From binding to breakdown: biophysical and molecular insights into fluoroquinolone induced F-actin perturbation

From binding to breakdown: biophysical and molecular insights into fluoroquinolone induced F-actin perturbation | 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 Article From binding to breakdown: biophysical and molecular insights into fluoroquinolone induced F-actin perturbation Rahul Gupta, Hridhya Nair, Tanveera Rounaque Sarhadi, Shirisha Nagotu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6693104/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Neurodegenerative disorders involving actin dysregulation demand safer yet potent actin-disrupting compounds. Previous studies have reported the actin-destabilizing effects of fluoroquinolones (FQs), suggesting their potential for repurposing. FQs are widely prescribed broad-spectrum antibiotics known for their efficacy against bacterial infections. The newer generation FQs are also known to effectively cross the Blood Brain Barrier (BBB). In this study, we demonstrate that FQs irreversibly disrupts F-actin filaments in a concentration-dependent manner using scattering based assay. Electron microscopy and gel filtration confirms generation-dependent disruption activity. In particular, Gen3 and Gen4 FQs reduces actin aggregates in more than 60% yeast cells. FQ treatment alters the thermal stability of F-actin at 1:30 and 1:50 molar ratios with minor secondary structural changes. While exploring the molecular insights of FQs interaction with F-actin, STD NMR combined with MD simulations revealed the importance of the fluorinated quinolone core, which is common to all FQs. These studies also highlight the involvement of R5 amino, bulky piperazine and azabicyclo rings at the R7 position in F-actin’s intermonomer interface binding, ultimately leading to the disruption of F-actin. We propose modifying the identified positions on the quinolone core to enhance the potency of F-actin disruption. For targeting actin mis-aggregation related. neuro degenerative diseases (NDs), these positions can be modified with the functional groups that might increase the lipophilicity of the molecule to cross the BBB. Health sciences/Diseases/Neurological disorders Biological sciences/Biophysics Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Protein analysis/Protein sequence analyses Biological sciences/Structural biology/Molecular modelling Biological sciences/Structural biology/Nmr spectroscopy/Solution state nmr F-actin Fluoroquinolones drug repurposing neurodegenerative disorders STD NMR molecular dynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Neurodegeneration is characterized by the progressive loss of neurons and abnormalities in neuronal synapses often appearing in middle or later stages of life 1 , 2 . The neurodegenerative disorders (NDs), which impact over 50 million people worldwide, are often linked to synaptic dysfunction, disruptions in neural networks, and the accumulation of abnormal protein variants in the brain 2 , 3 . Common NDs include Alzheimer's disease, Parkinson's disease, Amyotrophic lateral sclerosis (ALS), motor neuron disease, Huntington's disease, spinal muscular atrophy, prion diseases, and spinocerebellar ataxia 3 – 7 . Neurons naturally harbour dynamic actin filaments and under certain conditions can assemble them into rod like aggregates composed primarily of actin and cofilin 8 . When exposed to oxidative or energetic stressors those reactive molecules disrupt the regulation of actin and its associated proteins and impair filament polymerization dynamics that are essential for maintaining dendritic spine structure and synaptic plasticity 9 , 10 . As polymerization stalls, actin and cofilin coalesce into distinctive rods. In the short term these actin cofilin rods help protect neurons by delaying cytochrome C release from mitochondria and temporarily staving off apoptosis 11 , 12 . However, persistent rods obstruct intracellular transport and serve as nucleation sites for amyloid precursor protein (APP) and tau accumulation, which accelerates fibril formation 13 . Such rods have been identified in the brains of patients with Guam amyotrophic lateral sclerosis parkinsonism dementia complex, Alzheimer’s disease, and Pick’s disease 14 – 16 . Over time these aggregates mature into paracrystalline Hirano bodies whose complex contributes to neurodegeneration, evident in transgenic models, make them both early markers of disease and intriguing targets for therapeutic intervention 17 – 19 . A number of marine macrolides, such as reidispongiolides, sphinxolides, aplyronines, and ulapualides bind to the barbed end of actin and disrupt actin filament, however, no actin-binding drugs have broken beyond the preclinical stage due to their extreme cytotoxicity 20 . Therefore, it is pertinent to identify the potential small molecules could reverse actin mis-aggregation and disrupt the actin cofilin rods into smaller soluble forms without being cytotoxic to the cells. Likewise, for FDA approved Rifampicin and Colchicine actin disruption ability has been reported 21 , 22 . However, their poor permeability through Blood Brain Barrier (BBB) makes them unsuitable to treat actin mediated neuropathies 23 , 24 . FQs with moderate lipophilic nature, absence of charge at physiological pH, and low plasma protein binding capacity that favours blood-brain barrier penetration makes them suitable candidates against brain actinopathies 25 , 26 . Pathak et al. showed tetracycline family and a second generation fluoroquinolone ofloxacin as disruptor of actin aggregates and thus apart from being good antibiotics these compounds can also be repurposed for actin disruption 27 , 28 . To fully characterise applicability of FQ’s for actin driven neuropathies, in the current study, the effect of five generations (Gen) of FQs (Gen1, Gen2a, Gen2b, Gen3 and Gen4) on F-actin disruption has been investigated using an interdisciplinary in vitro , and in silico approach. FQs mediated disruption of F-actin using scattering of F-actin, further confirmed using electron microscopy, gel filtration and in Saccharomyces cerevisiae ∆end3 strain. Secondary structure and thermal stability of FQ treated F-actin samples were assessed using circular dichroism (CD) and differential scanning calorimetry (DSC) respectively. Through Saturation Transfer Difference (STD NMR) and Molecular dynamics simulations (MD) we have identified critical functional groups of the FQs interacting with F-actin and characterised the nature of these interactions. This allows for suggesting probable modifications to the structure of the FQs to improve the potency of these FDA approved drug molecules. Moxifloxacin (MFX) and Sparfloxacin (SFX) standout for their efficacy in disrupting actin filaments and can be repurposed for actinopathies inside and outside the brain respectively. Results We studied the effects of five generations of FQ’s (Gen1: Nalidixic acid (NDA); Gen2a: Ciprofloxacin (CFX) and Norfloxacin (NFX); Gen2b: Ofloxacin (OFX) and Levofloxacin (LFX); Gen3: Sparfloxacin (SFX); Gen4: Moxifloxacin (MFX)) on F-actin using a plethora of spectroscopic techniques. Fluoroquinolones efficiently and irreversibly disrupt F-actin Right Angle Light Scattering (RALS) is used to measure the F-actin disruption efficacy of aforementioned FQ’s. The protein control of untreated 3 µM F-actin in F-buffer, exhibited a high level of scattering exceeding 100 a.u. All FQ’s began disrupting F-actin at a 1:15 molar ratio, with increasing concentration dependent increase in efficacy (Fig.1 and Fig.S1). While NDA’s efficiency was more prevalent at 1:100, Gen2a FQs required higher concentrations of 1:200 to achieve substantial activity (Fig.S1a, b) In contrast, Gen2b FQs, were more effective even at 1:60 molar ratio (Fig.S1c, d). SFX exhibited marked F-actin disruption at a 1:15 molar ratio, with a reduction in scattering intensity exceeding 50%, indicating its effectiveness (Fig.1a). MFX, a Gen4 FQ, was the most effective F-actin disruptor among all FQs with complete disruption after a 1:30 treatment (Fig.1b). It must be noted that we do not observe any reversibility in the treated samples, i.e., reformation of F-actin even after 72 h except for NDA (Fig.S1e). Heterogeneity of disrupted F-actin oligomers To assess the extent of F-actin disruption observed in previous size-based measurements, untreated and FQ-treated F-actin samples were studied using gel filtration chromatography. Untreated F-actin eluted as a broad peak at 9.2 mL, near the column’s void volume which is consistent with its large filamentous structure (Fig.2). F-actin treated with Gen1 NDA exhibited a sharper elution peak at 15.6 mL, indicating a more homogeneous population of disrupted filaments compared to other FQ-treated samples. Notably, treatment with Gen2a CFX and NFX, resulted in the formation of visible white precipitate thereby preventing their analysis by gel filtration chromatography. Gen2b treated F-actin began eluting near the void volume, with peak maxima at 21.6 mL for OFX and 21.3 mL for LFX, suggesting marked filament disruption. Similarly, Gen3 SFX and Gen4 MFX treated F-actin exhibited elution profiles comparable to those of Gen2b compounds, with elution starting from the void volume and peak maxima at 22.4 mL for SFX and 22.2 mL for MFX. The diversity in elution profiles suggests heterogeneity of actin oligomer populations in the treated samples because of the plausible differences in the molecular disruption mechanism. Morphological changes in disrupted filaments The morphologies of the disrupted F-actin filaments are characterised with TEM imaging. Actin, when negatively stained in G-buffer and F-buffer, exhibits distinct morphologies. G-actin primarily consists of actin monomers and oligomers, while polymerized F-actin shows the presence of filamentous structures in solution (Fig.3a). F-actin treated with Gen1 NDA at 1:30 and 1:50 molar ratios resulted in the disruption of filaments, with larger oligomers predominating in the solution (Fig.S2). In contrast, treatment with Gen2a CFX and NFX at the same molar ratios (1:30 and 1:50) led to the perturbed filaments morphology, which exhibited distinct morphologies compared to the control F-actin. These perturbed filaments showed multiple kinks, bends, and discontinuities, though complete filament disruption was not observed (Fig.S3, S4). Similarly, Gen2b LFX and OFX treatment at a 1:30 molar ratio also resulted in perturbed F-actin morphology, similar to Gen2a treated samples, with kinks, bends, and oligomers present, but without complete filament disruption. However, at a higher molar ratio of 1:50, OFX treatment resulted in the complete loss of filamentous structures, indicating observable disruption of F-actin at this concentration (Fig.S5). LFX, despite being from the same generation, did not show this effect (Fig.S6). Treatment with Gen3 SFX and Gen4 MFX led to complete disruption of filaments at both 1:30 and 1:50 molar ratios (Fig.3b, Fig.S7). Furthermore, the oligomers generated post-filament disruption were predominantly smaller in size in comparison to earlier generations of FQs. Fluoroquinolones induce minor secondary structural changes in actin F-actin treatment with the FQs disrupts the filaments as observed in various size-based measurements. To confirm the possible secondary structural changes of the resulting populations of disrupted F-actin filaments, far UV CD spectroscopic measurements were performed. The two negative bands at 208 nm and 222 nm represent the characteristic of alpha helical structure while the negative band at 218 nm is associated with β sheets structure 29 . Actin is a rather rigid protein molecule and has secondary structure conformations of 30% α-helix, 23.5% β-sheet, 12.2% turns, and 34.3% random coil. These values closely match with the previously reported secondary structure of actin protein 30 . Gen1 NDA treatment of F-actin at 1:30 molar ratio, increases the α helical content by 4.1% and decreases the sheet content by 2.6% (Fig.S8e). Gen2a CFX treatment of F-actin decreases the α helical content by 3% and increases the sheet content by 3.5% with minor changes in the turns and other conformations such as random coils (Fig.S8a). Gen2a NFX treatment of F-actin increases the α helical content by 2.2% and decreases the sheet content by 4.2% (Fig.S8b). Gen2b OFX treatment of F-actin decreases the α helical content by 4.6 % and increases the sheet content by 3% (Fig.S8c). Gen2b LFX treatment of F-actin decreases the α helical content by 7.6 % and increases the sheet content by 4.8% (Fig.S8d). Gen3 SFX treatment of F-actin induces minor change in the α helical content but decreases the sheet content by 1.3% with minor changes in the turns and other conformations such as random coils (Fig.4). Gen4 MFX treatment of F-actin induces minor change in the α helical content but decreases the sheet content by 3.9% with minor changes in the turns and other conformations such as random coils (Fig.S8f). The secondary structure data has been provided in supplementary Table S1. Thermal stability of fluoroquinolones treated F-actin samples The primary goal of DSC measurements was to assess the thermal stability of F-actin filaments treated with different generations of FQ’s. In case of thermal denaturation of biological samples, a difference of 1 °C in melting temperature (T m ) is considered significant 31 . Actin protein concentration can have impact on the different denaturation parameters due to changes in concentration dependent intermolecular forces and charges; therefore, the protein concentration was fixed at 50 µM. The denaturation peaks of G-actin (T m = 59.0 °C) and F-actin (T m = 69.8 °C) are consistent with previously reported literature values 32 . After FQ treatment the F-actin undergoes disruption as observed in size-based measurements. Therefore, we expect FQ mediated F-actin disruption should also cause protein structure to destabilize. The disrupted oligomers should be more globular compared to the compact F-actin structure thus reducing the melting temperature of F-actin treated with FQs (Fig.5). After the FQs treatment at 1:30 and 1:50 molar ratios, marked reduction in T m and ΔH cal of treated F-actin is observed (Table 1). Table 1: Thermal parameters of the denaturation of native and FQs treated F-actin protein Tm (°C) ΔH cal (kJ.mol -1 ) T m (°C) ΔH cal (kJ.mol -1 ) G-actin (untreated) 59.0 480.9 ± 48.9 F-actin (untreated) 69.8 1012.0 ± 48.3 F-actin treated with FQs 1.5mM treatment 2.5mM treatment Nalidixic acid 66.8 591.5 ± 23.2 61.5 445.4 ± 9.3 CFX 65.6 666.4 ± 21.5 65.4 620.8 ± 15.0 NFX 67.5 771.8 ± 28.8 67.0 690.6 ± 28.7 OFX 68.1 714.3 ± 29.6 65.7 688.3 ± 21.7 LFX 68.0 617.4 ± 22.8 68.3 753.0 ± 26.2 SFX 66.9 667.1 ± 21.9 64.1 619.7 ± 19.6 MFX 69.1 837.6 ± 40.5 68.2 793.3 ± 30.7 Fluoroquinolone mediated actin disaggregation in D end3 S. cerevisiae The effects of FQs on actin bundles was investigated in the S. cerevisiae ∆end3 strain. This strain has been widely used in studies involving actin-binding small molecules as the F-actin dynamics is reduced during the stationary phase of growth and its larger cell size facilitates the visualization of actin structures 33 . After the cells reached the stationary phase, they were treated with 300 μM FQ compounds and stained with Rh-phalloidin to assess morphological changes in actin aggregates using fluorescence microscopy. In untreated control cells, large aggregated patches of F-actin were observed whereas FQ-treated cells exhibited dispersed actin bundles and a disaggregated F-actin morphology. 