Alternative Splicing Guided Stoichiometric Competition Model of Vesicular Polyamine Transporter Reveals Modulatory Drug Targets

Alternative Splicing Guided Stoichiometric Competition Model of Vesicular Polyamine Transporter Reveals Modulatory Drug Targets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Alternative Splicing Guided Stoichiometric Competition Model of Vesicular Polyamine Transporter Reveals Modulatory Drug Targets Alper Karagöl, Taner Karagöl This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8649051/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alternative splicing represents a potent mechanism for the post-transcriptional regulation of solute carrier function, yet the structural biophysics governing the Vesicular Polyamine Transporter (VPAT) interactome remain elusive. Here, we present a comprehensive thermodynamic and topological analysis of canonical VPAT and its splice variants (A0A3B3ITL9, A0A3B3IU67, A0A3B3IT67, and A0A3B3ISL3) utilizing explicit membrane Molecular Dynamics (MD) simulations coupled with MM/PBSA free energy decomposition. We identify isoform A0A3B3ITL9 (10-TM) as a specific, equipotent competitive inhibitor of canonical VPAT homodimerization. While the canonical homodimer (ΔG bind=-29.29 kcal/mol) is stabilized by a conserved C-terminal lock involving Trp333 and Tyr272, the truncated ITL9 isoform bypasses this interface. Instead, ITL9 exploits a conformational switch to recruit the VPAT N-terminus via a high-affinity electrostatic anchor at Arg20, resulting in a heterodimeric complex of identical stability (ΔG bind=-30.10 kcal/mol). Conversely, we demonstrate that other isoforms are functionally segregated from this competitive pool: A0A3B3IU67 (11-TM) is destabilized by topological mismatch within the lipid bilayer, while A0A3B3IT67 (8-TM) functions as an inert allocrite driven by obligate homodimerization. These findings define the VPAT regulatory landscape as a stoichiometric competition model, highlighting the N-terminal Arg pocket as a isoform-specific equipotent therapeutic target for modulating polyamine transport. Drug Discovery, Design, & Development Cellular & Molecular Neuroscience Applied Biochemistry Computational Biology Truncated isoforms Monoamine transporters mRNA Synaptic vesicle Neurotransmission Figures Figure 1 Figure 2 Introduction The regulated storage and release of neurotransmitters and neuromodulators are fundamental processes underlying synaptic plasticity, learning, and memory [ 1 , 2 , 3 ]. Among the diverse array of signaling molecules sequestered within synaptic vesicles, polyamines have emerged as critical regulators of neuronal excitability [ 4 , 5 , 6 , 7 , 8 ]. These polycationic molecules modulate the activity of key ion channels, including NMDA and AMPA receptors, and potassium channels, thereby shaping the spatiotemporal dynamics of synaptic transmission [ 6 , 7 , 8 ]. The vesicular sequestration of polyamines is mediated by the Vesicular Polyamine Transporter (VPAT), encoded by the SLC18B1 gene [ 4 , 5 ]. VPAT is also responsible for vesicular storage and release of polyamines from mast cells [ 9 ]. Given the neuroprotective and neuromodulatory roles of polyamines, the functional regulation of VPAT is of paramount physiological significance [ 10 , 11 ]. Dysregulation of polyamine transport has been implicated in oxidative stress, neurodegeneration, and cognitive deficits, underscoring the need to understand the molecular mechanisms governing VPAT activity [ 10 , 11 ]. VPAT belongs to the Major Facilitator Superfamily (MFS) of transporters, a vast class of membrane proteins that typically share a conserved fold of 12 transmembrane helices organized into two pseudo-symmetrical domains [ 12 ]. While the classical "rocker-switch" mechanism of MFS transport is often conceptualized within the context of a monomeric functional unit [ 13 ], emerging structural and biochemical evidence suggests that oligomerization is a prevalent and often obligatory feature of membrane protein physiology [ 14 , 15 , 16 , 17 , 18 ]. Crucially, the requirement for oligomeric assembly creates a vulnerability: the presence of non-functional or truncated splice variants can exert potent dominant-negative effects for distinct membrane proteins [ 15 , 16 , 18 , 19 , 20 ]. By heterodimerizing with the wild-type (WT) transporter, these isoforms can inhibit canonical assemblies [ 15 , 16 ]. This mechanism of truncated isoform-mediated modulation could be a sophisticated layer of gene regulation [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. The SLC18B1 locus is subject to extensive alternative splicing, generating a repertoire of protein isoforms whose functions remain largely uncharacterized. While the canonical VPAT isoform is well-defined, the proteomic landscape includes variants such as A0A3B3ITL9, A0A3B3IU67, and A0A3B3IT67. We hypothesized that these specific isoforms function as inhibitors of VPAT signaling by effectively competing for the dimerization interface of the wild-type bio assembly. In this model, the efficacy of an isoform as a regulator is determined by a strict thermodynamic hierarchy: to act as a dominant negative, an isoform must bind to the wild-type VPAT with an affinity that matches or exceeds the stability of the wild-type homodimer. Conversely, isoforms that preferentially self-associate would form stable, sequestered homodimers with little impact on the functional VPAT pool. Unraveling this thermodynamic mechanism requires an energetic accounting of the protein-protein interfaces involved, a task well-suited for computational structural biology. However, membrane characteristic is another point that should be accounted for analyzing vesicular transporters [ 16 ]. A previous limitation concerns lipid composition in modeling studies [ 15 ]. While simplistic membrane models can provide valuable insights, they often fail to capture the specific VPAT localization at synaptic vesicles. In this study, we addressed this limitation by employing a realistic lipid composition, allowing a more accurate representation of synaptic vesicle–associated environments. Molecular dynamics simulations in a lipid bilayer provide a rigorous framework for estimating the relative binding free energies of macromolecular complexes by decomposing interactions into van der Waals, electrostatic, and solvation contributions [ 15 , 16 ]. This approach allows us to move beyond simple structural modeling to predict the competitive landscape of isoform interactions quantitatively. Here, we present a comprehensive thermodynamic analysis of VPAT dimerization and its modulation by the isoforms A0A3B3ITL9, A0A3B3IU67, and A0A3B3IT67. We demonstrate that VPAT homodimerization is a stable, energetically favorable process, providing a baseline for functional assembly. Strikingly, our calculations reveal that the isoforms A0A3B3ITL9 and A0A3B3IU67 form heterodimers with VPAT that are significantly more stable than the wild-type homodimer. In contrast, the A0A3B3IT67 isoform displays a distinct profile, characterized by hyper-stable self-association and weak affinity for VPAT, rendering it functionally inert as an inhibitor. These findings define a structural and thermodynamic basis for isoform-specific regulation of vesicular polyamine transport, providing insights into how alternative splicing expands the functional versatility of the SLC18 transporter family. Results and Discussions Topological and Biochemical Analyses of VPAT Isoforms Physicochemical and topological profiling of the VPAT isoform library necessitates the exclusion of isoform ISL3 (A0A3B3ISL3) from the comparative membrane-embedded dimerization analysis. Unlike the canonical VPAT transporter, which exhibits a robust integral membrane architecture characterized by 12 transmembrane (TM) helices and a distinct hydrophobic GRAVY index of 0.719, ISL3 presents as a severely truncated variant (Supplementary Table S1, Figure S1, Figure S2). The topological prediction assigns zero transmembrane domains to ISL3, and a near-neutral GRAVY score of 0.051. This hydropathic profile indicates that ISL3 lacks the necessary hydrophobic core to integrate into the lipid bilayer. The remaining isoforms; A0A3B3ITL9 (ITL9), A0A3B3IU67 (IU67), and A0A3B3IT67 (IT67); represent a spectrum of C-terminal truncations that fundamentally alter the transmembrane (TM) architecture of the transporter (Supplementary Figure S3, Figure S4, Figure S5). Unlike the canonical VPAT (525 aa, 12 TMs), these isoforms has reduced helical counts (10, 11, and 8 TMs, respectively), creating a symmetrical mismatch within the lipid bilayer. This structural deviation from the dodecyl-helical core may force these variants to adopt alternative interfacial arrangements to satisfy their energetic requirements, explaining the diverse TM modes observed in the residue decomposition analysis. Isoform IT67 (383 aa) represents the most severe membrane-competent truncation, retaining