0031410	Cytoplasmic vesicle	−41
	Local Network Cluster (STRING)
	CL:11079	NADH dehydrogenase (ubiquinone) activity	−41
	CL:11077	Respiratory chain complex	−37
	CL:11070	Respiratory chain complex, and Complex I biogenesis	−35
	CL:11080	NADH dehydrogenase (ubiquinone) activity	−35
	CL:11066	Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins, and respiratory chain complex IV	−32
	KEGG Pathways
	hsa05012	Parkinson disease	−111
	hsa05014	Amyotrophic lateral sclerosis	−94
	hsa05010	Alzheimer disease	−88
	hsa05020	Prion disease	−87
	hsa05016	Huntington disease	−82

.

Figure 2E demonstrates the 3D structure of human α-synuclein modeled by AlphaFold. According to this model, α-synuclein does not have a compact core, with the only structured element predicted in this protein being a long α-helix spanning residues 1–91. This is a rather unrealistic structure, as long α-helices typically cannot exist in isolation, as they need to be stabilized by interactions either with the compact protein core or via binding to specific partners, such as other proteins, nucleic acids, or membranes. Therefore, it is likely that in this case, AlphaFold predicts the 3D structure of a bound form of α-synuclein. In fact, comprehensive experimental analysis of purified α-synuclein in vitro using a multitude of techniques sensitive to different levels of protein structural organization revealed that this protein is highly disordered [3,4,6,143]. Although transient long-range interactions were observed within this protein [9,144,145,146] solution, NMR analysis did not show the presence of any stable structural elements in the unbound form of this protein. However, this protein has been shown to adopt a secondary structure of mostly helical nature upon interaction with negatively charged small, unilamellar vesicles (SUVs) or detergent micelle surfaces [3,5,155,156], and α-helical structure was induced in this protein in the presence of lipids [157] and organic solvents [158]. Furthermore, binding of α-synuclein to a micelle of the detergent sodium lauroyl sarcosinate (SLAS) was shown to be accompanied by the disorder-to-order transition resulting in the formation of two antiparallel micelle-bound α-helices (residues 1–31 and 41–91) [159]. In agreement with this NMR-EPR-based study, solution NMR analysis of the micelle-bound form of α-synuclein revealed the presence of the two anti-parallel curved α-helices (residues 3–37 and 45–92) connected via an extended but well-ordered linker [160].

Similar to α-synuclein, human β-synuclein is predicted to contain high levels of intrinsic disorder (see Figure 3). The major difference between these two proteins is the lack of 11 residues (residues 73–83) within the middle region of β-synuclein [19]. As a result, the overall percent of disordered residues (as per PONDR^® VSL2 analysis) decreases from 90.71% in α-synuclein to 87.31% in β-synuclein. On the contrary, the average prediction score increased from 0.7199 in α-synuclein to 0.7342 in β-synuclein (see Figure 3A). Figure 3B shows that human β-synuclein, being predicted to be mostly disordered by all the tools included in the D²P²-based analysis, is expected to have three MoRFs (residues 1–9, 65–89, and 100–134), indicating that intrinsic disorder plays a crucial role in its interactability. Furthermore, the function of β-synuclein can be modulated by various PTMs. At the same time, this protein has lost the capability to undergo spontaneous LLPS (its p_LLPS of 0.5427 is below the threshold of 0.6) together with the aggregation hot spot. However, β-synuclein can still act as a droplet client since it has a long DPR (residues 95–134) at its C-terminal tail (see Figure 3C). As per Figure 3D, human β-synuclein contains four regions with context-dependent interactions (residues 8–19, 21–58, 78–87, and 92–98). Therefore, this protein is also expected to act as a highly promiscuous binder. The idea is supported by Figure 5B, which shows the β-synuclein-centered PPI network generated by STRING, which contains 85 nodes connected by 715 edges. The average node degree of this network is 16.8, and its average local clustering coefficient is 0.682. Furthermore, this network has significantly more interactions than expected (715 vs. 143), as characterized by the PPI enrichment p-value of <1.0 × 10⁻¹⁶. The five most enriched biological processes, molecular functions, and cellular components (as per Gene Ontology annotations) of the members of this network, as well as the most enriched local STRING network clusters and KEGG pathways, are listed in Table 1. Among the functional differences among the members of the α-synuclein- and β-synuclein-centered PPI networks is a remarkable change in the KEGG pathways from exclusively disease-oriented pathways in the α-synuclein-centered network (PD, ALS, AD, Prion disease, and Huntington’s disease) to the synaptic vesicle cycle, PD, nicotine addiction, serotonergic synapse, and insulin secretion pathways in the β-synuclein-centered PPI network.