81% of the untreated yeast cell population displayed aggregated F-actin patches, while only 19% showed disaggregated morphology (Fig.6b). Upon FQ treatment, the F-actin distribution was significantly altered. In cells treated with the Gen1 NDA, 31% retained aggregated actin, while 69% showed a marked increase in disaggregated F-actin. Similarly, treatment with Gen3 SFX and Gen4 MFX resulted in a marked increase in the proportion of cells (67% and 63%) exhibiting disaggregated F-actin morphology compared to the control. Cells treated with NFX, OFX, and LFX (Gen2 compounds) also showed a substantial disaggregation (62%, 59% and 58% respectively) significantly higher than the untreated group (Fig.6b). In CFX-treated cells, the percentage of cells with aggregated F-actin remained relatively higher (63%) when compared to the other FQ-treated samples. Nevertheless, CFX treatment still led to a higher proportion cells (37%) displaying disaggregated F-actin compared to the untreated control (19%) (Fig. 6b). Differential binding of fluoroquinolones to actin using saturation transfer difference NMR The interaction between actin protein and FQs at a 1:50 molar excess ratio was studied using STD NMR analysis. The actin-FQs mixture contained 10% D₂O in F-buffer at 25°C for acquiring on-resonance and off-resonance spectra. The presence of peaks in the STD (difference) spectrum provides clear evidence of FQs binding to actin protein. The binding epitopes of the compounds (proton-containing functional groups) were identified by comparing the STD spectra with the corresponding ¹H NMR spectra of the compounds, thus providing atomic-level insight into their interaction with the protein. The STD NMR spectra for all FQs exhibited signals, indicating that each compound readily interacts with the F-actin. The characteristic signals of protons from the aromatic quinolone bicyclic core, methyl (-CH₃), and methylene (-CH₂-) groups in the FQs were prominent. The protons from OH and NH functional groups were difficult to observe due to fast exchange with water. Group epitope mapping studies using saturation transfer difference NMR Gen3 SFX exhibited stronger STD effects than the other FQs. The H12 and H15 protons from the methyl groups attached to the piperazine ring had a 100% STD effect, while H10 and H14 protons from the same ring contributed 7.0% (Fig.7). The H17 and H18 protons from the cyclopropyl group exhibited a moderately weak STD effect of 17.6%. The proton from the NH₂ group (4.2%) attached to the quinolone core and the proton from the NH group (7.3%) of the piperazine ring had weak STD effects. The putative NH signals in the STD NMR spectrum were unexpected; hence, they were verified by acquiring and comparing spectra of SFX in pure D₂O. These signals are weak and typically not visible in aqueous solutions due to fast exchange with water. The only aromatic proton, H3 (6.7%), exhibited a weak STD effect. The signals in the STD spectrum indicate that, in conjunction with the quinolone core, the piperazine ring also contributes to the binding interactions between SFX and actin protein. Fig.S9 shows Gen1 NDA in complex with actin protein. The strongest signal in the STD NMR spectrum, corresponding to the H12 protons of the methyl group, was considered to have a 100% STD effect. The STD effects of other signals in the spectrum were calculated relative to this strongest signal by comparing their intensities. The H9 protons of the methyl group showed a moderate STD effect of 20.9%, whereas the aromatic protons from the naphthyridone core, such as H3 (7.1%), H6 (4.2%), and H8 (4.8%), exhibited very weak STD effects. Gen2a CFX and NFX displayed weak overall STD effects. These were also the consistent weakest binders among all FQs tested. For CFX, the H15 and H16 protons from the cyclopropyl group showed the highest STD effects at 100% and 91.3%, respectively. The aromatic protons from the quinolone core, such as H2 (21.9%), H8 (9.8%), and H4 (11.5%), exhibited moderate to weak STD effects (Fig.S10). NFX, which belongs to the same generation, showed only four signals in the STD NMR spectrum, confirming its lower binding activity as observed in previous data. The H15 proton from the methyl group exhibited a 100% STD effect, while the aromatic protons from the quinolone core, such as H8 (12.2%), H6 (10.0%), and H3 (11.7%), showed weak STD effects similar to CFX (Fig.S11). Gen2b OFX and LFX showed that the H10 proton from the methyl group exhibited a 100% STD effect. The H15 protons from the methyl group attached to the piperazine ring showed a moderate STD effect of 45.7% in OFX and 40.7% in LFX, indicating good contact with actin protein and participation in binding interactions. The aromatic protons from the quinolone core of OFX exhibited weak STD effects, as indicated by H8 (13.0%), H9 (6.9%), and H1 (11.7%) (Fig.S12). Similarly, LFX showed weak STD effects for H8 (9.2%), H9 (6.7%), and H1 (9.7%) (Fig.S13). Gen4 MFX also exhibited strong STD effects but followed a binding mode similar to CFX. The H19 and H20 protons from the cyclopropyl group contributed the highest STD effects at 100%. The azabicyclo group protons, such as H10 and H16, together exhibited a strong STD effect of 79%. Additionally, the NH protons (34.1%) and H14 (48.4%) showed moderate STD effects, indicating the prominent contribution of the azabicyclo group to actin binding. The aromatic protons from the quinolone core, such as H6 (32.6%) and H7 (18.8%), displayed moderate to weak STD effects (Fig.S14). Based on the number of signals and their STD effects in the spectrum, MFX demonstrates strong binding to actin protein. Atomic level structural insights into binding of fluoroquinolones to F-actin The mode of binding and interaction of FQ’s was studied with a pentameric model of F-actin. In the MD simulations, both the Gen2a CFX and NFX deviate away from the docked sites. CFX, exhibits a steadily increasing RMSD (Fig.S16a), reaching over 5.0 nm by the end of simulation over the period of ~38ns (supplementary movie SM1). This progressive deviation suggests major conformational and spatial rearrangement of CFX, leading to “diffusion away” from its initial binding site of the protein, implying an unstable interaction with relatively stable F-actin over the function of time. On the other hand, another Gen2a drug, NFX does not even bind at the interface and is observed to be interacting with an ATP/ADP binding site (Fig.S17a). Gen2b OFX (Fig.S18a), suffers a similar fate in one of the replicas. Along with CFX and NFX, it was excluded from further investigation, as these compounds were categorized either as weak binders or as ligands that may require further refinement in future docking studies. Gen1 NDA, Gen2b LFX, Gen 3 SFX, and Gen4 MFX maintain low and stable RMSD values (~0.2-0.6 nm) over ~90ns (Fig.S15a, Fig.S19a, Fig.8a, and Fig.S20a, supplementary movie SM2, SM6, SM7, and SM8), with minimal deviation from the initial pose and causing negligible structural perturbation to F-actin. Gen3 SFX binds at the multimeric interface of chain A, chain B and chain C (Fig.8c). The frequently interacting amino acid residues from different subunits of F-actin with SFX are depicted in in Fig.8b as a heatmap. The prominent residues of the interface such as Phe266, Pro172, Ile267, Lys191 and Ile267 form strong and persistent contact (dark green). Additionally, the other interface forming residues Tyr188, Phe375, His173, Gly268, Ile175, Arg256, Ser265, Cys374, Leu110, Ile192, and His40 are also found to be interacting with SFX as also visualized in Fig.8d. To identify the highly interacting functional groups of SFX with F-actin, atom-specific interaction frequencies were analyzed over the full MD trajectory (total interactions: 838,311). The analysis revealed that atoms N4, O1, O2, O3, and F2 located on the quinolone core of SFX (as mapped in Fig.8f) exhibited the highest interaction frequencies compared to other functional groups. Gen1 NDA interacts with the interface formed by chain C and D (Fig.S16c) with prominent interacting amino acids include Pro172, Leu110, Phe375, Pro109, Lys113, Asn111, Arg116, Cys374 of chain C and Glu195, Lys191, Tyr188, Ile192, Ile267, Arg256, Phe266 of chain D. These interactions remain consistent over the three replicas (Fig.S16b and Fig.S16d). Atoms O2, O3, O1 and C6 (Fig.S16e) of the quinolone part show the highest interactions frequencies with actin. Gen4 MFX binds at the interphase of chain C and D (Fig.S20c). Frequently interacting amino acid residues with MFX are shown as the dark green patch in the heatmap (Fig.S20b). Prominent amino acids that are in close proximity with MFX thus having intermittent interactions are Lys284, Asn280, Ile175, His173, Met176, Arg177 of chain C and Lys191, Asp187, Ile267, Thr194, Phe200, Met190 of chain D as visualized in Fig.S20d. The total number of interactions between MFX and F-actin during the simulation was calculated to be 555,792. The most frequently interacting atoms were O1, O3, O4, and F from the quinolone core (Fig.S20f). Discussion Given the challenges of finding new compounds that can address actin mis-aggregation for the treatment of various actinopathies, repurposing of FDA approved compounds is an attractive strategy. Our experiments confirm that Gen4 MFX exhibits the most potent activity, effectively disrupting F-actin at an equimolar ratio. This was followed by Gen3 SFX, which required a fivefold molar excess. Interestingly, the smallest molecule, Gen1 NDA, showed moderate disruption activity. Notably, F-actin disruption induced by all FQs was found to be irreversible, with the exception of NDA. At lower concentrations, NDA-treated samples showed increased scattering intensity at 72 hours (Fig. S1 e), suggesting that insufficient drug concentration may allow re-bundling of smaller F-actin fragments, ruling it out as a potential candidate for further clinical investigation. Gen2b (OFX, LFX) displayed weak activity, while Gen2a (NFX, CFX) were the least effective, with CFX requiring a sixty-fold excess to induce observable disruption which is clearly less potent than MFX and SFX. Despite differences in potency, all FQs maintained smaller actin fragments post-disruption for at least 72 hours, supporting their irreversible mechanism of action. The clear shift of F-actin towards lower molecular weight oligomers, confirms effective filament disruption in the gel filtration profiles for Gen2b, Gen3 and Gen4 FQs. These profiles exhibited broad peaks, reflecting the heterogeneity of the fragmented actin population. In contrast, Gen1 NDA yielded larger, more homogenous actin fragments, suggesting a different mode or efficiency of disruption. TEM analysis further provides morphological validation of F-actin disruption. Gen1 NDA, Gen3 SFX, and Gen4 MFX caused complete filament fragmentation at both thirty- and fifty-fold drug excess. Gen2b OFX disrupted filaments into larger oligomers, whereas Gen2a CFX and NFX, and Gen2b LFX, only showed minimal morphological changes/structural perturbations, even at higher concentrations. Polymerization driven stabilization increases the F-actin melting temperature by ~ 11°C when compared to the G-actin 32 . Interestingly, treatment of F-actin with all the FQs under study, shifted its melting profile toward that of G-actin, confirming disruption of filament integrity to a varying extent (Fig. 5 ). Despite of F-actin being disrupted on treatment with FQs, only minor changes in α-helical, β-sheet, and random coil content are seen in CD spectra. This indicates that the FQs do not alter the secondary structure of actin making them potential candidates for drug repurposing against actin mis-aggregation. Isothermal titration calorimetry (ITC) failed to yield conclusive binding interactions (data not shown) indicating weak binding between FQs and F-actin. Due to this weak interaction, STD-NMR becomes a method of choice to identify the key interacting protons of FQs. Common protons present at positions 2 and 5 of the core quinolone rings (Fig. 9 a) are involved in protein interactions across all FQs, thereby emphasizing their vital role in drug binding. MD simulations corroborated these findings, showing that position 2 is involved in van der Waals interactions, while position 5 engages in hydrophobic contacts (Fig. 9 ). NDA exhibited additional interactions via proton at position 6, which is absent in other FQs due to fluorine substitution (Fig. 9 b). CFX and NFX displayed weak STD signals and unstable MD trajectories, consistent with their poor experimental performance. In contrast, SFX and MFX showed strong and sustained interactions, attributed to bulky R7 substitutions such as dimethyl piperazine (SFX) and azabicyclo ring (MFX) (Fig. 9 g, h). Further, analysis of the MD data captures the important amino acids for actin interacting with FQ molecules. Gen1 NDA and Gen3 SFX are observed to be effective disruptors when compared to the other FQs as both of them are interacting with Arg256 of F-actin (PDB ID: 8A2T). The equivalent Arg257 have been reported to form a salt bridge with Asp196, thus playing a vital role in stability of F-actin isoform. The mutational at Arg257Cys reported by Ceron et al and Chiappori et al suggest that this arginine residue is vital for maintaining the structural integrity of F-actin 34 , 35 . This mutation either destabilizes or depolymerizes the filamentous actin 34 . In light of the known data both NDA and SFX seems to be involved in disrupting the salt bridge that Arg256 would be forming with Asp194. The Gen3 SFX quinolone core atoms such as N4, O1, O3, F1 and F2 forms hydrogen bonds with Arg256 (Fig. 8f) as observed in simulation data. O2, O3, N2 are the prominent atoms of Gen1 NDA that forms hydrogen bonds with Arg256 (Fig.S15f). It is pertinent to note that SFX exhibits greater potency compared to NDA, due to the presence of an –NH₂ group at position R5 and two fluorine atoms at R6 and R8, which enable more effective hydrogen bonding with Arg256. However, owing to the small size of NDA, effective F-actin disruption observed in preceding experiments could be achieved at the higher concentrations. It is likely that SFX and NDA mediated abolition of the salt bridge destabilizes lateral interstrand contact thus explaining the destabilization and disruption of F-actin filaments. The yeast cell assay clearly reveals that Gen1 NDA, Gen3 SFX and Gen4 MFX exhibit the strongest effect while Gen2a NFX, Gen2b OFX and LFX demonstrate the moderate disruption activity towards actin aggregates. The oxygen atoms from carboxylic and the keto groups of the FQs core in combination with fluorine at “R6” position formed hydrogen bond with the F-actin (Fig. 9 ). This signifies the role of common core of FQs in forming the hydrogen bonds which forms the primary means of interaction with F-actin. SFX additionally contains -NH 2 present at position R5. As evident from our MD data, this -NH 2 group is involved in more hydrogen bonded interactions with Arg256 and this residue form a vital salt bridge interactions with Asp194 which vital for F-actin stability 35 . The MD simulation studies suggest that the -NH2 group of SFX destabilizes the structural F-actin integrity by interfering with this crucial salt bridge. Another important position implicated in disruption of F-actin is “R7” position (Fig. 9 ). The piperazine ring at “R7” position in Gen2a doesn’t have much interaction as observed in STD NMR and MD simulations (Fig. 9 c, d). The methylation of the same piperazine in Gen2b increases its bulk and the methyl group forms hydrophobic interactions