only 8 transmembrane domains (Supplementary Figure S3). Isoform ITL9 (373 aa) exhibits a deletion of two transmembrane domains, resulting in a 10-TM topology (Supplementary Figure S4). Biophysically, this specific truncation is the most consequential. Isoform IU67 (409 aa) presents with 11 transmembrane domains, a unique odd-numbered topology that implies a potential inversion of the C-terminal orientation relative to the membrane leaflet (Supplementary Figure S5). This topological inversion likely disrupts the alignment of the dimerization interface. Comparative Docking of VPAT Isoform Complexes To elucidate the molecular mechanism by which alternative splice isoforms modulate VPAT function, we performed Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy calculations (Supplementary Table S2). We hypothesized that specific isoforms function as dominant-negative inhibitors by forming stable heterodimers with VPAT, thereby sequestering the wild-type protein and preventing the formation of functional VPAT homodimers. To test this, we analyzed the binding free energies (ΔG bind) of the wild-type VPAT homodimer compared to three isoform-VPAT heterodimeric complexes (VPAT-A0A3B3ITL9, VPAT-A0A3B3IU67, and VPAT-A0A3B3IT67). The wild-type VPAT homodimer exhibited a predicted MMGBSA binding free energy of -152.09 kcal/mol, driven primarily by van der Waals interactions (ΔEvdw =-100.17 kcal/mol) and electrostatic contributions (ΔE ele=-56.06 kcal/mol). This establishes a thermodynamic baseline for homotypic self-association. Our analysis revealed a hierarchy of binding stabilities that strongly supports a competitive inhibition mechanism for specific isoforms. Strikingly, the interaction between VPAT and the A0A3B3ITL9 (ITL9 isoform) resulted in a significantly more stable complex, with a total binding free energy of -283.85 kcal/mol. This represents a dramatic energetic advantage (ΔΔG≈-131 kcal/mol) over the homotypic VPAT-VPAT interaction. The stability of this heterodimer is anchored by a substantial increase in van der Waals contacts (ΔE vdw =-177.64 kcal/mol) and favorable solvation effects. Similarly, the A0A3B3IU67 isoform also formed a highly stable heterodimer with VPAT (ΔGbind =-193.65 kcal/mol), surpassing the stability of the VPAT homodimer by approximately 41 kcal/mol. These thermodynamic profiles indicate that both A0A3B3ITL9 and A0A3B3IU67 (IU67 isoform) are thermodynamically favored to disrupt VPAT homodimers in a competitive environment. Per-residue energy decomposition further identified critical residues driving the potent inhibition by A0A3B3ITL9. In the VPAT-ITL9 complex, Arg3 (Arg20) on the receptor chain made the largest energetic contribution (-11.08 kcal/mol), an interaction not observed in the homodimer interface. Furthermore, strong hydrophobic contributions were provided by Phe and Tyr from the ligand isoform chain. These distinct interfacial contacts underscore the structural basis for the isoform's enhanced affinity. In contrast, the A0A3B3IT67 isoform formed the least stable heterodimer with VPAT (ΔGbind=-138.58 kcal/mol), which is energetically weaker than the wild-type homodimer. Interestingly, the A0A3B3IT67 homodimer was found to be exceptionally stable (ΔG bind=-408.68 kcal/mol), suggesting that this isoform preferentially self-associates rather than interfering with VPAT. Collectively, our computational data identify A0A3B3ITL9 and A0A3B3IU67 as potent thermodynamic traps that can effectively blockade VPAT homodimerization. Differential Residue & Interface Analysis The self-association of canonical VPAT is driven principally by a hydrophobic patch located near the C-terminus. The analysis identifies residues Trp305, Tyr244, Leu247, and Leu304 as the primary energy contributors (corresponding to Trp333 and Tyr272, Leu275, and Leu332). These residues form a classic "hydrophobic lock", burying aromatic side chains to stabilize the dimer. This interface is heavily reliant on shape complementarity and hydrophobic exclusion but lacks strong electrostatic salt bridges, which limits its maximal binding energy compared to the isoforms. The increase in stability seen in the VPAT-ITL9 complex is driven by a shift to the N-terminus. The N-terminal Arg residue on VPAT acts as a critical linchpin in this interaction, contributing over − 11 kcal/mol to the binding energy, a magnitude of contribution not seen in the homodimer. This suggests the formation of a high-energy salt bridge or hydrogen bond network specific to the ITL9 complex. Additionally, a central cluster becomes highly active. This indicates that ITL9 engages VPAT in a different orientation, clamping down on the N-terminus and central domains rather than the C-terminus. The interaction with IU67 reveals a third distinct interface. This binding mode is dominated by Arg94 and Phe73, which show significantly stronger contributions here than in the homodimer (an improvement of ~ 8.5 kcal/mol). The recruitment of Arg alongside His and Ile points to an interface that combines hydrophobic stacking with specific charge-charge interactions. Free Energy Calculations in the Synaptic Membrane Environment Based on the refinement of the binding free energies using membrane full-atom Molecular Dynamics (MD) simulations (MM/PBSA on the last 20 ns of 50 ns trajectories), the interaction landscape has been updated to reflect physiological conditions more accurately (Table 1 ). The inclusion of the membrane environment significantly modulates the predicted stabilities, revealing that isoform A0A3B3ITL9 (ITL9) is a direct, equipotent competitor to the canonical VPAT, whereas isoform A0A3B3IU67 (IU67) is significantly weaker and unlikely to disrupt the canonical complex under standard conditions (Supplementary Figure S6, Figure S7, Figure S8, Figure S9, Figure S10, Figure S11, Figure S12, Figure S13). Table 1 Interface composition and binding free energies (ΔΔGs) of the dimer complexes in an atomistic bilayer model. Interaction Pair Interaction Type Membrane Binding Energy (ΔGbind​) Stability Relative to VPAT Homodimer VPAT – A0A3B3ITL9 Heterodimer -30.10 ± 5.65 kcal/mol Equipotent VPAT – VPAT (Canonical) Homodimer -29.29 ± 5.41 kcal/mol Baseline VPAT – A0A3B3IU67 Heterodimer -9.80 ± 3.83 kcal/mol Significantly Weaker A0A3B3IT67 – A0A3B3IT67 Homodimer -9.16 ± 6.04 kcal/mol Unstable / Transient ITL9 does not engage the canonical C-terminal hydrophobic lock. Instead, it recruits the VPAT N-terminus. Unlike the initial screening which suggested ITL9 was a "super-binder", membrane simulations reveal it binds with an affinity (-30.10 kcal/mol) nearly identical to the canonical VPAT homodimer (-29.29 kcal/mol). This implies a 1:1 competitive landscape where the ratio of heterodimer (VPAT-ITL9) to homodimer (VPAT-VPAT) is determined purely by the relative expression levels of the two proteins, rather than thermodynamic dominance (Fig. 1 ). Rather than rendering the protein inert, this structural "change" exposes the N-terminal domain, specifically the Arg3 (Arg20) motif, allowing it to act as a surrogate interface. The preservation of equipotent binding energy ( kcal/mol) despite a ~ 30% reduction in mass indicates that ITL9 retains a high degree of conformational stability. The 10-TM architecture likely relaxes steric constraints, permitting the high-affinity electrostatic capture of canonical VPAT monomers that would otherwise be sterically occluded in the full-length 12-TM structure. The IU67 isoform shows a drastic reduction in stability (-9.80 kcal/mol) compared to the homodimer. This suggests that in a realistic lipid bilayer, IU67 cannot effectively displace VPAT from its homodimeric state. The "orphan" 11th helix may introduce steric clashes or entropic penalties within the lipid environment, preventing the formation of a tight complex. Consequently, the structural change in IU67 shifts it from a competitive inhibitor to a low-affinity bystander in the membrane context. Isoform A0A3B3IT67 Homodimerization as a Protective Mechanism The IT67 homodimer exhibits an exceptionally high binding free energy (-408.68 kcal/mol) in solvent MMGBSA, far exceeding the affinity of any other complex analyzed, including its interaction with VPAT (-138.58 kcal/mol). The loss of one-third of the transmembrane core creates a highly unstable inrteraction with canonical VPAT. The structural change here is catastrophic for heterodimerization; the 8-TM core lacks the properties to stabilize an interface with the 12-TM canonical VPAT. Instead, this truncation exposes a massive hydrophobic surface that drives the "super-stable" homodimerization seen in soluble screening. Biophysically, this suggests that IT67 functions as an inert allocrite in the context of VPAT signaling. Although membrane insertion appears to destabilize this homodimer (MM/PBSA ΔG bind ≈ -9 kcal/mol, Supplementary Figure S14, Figure S15), the massive stability observed in the soluble phase implies that IT67 likely aggregates or