Similar to α-synuclein, human β-synuclein was shown experimentally to be extensively disordered [6,8,9,72], with β-synuclein being somewhat more disordered than α-synuclein [6]. These experimental observations are supported by the results of our computational analysis. Figure 3E represents the AlphaFold-generated 3D structural model of human β-synuclein, showing the presence of a single, long, horseshoe-like α-helix (residues 2–80). Solution NMR analysis of this protein in its unbound form revealed that its residual structure was shown to noticeably differ from that of α-synuclein, with the helical propensity of β-synuclein being clearly reduced between residues 66 and 83 [9]. This difference in the residual structure of the unbound state was shown to propagate to its micelle-bound form, as the NMR analysis revealed that although the lipid-binding domain of β-synuclein, which is missing 11 residues, remains predominantly helical in the micelle-bound form and preserves the break around position 42, it is characterized by a dramatic decrease in the stability of the helical structure within the 65–83 region [8].

Figure 4 shows that human γ-synuclein (which is different from other members of the human synuclein family by the absence of the tyrosine-rich C-terminal signature [19]) is also predicted to be a highly disordered protein. In fact, it seems that it is the most disordered member of the family, since its overall percent disordered residues (as per PONDR^® VSL2 analysis) is 100% and its average prediction score is 0.8328 (see Figure 4A). Figure 4B represents the functional disorder profile of human γ-synuclein generated by the D²P² platform and also shows the high prevalence of disorder in this protein, which is also expected to have three MoRFs (residues 1–10, 68–77, and 87–97) and several PTMs. As per FuzDrop analysis (see Figure 4C), γ-synuclein is not expected to undergo spontaneous LLPS but can serve as a droplet client and also contains an aggregation hotspot (residues 94–106). These features make this protein closer to α-synuclein than to β-synuclein. This hypothesis is supported by experimental analyses that revealed the closer structural similarity of these two proteins [6,9,161]. The decreased aggregation potential of γ-synuclein in comparison with that of α-synuclein was attributed to an increased α-helical propensity in the amyloid-forming region that is critical for α-synuclein fibrillation, suggesting that increased structural stability in this region may protect against γ-synuclein aggregation [161]. Figure 4D shows the presence of four regions with context-dependent interactions (residues 4–66, 70–75, 83–89, and 94–106). Two of these regions overlap with MoRFs. Figure 5C represents the γ-synuclein-centered PPI network, which contains 32 nodes and 117 edges. Although this network is the smallest one among the synuclein family members, it still has significantly more interactions than expected (117 vs. 46). It is characterized by a PPI enrichment p-value of <1.0 × 10⁻¹⁶, an average node degree of 7.31, and a high average local clustering coefficient of 0.752. The five most enriched biological processes, molecular functions, and cellular components (as per Gene Ontology annotations) of the members of this network, as well as the most enriched local STRING network clusters and KEGG pathways, are listed in Table 1. Finally, Figure 4E represents a 3D model of human γ-synuclein generated by AlphaFold. In line with all other data discussed in this section, this structural model is very similar to that generated for α-synuclein, where a single long α-helix (residues 2–91) is observed.