with the protein which might explain the improved efficacy of Gen2b FQs (Fig. 9 e, f). The bulkier dimethylated piperazine occupying “R7” position in Gen3 SFX has considerable overall interaction thus improving the effectiveness of the compound (Fig. 9 g). Similarly, azabicyclo ring in Gen4 MFX enhances the F-actin interaction compared to Gen2 therefore this binding might facilitate better F-actin disruption (Fig. 9 h). CFX, SFX and MFX contain cyclopropyl group at “R1” position, the interaction of cyclopropyl group with F-actin is evident in STD NMR spectra, majorly driven by hydrophobic and Van der Waals interactions (Fig. 9 ). The actin disruption activity of SFX and MFX is far greater than CFX regardless of the presence of cyclopropyl group thereby indicating that this cyclopropyl ring is important in interaction but not in the disruption of actin filaments. Likewise, for moderately effective OFX and LFX the presence of oxazine ring-structure between positions 1 and 8 doesn’t seem to have pronounce effect on reduction in size of actin aggregates when compared to Gen3 and Gen4 FQs. It is important to note that, for NDA, the 'X' position (Fig. 9 b) is a nitrogen, placing it in the naphthyridone family rather than among fluoroquinolones. Amongst all the other reported compounds used in this study, Gen3 SFX and Gen4 MFX seems to have a best disruption activity of actin aggregates. Also, the smaller bulk of the Gen1 NDA might also be responsible for a better uptake by the yeast cells and thereby showing a significant actin aggregate disruption. However, NDA exhibits activity only at higher concentrations, as indicated by RALS and gel filtration data. FQs have been reported to have a better Blood Brain Barrier (BBB) permeability compared to other antibiotics 36 , 37 . Gen1 NDA; Gen2a CFX and NFX; and Gen3 SFX are known to have poor BBB permeability, whereas Gen2b OFX; LFX have moderate permeability 38 . Gen4 MFX is known to have better crossing ability through BBB 25 . Based on the presented experimental data Gen3 SFX and Gen4 MFX are the ideal candidates towards the F-actin disruption. The presented data highlights the roles of position number “R5” and “R7” functional groups of already existing FQs in F-actin disruption. The lipophilicity rendered to the MFX by functional group at “R7” position is vital for its ability to cross BBB and also found to be effective towards F-actin disruption. This makes MFX a potential candidate to treat actin-mediated neuropathies. However, the equally efficient SFX can be administered to treat actin-mis-aggregation in other parts of the body. By altering the lipophilicity of the “R7” position, even SFX might become a potent candidate to treat neurological disorders. Materials and Methods All chemicals and drug compounds were of the highest purity grade obtained from Sigma Aldrich, Hi-media, TCI Chemical, and SD Fine chemicals, else mentioned. It must be noted that Actin and FQ concentrations were adjusted to suit the sensitivity of the experiments while preserving the Actin to FQ compound molar ratio. Actin protein purification Actin was purified from the cytoskeletal muscle acetone powder using G-buffer (2 mM tris.HCl pH 7.5, 0.2 mM CaCl 2 , 0.5 mM ATP, 0.5 mM DTT, 1 mM NaN 3 ) as per Pardee & Spudich protocol with some minor modifications 39 . The F- actin filaments were resuspended in F-buffer (10 mM tris.HCl pH 7.5, 50 mM KCl, 2 mM MgCl 2 , 1 mM ATP, 1 mM NaN 3 ) and were stored at -80°C. The actin protein concentration was determined spectrophotometrically (Implen nanophotometer NP80) at 290 nm (note longer wavelength) corrected for background scattering at 340 nm 40 , 41 . The purified protein was identified by Peptide Mass fingerprinting using MALDI TOF MS (Bruker corporation, USA) at TIFR, Mumbai. Right Angle light scattering (RALS) measurements RALS measurements were performed using Cary Eclipse Fluorescence Spectrophotometer (Agilent technologies, USA) using a 1 cm x 1 cm quartz cuvette with 1 cm path length (Starna Scientific, UK). The excitation and emission wavelength were both set at 350 nm, the excitation and emission slit width was kept 5 nm, the excitation and emission filter were set to auto and PMT voltage set to medium. The concentration of actin protein was fixed at 3 µM and was incubated with varying concentrations of FQs from 0 hours to 72 hours in F- buffer. Size Exclusion Chromatography (SEC) SEC experiments were performed using Biorad NGC (Biorad, USA) at Department of chemical Sciences, TIFR Mumbai, India. The 30 µM F-actin protein was treated with FQs at protein-to-compound molar ratios of 1:15 and incubated for 1 hour in F-buffer. The running F-buffer and the fluoroquinolone treated F-actin protein solution was degassed prior to applying the sample onto the Superdex 200 10/300 GL column (GE Healthcare, USA). The chromatogram was analysed and plotted with Origin 9.0 software. Transmission Electron Microscopy Protein concentration of 5 µM F-actin was used for these studies. F-actin was treated with fluoroquinolone compounds at protein-to-compound molar ratios of 1:30, and 1:50 in F-buffer. 5 µL of F-actin control and fluoroquinolone treated F-actin were adsorbed on 400 mesh formvar/carbon coated copper grids for an hour before negative staining with 1% uranyl acetate, the excess stain solution was removed with a filter paper as previously described 42 . The negatively stained grids were imaged on JEOL JEM-1400 PLUS electron microscope (JEOL, Japan) equipped with EMSIS Tengra CCD camera and LaB 6 filament operating at an accelerating voltage of 120 kV at ACTREC Mumbai, India. Circular Dichroism spectroscopy Circular Dichroism spectra were acquired on Jasco J-1500 spectrometer (Jasco Corporation, Japan) between 260 nm and 190 nm in 0.2 cm pathlength J/21 quartz cells (Jasco Corporation, Japan). The measurements were performed with a 1 nm step size with the digital integration time of 4 seconds at a scan rate of 50 nm/min, at least three accumulations were averaged for each sample. The baseline scan of the F-buffer containing FQs was subtracted from the F-actin treated with FQs. The protein concentration was maintained at 3 µM and treated with FQs at protein-to-compound molar ratios of 1:5, 1:15, and 1:30. The spectra from 260 nm to 200 nm were analysed using JASCO spectra analysis software and protein secondary structural content was estimated using CD Multivariate SSE program provided by JASCO Corporation. The CD spectrums are expressed in the mean residue molar ellipticity [θ] calculated from the following equation: [θ] = θ obs /10.n.c.l (deg.cm − 2 .dmol − 1 ); where θ obs is the observed ellipticity in degrees, n is the number of amino acid residues in the protein, c is the final molar concentration of the protein, and l is the path length in cm 30 . Differential Scanning Calorimetry The experiments were performed on Nano DSC instrument (TA instruments, USA). All measurements were performed using F-buffer and G-buffer by replacing Tris-HCl with HEPES. The protein was dialysed against their respective buffers and fluoroquinolone compounds solutions were prepared by dissolving the compounds in the dialyzed buffer. A protein concentration of 50 µM was measured at 25–90°C, with a scan rate of 2°C per minute at the constant pressure of 3 atm 43 , 44 . The DSC thermograms were analysed using NanoAnalyze (TA instruments, USA) processing software to obtain the temperature-dependent calorimetric enthalpy change (ΔH cal ) and the transition temperature (T m ). Actin depolymerization dynamics in Saccharomyces cerevisiae ∆end3 yeast assay Media and growth conditions The Saccharomyces cerevisiae ∆end3 strain YNL084C (MATα; his3∆1; leu2∆0; lys2∆0; ura3∆0) purchased from Dharmacon Inc. was used in this study. A loop full of cells from a YPD plate (1% yeast extract, 2% Bacto-peptone, 1% glucose, and 2% agar) were inoculated in 25 mL liquid YPD medium (1% yeast extract, 2% Bacto-peptone, and 1% glucose) and were grown overnight to saturation in a shaker incubator at 30°C and 200 rpm. The cells were subsequently shifted to 0.1 OD 600nm in fresh YPD medium and grown until they reached stationary phase (24 hours) 28 . The stationary phase cells were treated with 300 µM FQs and allowed to grow for another 6 hours. Rhodamine-phalloidin staining of actin aggregates Actin staining in ∆end3 cells was performed as described in Pathak et al, 2021. Briefly, cells were fixed using 4% formaldehyde for 10 minutes at 30°C. These cells were harvested by centrifugation at 3000 rpm for 5 minutes and the pellet was resuspended in 6 mL phosphate-buffered saline (136.89 mM NaCl, 2 mM KCl, 10.14 mM Na 2 HPO 4 , 1.76 mM KH 2 PO 4 ) (pH 7.4) and were fixed by adding 4 mL 4% formaldehyde for 1 hour at room temperature. Cells were washed twice with PBS and resuspended in 0.5 mL PBS. 50 µL of this cell suspension was stained with 0.07 µM Rhodamine-phalloidin (Ex/Em: 540/565 nm; Themofisher Scientific, USA), followed by incubation of the cell-dye mixture for 1 hour in dark. The cells were washed five times with PBS and subsequently visualized using an Olympus IX83 inverted microscope equipped with a DP80 CCD camera using a 100x/1.4 NA oil immersion objective. A pE-300white CoolLED light source was used for sample illumination. Images were acquired, processed, and analyzed using CellSens Dimension software (Olympus, Japan). Final images were assembled using Adobe Photoshop. Saturation Transfer Difference (STD) NMR STD NMR studies were performed with a 30 µM solution of F-actin and 1.5 mM fluoroquinolone compounds in F-buffer. The solution contained a 50-fold molar excess of FQs over actin, with 10% D₂O added to the actin-FQs mixture. NMR spectra were acquired using a Bruker Ascend 400.3 MHz spectrometer (¹H nucleus) equipped with an HR-BBO400S1-BBF/H/D-5.0-Z SP probe (¹H, ¹³C, ³¹P, ¹⁵N) and pulsed-field gradients along the z-direction at a temperature of 298 K. The spectrometer was operated with Bruker TopSpin 4.1 software for data collection and processing. The standard STD NMR pulse sequence stddiffesgp.3 was utilized to obtain the spectra. This pulse sequence applies a selective train of Gaussian-shaped pulses with a 1% truncation, a pulse length of 49 ms, and a 2 ms separation between pulses. A total of 256 scans were acquired, with a saturation time of 2.0 s for the STD studies. The deuterated water (HOD) signal was suppressed using gradient-tailored water suppression encoded in the standard STD NMR pulse sequence. Protons of actin protein were selectively irradiated for saturation, with the on-resonance frequency set at 1.22 ppm after multiple experiments and the off-resonance frequency set at -40 ppm. The STD effect percentage was calculated by subtracting the on-resonance spectrum from the off-resonance (reference) spectrum, yielding the STD (difference) spectrum. In-Silico studies of actin and actin-fluoroquinolone interactions The pentameric Cryo-EM structure of the F-actin protein (PDB ID: 8A2T) was used for all the in-silico studies 45 . Missing residues were modelled using the COOT software 46 . CHARMM-GUI 47 was then used to prepare the molecule for GROMACS2022 48 to perform molecular dynamics (MD) simulations. CHARMM-GUI v3.7 Solution Builder 49 module was used to solvate the protein in an octahedral water box, with an edge distance of 10 Å. NaCl ions were added to neutralize the system. Electrostatics were treated using Particle Mesh Ewald algorithm 50 . Protein was modeled using CHARMM36m force field 51 and water molecules were modeled using the TIP3P model 52 , 53 . Forcefield parameters for ADP and FQ’s compounds was generated using CHARMM-GUI. Energy minimization was performed using the steepest descent algorithm until the maximum force acting on the system dropped below 1000 kJ.mol − 1 .nm − 1 . Subsequently, temperature equilibration was carried out to 303.15K in 250 ps using the V-rescale 54 thermostat with a τ t of 1 ps. Solute and solvent groups were coupled to independent thermostats. Pressure equilibration followed, where the system was equilibrated to 1 atm in 500 ps using the C-rescale barostat 55 with isotropic pressure scaling, τ p of 1 ps and compressibility factor of 4.5x10 − 5 bar − 1 . During both equilibration steps, heavy atoms were subjected to position restraints with a force constant of 1000 kJ.mol − 1 .nm − 2 . The structure obtained after pressure equilibration was used as the starting point for the production runs. Three independent production simulations for F-actin (pentameric actin with ADP and Mg 2+ ) were performed, each lasting around 250 ns with a 2 fs time step. A velocity rescaling thermostat 54 regulated the temperature at 303.15 K (τ t : 1 ps), while pressure was maintained at 1 atm using the C-rescale barostat 56 with a τ p of 5 ps. Electrostatic interactions were handled via the Particle Mesh Ewald method 50 , and all h-bonds were constrained using the LINCS algorithm 57 . Structural snapshots were recorded every 20 ps for further analysis. Equilibrated actin structure with ADP and Mg 2+ were used for further docking steps and simulations with FQ compounds. Fluoroquinolone compound preparation for docking FQ compounds Nalidixic acid (NDA), Ciprofloxacin (CFX), Norfloxacin (NFX), Ofloxacin (OFX), Levofloxacin (LFX), Sparfloxacin (SFX), and Moxifloxacin (MFX) were retrieved from the PubChem Database. Energy minimization was performed using ArgusLab ( http://www.arguslab.com/arguslab.com/ArgusLab.html ) with the BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm 58 and the Universal Force Field 59 . Molecular Docking and Simulations From the energy minimized structure of HACT, Mg + 2 and ADP were removed to perform molecular docking. Hydrogen atoms and Kollman charges were added using AutoDock v1.5.7 60 . FQs compounds were assigned Gasteiger charges before performing blind docking using AutoDock Vina v1.1.2 61,62 . The best pose of the FQs were selected on the basis of the lowest binding affinity. The affinity values (kcal.mol − 1 ) for all the FQs are mentioned in Supplementary Table S2. FQs were parameterized using the CHARMM General Force Field (CGenFF) 63 via the Ligand Reader & Modeller module in CHARMM-GUI v3.7. Solution Builder was then used to generate CHARMM force field topologies for the ligand-parameterized protein complexes and simulations were setup as described above. Production run simulations with three replicas per ligand complex, 80ns each were used for data collection. MD data was analysed using GROMACS tools in combination with inhouse python scripts and PyMol 3.1.0 was used for visualization 64 . Statistical analysis Quantitative data are expressed as mean ± SEM. Statistical analysis was performed using the GraphPad Prism Version 8.4.2 (GraphPad Software, Inc., La Jolla, CA, USA). Statistical differences were evaluated using one-way analysis of variance (ANOVA) where P-values less than 0.05 were considered to be significant. Specific information on the statistical tests are given the respective figure legends. Declarations Funding This work was funded and supported by the Department of Atomic Energy. The MD simulations were performed using high-performance computing time allocated by the UK High-End Computing Consortium for Biomolecular Simulation (HECBioSim; http://hecbiosim.ac.uk ), supported by the EPSRC under grant EP/X035603/1. R.G. is supported by UGC-CSIR NET JRF PhD fellowship no. 786/(CSIR-UGC NET DEC. 2018). T.R.S. is supported by IIT Guwahati through her fellowship. Author Contribution R.G. conceptualized the study, designed and conducted all in vitro experiments, performed data analysis and visualization, and contributed to the writing, reviewing, and editing of the manuscript. H.N. performed all in silico experiments, carried out data analysis and visualization, and contributed to the writing and reviewing of the manuscript. T.R.S. performed the yeast cell assays and analyzed the corresponding data. S.N. supervised the yeast cell assays and contributed to data analysis. P.P.G. provided guidance for the in silico experiments. S.C.D. supervised the in silico experiments and computational analyses, managed computational resources, and contributed to the writing, reviewing, and editing of the manuscript. A.K. conceptualized the study, provided overall supervision, managed the project and resources, conducted data analysis, and contributed to the writing, reviewing, and editing of the manuscript. All authors reviewed and approved the final manuscript drafts. Acknowledgement We thank Dr. Debasis Das from the Department of Biological Sciences, TIFR, Mumbai, for granting us access to the ultracentrifugation facility. We also acknowledge the support of Dr. Sri Rama Koti Ainavarapu from the Department of Chemical Sciences, TIFR, Mumbai, for providing access to the Bio-Rad NGC chromatography system. We are grateful to Ms. Siddhi A. Redkar for her assistance with TEM image acquisition at the Electron Microscopy Facility, ACTREC. We also thank Mr. Sudipta Goswami at Bruker, Bangalore, for his technical assistance with the STD NMR experiments. Data Availability The data used to support the findings of this study are available from the corresponding author upon request. References Herrero, M. T. & Morelli, M. Multiple mechanisms of neurodegeneration and progression. Prog. Neurobiol. 