traffics as a pre-formed homodimer, thereby insulating the canonical VPAT machinery from interference. This obligate homodimerization serves as a built-in specificity filter, ensuring that only fusion-competent isoforms (like ITL9) can enter the competitive regulatory pool. Co-evolutionary Profilling of VMAT Isoform Regulation Molecular evolution of transmembrane proteins is non-linear and complex [ 21 , 22 , 23 ] and involves co-evolutionary dependencies [ 21 , 22 , 23 , 24 , 25 ]. Previously, VMAT and EAAT isoforms showed clear sign of evolutionary couplings in the interaction domains [ 15 , 16 ]. Unlike VPAT stoichiometric isoform interaction, they showed increased binding affinity to canonical dimer. The N-terminal anchor residue, Arg3 (corresponding to Arg20), does not show strong co-evolutionary signals with the canonical C-terminal sector, supporting the hypothesis that the VPAT–ITL9 heterodimer is a product of evolutionary neofunctionalization (Fig. 2 ). Rather than utilizing a conserved structural scaffold, ITL9 exploits an electrostatic surface on the canonical monomer. This suggests that the alternative splicing event generating ITL9 evolved to hijack an accessible, non-constrained region of the protein (the N-terminus) to function as a regulatory switch, allowing for dynamic modulation of transport activity without redesigning the core oligomerization machinery. The dichotomy in evolutionary couplings explains the differential stability observed in the membrane environment. This evolutionary divergence implies that the regulatory potency of ITL9 is likely species-specific or adaptable, unlike the universally conserved transport function of the homodimer. Structural Implications for Therapeutic Targeting The spatial segregation of binding interfaces between the canonical homodimer and the pathogenic/regulatory heterodimers offers a precise structural rationale for drug design (Table 2 ). To selectively inhibit the formation of the VPAT–ITL9 heterodimer while preserving canonical VPAT function, therapeutic ligands should be designed to target the N-terminal Arg pocket. Occupancy of this site would sterically occlude ITL9 binding without perturbing the C-terminal interface required for VPAT homodimerization. Conversely, pathological overexpression of VPAT homodimers could be dampened by allosteric inhibitors targeting the Trp-Tyr hydrophobic cleft, a site that is structurally distinct and distal from the regulatory interface used by ITL9. Table 2 Possible targets. Target Strategy Target Residues Rationale Disrupt VPAT Homodimer Only TRP-305, TYR-244 These are critical for VPAT-VPAT binding but are not the primary drivers for the high-affinity ITL9 or IU67 heterodimers. Mutating or binding a small molecule here would block homodimers while likely preserving heterodimer functions. Block VPAT-ITL9 Interaction ARG-3 This residue is the "linchpin" of the super-strong ITL9 interaction. Targeting the N-terminus or ARG-3 specifically would ablate this interaction without affecting the canonical homodimer (which relies on residue 244/305). Block VPAT-IU67 Interaction ARG-94, PHE-73 These residues define the IU67 binding footprint. They are unique to this interaction interface. The integration of topological profiling, implicit solvent screening, and rigorous membrane-embedded simulation establishes the VPAT interactome as a highly structured, competitive regulatory network rather than a stochastic assembly of splice variants. The data fundamentally revises the understanding of VPAT regulation, shifting the paradigm from simple dominant-negative suppression to a precise stoichiometric competition model driven primarily by isoform A0A3B3ITL9 (ITL9). Methods Protein Sequences and Other Characteristics Similar to our previous studies [15,16], sequence data was mined from the UniProt database [26], utilizing FASTA formatting for downstream processing. Isoforms were identified via UniProt’s gene-centric referencing system, which integrates manual curation with automated mapping to eukaryotic reference proteomes [26]. The dataset was filtered to isolate viable protein-coding variants, specifically constraining selection to lengths spanning 0.15–0.85 of the canonical transcript to ensure structural relevance. Topological orientation and feature mapping were visualized via Protter [27]. Key physicochemical parameters, including molecular weight, residue composition, thermodynamic instability metrics, and hydropathicity (GRAVY), were derived using Expasy tools [28,29,30]. Model ranking Tertiary and quaternary conformational ensembles (specifically monomers, dimers, and trimers) were predicted using the AlphaFold3 [31]. To ensure high-fidelity discrimination in inhibitor ranking, the computationally tractable Variable Dielectric Molecular Mechanics with Generalized Born and Surface Area (VD-MM/GBSA) algorithm was employed [32]. Post-docking rescoring was executed via the HawkDock platform [32,33,34] following atomistic reconstruction, specifically the addition of missing hydrogen and heavy atoms, using the Amber16 tleap module. Calculations utilized a variable dielectric generalized Born model, which integrates residue-specific dielectric constants to achieve superior accuracy over classical GB formulations [33]. The complex underwent a 5,000-step geometry optimization protocol (comprising 2,000 steps of steepest descent followed by 3,000 steps of conjugate gradient minimization) with a 12 Å cutoff applied to van der Waals interactions. Finally, per-residue free energy decomposition was applied to delineate the thermodynamic contributions of critical residues at the canonical transporter–isoform interface. Atomistic synaptic vesicle membrane systems Molecular dynamics (MD) simulations were performed on AlphaFold-predicted membrane-embedded protein structures using GROMACS v2024.3 [45], following simulation workflows previously validated in our studies of glutamate transporter isoforms and neuronal membrane proteins [15,16,36,37]. All computations were conducted on a distributed high-performance environment consisting of three Google Colab instances, each provisioned with an NVIDIA L4 GPU, 126 GB GPU memory, 318 GB system RAM, and Intel® Xeon® processors [38]. To optimize computational throughput, GROMACS was custom-compiled with CUDA acceleration, facilitating efficient GPU offloading and multi-core parallel execution of force calculations and integration steps [38]. Adhering to the computational framework established in our prior investigations [15,16,36,37], all-atom membrane systems were assembled via the CHARMM-GUI Membrane Builder [39,40,41]. The translational and rotational positioning of the protein within the bilayer was optimized using the PPM 2.0 algorithm, which accounts for anisotropic dielectric constants and hydrogen bonding potential at the lipid-solvent interface [42]. To recapitulate a representative plasma membrane environment, a complex lipid matrix was generated, maintaining concordance with our established models. This bilayer comprised a symmetrical distribution of cholesterol, POPC, POPE, POPS, POPI, and palmitoyl-sphingomyelin (PSM) across both leaflets [43,44]. Electroneutrality and a physiological ionic strength of 0.15 M (K +/Cl-) were achieved via 2,000 steps of Monte Carlo ion displacement employing a primitive ion model. All simulations were parameterized using the CHARMM36m all-atom force field [45]. Molecular dynamics simulations Prior to dynamic simulations, the system was subjected to energy minimization using the steepest descent algorithm for up to 5,000 iterations, with convergence defined by a maximum force threshold of 1,000 kJ·mol⁻¹·nm⁻¹. Subsequent equilibration was conducted following the standard six-stage CHARMM-GUI workflow, during which positional restraints were progressively relaxed to ensure a smooth transition toward an unrestrained system. The terminal equilibration phase consisted of a 500 ps simulation employing the velocity-rescaling (v-rescale) thermostat to maintain the system temperature at 303.15 K (τₜ = 1.0 ps) and the C-rescale barostat to regulate pressure at 1 bar under semi-isotropic coupling conditions (τₚ = 5.0 ps). Production molecular dynamics simulations were then performed for 50 ns, with trajectory snapshots recorded at 0.5 ns intervals, consistent with our previous work [15,16,36,37]. Long-range electrostatic interactions were treated using the Particle Mesh Ewald (PME) method, while short-range Coulombic and van der Waals interactions were truncated at 1.2 nm. During production runs, temperature and pressure were maintained at 303.15 K and 1 bar using the Nosé–Hoover thermostat and the Parrinello–Rahman barostat, respectively, under semi-isotropic pressure coupling. All covalent bonds involving hydrogen atoms were constrained using the LINCS algorithm, enabling the use of the specified interaction cutoffs for short-range nonbonded interactions. System stability and conformational