To understand the general similarity and difference in the functionality of proteins interacting with human α-, β-, and γ-synucleins, we conducted a comparative analysis of the functional enrichment of the members of the corresponding PPI networks. To this end, we looked at the abundance of these proteins in Kyoto Encyclopedia of Genes and Genome (KEGG) pathways [162,163]. Based on the analysis of the networks generated by STRING using the parameters utilized in this study, α-synuclein interactors were found to be associated with 158 different KEGG pathways. These findings are summarized in Supplementary Table S2. A detailed description of these pathways is outside the scope of this study. However, it is important to mention that via its interactors, α-synuclein is involved in numerous pathological pathways, including those associated with neurodegenerative diseases such as Alzheimer disease, amyotrophic lateral sclerosis, Huntington disease, Parkinson disease, Prion disease, and Spinocerebellar ataxia, as well as various types of cancer and metabolic diseases (see Supplementary Table S2). On the other hand, interactors in the β- and γ-synuclein-centered PPI networks were associated with 11 KEGG pathways each. These observations indicate that, relative to the β- and γ-synuclein interactomes, α-synuclein interactors are involved in a much broader spectrum of highly diversified functional pathways. One cannot exclude the possibility that this observation could be related to the fact that there are much more studies dedicated to α-synuclein than to two other members of this protein family. Although one would expect that the α-synuclein interactors should be involved in most of the functions conducted by the members of the β- and γ-synuclein interactomes, Figure 6 shows that there are only three common KEGG pathways shared by the interactors of three human synucleins: synaptic vesicle cycle (hsa04721), serotonergic synapse (hsa04726), and retrograde endocannabinoid signaling (hsa04723). On the other hand, α- and β-synucleins have 8 common pathways, whereas interactors of β- and γ-synucleins share 3 KEGG pathways. Furthermore, via their interactors, β-synucleins are associated with several unique KEGG pathways, such as porphyrin and chlorophyll metabolism (hsa00860), nicotine addiction (hsa05033), neuroactive ligand-receptor interaction (hsa04080), and morphine addiction (hsa05032). However, no such unique pathways were found for the γ-synuclein interactome.

2.2. Effect of Familial Point Mutations on the Intrinsic Disorder Propensity of Human α-Synuclein

It is known that the residual structure of α-synuclein is affected by the familial PD missense mutations. There are at least six such mutations: A53T [138], A30P [164], E46K [165], H50Q [54,55], G51D [166,167], and A53E [168]. To understand how these point mutations associated with the early-onset familial cases of PD affect the propensity of α-synuclein for intrinsic disorder, we analyzed the corresponding sequences of the wild type protein (WT) as well as the A30P, E46K, H50Q, G51D, A53T, and A53E mutants using PONDR^® VSL2. Results of this analysis are shown in Figure 7A, whereas Figure 7B represents the “difference disorder spectra” calculated by subtracting the wild type per-residue disorder propensities from the corresponding data for the mutants. The use of “difference disorder spectra” simplifies the understanding of the effects of mutations, as positive (or negative) peaks in these plots show regions in mutant proteins with an increased (or decreased) local disorder propensity relative to the wild type protein. Since, with the exception of G51D, all “difference disorder spectra” contain positive peaks, the disease-associated mutations A30P, E46K, H50Q, A53T, and A53E caused some increase in the local disorder propensity. On the other hand, local intrinsic disorder propensity is absent in the G51D mutant. Note that the observed effects are mostly local and small (in a range from 0.01 for A30P and E46K to ~0.08 for H50Q and A53E). Since for estimation of the per-residue disorder scores, the disorder predictors use sliding windows, it is expected that changes in the disorder propensity would propagate outside the mutation site and affect a region containing the analyzed point mutation. The length of a region that “feels” mutation would depend, among other factors, on a window size utilized by the predictor and on the actual scale of the disorder score change at the mutation site. This is illustrated by the comparison of the “difference disorder spectra” generated for A53T and A53E mutants, with the A53T “spectrum” being narrower and less intensive than the A53E “difference disorder spectrum”.