155 , 1 (2017). Soto, C. Unfolding the role of protein misfolding in neurodegenerative diseases. Nat. Rev. Neurosci. 4 , 49–60 (2003). Lamptey, R. N. L. et al. A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Nanotherapeutics. Int. J. Mol. Sci. 23 , 1851 (2022). Hoover, B. R. et al. Tau Mislocalization to Dendritic Spines Mediates Synaptic Dysfunction Independently of Neurodegeneration. Neuron 68 , 1067–1081 (2010). Milnerwood, A. J. & Raymond, L. A. Early synaptic pathophysiology in neurodegeneration: insights from Huntington’s disease. Trends Neurosci. 33 , 513–523 (2010). Scott, D. A. et al. A Pathologic Cascade Leading to Synaptic Dysfunction in -Synuclein-Induced Neurodegeneration. J. Neurosci. 30 , 8083–8095 (2010). Steinmetz, J. D. et al. Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol. 23 , 344–381 (2024). Bamburg, J. R., Minamide, L. S., Wiggan, N., Tahtamouni, L. H. & Kuhn, T. B. Cofilin and actin dynamics: Multiple modes of regulation and their impacts in neuronal development and degeneration. mdpi.com (2021) doi:10.3390/cells10102726. Alsegiani, A. & Shah, Z. The role of cofilin in age-related neuroinflammation. Neural Regen. Res. 15 , 1451 (2020). Kregel, K. C. & Zhang, H. J. An integrated view of oxidative stress in aging: basic mechanisms, functional effects, and pathological considerations. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 292 , R18–R36 (2007). Furgerson, M., Fechheimer, M. & Furukawa, R. Model Hirano Bodies Protect against Tau-Independent and Tau-Dependent Cell Death Initiated by the Amyloid Precursor Protein Intracellular Domain. PLoS ONE 7 , e44996 (2012). Ha, S., Furukawa, R. & Fechheimer, M. Association of AICD and Fe65 with Hirano bodies reduces transcriptional activation and initiation of apoptosis. Neurobiol. Aging 32 , 2287–2298 (2011). Minamide, L. S., Striegl, A. M., Boyle, J. A., Meberg, P. J. & Bamburg, J. R. Neurodegenerative stimuli induce persistent ADF/cofilin-actin rods that disrupt distal neurite function. Nat. Cell Biol. 2 , 628–636 (2000). Gibson, P. H. & Tomlinson, B. E. Numbers of Hirano bodies in the hippocampus of normal and demented people with Alzheimer’s disease. J. Neurol. Sci. 33 , 199–206 (1977). Kang, D. E. & Woo, J. A. Cofilin, a Master Node Regulating Cytoskeletal Pathogenesis in Alzheimer’s Disease. J. Alzheimers Dis. 72 , S131–S144 (2019). Spears, W. et al. Hirano bodies differentially modulate cell death induced by tau and the amyloid precursor protein intracellular domain. BMC Neurosci. 15 , 1–20 (2014). Bamburg, J. R. & Bernstein, B. W. Actin dynamics and cofilin-actin rods in alzheimer disease. Cytoskeleton 73 , 477–497 (2016). Furgerson, M. et al. Hirano body expression impairs spatial working memory in a novel mouse model. Acta Neuropathol. Commun. 2 , 131 (2014). Jang, D.-H. et al. Cofilin expression induces cofilin-actin rod formation and disrupts synaptic structure and function in Aplysia synapses. Proc. Natl. Acad. Sci. 102 , 16072–16077 (2005). Tanaka, J., Blain, J. C. & Allingham, J. S. Actin-binding toxin ‘tail’ wags the dog. Chem. Biol. 15 , 205–207 (2008). Pathak, S., Deori, N., Sharma, A., Nagotu, S. & Kale, A. In vitro, in vivo and in silico rationale for the muscle loss due to therapeutic drugs used in the treatment of Mycobacterium tuberculosis infection. J. Biomol. Struct. Dyn. 40 , 44–60 (2022). Pathak, S. et al. The role of Colchicine on actin polymerization dynamics: as a potent anti-angiogenic factor. J. Biomol. Struct. Dyn. 40 , 11729–11743 (2022). Desrayaud, S., Guntz, P., Scherrmann, J.-M. & Lemaire, M. Effect of the P-glycoprotein inhibitor, SDZ PSC 833, on the blood and brain pharmacokinetics of colchicine. Life Sci. 61 , 153–163 (1997). Liu, L. et al. Modulation of P-glycoprotein at the Human Blood-Brain Barrier by Quinidine or Rifampin Treatment: A Positron Emission Tomography Imaging Study. Drug Metab. Dispos. Biol. Fate Chem. 43 , 1795–1804 (2015). Alffenaar, J. W. C. et al. Plasma and cerebrospinal fluid pharmacokinetics of moxifloxacin in a patient with tuberculous meningitis. Antimicrob. Agents Chemother. 52 , 2293–2295 (2008). Bergogne-Bérézin, E. Clinical role of protein binding of quinolones. Clin. Pharmacokinet. 41 , 741–750 (2002). Pathak, S., Parkar, H., Tripathi, S. & Kale, A. Ofloxacin as a Disruptor of Actin Aggresome “Hirano Bodies”: A Potential Repurposed Drug for the Treatment of Neurodegenerative Diseases. Front. Aging Neurosci. 12 , 1–18 (2020). Pathak, S. et al. Effect of tetracycline family of antibiotics on actin aggregation, resulting in the formation of Hirano bodies responsible for neuropathological disorders. J. Biomol. Struct. Dyn. 39 , 236–253 (2021). Miles, A. J., Janes, R. W. & Wallace, B. A. Tools and methods for circular dichroism spectroscopy of proteins: a tutorial review. Chem. Soc. Rev. 50 , 8400–8413 (2021). Tolchard, J. et al. The intrinsically disordered Tarp protein from chlamydia binds actin with a partially preformed helix. Sci. Rep. 8 , 1–11 (2018). Farkas, P., Szatmári, D., Könczöl, F. & Lőrinczy, D. Cyclophosphamide treatment evoked side effect on skeletal muscle actin, monitored by DSC. J. Therm. Anal. Calorim. 147 , 3609–3614 (2022). Le Bihan, T. & Gicquaud, C. Stabilization of actin by phalloidin: a differential scanning calorimetric study. Biochem. Biophys. Res. Commun. 181 , 542–547 (1991). Bénédetti, H., Raths, S., Crausaz, F. & Riezman, H. The END3 gene encodes a protein that is required for the internalization step of endocytosis and for actin cytoskeleton organization in yeast. Mol. Biol. Cell 5 , 1023 (1994). Ceron, R. H. et al. Molecular mechanisms linking missense ACTG2 mutations to visceral myopathy. Sci. Adv. 10 , eadn6615 (2024). Chiappori, F., Palma, F. D., Cavalli, A., de Rosa, M. & Viti, F. Dynamical features of smooth muscle actin pathological mutants: The arginine-257(258)-Cysteine cases. Comput. Struct. Biotechnol. J. 27 , 753–764 (2025). Thwaites, G. E. et al. Randomized pharmacokinetic and pharmacodynamic comparison of fluoroquinolones for tuberculous meningitis. Antimicrob. Agents Chemother. 55 , 3244–3253 (2011). Thwaites, G. E., Van Toorn, R. & Schoeman, J. Tuberculous meningitis: more questions, still too few answers. Lancet Neurol. 12 , 999–1010 (2013). de Lange, E. C. et al. In vitro and in vivo investigations on fluoroquinolones; effects of the P-glycoprotein efflux transporter on brain distribution of sparfloxacin. Eur. J. Pharm. Sci. Off. J. Eur. Fed. Pharm. Sci. 12 , 85–93 (2000). Pardee, J. D. & Spudich, J. A. Purification of muscle actin. Methods Cell Biol 24 , 271–289 (1982). Hundt, N. et al. Direct observation of the molecular mechanism underlying protein polymerization. Sci. Adv. 8 , (2022). Zimmerle, C. T. & Frieden, C. Effect of Temperature on the Mechanism of Actin Polymerization. Biochemistry 25 , 6432–6438 (1986). Ohi, M., Li, Y., Cheng, Y. & Walz, T. Negative Staining and Image Classification – Powerful Tools in Modern Electron Microscopy. Biol. Proced. Online 6 , 23 (2004). Kremneva, E. V., Nikolaeva, O. P., Gusev, N. B. & Levitsky, D. I. Effects of Troponin on Thermal Unfolding of Actin-Bound Tropomyosin. Biochem. Mosc. 68 , 802–809 (2003). Levitsky, D. I., Pivovarova, A. V., Mikhailova, V. V. & Nikolaeva, O. P. Thermal unfolding and aggregation of actin: Stabilization and destabilization of actin filaments. FEBS J. 275 , 4280–4295 (2008). Oosterheert, W., Klink, B. U., Belyy, A., Pospich, S. & Raunser, S. Structural basis of actin filament assembly and aging. Nature 611 , 374–379 (2022). Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Biol. Crystallogr. 60 , 2126–2132 (2004). Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM‐GUI: A web‐based graphical user interface for CHARMM. J. Comput. Chem. 29 , 1859–1865 (2008). Park, S.-J., Kern, N., Brown, T., Lee, J. & Im, W. CHARMM-GUI PDB Manipulator : Various PDB Structural Modifications for Biomolecular Modeling and Simulation. J. Mol. Biol. 435 , 167995 (2023). Lee, J. et al. CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J. Chem. Theory Comput. 12 , 405–413 (2016). Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103 , 8577–8593 (1995). Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14 , 71–73 (2017). Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79 , 926–935 (1983). MacKerell, A. D. Jr. et al. All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. J. Phys. Chem. B 102 , 3586–3616 (1998). Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126 , 014101 (2007). Bernetti, M. & Bussi, G. Pressure control using stochastic cell rescaling. J. Chem. Phys. 153 , 114107 (2020). Bussi, G. & Parrinello, M. Accurate sampling using Langevin dynamics. Phys. Rev. E 75 , 056707 (2007). Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 18 , 1463–1472 (1997). Fletcher, R. Practical Methods of Optimization . (Wiley, 2000). doi:10.1002/9781118723203. Rappe, A. K., Casewit, C. J., Colwell, K. S., Goddard III, W. A. & Skiff, W. M. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. ACS Publications https://pubs.acs.org/doi/pdf/10.1021/ja00051a040 (2002) doi:10.1021/ja00051a040. Morris, G. M. et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 30 , 2785–2791 (2009). Hetényi, C. & van der Spoel, D. Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Lett. 580 , 1447–1450 (2006). Trott, O. & Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31 , 455–461 (2010). Vanommeslaeghe, K. et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31 , 671–690 (2010). Schrödinger, LLC. The PyMOL Molecular Graphics System, Version 1.8. (2023). Additional Declarations No competing interests reported. Supplementary Files NSRSupplementaryRG18052025.pdf Cite Share Download PDF Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviews received at journal 23 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviewers agreed at journal 03 Jul, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers invited by journal 05 Jun, 2025 Editor assigned by journal 05 Jun, 2025 Submission checks completed at journal 23 May, 2025 First submitted to journal 23 May, 2025 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6693104","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":467943895,"identity":"6a56ce1c-62be-498e-a94a-19b4ee70cf29","order_by":0,"name":"Rahul Gupta","email":"","orcid":"","institution":"School of Chemical Sciences, UM-DAE Center for excellence in basic sciences, University of Mumbai, Mumbai, Maharashtra, India","correspondingAuthor":false,"prefix":"","firstName":"Rahul","middleName":"","lastName":"Gupta","suffix":""},{"id":467943896,"identity":"5d7f17b1-ff90-42d1-8724-0763c139e3e3","order_by":1,"name":"Hridhya Nair","email":"","orcid":"","institution":"School of Chemical Sciences, UM-DAE Center for excellence in basic sciences, University of Mumbai, Mumbai, Maharashtra, India","correspondingAuthor":false,"prefix":"","firstName":"Hridhya","middleName":"","lastName":"Nair","suffix":""},{"id":467943897,"identity":"0d55585e-19f8-49e1-8800-c247851f5c14","order_by":2,"name":"Tanveera Rounaque Sarhadi","email":"","orcid":"","institution":"Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India","correspondingAuthor":false,"prefix":"","firstName":"Tanveera","middleName":"Rounaque","lastName":"Sarhadi","suffix":""},{"id":467943898,"identity":"31225ceb-98ac-4735-86a0-12fb0b8a173a","order_by":3,"name":"Shirisha Nagotu","email":"","orcid":"","institution":"Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India","correspondingAuthor":false,"prefix":"","firstName":"Shirisha","middleName":"","lastName":"Nagotu","suffix":""},{"id":467943899,"identity":"66351bd4-efdb-413c-ab46-5269ed4885bf","order_by":4,"name":"Pramodkumar P. Gupta","email":"","orcid":"","institution":"School of Biotechnology and Bioinformatics, D Y Patil Deemed to be University, Plot 50 Sector 15, CBD Belapur, Navi Mumbai 400614, Maharashtra, India","correspondingAuthor":false,"prefix":"","firstName":"Pramodkumar","middleName":"P.","lastName":"Gupta","suffix":""},{"id":467943902,"identity":"fe29559e-33d2-4e3a-b79c-3c53f602ea92","order_by":5,"name":"Sarath Chandra Dantu","email":"","orcid":"","institution":"Department of Computer Sciences, Brunel University London, Uxbridge UB8 3PH, United Kingdom","correspondingAuthor":false,"prefix":"","firstName":"Sarath","middleName":"Chandra","lastName":"Dantu","suffix":""},{"id":467943904,"identity":"96236de5-641f-46a5-803b-823656fff372","order_by":6,"name":"Avinash Kale","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3RsUoDMRzH8V84aJdI1oTD8xWuOAlCX8Vw4FTfwOFfCrlF93sUxx5/0KXg6tChQbipQ8eKNxgrdWu80SGfIfyXL3+SAEnyLwkKxxIaGWEXhoObYYkg0QxLDn6STB6TGNVUbrPv1zB1O3+/vl9PMeYN/NPpRL/ZevLoOuTSLi5nz50leVvCriJrVq3TZ8QoYF1+RxxuMUOYTxcXITF9HxLl688r4inUNp6UL3OXyxEj19ZlgliQ/mPL5Ds5dyxN4xfmIdzF6a5cxpKCx53Z9lzo16rd7cOLKVV5/xFJjuTvNAKG/E6SJEkS8wUHa1Wz18f1lwAAAABJRU5ErkJggg==","orcid":"","institution":"School of Chemical Sciences, UM-DAE Center for excellence in basic sciences, University of Mumbai, Mumbai, Maharashtra, India","correspondingAuthor":true,"prefix":"","firstName":"Avinash","middleName":"","lastName":"Kale","suffix":""}],"badges":[],"createdAt":"2025-05-18 17:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6693104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6693104/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-36089-x","type":"published","date":"2026-02-18T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84230521,"identity":"39b13fba-0048-44ba-8eff-ee273660d32c","added_by":"auto","created_at":"2025-06-09 13:45:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":560769,"visible":true,"origin":"","legend":"\u003cp\u003eF-actin disruption assay using Right Angle Light Scattering (RALS) measurements. a) F-actin treated with SFX at various concentrations (3-900 µM) for 72 hours. The different colours bars represent measurement time points. Data represents mean and the standard error of mean (mean ± SEM), n=3; b) Percentage F-actin disruption upon treatment with various molar ratios of FQs at 0 hours. Data represents mean and the standard error of mean (mean ± SEM), n=3.