behavior were assessed through post-simulation trajectory analyses. Backbone structural deviations were quantified by calculating the root mean square deviation (RMSD), while global compactness was evaluated via the radius of gyration (R_g), both computed using the GROMACS gmx rms and gmx gyrate utilities. Local residue-level flexibility was characterized by determining the root mean square fluctuation (RMSF) of Cα atoms. Solvent exposure of protein side chains was quantified by calculating the solvent-accessible surface area (SASA) using the gmx sasa module with a probe radius of 1.4 Å [46]. For binding free energy calculations, only the final 20 ns (31–50 ns) of the equilibrated production trajectories were considered. Binding Free Energy Calculations Binding free energies were estimated using the Molecular Mechanics–Poisson–Boltzmann Surface Area (MM–PBSA) formalism as implemented in gmx_MMPBSA, adapted explicitly for membrane-embedded systems [47,48]. Dielectric constants were parameterized to reflect heterogeneous environments, with values of 7.0 for the membrane phase, 4.0 for the solute, and 80.0 for the aqueous solvent. Solvent-excluded surfaces were constructed using a dual-probe approach, employing a 1.40 Å probe radius for water and a 2.70 Å probe radius for the membrane environment. Long-range electrostatic interactions and forces were computed using the particle–particle particle–mesh (P3M) algorithm.Per-residue free energy decomposition was performed using the pairwise decomposition scheme (idecomp = 2) to resolve individual residue contributions to electrostatic and van der Waals interaction components. Binding free energies were calculated as ensemble averages over selected trajectory frames, and the associated standard error of the mean (SEM) was determined via propagation of uncertainty. Interfacial residue contacts within a 4 Å cutoff were quantified and visualized using gmx_MMPBSA_ana and UCSF ChimeraX [49]. All statistical plots were generated using Grace (https://plasma-gate.weizmann.ac.il/Grace/). Co-evolutionary Profilling Evolutionary couplings (ECs) were inferred using the EVcouplings framework, which applies a global maximum entropy model constrained by empirical multiple sequence alignment (MSA) statistics [15,16,50]. To ensure comparability across proteins of differing lengths and alignment depths, EC strengths were normalized using length-adjusted bitscores. Alignment quality was evaluated using the ratio of the effective number of sequences to protein length (n_eff/L), where values exceeding 5 are indicative of statistically robust coupling inference. The resulting n_eff/L values were 15.49 for canonical VPAT, 20.80 for the ISL3 isoform, 33.34 for the IU67 isoform, 120.56 for the ITL9 isoform, and 21.13 for the IT67 isoform. Declarations Ethics Approval Ethics approval was not required for this computational study as it did not involve animal subjects, human participants, and identifiable data. Consent to participate Not applicable. This computational study did not involve human participants. Consent for publication Not applicable. This computational study did not involve human participants. Availability of data and materials The AlphaFold DB (https://alphafold.ebi.ac.uk), a database developed by DeepMind and the European Bioinformatics Institute (EMBL-EBI) at EMBL, is a repository for AlphaFold2 predictions, with over 200 million protein structures. Each statistical and computational analysis of this study, included with step-by-step instructions where possible, are publicly available to ensure repeatability. For more detailed information on the statistical analyses, input files and detailed outputs, including the AlphaFold calculations and codes to regenerate analyses, please visit the website: https://github.com/karagol-alper/VPAT-isoforms Competing financial interests None. Funding The author(s) received no specific funding for this work. Author Contributions A.K and T.K contributed equally to this work. All authors have read and agreed to the published version of the manuscript. Acknowledgements None. References Gholami A, Mortezaee K (2025) Neurotransmitters in Neural Circuits and Neurological Diseases. ACS Chem Neurosci 16(19):3653–3664. https://doi.org/10.1021/acschemneuro.5c00426 Fuchsberger T, Paulsen O (2022) Modulation of hippocampal plasticity in learning and memory. Curr Opin Neurobiol 75:102558. https://doi.org/10.1016/j.conb.2022.102558 Magee JC, Grienberger C (2020) Synaptic Plasticity Forms and Functions. 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Protein Sci 32(11):e4792. https://doi.org/10.1002/pro.4792 Thomas A, Hopf AG, Green B, Schubert S, Mersmann, Charlotta PI, Schärfe JB, Ingraham A, Brock T-PK, Riesselman AJ, Palmedo P, Kang C, Sheridan R, Eli J, Draizen C, Dallago C, Sander, Debora S, Marks (2019) The EVcouplings Python framework for coevolutionary sequence analysis, Bioinformatics, Volume 35, Issue 9, 1 May 2019, Pages 1582–1584. https://doi.org/10.1093/bioinformatics/bty862 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryFile.pdf Supplementary File Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8649051","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577446320,"identity":"b0ddc2c2-34ae-4825-b7bb-54ab2ceef3bd","order_by":0,"name":"Alper 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18:15:18","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133955,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8649051/v1/02a52caee2abe0e5bfbd4396.html"},{"id":100822572,"identity":"43364f88-91c4-43bf-a764-cf66da25fb86","added_by":"auto","created_at":"2026-01-21 18:15:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4134607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Stoichiometric Competition Model of VPAT Regulation. \u003c/strong\u003ea) The canonical VPAT homodimer (blue/blue) stabilized by the C-terminal hydrophobic lock (Trp305/Tyr244) in the lipid bilayer. The regulatory VPAT-ITL9 heterodimer (blue/red). ITL9 (10-TM) bypasses the C-terminal interface to engage the VPAT N-terminus via the Arg3 electrostatic anchor. The interaction energies (ΔG) are equivalent, indicating competitive regulation. (b) The Rejection Pathways: Isoform IU67 (green) fails to bind due to topological mismatch, while isoform IT67 (yellow) forms an inert, soluble homodimeric sink.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8649051/v1/f2be7053ec5c20ef561c37d0.png"},{"id":100822577,"identity":"ec2448fe-3e5c-45f2-9be5-664e6a570e05","added_by":"auto","created_at":"2026-01-21 18:15:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3011313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvolutionary coupling analysis between VPAT canonical protein and splice isoforms. \u003c/strong\u003eThere is a strong co-evolution (colored purple) of the residues contributing to the binding interfaces.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8649051/v1/cd75799b8ea8f5dd38ad531f.png"},{"id":102298477,"identity":"77d33baa-f781-4224-b914-527971454ff0","added_by":"auto","created_at":"2026-02-10 10:39:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11165706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8649051/v1/3e41e7ea-8899-4f1c-b14e-63591befc91c.pdf"},{"id":100858803,"identity":"0ff2ebec-54fb-458d-98d0-687887ceff11","added_by":"auto","created_at":"2026-01-22 07:24:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1587322,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary File\u003c/p\u003e","description":"","filename":"SupplementaryFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8649051/v1/3f187cdb24b71794611b6192.