Figure 8 illustrates the effect of these mutations on the propensity of human α-synuclein for spontaneous LLPS. Although the droplet-promoting region is located within the C-terminal region of this protein and although all the mutations are located within the N-terminal region, the A30P, E46K, H50Q, G51D, A53T, and A53E mutations show noticeable effects on the LLPS potential of this protein. In fact, based on their propensity for spontaneous liquid–liquid phase separation, p_LLPS, these forms of α-synuclein can be arranged in the following order: A53T (P_LLPS = 0.6416) > A30P (P_LLPS = 0.6413) > A53E (P_LLPS = 0.6350) > WT (P_LLPS = 0.6249) > H50Q (P_LLPS = 0.6165) > E46K (P_LLPS = 0.5730) > G51D (P_LLPS = 0.5153). Based on these observations, one can hypothesize that the capability of α-synuclein to undergo spontaneous LLPS can be eliminated by point mutations E46K and G51D. Since the formation of LLPS is considered a step preceding fibril formation, these data indicate that the aggregation potential of α-synuclein is modulated by mutations. In agreement with these suppositions, these mutations associated with the early onset of PD were experimentally shown to differently modulate α-synuclein functions and aggregation propensity. The A30P mutation promoted the fast formation of non-fibrillar aggregates (such as oligomers or protofibrils) and not fibrils [48,169]. Two other PD mutants, A53T and E46K, were characterized by accelerated fibrillation [48,49,170,171]. Similarly, α-synuclein aggregation and fibrillation were dramatically accelerated by the H50Q mutant [56]. On the other hand, a significant reduction in the α-synuclein oligomerization and fibrillation rates was induced by the G51D and A53E mutations, with the G51D mutant forming amorphous aggregates [167,172] and the A53E mutant being able to slowly form very thin amyloid fibrils [172,173,174].

2.3. Intrinsic Disorder Potential of α-, β-, and γ-Synucleins from Other Species

Based on the experimental and computational data, all three human synucleins are known as highly disordered proteins, so we decided to evaluate the intrinsic disorder propensities of α-, β-, and γ-synucleins from other species. At the first step, we extracted the amino sequences of 381 α- synucleins, 320 β- synucleins, and 234 γ-synucleins from UniProt and checked their global intrinsically disordered predispositions. The results of these analyses are summarized in Figure 9, which shows the PONDR^® VSL2 score vs. PONDR^® VSL2 (%) plot for all these proteins. Typically, the percent of the predicted intrinsically disordered residues (PPIDR) is used to classify proteins as highly ordered, moderately disordered, or highly disordered if their corresponding PPIDR values are below 10%, between 10% and 30%, or above 30%, respectively [175,176]. Additional angle is provided by the analysis of the averaged disorder scores (ADS), which are calculated for each query protein as a protein length-normalized sum of all the per-residue disorder scores and classify them as highly ordered, moderately disordered/flexible, or highly disordered if their ADS < 0.15, 0.15 ≤ ADS < 0.5, and ADS ≥ 0.5. Based on these criteria, all synucleins analyzed in this study are clearly classified as highly disordered, being characterized by PPIDR values of 85.8 ± 14.0, 89.7 ± 8.4, and 94.3 ± 10.8 and ADS values of 0.686 ± 0.075, 0.751 ± 0.055, and 0.758 ± 0.074.

To check if the propensity for intrinsic disorder is an evolutionary conserved feature of the members of the synuclein family, we analyzed disorder propensity in a variety of evolutionary distinct species, such as Macaca fascicularis, Mus musculus, Monodelphis domestica, Tachyglossus aculeatus, Gallus gallus, Pelodiscus sinensis, Xenopus laevis, and Erpetoichthys calabaricus. In other words, our analysis encompassed mammals, including a marsupial and an egg-laying monotreme, a bird, a reptile, an amphibian, and a fish. Amino acid sequences of α- (where available), β-, and γ-synucleins from these species were used for the multiple sequence alignments and per-residue disorder analysis. We did not find sequences of α-synucleins from Monodelphis domestica and Tachyglossus aculeatus, and therefore these proteins were not included in subsequent analyses. The amino acid sequences of all proteins used in these analyses are shown in Supplementary Table S1.