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/5549f2334afd7c3b48652996.png"},{"id":84230522,"identity":"bd9692e4-b5cf-4535-bb3b-0ef7d691f310","added_by":"auto","created_at":"2025-06-09 13:45:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":363295,"visible":true,"origin":"","legend":"\u003cp\u003eGel filtration chromatograms of F-actin treated with various FQs at 1:15 molar ratios.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/cd582f154c7a2aa1f07a653d.png"},{"id":84231817,"identity":"b41f4e12-c76f-4f6f-a57d-bcbb9d732d0e","added_by":"auto","created_at":"2025-06-09 14:01:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5913138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e Actin controls visualized by TEM. (a) G-actin; (b) F-actin. Images were captured at 10,000× (scale bar: 500 nm), 25,000× (scale bar: 200 nm), and 40,000× (scale bar: 100 nm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e. SFX-induced morphological changes in F-actin visualized by TEM. (a) F-actin treated with SFX at a 1:30 molar ratio; (b) F-actin treated with SFX at a 1:50 molar ratio. Images were captured at 10,000× (scale bar: 500 nm), 25,000× (scale bar: 200 nm), and 40,000× (scale bar: 100 nm).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/e8cb5eeccfa9d60a4e28ee26.png"},{"id":84230524,"identity":"577963fe-4911-4ab9-919e-f8b177202903","added_by":"auto","created_at":"2025-06-09 13:45:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":201670,"visible":true,"origin":"","legend":"\u003cp\u003eCD spectra of\u003cstrong\u003e \u003c/strong\u003eF-actin treated with SFX at 1:5 (red curve), 1:10 (cyan curve), and 1:30 (olive curve) molar ratios. Untreated F-actin is shown as grey curve.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/a18994371874bea9173d9135.png"},{"id":84230534,"identity":"e7cf2e8a-3428-4b1b-9ac8-035eba917080","added_by":"auto","created_at":"2025-06-09 13:45:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":599493,"visible":true,"origin":"","legend":"\u003cp\u003eDSC curves of F-actin treated with various FQs at (a) 1:30 and (b) 1:50 molar ratios. The concentration of G-actin and F-actin was maintained at 50 μM (represented by the dashed curve). F-actin treated with different FQs is represented by distinct colored solid curves.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/c4a07711f821ec9cf4707c0d.png"},{"id":84232674,"identity":"6126067c-7cd8-4425-8f10-b10451b123e7","added_by":"auto","created_at":"2025-06-09 14:17:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1437880,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of FQ compounds on actin aggregates in yeast cells. (a) Representative fluorescence microscopy images depicting \u003cem\u003e∆end3\u003c/em\u003e cells stained with Rh-phalloidin post treatment with FQ compounds. Scale bar - 5 μm; (b) The bar graphs represent the percentage of cells showing aggregation and disaggregation of actin upon treatment. For quantification, 50 random yeast cells were counted from the acquired images from two separate experiments. Statistical analysis was performed for disaggregation of actin of each treated sample with the untreated control group using one-way ANOVA (non-parametric) test. Data represent mean and the standard error of the mean (mean ± SEM), n=2 with duplicates (\u003cem\u003e*p \u003c/em\u003e\u0026lt; 0.05, \u003cem\u003e**p \u003c/em\u003e\u0026lt; 0.01, \u003cem\u003e***p \u003c/em\u003e\u0026lt; 0.001, \u003cem\u003e****p \u003c/em\u003e\u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/98ed8871d65a391a2b822daa.png"},{"id":84232488,"identity":"181cc608-a7f7-497c-98d6-a1be95f07a41","added_by":"auto","created_at":"2025-06-09 14:09:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":224516,"visible":true,"origin":"","legend":"\u003cp\u003e(a) STD spectrum of SFX in presence of actin; (b) Reference spectrum (off resonance) of SFX in presence of actin. The compound was added to 2mM final concentration to result in a 50-fold excess of the ligand.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/0d792cc8da146ae0ca1d3955.png"},{"id":84230921,"identity":"7a575f3f-01d1-4fbf-bb5d-7c408bb00208","added_by":"auto","created_at":"2025-06-09 13:53:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1422926,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/15c52ae0b74641641ec56654.png"},{"id":84230566,"identity":"38a00230-e272-424c-bc85-89046efc4b9b","added_by":"auto","created_at":"2025-06-09 13:45:10","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1080340,"visible":true,"origin":"","legend":"\u003cp\u003eAnnotated chemical structures of different generations of FQs. Functional groups involved in hydrophobic (green), hydrogen bonding (yellow), and van der Waals (pink) interactions are highlighted in respective colors. Functional groups interacting with F-actin, as identified by STD NMR, are indicated with red asterisks.\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/ec3ccd16da9dbfbd97232400.png"},{"id":103252246,"identity":"a2168bb6-01f5-47fe-8cfb-2fc84f1f9051","added_by":"auto","created_at":"2026-02-23 16:13:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13536877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/6255d465-e84d-4adb-b4bd-3f8e8bb44c9c.pdf"},{"id":84230533,"identity":"4d99c495-5852-4ed6-a904-164e63a5c36a","added_by":"auto","created_at":"2025-06-09 13:45:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6076652,"visible":true,"origin":"","legend":"","description":"","filename":"NSRSupplementaryRG18052025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6693104/v1/58713b3cd5ea5a01a2547e54.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From binding to breakdown: biophysical and molecular insights into fluoroquinolone induced F-actin perturbation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeurodegeneration is characterized by the progressive loss of neurons and abnormalities in neuronal synapses often appearing in middle or later stages of life\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The neurodegenerative disorders (NDs), which impact over 50\u0026nbsp;million people worldwide, are often linked to synaptic dysfunction, disruptions in neural networks, and the accumulation of abnormal protein variants in the brain\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Common NDs include Alzheimer's disease, Parkinson's disease, Amyotrophic lateral sclerosis (ALS), motor neuron disease, Huntington's disease, spinal muscular atrophy, prion diseases, and spinocerebellar ataxia\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNeurons naturally harbour dynamic actin filaments and under certain conditions can assemble them into rod like aggregates composed primarily of actin and cofilin\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. When exposed to oxidative or energetic stressors those reactive molecules disrupt the regulation of actin and its associated proteins and impair filament polymerization dynamics that are essential for maintaining dendritic spine structure and synaptic plasticity\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. As polymerization stalls, actin and cofilin coalesce into distinctive rods. In the short term these actin cofilin rods help protect neurons by delaying cytochrome C release from mitochondria and temporarily staving off apoptosis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, persistent rods obstruct intracellular transport and serve as nucleation sites for amyloid precursor protein (APP) and tau accumulation, which accelerates fibril formation\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Such rods have been identified in the brains of patients with Guam amyotrophic lateral sclerosis parkinsonism dementia complex, Alzheimer\u0026rsquo;s disease, and Pick\u0026rsquo;s disease \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Over time these aggregates mature into paracrystalline Hirano bodies whose complex contributes to neurodegeneration, evident in transgenic models, make them both early markers of disease and intriguing targets for therapeutic intervention\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA number of marine macrolides, such as reidispongiolides, sphinxolides, aplyronines, and ulapualides bind to the barbed end of actin and disrupt actin filament, however, no actin-binding drugs have broken beyond the preclinical stage due to their extreme cytotoxicity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Therefore, it is pertinent to identify the potential small molecules could reverse actin mis-aggregation and disrupt the actin cofilin rods into smaller soluble forms without being cytotoxic to the cells. Likewise, for FDA approved Rifampicin and Colchicine actin disruption ability has been reported \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, their poor permeability through Blood Brain Barrier (BBB) makes them unsuitable to treat actin mediated neuropathies\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFQs with moderate lipophilic nature, absence of charge at physiological pH, and low plasma protein binding capacity that favours blood-brain barrier penetration makes them suitable candidates against brain actinopathies\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Pathak et al. showed tetracycline family and a second generation fluoroquinolone ofloxacin as disruptor of actin aggregates and thus apart from being good antibiotics these compounds can also be repurposed for actin disruption\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo fully characterise applicability of FQ\u0026rsquo;s for actin driven neuropathies, in the current study, the effect of five generations (Gen) of FQs (Gen1, Gen2a, Gen2b, Gen3 and Gen4) on F-actin disruption has been investigated using an interdisciplinary \u003cem\u003ein vitro\u003c/em\u003e, and \u003cem\u003ein silico\u003c/em\u003e approach. FQs mediated disruption of F-actin using scattering of F-actin, further confirmed using electron microscopy, gel filtration and in \u003cem\u003eSaccharomyces cerevisiae ∆end3\u003c/em\u003e strain. Secondary structure and thermal stability of FQ treated F-actin samples were assessed using circular dichroism (CD) and differential scanning calorimetry (DSC) respectively. Through Saturation Transfer Difference (STD NMR) and Molecular dynamics simulations (MD) we have identified critical functional groups of the FQs interacting with F-actin and characterised the nature of these interactions. This allows for suggesting probable modifications to the structure of the FQs to improve the potency of these FDA approved drug molecules. Moxifloxacin (MFX) and Sparfloxacin (SFX) standout for their efficacy in disrupting actin filaments and can be repurposed for actinopathies inside and outside the brain respectively.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe studied the effects of five generations of FQ\u0026rsquo;s (Gen1: Nalidixic acid (NDA); Gen2a: Ciprofloxacin (CFX) and Norfloxacin (NFX); Gen2b: Ofloxacin (OFX) and Levofloxacin (LFX); Gen3: Sparfloxacin (SFX); Gen4: Moxifloxacin (MFX)) on F-actin using a plethora of spectroscopic techniques.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFluoroquinolones\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eefficiently and irreversibly disrupt F-actin\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRight Angle Light Scattering (RALS) is used to measure the F-actin disruption efficacy of aforementioned FQ\u0026rsquo;s. The protein control of untreated 3 \u0026micro;M F-actin in F-buffer, exhibited a high level of scattering exceeding 100 a.u. All FQ\u0026rsquo;s began disrupting F-actin at a 1:15 molar ratio, with increasing concentration dependent increase in efficacy (Fig.1 and Fig.S1). While NDA\u0026rsquo;s efficiency was more prevalent at 1:100, Gen2a FQs required higher concentrations of 1:200 to achieve substantial activity (Fig.S1a, b) In contrast, Gen2b FQs, were more effective even at 1:60 molar ratio (Fig.S1c, d). SFX exhibited marked F-actin disruption at a 1:15 molar ratio, with a reduction in scattering intensity exceeding 50%, indicating its effectiveness (Fig.1a). MFX, a Gen4 FQ, was the most effective F-actin disruptor among all FQs with complete disruption after a 1:30 treatment (Fig.1b). It must be noted that we do not observe any reversibility in the treated samples, i.e., reformation of F-actin even after 72 h except for NDA (Fig.S1e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity of disrupted F-actin oligomers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the extent of F-actin disruption observed in previous size-based measurements, untreated and FQ-treated F-actin samples were studied using gel filtration chromatography. Untreated F-actin eluted as a broad peak at 9.2 mL, near the column\u0026rsquo;s void volume which is consistent with its large filamentous structure (Fig.2). F-actin treated with Gen1 NDA exhibited a sharper elution peak at 15.6 mL, indicating a more homogeneous population of disrupted filaments compared to other FQ-treated samples. Notably, treatment with Gen2a CFX and NFX, resulted in the formation of visible white precipitate thereby preventing their analysis by gel filtration chromatography. Gen2b treated F-actin began eluting near the void volume, with peak maxima at 21.6 mL for OFX and 21.3 mL for LFX, suggesting marked filament disruption. Similarly, Gen3 SFX and Gen4 MFX treated F-actin exhibited elution profiles comparable to those of Gen2b compounds, with elution starting from the void volume and peak maxima at 22.4 mL for SFX and 22.2 mL for MFX. The diversity in elution profiles suggests heterogeneity of actin oligomer populations in the treated samples because of the plausible differences in the molecular disruption mechanism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMorphological changes in disrupted filaments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe morphologies of the disrupted F-actin filaments are characterised with TEM imaging. Actin, when negatively stained in G-buffer and F-buffer, exhibits distinct morphologies. G-actin primarily consists of actin monomers and oligomers, while polymerized F-actin shows the presence of filamentous structures in solution (Fig.3a). F-actin treated with Gen1 NDA at 1:30 and 1:50 molar ratios resulted in the disruption of filaments, with larger oligomers predominating in the solution (Fig.S2). In contrast, treatment with Gen2a CFX and NFX at the same molar ratios (1:30 and 1:50) led to the perturbed filaments morphology, which exhibited distinct morphologies compared to the control F-actin. These perturbed filaments showed multiple kinks, bends, and discontinuities, though complete filament disruption was not observed (Fig.S3, S4). Similarly, Gen2b LFX and OFX treatment at a 1:30 molar ratio also resulted in perturbed F-actin morphology, similar to Gen2a treated samples, with kinks, bends, and oligomers present, but without complete filament disruption. However, at a higher molar ratio of 1:50, OFX treatment resulted in the complete loss of filamentous structures, indicating observable disruption of F-actin at this concentration (Fig.S5). LFX, despite being from the same generation, did not show this effect (Fig.S6). Treatment with Gen3 SFX and Gen4 MFX led to complete disruption of filaments at both 1:30 and 1:50 molar ratios (Fig.3b, Fig.S7). Furthermore, the oligomers generated post-filament disruption were predominantly smaller