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAlternative Splicing Guided Stoichiometric Competition Model of Vesicular Polyamine Transporter Reveals Modulatory Drug Targets\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe regulated storage and release of neurotransmitters and neuromodulators are fundamental processes underlying synaptic plasticity, learning, and memory [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among the diverse array of signaling molecules sequestered within synaptic vesicles, polyamines have emerged as critical regulators of neuronal excitability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These polycationic molecules modulate the activity of key ion channels, including NMDA and AMPA receptors, and potassium channels, thereby shaping the spatiotemporal dynamics of synaptic transmission [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The vesicular sequestration of polyamines is mediated by the Vesicular Polyamine Transporter (VPAT), encoded by the SLC18B1 gene [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. VPAT is also responsible for vesicular storage and release of polyamines from mast cells [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Given the neuroprotective and neuromodulatory roles of polyamines, the functional regulation of VPAT is of paramount physiological significance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Dysregulation of polyamine transport has been implicated in oxidative stress, neurodegeneration, and cognitive deficits, underscoring the need to understand the molecular mechanisms governing VPAT activity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVPAT belongs to the Major Facilitator Superfamily (MFS) of transporters, a vast class of membrane proteins that typically share a conserved fold of 12 transmembrane helices organized into two pseudo-symmetrical domains [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While the classical \"rocker-switch\" mechanism of MFS transport is often conceptualized within the context of a monomeric functional unit [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], emerging structural and biochemical evidence suggests that oligomerization is a prevalent and often obligatory feature of membrane protein physiology [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Crucially, the requirement for oligomeric assembly creates a vulnerability: the presence of non-functional or truncated splice variants can exert potent dominant-negative effects for distinct membrane proteins [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. By heterodimerizing with the wild-type (WT) transporter, these isoforms can inhibit canonical assemblies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This mechanism of truncated isoform-mediated modulation could be a sophisticated layer of gene regulation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe SLC18B1 locus is subject to extensive alternative splicing, generating a repertoire of protein isoforms whose functions remain largely uncharacterized. While the canonical VPAT isoform is well-defined, the proteomic landscape includes variants such as A0A3B3ITL9, A0A3B3IU67, and A0A3B3IT67. We hypothesized that these specific isoforms function as inhibitors of VPAT signaling by effectively competing for the dimerization interface of the wild-type bio assembly. In this model, the efficacy of an isoform as a regulator is determined by a strict thermodynamic hierarchy: to act as a dominant negative, an isoform must bind to the wild-type VPAT with an affinity that matches or exceeds the stability of the wild-type homodimer. Conversely, isoforms that preferentially self-associate would form stable, sequestered homodimers with little impact on the functional VPAT pool.\u003c/p\u003e \u003cp\u003eUnraveling this thermodynamic mechanism requires an energetic accounting of the protein-protein interfaces involved, a task well-suited for computational structural biology. However, membrane characteristic is another point that should be accounted for analyzing vesicular transporters [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A previous limitation concerns lipid composition in modeling studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While simplistic membrane models can provide valuable insights, they often fail to capture the specific VPAT localization at synaptic vesicles. In this study, we addressed this limitation by employing a realistic lipid composition, allowing a more accurate representation of synaptic vesicle\u0026ndash;associated environments. Molecular dynamics simulations in a lipid bilayer provide a rigorous framework for estimating the relative binding free energies of macromolecular complexes by decomposing interactions into van der Waals, electrostatic, and solvation contributions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This approach allows us to move beyond simple structural modeling to predict the competitive landscape of isoform interactions quantitatively.\u003c/p\u003e \u003cp\u003eHere, we present a comprehensive thermodynamic analysis of VPAT dimerization and its modulation by the isoforms A0A3B3ITL9, A0A3B3IU67, and A0A3B3IT67. We demonstrate that VPAT homodimerization is a stable, energetically favorable process, providing a baseline for functional assembly. Strikingly, our calculations reveal that the isoforms A0A3B3ITL9 and A0A3B3IU67 form heterodimers with VPAT that are significantly more stable than the wild-type homodimer. In contrast, the A0A3B3IT67 isoform displays a distinct profile, characterized by hyper-stable self-association and weak affinity for VPAT, rendering it functionally inert as an inhibitor. These findings define a structural and thermodynamic basis for isoform-specific regulation of vesicular polyamine transport, providing insights into how alternative splicing expands the functional versatility of the SLC18 transporter family.\u003c/p\u003e"},{"header":"Results and Discussions","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTopological and Biochemical Analyses of VPAT Isoforms\u003c/h2\u003e \u003cp\u003ePhysicochemical and topological profiling of the VPAT isoform library necessitates the exclusion of isoform ISL3 (A0A3B3ISL3) from the comparative membrane-embedded dimerization analysis. Unlike the canonical VPAT transporter, which exhibits a robust integral membrane architecture characterized by 12 transmembrane (TM) helices and a distinct hydrophobic GRAVY index of 0.719, ISL3 presents as a severely truncated variant (Supplementary Table S1, Figure S1, Figure S2). The topological prediction assigns zero transmembrane domains to ISL3, and a near-neutral GRAVY score of 0.051. This hydropathic profile indicates that ISL3 lacks the necessary hydrophobic core to integrate into the lipid bilayer.\u003c/p\u003e \u003cp\u003eThe remaining isoforms; A0A3B3ITL9 (ITL9), A0A3B3IU67 (IU67), and A0A3B3IT67 (IT67); represent a spectrum of C-terminal truncations that fundamentally alter the transmembrane (TM) architecture of the transporter (Supplementary Figure S3, Figure S4, Figure S5). Unlike the canonical VPAT (525 aa, 12 TMs), these isoforms has reduced helical counts (10, 11, and 8 TMs, respectively), creating a symmetrical mismatch within the lipid bilayer. This structural deviation from the dodecyl-helical core may force these variants to adopt alternative interfacial arrangements to satisfy their energetic requirements, explaining the diverse TM modes observed in the residue decomposition analysis.\u003c/p\u003e \u003cp\u003eIsoform IT67 (383 aa) represents the most severe membrane-competent truncation, retaining only 8 transmembrane domains (Supplementary Figure S3). Isoform ITL9 (373 aa) exhibits a deletion of two transmembrane domains, resulting in a 10-TM topology (Supplementary Figure S4). Biophysically, this specific truncation is the most consequential. Isoform IU67 (409 aa) presents with 11 transmembrane domains, a unique odd-numbered topology that implies a potential inversion of the C-terminal orientation relative to the membrane leaflet (Supplementary Figure S5). This topological inversion likely disrupts the alignment of the dimerization interface.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparative Docking of VPAT Isoform Complexes\u003c/h3\u003e\n\u003cp\u003eTo elucidate the molecular mechanism by which alternative splice isoforms modulate VPAT function, we performed Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy calculations (Supplementary Table S2). We hypothesized that specific isoforms function as dominant-negative inhibitors by forming stable heterodimers with VPAT, thereby sequestering the wild-type protein and preventing the formation of functional VPAT homodimers. To test this, we analyzed the binding free energies (ΔG bind) of the wild-type VPAT homodimer compared to three isoform-VPAT heterodimeric complexes (VPAT-A0A3B3ITL9, VPAT-A0A3B3IU67, and VPAT-A0A3B3IT67). The wild-type VPAT homodimer exhibited a predicted MMGBSA binding free energy of -152.09 kcal/mol, driven primarily by van der Waals interactions (ΔEvdw =-100.17 kcal/mol) and electrostatic contributions (ΔE ele=-56.06 kcal/mol). This establishes a thermodynamic baseline for homotypic self-association.\u003c/p\u003e \u003cp\u003eOur analysis revealed a hierarchy of binding stabilities that strongly supports a competitive inhibition mechanism for specific isoforms. Strikingly, the interaction between VPAT and the A0A3B3ITL9 (ITL9 isoform) resulted in a significantly more stable complex, with a total binding free energy of -283.85 kcal/mol. This represents a dramatic energetic advantage (ΔΔG\u0026asymp;-131 kcal/mol) over the homotypic VPAT-VPAT interaction. The stability of this heterodimer is anchored by a substantial increase in van der Waals contacts (ΔE vdw =-177.64 kcal/mol) and favorable solvation effects. Similarly, the A0A3B3IU67 isoform also formed a highly stable heterodimer with VPAT (ΔGbind =-193.65 kcal/mol), surpassing the stability of the VPAT homodimer by approximately 41 kcal/mol. These thermodynamic profiles indicate that both A0A3B3ITL9 and A0A3B3IU67 (IU67 isoform) are thermodynamically favored to disrupt VPAT homodimers in a competitive environment.