Figure 10 represents the results of multiple sequence alignments of these proteins conducted using Clustal Omega [177] and shows remarkable sequence similarity among these intrinsically disordered proteins. In fact, the percent of sequence identity of human protein with the α-synucleins from other species ranged from 76.3% (Erpetoichthys calabaricus) to 98.57% (Macaca fascicularis) (see Supplementary Figure S1). In the case of human β-synuclein, the percent of sequence identity ranged from 61.07% (Monodelphis domestica) to 97.74% (Mus musculus) (see Supplementary Figure S2). Finally, human γ-synuclein was shown to have the highest (96.06%) and lowest percent of sequence identity (61.34%) with Macaca fascicularis and Erpetoichthys calabaricus, respectively (see Supplementary Figure S3).

Furthermore, the global multiple sequence alignment of all 25 synuclein proteins selected for the analysis revealed that these proteins as a group have a sequence similarity that ranges from 30.00% to 98.57% (see Supplementary Figure S4). Based on these observations, it was not surprising to find that the members of the synuclein family are characterized by rather strong conservation of their within-group per-residue disorder profiles (see Figure 11). This analysis indicated that proteins with high levels of intrinsic disorder can be characterized by remarkable evolutionary conservation.

Based on the phylogenetic analysis of the 252 unique synuclein sequences from 73 organisms, it was concluded that γ-synuclein can be considered a common ancestor of the α- and β-synucleins [178]. Furthermore, in line with the results of our analyses, all three synuclein subfamilies were found to be highly conserved [178]. However, it should be emphasized here that the detailed analysis of the evolution of the synuclein family and comprehensive examination of the evolutionary peculiarities of the intrinsic disorder distribution in these proteins are outside the scope of this article, being an exciting and interesting subject of the dedicated study.

It was emphasized that although the analysis of the synucleins of non-mammalian origin would be useful for a better understanding of the evolution and physiological roles of these proteins, currently reported research on synucleins in non-mammalian vertebrates constitutes a very small percentage of the overall publications on this topic [179]. In fact, there are only a very few studies that provide information on the synucleins of amphibians [179,180,181,182,183], birds [184,185,186], fish [187,188,189,190,191], and reptiles [192].

For example, a comprehensive analysis of the spatial and temporal expression patterns of three synucleins during the early embryonic development of Xenopus laevis revealed that genes encoding these proteins are most intensely expressed in the nervous system [182]. Based on the facts that at the tadpole stages, synucleins showed distinct expression patterns, with snca and sncbb being expressed in the brain and retina, sncbb showing high expression in the spinal cord, and sncg being mainly expressed in the peripheral nervous system, it was concluded that during embryonic development, these proteins have different functions [182]. Since the observed expression patterns of synuclein genes in Xenopus laevis were similar to the expression patterns of synucleins in zebrafish [193], Takifugu rupribes [188], and chickens [184], it was also indicated that synucleins may have a conserved function in nervous system development [182]. It was also shown that the expression levels of the three synucleins in the green lizard’s Anolis carolinensis nervous system were similar to those of human synucleins, confirming the evolutionarily conserved functions of these proteins [192].

In line with the results of our bioinformatics analysis, the recently published study revealed that α-, β-, and γ-synucleins from Xenopus laevis are intrinsically disordered in aqueous media but can undergo disorder-to-order transition into an α-helical structure in the presence of the anionic detergent SDS [179].

2.4. Functional Disorder Analysis of Human Proteins Engaged in Interaction with Members of Synuclein Family

At the next stage, we checked the prevalence of intrinsic disorders in human proteins involved in interactions with α-, β-, and γ-synucleins. PPI networks generated for individual proteins are shown in Figure 5, whereas a global PPI network centered on all three synucleins is shown in Figure 12. This network was generated using a confidence level of 0.45 as the minimum required interaction score. The network includes 469 proteins involved in 10,731 interactions, which significantly exceed the 4889 interactions expected to happen in a random set of proteins of the same size and degree distribution drawn from the genome. The average node degree of this network is 45.8, whereas its average local clustering coefficient is 0.585. The five most enriched biological processes, molecular functions, and cellular components (as per Gene Ontology annotations) of the members of this network, as well as the most enriched local STRING network clusters and KEGG pathways, are listed in Table 1.