in size in comparison to earlier generations of FQs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFluoroquinolones induce minor secondary structural changes in actin\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF-actin treatment with the FQs disrupts the filaments as observed in various size-based measurements. To confirm the possible secondary structural changes of the resulting populations of disrupted F-actin filaments, far UV CD spectroscopic measurements were performed. The two negative bands at 208 nm and 222 nm represent the characteristic of alpha helical structure while the negative band at 218 nm is associated with \u0026beta; sheets structure \u003csup\u003e29\u003c/sup\u003e. Actin is a rather rigid protein molecule and has secondary structure conformations of 30% \u0026alpha;-helix, 23.5% \u0026beta;-sheet, 12.2% turns, and 34.3% random coil. These values closely match with the previously reported secondary structure of actin protein\u003csup\u003e30\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGen1 NDA treatment of F-actin at 1:30 molar ratio, increases the \u0026alpha; helical content by 4.1% and decreases the sheet content by 2.6% (Fig.S8e). Gen2a CFX treatment of F-actin decreases the \u0026alpha; helical content by 3% and increases the sheet content by 3.5% with minor changes in the turns and other conformations such as random coils (Fig.S8a). Gen2a NFX treatment of F-actin increases the \u0026alpha; helical content by 2.2% and decreases the sheet content by 4.2% (Fig.S8b). Gen2b OFX treatment of F-actin decreases the \u0026alpha; helical content by 4.6 % and increases the sheet content by 3% (Fig.S8c). Gen2b LFX treatment of F-actin decreases the \u0026alpha; helical content by 7.6 % and increases the sheet content by 4.8% (Fig.S8d). Gen3 SFX treatment of F-actin induces minor change in the \u0026alpha; helical content but decreases the sheet content by 1.3% with minor changes in the turns and other conformations such as random coils (Fig.4). Gen4 MFX treatment of F-actin induces minor change in the \u0026alpha; helical content but decreases the sheet content by 3.9% with minor changes in the turns and other conformations such as random coils (Fig.S8f). The secondary structure data has been provided in supplementary Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThermal stability of fluoroquinolones treated F-actin samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary goal of DSC measurements was to assess the thermal stability of F-actin filaments treated with different generations of FQ\u0026rsquo;s. In case of thermal denaturation of biological samples, a difference of 1 \u0026deg;C in melting temperature (T\u003csub\u003em\u003c/sub\u003e) is considered significant\u003csup\u003e31\u003c/sup\u003e. Actin protein concentration can have impact on the different denaturation parameters due to changes in concentration dependent intermolecular forces and charges; therefore, the protein concentration was fixed at 50\u0026nbsp;\u0026micro;M. The denaturation peaks of G-actin (T\u003csub\u003em\u003c/sub\u003e = 59.0 \u0026deg;C) and F-actin (T\u003csub\u003em\u003c/sub\u003e = 69.8 \u0026deg;C) are consistent with previously reported literature values\u003csup\u003e32\u003c/sup\u003e. After FQ treatment the F-actin undergoes disruption as observed in size-based measurements. Therefore, we expect FQ mediated F-actin disruption should also cause protein structure to destabilize. The disrupted oligomers should be more globular compared to the compact F-actin structure thus reducing the melting temperature of F-actin treated with FQs\u0026nbsp;(Fig.5). After the FQs treatment at 1:30 and 1:50 molar ratios, marked reduction in T\u003csub\u003em\u003c/sub\u003e and \u0026Delta;H\u003csub\u003ecal\u003c/sub\u003e of treated F-actin is observed (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Thermal parameters of the denaturation of native and FQs treated F-actin protein\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTm (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;H\u003csub\u003ecal\u003c/sub\u003e (kJ.mol\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003csub\u003em\u003c/sub\u003e (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;H\u003csub\u003ecal\u003c/sub\u003e (kJ.mol\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG-actin (untreated)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e480.9 \u0026plusmn; 48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-actin (untreated)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e69.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1012.0 \u0026plusmn; 48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-actin treated with FQs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e1.5mM treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e2.5mM treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNalidixic acid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e66.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e591.5 \u0026plusmn; 23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e445.4 \u0026plusmn; 9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCFX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e65.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e666.4 \u0026plusmn; 21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e65.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e620.8 \u0026plusmn; 15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e771.8 \u0026plusmn; 28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e67.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e690.6 \u0026plusmn; 28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOFX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e68.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e714.3 \u0026plusmn; 29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e65.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e688.3 \u0026plusmn; 21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLFX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e68.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e617.4 \u0026plusmn; 22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e68.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e753.0 \u0026plusmn; 26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSFX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e66.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e667.1 \u0026plusmn; 21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e619.7 \u0026plusmn; 19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMFX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e69.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e837.6 \u0026plusmn; 40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e68.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e793.3 \u0026plusmn; 30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFluoroquinolone mediated actin disaggregation in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eD\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eend3 S. cerevisiae\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effects of FQs on actin bundles was investigated in the \u003cem\u003eS. cerevisiae ∆end3\u003c/em\u003e strain. This strain has been widely used in studies involving actin-binding small molecules as the F-actin dynamics is reduced during the stationary phase of growth and its larger cell size facilitates the visualization of actin structures\u003csup\u003e33\u003c/sup\u003e. After the cells reached the stationary phase, they were treated with 300 \u0026mu;M FQ compounds and stained with Rh-phalloidin to assess morphological changes in actin aggregates using fluorescence microscopy.\u003c/p\u003e\n\u003cp\u003eIn untreated control cells, large aggregated patches of F-actin were observed whereas FQ-treated cells exhibited dispersed actin bundles and a disaggregated F-actin morphology. 81% of the untreated yeast cell population displayed aggregated F-actin patches, while only 19% showed disaggregated morphology (Fig.6b). Upon FQ treatment, the F-actin distribution was significantly altered. In cells treated with the Gen1 NDA, 31% retained aggregated actin, while 69% showed a marked increase in disaggregated F-actin. Similarly, treatment with Gen3 SFX and Gen4 MFX resulted in a marked increase in the proportion of cells (67% and 63%) exhibiting disaggregated F-actin morphology compared to the control. Cells treated with NFX, OFX, and LFX (Gen2 compounds) also showed a substantial disaggregation (62%, 59% and 58% respectively) significantly higher than the untreated group (Fig.6b). In CFX-treated cells, the percentage of cells with aggregated F-actin remained relatively higher (63%) when compared to the other FQ-treated samples. Nevertheless, CFX treatment still led to a higher proportion cells (37%) displaying disaggregated F-actin compared to the untreated control (19%) (Fig. 6b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential binding of fluoroquinolones to actin using\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esaturation transfer difference NMR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interaction between actin protein and FQs at a 1:50 molar excess ratio was studied using STD NMR analysis. The actin-FQs mixture contained 10% D₂O in F-buffer at 25\u0026deg;C for acquiring on-resonance and off-resonance spectra. The presence of peaks in the STD (difference) spectrum provides clear evidence of FQs binding to actin protein. The binding epitopes of the compounds (proton-containing functional groups) were identified by comparing the STD spectra with the corresponding \u0026sup1;H NMR spectra of the compounds, thus providing atomic-level insight into their interaction with the protein. The STD NMR spectra for all FQs exhibited signals, indicating that each compound readily interacts with the F-actin. The characteristic signals of protons from the aromatic quinolone bicyclic core, methyl (-CH₃), and methylene (-CH₂-) groups in the FQs were prominent. The protons from OH and NH functional groups were difficult to observe due to fast exchange with water.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGroup epitope mapping studies using saturation transfer difference NMR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGen3 SFX exhibited stronger STD effects than the other FQs. The H12 and H15 protons from the methyl groups attached to the piperazine ring had a 100% STD effect, while H10 and H14 protons from the same ring contributed 7.0% (Fig.7). The H17 and H18 protons from the cyclopropyl group exhibited a moderately weak STD effect of 17.6%. The proton from the NH₂ group (4.2%) attached to the quinolone core and the proton from the NH group (7.3%) of the piperazine ring had weak STD effects. The putative NH signals in the STD NMR spectrum were unexpected; hence, they were verified by acquiring and comparing spectra of SFX in pure D₂O. These signals are weak and typically not visible in aqueous solutions due to fast exchange with water. The only aromatic proton, H3 (6.7%), exhibited a weak STD effect. The signals in the STD spectrum indicate that, in conjunction with the quinolone core, the piperazine ring also contributes to the binding interactions between SFX and actin protein.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig.S9 shows Gen1 NDA in complex with actin protein. The strongest signal in the STD NMR spectrum, corresponding to the H12 protons of the methyl group, was considered to have a 100% STD effect. The STD effects of other signals in the spectrum were calculated relative to this strongest signal by comparing their intensities. The H9 protons of the methyl group showed a moderate STD effect of 20.9%, whereas the aromatic protons from the naphthyridone core, such as H3 (7.1%), H6 (4.2%), and H8 (4.8%), exhibited very weak STD effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGen2a CFX and NFX displayed weak overall STD effects. These were also the consistent weakest binders among all FQs tested. For CFX, the H15 and H16 protons from the cyclopropyl group showed the highest STD effects at 100% and 91.3%, respectively. The aromatic protons from the quinolone core, such as H2 (21.9%), H8 (9.8%), and H4 (11.5%), exhibited moderate to weak STD effects (Fig.S10). NFX, which belongs to the same generation, showed only four signals in the STD NMR spectrum, confirming its lower binding activity as observed in previous data. The H15 proton from the methyl group exhibited a 100% STD effect, while the aromatic protons from the quinolone core, such as H8 (12.2%), H6 (10.0%), and H3 (11.7%), showed weak STD effects similar to CFX (Fig.S11).\u003c/p\u003e\n\u003cp\u003eGen2b OFX and LFX showed that the H10 proton from the methyl group exhibited a 100% STD effect. The H15 protons from the methyl group attached to the piperazine ring showed a moderate STD effect of 45.7% in OFX and 40.7% in LFX, indicating good contact with actin protein and participation in binding interactions. The aromatic protons from the quinolone core of OFX exhibited weak STD effects, as indicated by H8 (13.0%), H9 (6.9%), and H1 (11.7%) (Fig.S12). Similarly, LFX showed weak STD effects for H8 (9.2%), H9 (6.7%), and H1 (9.7%) (Fig.S13).\u003c/p\u003e\n\u003cp\u003eGen4 MFX also exhibited strong STD effects but followed a binding mode similar to CFX. The H19 and H20 protons from the cyclopropyl group contributed the highest STD effects at 100%. The azabicyclo group protons, such as H10 and H16, together exhibited a strong STD effect of 79%. Additionally, the NH protons (34.1%) and H14 (48.4%) showed moderate STD effects, indicating the prominent contribution of the azabicyclo group to actin binding. The aromatic protons from the quinolone core, such as H6 (32.6%) and H7 (18.8%), displayed moderate to weak STD effects (Fig.S14). Based on the number of signals and their STD effects in the spectrum, MFX demonstrates strong binding to actin protein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAtomic level structural insights into binding of fluoroquinolones to F-actin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mode of binding and interaction of FQ\u0026rsquo;s was studied with a pentameric model of F-actin.\u003c/p\u003e\n\u003cp\u003eIn the MD simulations, both the Gen2a CFX and NFX deviate away from the docked sites. \u0026nbsp;CFX, exhibits a steadily increasing RMSD (Fig.S16a), reaching over 5.0 nm by the end of simulation over the period of ~38ns (supplementary movie SM1). This progressive deviation suggests major conformational and spatial rearrangement of CFX, leading to \u0026ldquo;diffusion away\u0026rdquo; from its initial binding site of the protein, implying an unstable interaction with relatively stable F-actin over the function of time. On the other hand, another Gen2a drug, NFX does not even bind at the interface and is observed to be interacting with an ATP/ADP binding site (Fig.S17a). Gen2b OFX (Fig.S18a), suffers a similar fate in one of the replicas. Along with CFX and NFX, it was excluded from further investigation, as these compounds were categorized either as weak binders or as ligands that may require further refinement in future docking studies.\u003c/p\u003e\n\u003cp\u003eGen1 NDA, Gen2b LFX, Gen 3 SFX, and Gen4 MFX maintain low and stable RMSD values (~0.2-0.6 nm) over ~90ns (Fig.S15a, Fig.S19a, Fig.8a, and Fig.S20a, supplementary movie SM2, SM6, SM7, and SM8), with minimal deviation from the initial pose and causing negligible structural perturbation to F-actin.