\u003c/p\u003e \u003cp\u003ePer-residue energy decomposition further identified critical residues driving the potent inhibition by A0A3B3ITL9. In the VPAT-ITL9 complex, Arg3 (Arg20) on the receptor chain made the largest energetic contribution (-11.08 kcal/mol), an interaction not observed in the homodimer interface. Furthermore, strong hydrophobic contributions were provided by Phe and Tyr from the ligand isoform chain. These distinct interfacial contacts underscore the structural basis for the isoform's enhanced affinity. In contrast, the A0A3B3IT67 isoform formed the least stable heterodimer with VPAT (ΔGbind=-138.58 kcal/mol), which is energetically weaker than the wild-type homodimer. Interestingly, the A0A3B3IT67 homodimer was found to be exceptionally stable (ΔG bind=-408.68 kcal/mol), suggesting that this isoform preferentially self-associates rather than interfering with VPAT. Collectively, our computational data identify A0A3B3ITL9 and A0A3B3IU67 as potent thermodynamic traps that can effectively blockade VPAT homodimerization.\u003c/p\u003e\n\u003ch3\u003eDifferential Residue \u0026 Interface Analysis\u003c/h3\u003e\n\u003cp\u003eThe self-association of canonical VPAT is driven principally by a hydrophobic patch located near the C-terminus. The analysis identifies residues Trp305, Tyr244, Leu247, and Leu304 as the primary energy contributors (corresponding to Trp333 and Tyr272, Leu275, and Leu332). These residues form a classic \"hydrophobic lock\", burying aromatic side chains to stabilize the dimer. This interface is heavily reliant on shape complementarity and hydrophobic exclusion but lacks strong electrostatic salt bridges, which limits its maximal binding energy compared to the isoforms.\u003c/p\u003e \u003cp\u003eThe increase in stability seen in the VPAT-ITL9 complex is driven by a shift to the N-terminus. The N-terminal Arg residue on VPAT acts as a critical linchpin in this interaction, contributing over \u0026minus;\u0026thinsp;11 kcal/mol to the binding energy, a magnitude of contribution not seen in the homodimer. This suggests the formation of a high-energy salt bridge or hydrogen bond network specific to the ITL9 complex. Additionally, a central cluster becomes highly active. This indicates that ITL9 engages VPAT in a different orientation, clamping down on the N-terminus and central domains rather than the C-terminus.\u003c/p\u003e \u003cp\u003eThe interaction with IU67 reveals a third distinct interface. This binding mode is dominated by Arg94 and Phe73, which show significantly stronger contributions here than in the homodimer (an improvement of ~\u0026thinsp;8.5 kcal/mol). The recruitment of Arg alongside His and Ile points to an interface that combines hydrophobic stacking with specific charge-charge interactions.\u003c/p\u003e\n\u003ch3\u003eFree Energy Calculations in the Synaptic Membrane Environment\u003c/h3\u003e\n\u003cp\u003eBased on the refinement of the binding free energies using membrane full-atom Molecular Dynamics (MD) simulations (MM/PBSA on the last 20 ns of 50 ns trajectories), the interaction landscape has been updated to reflect physiological conditions more accurately (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The inclusion of the membrane environment significantly modulates the predicted stabilities, revealing that isoform A0A3B3ITL9 (ITL9) is a direct, equipotent competitor to the canonical VPAT, whereas isoform A0A3B3IU67 (IU67) is significantly weaker and unlikely to disrupt the canonical complex under standard conditions (Supplementary Figure S6, Figure S7, Figure S8, Figure S9, Figure S10, Figure S11, Figure S12, Figure S13).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterface composition and binding free energies (ΔΔGs) of the dimer complexes in an atomistic bilayer model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Pair\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMembrane Binding Energy (ΔGbind​)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStability Relative to VPAT Homodimer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVPAT \u0026ndash; A0A3B3ITL9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeterodimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-30.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.65 kcal/mol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEquipotent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVPAT \u0026ndash; VPAT (Canonical)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomodimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-29.29\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41 kcal/mol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVPAT \u0026ndash; A0A3B3IU67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeterodimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83 kcal/mol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificantly Weaker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA0A3B3IT67 \u0026ndash; A0A3B3IT67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomodimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.16\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04 kcal/mol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnstable / Transient\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eITL9 does not engage the canonical C-terminal hydrophobic lock. Instead, it recruits the VPAT N-terminus. Unlike the initial screening which suggested ITL9 was a \"super-binder\", membrane simulations reveal it binds with an affinity (-30.10 kcal/mol) nearly identical to the canonical VPAT homodimer (-29.29 kcal/mol). This implies a 1:1 competitive landscape where the ratio of heterodimer (VPAT-ITL9) to homodimer (VPAT-VPAT) is determined purely by the relative expression levels of the two proteins, rather than thermodynamic dominance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Rather than rendering the protein inert, this structural \"change\" exposes the N-terminal domain, specifically the Arg3 (Arg20) motif, allowing it to act as a surrogate interface. The preservation of equipotent binding energy ( kcal/mol) despite a\u0026thinsp;~\u0026thinsp;30% reduction in mass indicates that ITL9 retains a high degree of conformational stability. The 10-TM architecture likely relaxes steric constraints, permitting the high-affinity electrostatic capture of canonical VPAT monomers that would otherwise be sterically occluded in the full-length 12-TM structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe IU67 isoform shows a drastic reduction in stability (-9.80 kcal/mol) compared to the homodimer. This suggests that in a realistic lipid bilayer, IU67 cannot effectively displace VPAT from its homodimeric state. The \"orphan\" 11th helix may introduce steric clashes or entropic penalties within the lipid environment, preventing the formation of a tight complex. Consequently, the structural change in IU67 shifts it from a competitive inhibitor to a low-affinity bystander in the membrane context.\u003c/p\u003e\n\u003ch3\u003eIsoform A0A3B3IT67 Homodimerization as a Protective Mechanism\u003c/h3\u003e\n\u003cp\u003eThe IT67 homodimer exhibits an exceptionally high binding free energy (-408.68 kcal/mol) in solvent MMGBSA, far exceeding the affinity of any other complex analyzed, including its interaction with VPAT (-138.58 kcal/mol). The loss of one-third of the transmembrane core creates a highly unstable inrteraction with canonical VPAT. The structural change here is catastrophic for heterodimerization; the 8-TM core lacks the properties to stabilize an interface with the 12-TM canonical VPAT. Instead, this truncation exposes a massive hydrophobic surface that drives the \"super-stable\" homodimerization seen in soluble screening. Biophysically, this suggests that IT67 functions as an inert allocrite in the context of VPAT signaling. Although membrane insertion appears to destabilize this homodimer (MM/PBSA ΔG bind \u0026asymp; -9 kcal/mol, Supplementary Figure S14, Figure S15), the massive stability observed in the soluble phase implies that IT67 likely aggregates or traffics as a pre-formed homodimer, thereby insulating the canonical VPAT machinery from interference. This obligate homodimerization serves as a built-in specificity filter, ensuring that only fusion-competent isoforms (like ITL9) can enter the competitive regulatory pool.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCo-evolutionary Profilling of VMAT Isoform Regulation\u003c/h2\u003e \u003cp\u003eMolecular evolution of transmembrane proteins is non-linear and complex [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and involves co-evolutionary dependencies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Previously, VMAT and EAAT isoforms showed clear sign of evolutionary couplings in the interaction domains [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Unlike VPAT stoichiometric isoform interaction, they showed increased binding affinity to canonical dimer.