Next, we compared the levels of intrinsic disorder in all these interactomes with the disorder status of all proteins in the human brain. The results of this analysis are shown in Figure 13, which clearly indicates that all analyzed protein sets contain noticeable levels of intrinsic disorder. Figure 13A summarizes the results of this analysis in the form of the PONDR^® VSL2 score vs. PONDR^® VSL2 (%) plot. Based on the results of these analyses, proteins can be classified using the percent of predicted intrinsically disordered residues (PPIDR), i.e., the percent of residues with a disorder score of 0.5 or higher. Here, a PPIDR value of less than 10% is taken to correspond to a highly ordered protein; PPIDR between 10% and 30% is ascribed to a moderately disordered protein; and PPIDR greater than 30% corresponds to a highly disordered protein [175,176]. In addition to PPIDR, the average disorder score (ADS) was calculated for each query protein as a protein length-normalized sum of all the per-residue disorder scores. The resulting ADS values can be used for protein classification as highly ordered (ADS < 0.15), moderately disordered or flexible (ADS between 0.15 and 0.5), and highly disordered (ADS ≥ 0.5). Figure 13B represents the results of global disorder analysis in the form of the ΔCH-ΔCDF plot that can be used for further classification of proteins as mostly ordered, molten globule-like or hybrid, or highly disordered based on their positions within the resulting CH-CSD phase space [109,194,195,196]. The results of the corresponding classification are summarized in

Table 2. Distribution of human synuclein-interacting proteins among different disorder categories.

Dataset	Protein Number	PONDR® VSL2 Score vs. PONDR® VSL2 (%) Plot					CH-CDF Plot
		Blue	Cyan	Dark Pink	Pink	Red	Q1	Q2	Q3	Q4
Human brain proteome	10,611	15 (0.15%)	411 (3.87%)	3593 (33.86%)	2335 (22.00%)	4257 (40.12%)	6203 (58.5%)	2938 (27.7%)	1193 (11.2%)	277 (2.6%)
Joint α-β-γ interactome	467	0 (0.0%)	22 (4.7%)	172 (36.8%)	110 (23.6%)	163 (34.9%)	292 (62.5%)	105 (22.5%)	61 (13.1%)	9 (1.9%)
α-Synuclein interactome	356	0 (0.0%)	20 (5.6%)	135 (37.9%)	89 (25.0%)	112 (31.5%)	234 (65.7%)	65 (18.3%)	48 (13.5%)	9 (2.5%)
β-Synuclein interactome	85	0 (0.0%)	0 (0.0%)	30 (35.3%)	19 (22.3%)	36 (32.4%)	48 (56.5%)	26 (30.6%)	11 (12.9%)	0 (0.0%)
γ-Synuclein interactome	32	0 (0.0%)	0 (0.0%)	12 (37.5%)	4 (12.5%)	16 (50.0%)	14 (43.75%)	14 (43.75%)	4 (12.5%)	0 (0.0%)

. This analysis revealed that although proteins in the joint α-β-γ synuclein interactome and especially proteins interacting with human α-synuclein are somewhat less disordered than proteins in the human brain proteome, interactors of β- and especially γ-synuclein are noticeably more disordered. In fact, as per PONDR^® VSL2 analysis, all proteins interacting with β- and γ-synucleins are moderately or highly disordered.

Table 2 provides further illustration for this observation and also shows that, on average, most of the proteins in these various sets are classified as moderately or highly disordered, emphasizing the potential importance of intrinsic disorder for the functionality of these proteins.