\u003c/p\u003e\n\u003cp\u003eGen3 SFX binds at the multimeric interface of chain A, chain B and chain C (Fig.8c). The frequently interacting amino acid residues from different subunits of F-actin with SFX are depicted in in Fig.8b as a heatmap. The prominent residues of the interface such as Phe266, Pro172, Ile267, Lys191 and Ile267 form strong and persistent contact (dark green). Additionally, the other interface forming residues Tyr188, Phe375, His173, Gly268, Ile175, Arg256, Ser265, Cys374, Leu110, Ile192, and His40 are also found to be interacting with SFX as also visualized in Fig.8d. To identify the highly interacting functional groups of SFX with F-actin, atom-specific interaction frequencies were analyzed over the full MD trajectory (total interactions: 838,311). The analysis revealed that atoms N4, O1, O2, O3, and F2 located on the quinolone core of SFX (as mapped in Fig.8f) exhibited the highest interaction frequencies compared to other functional groups.\u003c/p\u003e\n\u003cp\u003eGen1 NDA interacts with the interface formed by chain C and D (Fig.S16c) with prominent interacting amino acids include Pro172, Leu110, Phe375, Pro109, Lys113, Asn111, Arg116, Cys374 of chain C and Glu195, Lys191, Tyr188, Ile192, Ile267, Arg256, Phe266 of chain D. These interactions remain consistent over the three replicas (Fig.S16b and Fig.S16d). Atoms O2, O3, O1 and C6 (Fig.S16e) of the quinolone part show the highest interactions frequencies with actin.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGen4 MFX binds at the interphase of chain C and D (Fig.S20c). Frequently interacting amino acid residues with MFX are shown as the dark green patch in the heatmap (Fig.S20b). Prominent amino acids that are in close proximity with MFX thus having intermittent interactions are Lys284, Asn280, Ile175, His173, Met176, Arg177 of chain C and Lys191, Asp187, Ile267, Thr194, Phe200, Met190 of chain D as visualized in Fig.S20d. The total number of interactions between MFX and F-actin during the simulation was calculated to be 555,792. The most frequently interacting atoms were O1, O3, O4, and F from the quinolone core (Fig.S20f).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGiven the challenges of finding new compounds that can address actin mis-aggregation for the treatment of various actinopathies, repurposing of FDA approved compounds is an attractive strategy. Our experiments confirm that Gen4 MFX exhibits the most potent activity, effectively disrupting F-actin at an equimolar ratio. This was followed by Gen3 SFX, which required a fivefold molar excess. Interestingly, the smallest molecule, Gen1 NDA, showed moderate disruption activity. Notably, F-actin disruption induced by all FQs was found to be irreversible, with the exception of NDA. At lower concentrations, NDA-treated samples showed increased scattering intensity at 72 hours (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ee), suggesting that insufficient drug concentration may allow re-bundling of smaller F-actin fragments, ruling it out as a potential candidate for further clinical investigation. Gen2b (OFX, LFX) displayed weak activity, while Gen2a (NFX, CFX) were the least effective, with CFX requiring a sixty-fold excess to induce observable disruption which is clearly less potent than MFX and SFX. Despite differences in potency, all FQs maintained smaller actin fragments post-disruption for at least 72 hours, supporting their irreversible mechanism of action. The clear shift of F-actin towards lower molecular weight oligomers, confirms effective filament disruption in the gel filtration profiles for Gen2b, Gen3 and Gen4 FQs. These profiles exhibited broad peaks, reflecting the heterogeneity of the fragmented actin population. In contrast, Gen1 NDA yielded larger, more homogenous actin fragments, suggesting a different mode or efficiency of disruption.\u003c/p\u003e \u003cp\u003eTEM analysis further provides morphological validation of F-actin disruption. Gen1 NDA, Gen3 SFX, and Gen4 MFX caused complete filament fragmentation at both thirty- and fifty-fold drug excess. Gen2b OFX disrupted filaments into larger oligomers, whereas Gen2a CFX and NFX, and Gen2b LFX, only showed minimal morphological changes/structural perturbations, even at higher concentrations. Polymerization driven stabilization increases the F-actin melting temperature by ~\u0026thinsp;11\u0026deg;C when compared to the G-actin\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Interestingly, treatment of F-actin with all the FQs under study, shifted its melting profile toward that of G-actin, confirming disruption of filament integrity to a varying extent (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Despite of F-actin being disrupted on treatment with FQs, only minor changes in α-helical, β-sheet, and random coil content are seen in CD spectra. This indicates that the FQs do not alter the secondary structure of actin making them potential candidates for drug repurposing against actin mis-aggregation.\u003c/p\u003e \u003cp\u003eIsothermal titration calorimetry (ITC) failed to yield conclusive binding interactions (data not shown) indicating weak binding between FQs and F-actin. Due to this weak interaction, STD-NMR becomes a method of choice to identify the key interacting protons of FQs. Common protons present at positions 2 and 5 of the core quinolone rings (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea) are involved in protein interactions across all FQs, thereby emphasizing their vital role in drug binding. MD simulations corroborated these findings, showing that position 2 is involved in van der Waals interactions, while position 5 engages in hydrophobic contacts (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). NDA exhibited additional interactions via proton at position 6, which is absent in other FQs due to fluorine substitution (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). CFX and NFX displayed weak STD signals and unstable MD trajectories, consistent with their poor experimental performance. In contrast, SFX and MFX showed strong and sustained interactions, attributed to bulky R7 substitutions such as dimethyl piperazine (SFX) and azabicyclo ring (MFX) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eg, h).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther, analysis of the MD data captures the important amino acids for actin interacting with FQ molecules. Gen1 NDA and Gen3 SFX are observed to be effective disruptors when compared to the other FQs as both of them are interacting with Arg256 of F-actin (PDB ID: 8A2T). The equivalent Arg257 have been reported to form a salt bridge with Asp196, thus playing a vital role in stability of F-actin isoform. The mutational at Arg257Cys reported by Ceron \u003cem\u003eet al\u003c/em\u003e and Chiappori \u003cem\u003eet al\u003c/em\u003e suggest that this arginine residue is vital for maintaining the structural integrity of F-actin\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This mutation either destabilizes or depolymerizes the filamentous actin\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In light of the known data both NDA and SFX seems to be involved in disrupting the salt bridge that Arg256 would be forming with Asp194. The Gen3 SFX quinolone core atoms such as N4, O1, O3, F1 and F2 forms hydrogen bonds with Arg256 (Fig.\u0026nbsp;8f) as observed in simulation data. O2, O3, N2 are the prominent atoms of Gen1 NDA that forms hydrogen bonds with Arg256 (Fig.S15f). It is pertinent to note that SFX exhibits greater potency compared to NDA, due to the presence of an \u0026ndash;NH₂ group at position R5 and two fluorine atoms at R6 and R8, which enable more effective hydrogen bonding with Arg256. However, owing to the small size of NDA, effective F-actin disruption observed in preceding experiments could be achieved at the higher concentrations. It is likely that SFX and NDA mediated abolition of the salt bridge destabilizes lateral interstrand contact thus explaining the destabilization and disruption of F-actin filaments.\u003c/p\u003e \u003cp\u003eThe yeast cell assay clearly reveals that Gen1 NDA, Gen3 SFX and Gen4 MFX exhibit the strongest effect while Gen2a NFX, Gen2b OFX and LFX demonstrate the moderate disruption activity towards actin aggregates. The oxygen atoms from carboxylic and the keto groups of the FQs core in combination with fluorine at \u0026ldquo;R6\u0026rdquo; position formed hydrogen bond with the F-actin (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This signifies the role of common core of FQs in forming the hydrogen bonds which forms the primary means of interaction with F-actin. SFX additionally contains -NH\u003csub\u003e2\u003c/sub\u003e present at position R5. As evident from our MD data, this -NH\u003csub\u003e2\u003c/sub\u003e group is involved in more hydrogen bonded interactions with Arg256 and this residue form a vital salt bridge interactions with Asp194 which vital for F-actin stability\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The MD simulation studies suggest that the -NH2 group of SFX destabilizes the structural F-actin integrity by interfering with this crucial salt bridge.\u003c/p\u003e \u003cp\u003eAnother important position implicated in disruption of F-actin is \u0026ldquo;R7\u0026rdquo; position (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The piperazine ring at \u0026ldquo;R7\u0026rdquo; position in Gen2a doesn\u0026rsquo;t have much interaction as observed in STD NMR and MD simulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec, d). The methylation of the same piperazine in Gen2b increases its bulk and the methyl group forms hydrophobic interactions with the protein which might explain the improved efficacy of Gen2b FQs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ee, f). The bulkier dimethylated piperazine occupying \u0026ldquo;R7\u0026rdquo; position in Gen3 SFX has considerable overall interaction thus improving the effectiveness of the compound (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eg). Similarly, azabicyclo ring in Gen4 MFX enhances the F-actin interaction compared to Gen2 therefore this binding might facilitate better F-actin disruption (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eh).\u003c/p\u003e \u003cp\u003eCFX, SFX and MFX contain cyclopropyl group at \u0026ldquo;R1\u0026rdquo; position, the interaction of cyclopropyl group with F-actin is evident in STD NMR spectra, majorly driven by hydrophobic and Van der Waals interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The actin disruption activity of SFX and MFX is far greater than CFX regardless of the presence of cyclopropyl group thereby indicating that this cyclopropyl ring is important in interaction but not in the disruption of actin filaments. Likewise, for moderately effective OFX and LFX the presence of oxazine ring-structure between positions 1 and 8 doesn\u0026rsquo;t seem to have pronounce effect on reduction in size of actin aggregates when compared to Gen3 and Gen4 FQs.\u003c/p\u003e \u003cp\u003eIt is important to note that, for NDA, the 'X' position (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb) is a nitrogen, placing it in the naphthyridone family rather than among fluoroquinolones. Amongst all the other reported compounds used in this study, Gen3 SFX and Gen4 MFX seems to have a best disruption activity of actin aggregates. Also, the smaller bulk of the Gen1 NDA might also be responsible for a better uptake by the yeast cells and thereby showing a significant actin aggregate disruption. However, NDA exhibits activity only at higher concentrations, as indicated by RALS and gel filtration data.\u003c/p\u003e \u003cp\u003eFQs have been reported to have a better Blood Brain Barrier (BBB) permeability compared to other antibiotics\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Gen1 NDA; Gen2a CFX and NFX; and Gen3 SFX are known to have poor BBB permeability, whereas Gen2b OFX; LFX have moderate permeability\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Gen4 MFX is known to have better crossing ability through BBB\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Based on the presented experimental data Gen3 SFX and Gen4 MFX are the ideal candidates towards the F-actin disruption.\u003c/p\u003e \u003cp\u003eThe presented data highlights the roles of position number \u0026ldquo;R5\u0026rdquo; and \u0026ldquo;R7\u0026rdquo; functional groups of already existing FQs in F-actin disruption. The lipophilicity rendered to the MFX by functional group at \u0026ldquo;R7\u0026rdquo; position is vital for its ability to cross BBB and also found to be effective towards F-actin disruption. This makes MFX a potential candidate to treat actin-mediated neuropathies. However, the equally efficient SFX can be administered to treat actin-mis-aggregation in other parts of the body. By altering the lipophilicity of the \u0026ldquo;R7\u0026rdquo; position, even SFX might become a potent candidate to treat neurological disorders.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eAll chemicals and drug compounds were of the highest purity grade obtained from Sigma Aldrich, Hi-media, TCI Chemical, and SD Fine chemicals, else mentioned. It must be noted that Actin and FQ concentrations were adjusted to suit the sensitivity of the experiments while preserving the Actin to FQ compound molar ratio.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eActin protein purification\u003c/h2\u003e \u003cp\u003eActin was purified from the cytoskeletal muscle acetone powder using G-buffer (2 mM tris.HCl pH 7.5, 0.2 mM CaCl\u003csub\u003e2\u003c/sub\u003e, 0.5 mM ATP, 0.5 mM DTT, 1 mM NaN\u003csub\u003e3\u003c/sub\u003e) as per Pardee \u0026amp; Spudich protocol with some minor modifications\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The F- actin filaments were resuspended in F-buffer (10 mM tris.HCl pH 7.5, 50 mM KCl, 2 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 1 mM ATP, 1 mM NaN\u003csub\u003e3\u003c/sub\u003e) and were stored at -80\u0026deg;C. The actin protein concentration was determined spectrophotometrically (Implen nanophotometer NP80) at 290 nm (note longer wavelength) corrected for background scattering at 340 nm\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The purified protein was identified by Peptide Mass fingerprinting using MALDI TOF MS (Bruker corporation, USA) at TIFR, Mumbai.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRight Angle light scattering (RALS) measurements\u003c/h2\u003e \u003cp\u003eRALS measurements were performed using Cary Eclipse Fluorescence Spectrophotometer (Agilent technologies, USA) using a 1 cm x 1 cm quartz cuvette with 1 cm path length (Starna Scientific, UK). The excitation and emission wavelength were both set at 350 nm, the excitation and emission slit width was kept 5 nm, the excitation and emission filter were set to auto and PMT voltage set to medium. The concentration of actin protein was fixed at 3 \u0026micro;M and was incubated with varying concentrations of FQs from 0 hours to 72 hours in F- buffer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSize Exclusion Chromatography (SEC)\u003c/h2\u003e \u003cp\u003eSEC experiments were performed using Biorad NGC (Biorad, USA) at Department of chemical Sciences, TIFR Mumbai, India. The 30 \u0026micro;M F-actin protein was treated with FQs at protein-to-compound molar ratios of 1:15 and incubated for 1 hour in F-buffer. The running F-buffer and the fluoroquinolone treated F-actin protein solution was degassed prior to applying the sample onto the Superdex 200 10/300 GL column (GE Healthcare, USA). The chromatogram was analysed and plotted with Origin 9.0 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTransmission Electron Microscopy\u003c/h2\u003e \u003cp\u003eProtein concentration of 5 \u0026micro;M F-actin was used for these studies. F-actin was treated with fluoroquinolone compounds at protein-to-compound molar ratios of 1:30, and 1:50 in F-buffer. 