\u003c/p\u003e \u003cp\u003eThe N-terminal anchor residue, Arg3 (corresponding to Arg20), does not show strong co-evolutionary signals with the canonical C-terminal sector, supporting the hypothesis that the VPAT\u0026ndash;ITL9 heterodimer is a product of evolutionary neofunctionalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Rather than utilizing a conserved structural scaffold, ITL9 exploits an electrostatic surface on the canonical monomer. This suggests that the alternative splicing event generating ITL9 evolved to hijack an accessible, non-constrained region of the protein (the N-terminus) to function as a regulatory switch, allowing for dynamic modulation of transport activity without redesigning the core oligomerization machinery. The dichotomy in evolutionary couplings explains the differential stability observed in the membrane environment. This evolutionary divergence implies that the regulatory potency of ITL9 is likely species-specific or adaptable, unlike the universally conserved transport function of the homodimer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStructural Implications for Therapeutic Targeting\u003c/h3\u003e\n\u003cp\u003eThe spatial segregation of binding interfaces between the canonical homodimer and the pathogenic/regulatory heterodimers offers a precise structural rationale for drug design (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To selectively inhibit the formation of the VPAT\u0026ndash;ITL9 heterodimer while preserving canonical VPAT function, therapeutic ligands should be designed to target the N-terminal Arg pocket. Occupancy of this site would sterically occlude ITL9 binding without perturbing the C-terminal interface required for VPAT homodimerization. Conversely, pathological overexpression of VPAT homodimers could be dampened by allosteric inhibitors targeting the Trp-Tyr hydrophobic cleft, a site that is structurally distinct and distal from the regulatory interface used by ITL9.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePossible targets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget Strategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget Residues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRationale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisrupt VPAT Homodimer Only\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRP-305,\u0026nbsp;TYR-244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThese are critical for VPAT-VPAT binding but are\u0026nbsp;not\u0026nbsp;the primary drivers for the high-affinity ITL9 or IU67 heterodimers. Mutating or binding a small molecule here would block homodimers while likely preserving heterodimer functions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock VPAT-ITL9 Interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARG-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis residue is the \"linchpin\" of the super-strong ITL9 interaction. Targeting the N-terminus or\u0026nbsp;ARG-3\u0026nbsp;specifically would ablate this interaction without affecting the canonical homodimer (which relies on residue 244/305).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock VPAT-IU67 Interaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARG-94,\u0026nbsp;PHE-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThese residues define the IU67 binding footprint. They are unique to this interaction interface.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe integration of topological profiling, implicit solvent screening, and rigorous membrane-embedded simulation establishes the VPAT interactome as a highly structured, competitive regulatory network rather than a stochastic assembly of splice variants. The data fundamentally revises the understanding of VPAT regulation, shifting the paradigm from simple dominant-negative suppression to a precise stoichiometric competition model driven primarily by isoform A0A3B3ITL9 (ITL9).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eProtein Sequences and Other Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to our previous studies [15,16], sequence data was mined from the UniProt database [26], utilizing FASTA formatting for downstream processing. Isoforms were identified via UniProt\u0026rsquo;s gene-centric referencing system, which integrates manual curation with automated mapping to eukaryotic reference proteomes [26]. The dataset was filtered to isolate viable protein-coding variants, specifically constraining selection to lengths spanning 0.15\u0026ndash;0.85 of the canonical transcript to ensure structural relevance. Topological orientation and feature mapping were visualized via Protter [27]. Key physicochemical parameters, including molecular weight, residue composition, thermodynamic instability metrics, and hydropathicity (GRAVY), were derived using Expasy tools [28,29,30].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel ranking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTertiary and quaternary conformational ensembles (specifically monomers, dimers, and trimers) were predicted using the AlphaFold3 [31]. To ensure high-fidelity discrimination in inhibitor ranking, the computationally tractable Variable Dielectric Molecular Mechanics with Generalized Born and Surface Area (VD-MM/GBSA) algorithm was employed [32]. Post-docking rescoring was executed via the HawkDock platform [32,33,34] following atomistic reconstruction, specifically the addition of missing hydrogen and heavy atoms, using the Amber16 tleap module. Calculations utilized a variable dielectric generalized Born model, which integrates residue-specific dielectric constants to achieve superior accuracy over classical GB formulations [33]. The complex underwent a 5,000-step geometry optimization protocol (comprising 2,000 steps of steepest descent followed by 3,000 steps of conjugate gradient minimization) with a 12 \u0026Aring; cutoff applied to van der Waals interactions. Finally, per-residue free energy decomposition was applied to delineate the thermodynamic contributions of critical residues at the canonical transporter\u0026ndash;isoform interface.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAtomistic synaptic vesicle membrane systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular dynamics (MD) simulations were performed on AlphaFold-predicted membrane-embedded protein structures using GROMACS v2024.3 [45], following simulation workflows previously validated in our studies of glutamate transporter isoforms and neuronal membrane proteins [15,16,36,37]. All computations were conducted on a distributed high-performance environment consisting of three Google Colab instances, each provisioned with an NVIDIA L4 GPU, 126 GB GPU memory, 318 GB system RAM, and Intel\u0026reg; Xeon\u0026reg; processors [38]. To optimize computational throughput, GROMACS was custom-compiled with CUDA acceleration, facilitating efficient GPU offloading and multi-core parallel execution of force calculations and integration steps [38]. Adhering to the computational framework established in our prior investigations [15,16,36,37], all-atom membrane systems were assembled via the CHARMM-GUI Membrane Builder [39,40,41]. The translational and rotational positioning of the protein within the bilayer was optimized using the PPM 2.0 algorithm, which accounts for anisotropic dielectric constants and hydrogen bonding potential at the lipid-solvent interface [42]. To recapitulate a representative plasma membrane environment, a complex lipid matrix was generated, maintaining concordance with our established models. This bilayer comprised a symmetrical distribution of cholesterol, POPC, POPE, POPS, POPI, and palmitoyl-sphingomyelin (PSM) across both leaflets [43,44]. Electroneutrality and a physiological ionic strength of 0.15 M (K +/Cl-) were achieved via 2,000 steps of Monte Carlo ion displacement employing a primitive ion model. All simulations were parameterized using the CHARMM36m all-atom force field [45].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to dynamic simulations, the system was subjected to energy minimization using the steepest descent algorithm for up to 5,000 iterations, with convergence defined by a maximum force threshold of 1,000 kJ\u0026middot;mol⁻\u0026sup1;\u0026middot;nm⁻\u0026sup1;. Subsequent equilibration was conducted following the standard six-stage CHARMM-GUI workflow, during which positional restraints were progressively relaxed to ensure a smooth transition toward an unrestrained system. The terminal equilibration phase consisted of a 500 ps simulation employing the velocity-rescaling (v-rescale) thermostat to maintain the system temperature at 303.15 K (\u0026tau;ₜ = 1.0 ps) and the C-rescale barostat to regulate pressure at 1 bar under semi-isotropic coupling conditions (\u0026tau;ₚ = 5.0 ps).