Next, we took a look at the intractability of different proteins from the joint α-β-γ synuclein interactome and compared the corresponding node degree of these proteins with their disorder status. The results of this analysis are shown in Figure 14. In this network, almost half of the proteins (207 of 467, 44.3%) are involved in more than 47 interactors each, indicating that these proteins can be considered hubs. These hub proteins are characterized by a mean node degree of 76 ± 41 and a mean PPID of 37.8 ± 22.8%. Our analysis revealed that 60 proteins with the least number of interactors (with 10 or fewer partners each) were characterized by a mean node degree of 6.0 ± 2.7 and a mean PPID of 51.4 ± 25.9%. On the other hand, the 60 most connected proteins were characterized by a mean node degree of 123 ± 43 and a mean PPID of 43.1 ± 21.4%. Curiously, the 60 most disordered proteins in this dataset had a mean node degree of 44.2 ± 60.6 and a mean PPID of 87.5 ± 8.4%, whereas the 60 most ordered proteins in this set were characterized by a mean node degree of 43.7 ± 32.0 and a mean PPID of 11.1 ± 11.6%. These data taken together indicated that generally, proteins with lower disorder levels are expected to engage in a bit more interactions. However, the situation changes if one compares the 20 most ordered proteins (PPID of 7.5 ± 1.8%) with the 20 most disordered proteins (PPID of 96.6 ± 3.9%), as their interactomes range from 4 to 430 and from 4 to 86 proteins, respectively.

We also looked for a correlation between the overall disorder status, intractability, and LLPS predisposition of human proteins in the joint α-β-γ synuclein interactome. The results of this analysis are summarized in Figure 15, which shows the corresponding outputs in the form of a 3D plot. This analysis revealed that proteins predicted by FuzDrop as droplet drivers (i.e., possessing p_LLPS ≥ 0.6) are on average more disordered than proteins that are not capable of spontaneous liquid–liquid phase separation. In fact, 130 proteins with p_LLPS ≥ 0.6 were characterized by a mean PPIDR of 66.3 ± 19.5%, whereas the remaining 337 proteins from the joint human α-β-γ synuclein interactome were characterized by a mean PPIDR of 31.4 ± 18.4%. On the other hand, LLPS drivers and non-drivers did not show a noticeable difference in their within network interactivity: within the joint human α-β-γ synuclein interactome, their corresponding mean node degrees were 40.4 ± 49.3% (drivers) and 48.0 ± 35.6% (non-drivers), respectively. Comparative analysis of the 130 most disordered proteins revealed that they are characterized by a mean PPIDR of 74.5 ± 14.0%, a mean node degree of 41.7 ± 48.2, and a mean p_LLPS of 0.753 ± 0.295. The remaining 337 proteins are characterized by a mean PPIDR of 28.3 ± 12.4%, a mean node degree of 47.4 ± 35.2, and a mean p_LLPS of 0.311 ± 0.229. Comparative analysis of the 130 most connected proteins with a mean node degree of 90.8 ± 45.6 revealed that they are characterized by a mean PPIDR of 38.4 ± 22.2% and a mean p_LLPS of 0.379 ± 0.273. The remaining less interactive human proteins in the joint α-β-γ synuclein interactome have a mean node degree of 28.6 ± 16.3, a mean PPIDR of 42.2 ± 25.2%, and a mean p_LLPS of 0.456 ± 0.331.

A detailed description of the prevalence and functionality of intrinsic disorder in the sets of 11 most disordered and 5 most ordered members of the joint α-β-γ synuclein interactome is presented in Appendix A and Appendix B, respectively.

3. Materials and Methods

3.1. Overview

In order to facilitate sequence-based and structure-based comparison of synuclein proteins of different species, we utilized web-based computational tools such as UniProt [197], NCBI Blast, and AlphaFold [198]. To perform disorder-based analysis and comparison, we utilized the RIDAO application [199], a computational tool, to identify the predicted disorder throughout the amino acid sequence. Further, we also utilized the D²P² tool [200] and the FuzDrop tool [147,148,201] to examine intrinsic disorder and predict liquid–liquid phase separation (LLPS). We conducted extensive analysis of interacting proteins through the STRING database [202] to enable disorder-based comparison of the human synuclein family with the proteins in their respective interactomes.