\u003c/p\u003e\n\u003cp\u003eProduction molecular dynamics simulations were then performed for 50 ns, with trajectory snapshots recorded at 0.5 ns intervals, consistent with our previous work [15,16,36,37]. Long-range electrostatic interactions were treated using the Particle Mesh Ewald (PME) method, while short-range Coulombic and van der Waals interactions were truncated at 1.2 nm. During production runs, temperature and pressure were maintained at 303.15 K and 1 bar using the Nos\u0026eacute;\u0026ndash;Hoover thermostat and the Parrinello\u0026ndash;Rahman barostat, respectively, under semi-isotropic pressure coupling. All covalent bonds involving hydrogen atoms were constrained using the LINCS algorithm, enabling the use of the specified interaction cutoffs for short-range nonbonded interactions.\u003c/p\u003e\n\u003cp\u003eSystem stability and conformational behavior were assessed through post-simulation trajectory analyses. Backbone structural deviations were quantified by calculating the root mean square deviation (RMSD), while global compactness was evaluated via the radius of gyration (R_g), both computed using the GROMACS gmx rms and gmx gyrate utilities. Local residue-level flexibility was characterized by determining the root mean square fluctuation (RMSF) of C\u0026alpha; atoms. Solvent exposure of protein side chains was quantified by calculating the solvent-accessible surface area (SASA) using the gmx sasa module with a probe radius of 1.4 \u0026Aring; [46]. For binding free energy calculations, only the final 20 ns (31\u0026ndash;50 ns) of the equilibrated production trajectories were considered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBinding Free Energy Calculations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBinding free energies were estimated using the Molecular Mechanics\u0026ndash;Poisson\u0026ndash;Boltzmann Surface Area (MM\u0026ndash;PBSA) formalism as implemented in gmx_MMPBSA, adapted explicitly for membrane-embedded systems [47,48]. Dielectric constants were parameterized to reflect heterogeneous environments, with values of 7.0 for the membrane phase, 4.0 for the solute, and 80.0 for the aqueous solvent. Solvent-excluded surfaces were constructed using a dual-probe approach, employing a 1.40 \u0026Aring; probe radius for water and a 2.70 \u0026Aring; probe radius for the membrane environment. Long-range electrostatic interactions and forces were computed using the particle\u0026ndash;particle particle\u0026ndash;mesh (P3M) algorithm.Per-residue free energy decomposition was performed using the pairwise decomposition scheme (idecomp = 2) to resolve individual residue contributions to electrostatic and van der Waals interaction components. Binding free energies were calculated as ensemble averages over selected trajectory frames, and the associated standard error of the mean (SEM) was determined via propagation of uncertainty. Interfacial residue contacts within a 4 \u0026Aring; cutoff were quantified and visualized using gmx_MMPBSA_ana and UCSF ChimeraX [49]. All statistical plots were generated using Grace (https://plasma-gate.weizmann.ac.il/Grace/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-evolutionary Profilling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvolutionary couplings (ECs) were inferred using the EVcouplings framework, which applies a global maximum entropy model constrained by empirical multiple sequence alignment (MSA) statistics [15,16,50]. To ensure comparability across proteins of differing lengths and alignment depths, EC strengths were normalized using length-adjusted bitscores. Alignment quality was evaluated using the ratio of the effective number of sequences to protein length (n_eff/L), where values exceeding 5 are indicative of statistically robust coupling inference. The resulting n_eff/L values were 15.49 for canonical VPAT, 20.80 for the ISL3 isoform, 33.34 for the IU67 isoform, 120.56 for the ITL9 isoform, and 21.13 for the IT67 isoform.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was not required for this computational study as it did not involve animal subjects, human participants, and identifiable data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This computational study did not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This computational study did not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AlphaFold DB (https://alphafold.ebi.ac.uk), a database developed by DeepMind and the European Bioinformatics Institute (EMBL-EBI) at EMBL, is a repository for AlphaFold2 predictions, with over 200 million protein structures. Each statistical and computational analysis of this study, included with step-by-step instructions where possible, are publicly available to ensure repeatability. For more detailed information on the statistical analyses, input files and detailed outputs, including the AlphaFold calculations and codes to regenerate analyses, please visit the website: https://github.com/karagol-alper/VPAT-isoforms\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting financial interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.K and T.K contributed equally to this work. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGholami A, Mortezaee K (2025) Neurotransmitters in Neural Circuits and Neurological Diseases. ACS Chem Neurosci 16(19):3653\u0026ndash;3664. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acschemneuro.5c00426\u003c/span\u003e\u003cspan address=\"10.1021/acschemneuro.5c00426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuchsberger T, Paulsen O (2022) Modulation of hippocampal plasticity in learning and memory. 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Protein Sci 32(11):e4792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pro.4792\u003c/span\u003e\u003cspan address=\"10.1002/pro.4792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas A, Hopf AG, Green B, Schubert S, Mersmann, Charlotta PI, Sch\u0026auml;rfe JB, Ingraham A, Brock T-PK, Riesselman AJ, Palmedo P, Kang C, Sheridan R, Eli J, Draizen C, Dallago C, Sander, Debora S, Marks (2019) The EVcouplings Python framework for coevolutionary sequence analysis, Bioinformatics, Volume 35, Issue 9, 1 May 2019, Pages 1582\u0026ndash;1584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/bty862\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/bty862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Truncated isoforms, Monoamine transporters, mRNA, Synaptic vesicle, Neurotransmission","lastPublishedDoi":"10.21203/rs.3.rs-8649051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8649051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlternative splicing represents a potent mechanism for the post-transcriptional regulation of solute carrier function, yet the structural biophysics governing the Vesicular Polyamine Transporter (VPAT) interactome remain elusive. Here, we present a comprehensive thermodynamic and topological analysis of canonical VPAT and its splice variants (A0A3B3ITL9, A0A3B3IU67, A0A3B3IT67, and A0A3B3ISL3) utilizing explicit membrane Molecular Dynamics (MD) simulations coupled with MM/PBSA free energy decomposition. We identify isoform A0A3B3ITL9 (10-TM) as a specific, equipotent competitive inhibitor of canonical VPAT homodimerization. While the canonical homodimer (ΔG bind=-29.29 kcal/mol) is stabilized by a conserved C-terminal lock involving Trp333 and Tyr272, the truncated ITL9 isoform bypasses this interface. Instead, ITL9 exploits a conformational switch to recruit the VPAT N-terminus via a high-affinity electrostatic anchor at Arg20, resulting in a heterodimeric complex of identical stability (ΔG bind=-30.10 kcal/mol). Conversely, we demonstrate that other isoforms are functionally segregated from this competitive pool: A0A3B3IU67 (11-TM) is destabilized by topological mismatch within the lipid bilayer, while A0A3B3IT67 (8-TM) functions as an inert allocrite driven by obligate homodimerization. These findings define the VPAT regulatory landscape as a stoichiometric competition model, highlighting the N-terminal Arg pocket as a isoform-specific equipotent therapeutic target for modulating polyamine transport.\u003c/p\u003e","manuscriptTitle":"Alternative Splicing Guided Stoichiometric Competition Model of Vesicular Polyamine Transporter Reveals Modulatory Drug Targets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 18:15:13","doi":"10.21203/rs.3.rs-8649051/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0beb105d-5579-425b-a492-37c7b519234d","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61436072,"name":"Drug Discovery, Design, \u0026 Development"},{"id":61436073,"name":"Cellular \u0026 Molecular Neuroscience"},{"id":61436074,"name":"Applied Biochemistry"},{"id":61436075,"name":"Computational Biology"}],"tags":[],"updatedAt":"2026-01-21T18:15:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 18:15:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8649051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8649051","identity":"rs-8649051","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}