3.2. Sequence and Structure-Based Analysis

We utilized the UniProt database [197] to extract the amino acid sequence information for human α-synuclein, β-synuclein, and γ-synuclein. UniProt is a database that provides the known amino acid sequences along with additional information regarding species and protein identity. Utilizing the extracted amino acid sequence, we visualized the predicted 3D structure of the proteins using the AlphaFold platform [198]. AlphaFold is an AI-based computational tool that predicts the 3D structure of a protein given its amino acid sequence. Having analyzed the sequence and structure of the synuclein family of proteins, we utilized the NCBI Blast tool to compare the human synuclein family sequences with those of other species. To this end, we used UniProt to find the amino acid sequences of α-, β-, and γ-synucleins from different species using “(protein_name:”alpha-synuclein”) AND (gene:SNCA) NOT fragment”, “(protein_name:”beta-synuclein”) AND (gene:SNCB) NOT fragment”, and “(protein_name:”gamma-synuclein”) AND (gene:SNCG) NOT fragment” as search criteria. This search resulted in 381 α- synucleins, 320 β- synucleins, and 234 γ-synucleins, which were used for global disorder analysis. Next, we selected eight species from different classes of animals, such as Macaca fascicularis, Mus musculus, Monodelphis domestica, Tachyglossus aculeatus, Gallus gallus, Pelodiscus sinensis, Xenopus laevis, and Erpetoichthys calabaricus. We extracted the amino acid sequences of the three synuclein proteins for each of these species using UniProt and performed a sequence-based comparison with the corresponding human synucleins using NCBI Blast. Further, we analyzed the intrinsic disorder of the synucleins of these species using the RIDAO platform.

3.3. Disorder-Based Analysis of the Interactomes of Human Synucleins

Having performed sequence- and structure-based comparisons of human synucleins with the synucleins of various species, we performed detailed intrinsic disorder analysis of the three human synucleins and the proteins in their interactomes. To this end, we utilized the STRING database [202] to identify proteins that are known to interact with human α-, β-, and γ-synucleins. The STRING database assembles information from different sources, such as laboratory experiments, previous research, and text mining models. The STRING database takes the query protein sequence as input and provides a network of interacting proteins with varying levels of confidence. The interacting proteins are sorted by confidence, with a score of 0.7 or above being termed high confidence, a score of 0.4 being considered medium confidence, and a score of 0.15 or below being taken as low confidence. Additional customization allows us to specify the maximum number of interactors in the first shell (the proteins directly interacting with the target protein). Known 3D structures of interactors and the nature of interactions (known interaction or predicted interaction) are also provided. We specify a maximum of 500 interactors in the first shell to enable an extensive search of the interacting proteins.

Further, we predict intrinsic disorder for each of these proteins using the RIDAO platform [199] and, based on the outputs of CH-CDF analysis incorporated into RIDAO, label them as ‘disordered’, ‘mixed’, ‘rare’, or ‘structured’. We selected the first 10 most disordered proteins in the interactomes of each member of the synuclein family and performed a detailed intrinsic disorder analysis with the RIDAO and the D²P² platforms [200]. Further, we analyze the propensity of these proteins for liquid–liquid phase separation (LLPS) with the FuzDrop computational platform [147,148,201].

4. Conclusions

This work provides a discussion of the sequence-based and structure-based functionality of the proteins of the human synuclein family. Through comparative inter-species sequence-based analysis, various insights regarding the similarity of α-, β-, and γ-synucleins from different species are obtained. Intrinsic disorder analysis demonstrates the presence of disordered, ordered, and mixed members in the human joint α-β-γ interactome. Interestingly, comprehensive disorder analysis reveals the presence of a significant percentage of intrinsically disordered interacting proteins in the interactomes of human α-, β-, and γ-synucleins. The analysis of the liquid–liquid phase separation probability of human synucleins and their interactors provides important insights into the potential roles of intrinsic disorder in the organization of synuclein-related MLOme. Finally, we explore the potential functionality of intrinsic disorder in a set of the most disordered members of the joint α-β-γ interactome using a set of bioinformatics tools.