5 \u0026micro;L of F-actin control and fluoroquinolone treated F-actin were adsorbed on 400 mesh formvar/carbon coated copper grids for an hour before negative staining with 1% uranyl acetate, the excess stain solution was removed with a filter paper as previously described\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The negatively stained grids were imaged on JEOL JEM-1400 PLUS electron microscope (JEOL, Japan) equipped with EMSIS Tengra CCD camera and LaB\u003csub\u003e6\u003c/sub\u003e filament operating at an accelerating voltage of 120 kV at ACTREC Mumbai, India.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCircular Dichroism spectroscopy\u003c/h2\u003e \u003cp\u003eCircular Dichroism spectra were acquired on Jasco J-1500 spectrometer (Jasco Corporation, Japan) between 260 nm and 190 nm in 0.2 cm pathlength J/21 quartz cells (Jasco Corporation, Japan). The measurements were performed with a 1 nm step size with the digital integration time of 4 seconds at a scan rate of 50 nm/min, at least three accumulations were averaged for each sample. The baseline scan of the F-buffer containing FQs was subtracted from the F-actin treated with FQs. The protein concentration was maintained at 3 \u0026micro;M and treated with FQs at protein-to-compound molar ratios of 1:5, 1:15, and 1:30. The spectra from 260 nm to 200 nm were analysed using JASCO spectra analysis software and protein secondary structural content was estimated using CD Multivariate SSE program provided by JASCO Corporation. The CD spectrums are expressed in the mean residue molar ellipticity [θ] calculated from the following equation: [θ] = θ\u003csub\u003eobs\u003c/sub\u003e/10.n.c.l (deg.cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.dmol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); where θ\u003csub\u003eobs\u003c/sub\u003e is the observed ellipticity in degrees, n is the number of amino acid residues in the protein, c is the final molar concentration of the protein, and l is the path length in cm\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Scanning Calorimetry\u003c/h2\u003e \u003cp\u003eThe experiments were performed on Nano DSC instrument (TA instruments, USA). All measurements were performed using F-buffer and G-buffer by replacing Tris-HCl with HEPES. The protein was dialysed against their respective buffers and fluoroquinolone compounds solutions were prepared by dissolving the compounds in the dialyzed buffer. A protein concentration of 50 \u0026micro;M was measured at 25\u0026ndash;90\u0026deg;C, with a scan rate of 2\u0026deg;C per minute at the constant pressure of 3 atm\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The DSC thermograms were analysed using NanoAnalyze (TA instruments, USA) processing software to obtain the temperature-dependent calorimetric enthalpy change (ΔH\u003csub\u003ecal\u003c/sub\u003e) and the transition temperature (T\u003csub\u003em\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eActin depolymerization dynamics in\u003c/b\u003e \u003cb\u003eSaccharomyces cerevisiae ∆end3\u003c/b\u003e \u003cb\u003eyeast assay\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMedia and growth conditions\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eSaccharomyces cerevisiae ∆end3\u003c/em\u003e strain YNL084C (MATα; his3∆1; leu2∆0; lys2∆0; ura3∆0) purchased from Dharmacon Inc. was used in this study. A loop full of cells from a YPD plate (1% yeast extract, 2% Bacto-peptone, 1% glucose, and 2% agar) were inoculated in 25 mL liquid YPD medium (1% yeast extract, 2% Bacto-peptone, and 1% glucose) and were grown overnight to saturation in a shaker incubator at 30\u0026deg;C and 200 rpm. The cells were subsequently shifted to 0.1 OD\u003csub\u003e600nm\u003c/sub\u003e in fresh YPD medium and grown until they reached stationary phase (24 hours)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The stationary phase cells were treated with 300 \u0026micro;M FQs and allowed to grow for another 6 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRhodamine-phalloidin staining of actin aggregates\u003c/h2\u003e \u003cp\u003eActin staining in \u003cem\u003e∆end3\u003c/em\u003e cells was performed as described in Pathak et al, 2021. Briefly, cells were fixed using 4% formaldehyde for 10 minutes at 30\u0026deg;C. These cells were harvested by centrifugation at 3000 rpm for 5 minutes and the pellet was resuspended in 6 mL phosphate-buffered saline (136.89 mM NaCl, 2 mM KCl, 10.14 mM Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, 1.76 mM KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e) (pH 7.4) and were fixed by adding 4 mL 4% formaldehyde for 1 hour at room temperature. Cells were washed twice with PBS and resuspended in 0.5 mL PBS. 50 \u0026micro;L of this cell suspension was stained with 0.07 \u0026micro;M Rhodamine-phalloidin (Ex/Em: 540/565 nm; Themofisher Scientific, USA), followed by incubation of the cell-dye mixture for 1 hour in dark. The cells were washed five times with PBS and subsequently visualized using an Olympus IX83 inverted microscope equipped with a DP80 CCD camera using a 100x/1.4 NA oil immersion objective. A pE-300white CoolLED light source was used for sample illumination. Images were acquired, processed, and analyzed using CellSens Dimension software (Olympus, Japan). Final images were assembled using Adobe Photoshop.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSaturation Transfer Difference (STD) NMR\u003c/h2\u003e \u003cp\u003eSTD NMR studies were performed with a 30 \u0026micro;M solution of F-actin and 1.5 mM fluoroquinolone compounds in F-buffer. The solution contained a 50-fold molar excess of FQs over actin, with 10% D₂O added to the actin-FQs mixture. NMR spectra were acquired using a Bruker Ascend 400.3 MHz spectrometer (\u0026sup1;H nucleus) equipped with an HR-BBO400S1-BBF/H/D-5.0-Z SP probe (\u0026sup1;H, \u0026sup1;\u0026sup3;C, \u0026sup3;\u0026sup1;P, \u0026sup1;⁵N) and pulsed-field gradients along the z-direction at a temperature of 298 K. The spectrometer was operated with Bruker TopSpin 4.1 software for data collection and processing. The standard STD NMR pulse sequence stddiffesgp.3 was utilized to obtain the spectra. This pulse sequence applies a selective train of Gaussian-shaped pulses with a 1% truncation, a pulse length of 49 ms, and a 2 ms separation between pulses. A total of 256 scans were acquired, with a saturation time of 2.0 s for the STD studies. The deuterated water (HOD) signal was suppressed using gradient-tailored water suppression encoded in the standard STD NMR pulse sequence. Protons of actin protein were selectively irradiated for saturation, with the on-resonance frequency set at 1.22 ppm after multiple experiments and the off-resonance frequency set at -40 ppm. The STD effect percentage was calculated by subtracting the on-resonance spectrum from the off-resonance (reference) spectrum, yielding the STD (difference) spectrum.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn-Silico\u003c/b\u003e \u003cb\u003estudies of actin and actin-fluoroquinolone interactions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe pentameric Cryo-EM structure of the F-actin protein (PDB ID: 8A2T) was used for all the \u003cem\u003ein-silico\u003c/em\u003e studies\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Missing residues were modelled using the COOT software\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. CHARMM-GUI\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e was then used to prepare the molecule for GROMACS2022\u003csup\u003e48\u003c/sup\u003e to perform molecular dynamics (MD) simulations. CHARMM-GUI v3.7 Solution Builder\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e module was used to solvate the protein in an octahedral water box, with an edge distance of 10 \u0026Aring;. NaCl ions were added to neutralize the system. Electrostatics were treated using Particle Mesh Ewald algorithm\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProtein was modeled using CHARMM36m force field\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e and water molecules were modeled using the TIP3P model\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Forcefield parameters for ADP and FQ\u0026rsquo;s compounds was generated using CHARMM-GUI. Energy minimization was performed using the steepest descent algorithm until the maximum force acting on the system dropped below 1000 kJ.mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.nm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Subsequently, temperature equilibration was carried out to 303.15K in 250 ps using the V-rescale\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e thermostat with a τ\u003csub\u003et\u003c/sub\u003e of 1 ps. Solute and solvent groups were coupled to independent thermostats. Pressure equilibration followed, where the system was equilibrated to 1 atm in 500 ps using the C-rescale barostat\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e with isotropic pressure scaling, τ\u003csub\u003ep\u003c/sub\u003e of 1 ps and compressibility factor of 4.5x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e bar \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. During both equilibration steps, heavy atoms were subjected to position restraints with a force constant of 1000 kJ.mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.nm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. The structure obtained after pressure equilibration was used as the starting point for the production runs.\u003c/p\u003e \u003cp\u003eThree independent production simulations for F-actin (pentameric actin with ADP and Mg\u003csup\u003e2+\u003c/sup\u003e) were performed, each lasting around 250 ns with a 2 fs time step. A velocity rescaling thermostat\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e regulated the temperature at 303.15 K (τ\u003csub\u003et\u003c/sub\u003e: 1 ps), while pressure was maintained at 1 atm using the C-rescale barostat\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e with a τ\u003csub\u003ep\u003c/sub\u003e of 5 ps. Electrostatic interactions were handled via the Particle Mesh Ewald method\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and all h-bonds were constrained using the LINCS algorithm\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Structural snapshots were recorded every 20 ps for further analysis. Equilibrated actin structure with ADP and Mg\u003csup\u003e2+\u003c/sup\u003e were used for further docking steps and simulations with FQ compounds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFluoroquinolone compound preparation for docking\u003c/h2\u003e \u003cp\u003eFQ compounds Nalidixic acid (NDA), Ciprofloxacin (CFX), Norfloxacin (NFX), Ofloxacin (OFX), Levofloxacin (LFX), Sparfloxacin (SFX), and Moxifloxacin (MFX) were retrieved from the PubChem Database. Energy minimization was performed using ArgusLab (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.arguslab.com/arguslab.com/ArgusLab.html\u003c/span\u003e\u003cspan address=\"http://www.arguslab.com/arguslab.com/ArgusLab.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the BFGS (Broyden\u0026ndash;Fletcher\u0026ndash;Goldfarb\u0026ndash;Shanno) algorithm\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and the Universal Force Field\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMolecular Docking and Simulations\u003c/h2\u003e \u003cp\u003eFrom the energy minimized structure of HACT, Mg\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e and ADP were removed to perform molecular docking. Hydrogen atoms and Kollman charges were added using AutoDock v1.5.7\u003csup\u003e60\u003c/sup\u003e. FQs compounds were assigned Gasteiger charges before performing blind docking using AutoDock Vina v1.1.2\u003csup\u003e61,62\u003c/sup\u003e. The best pose of the FQs were selected on the basis of the lowest binding affinity. The affinity values (kcal.mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for all the FQs are mentioned in Supplementary Table S2.\u003c/p\u003e \u003cp\u003eFQs were parameterized using the CHARMM General Force Field (CGenFF)\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e via the Ligand Reader \u0026amp; Modeller module in CHARMM-GUI v3.7. Solution Builder was then used to generate CHARMM force field topologies for the ligand-parameterized protein complexes and simulations were setup as described above. Production run simulations with three replicas per ligand complex, 80ns each were used for data collection. MD data was analysed using GROMACS tools in combination with inhouse python scripts and PyMol 3.1.0 was used for visualization\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eQuantitative data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Statistical analysis was performed using the GraphPad Prism Version 8.4.2 (GraphPad Software, Inc., La Jolla, CA, USA). Statistical differences were evaluated using one-way analysis of variance (ANOVA) where P-values less than 0.05 were considered to be significant. Specific information on the statistical tests are given the respective figure legends.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was funded and supported by the Department of Atomic Energy. The MD simulations were performed using high-performance computing time allocated by the UK High-End Computing Consortium for Biomolecular Simulation (HECBioSim; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hecbiosim.ac.uk\u003c/span\u003e\u003cspan address=\"http://hecbiosim.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), supported by the EPSRC under grant EP/X035603/1. R.G. is supported by UGC-CSIR NET JRF PhD fellowship no. 786/(CSIR-UGC NET DEC. 2018). T.R.S. is supported by IIT Guwahati through her fellowship.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.G. conceptualized the study, designed and conducted all in vitro experiments, performed data analysis and visualization, and contributed to the writing, reviewing, and editing of the manuscript. H.N. performed all in silico experiments, carried out data analysis and visualization, and contributed to the writing and reviewing of the manuscript. T.R.S. performed the yeast cell assays and analyzed the corresponding data. S.N. supervised the yeast cell assays and contributed to data analysis. P.P.G. provided guidance for the in silico experiments. S.C.D. supervised the in silico experiments and computational analyses, managed computational resources, and contributed to the writing, reviewing, and editing of the manuscript. A.K. conceptualized the study, provided overall supervision, managed the project and resources, conducted data analysis, and contributed to the writing, reviewing, and editing of the manuscript. All authors reviewed and approved the final manuscript drafts.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Dr. Debasis Das from the Department of Biological Sciences, TIFR, Mumbai, for granting us access to the ultracentrifugation facility. We also acknowledge the support of Dr. Sri Rama Koti Ainavarapu from the Department of Chemical Sciences, TIFR, Mumbai, for providing access to the Bio-Rad NGC chromatography system. We are grateful to Ms. Siddhi A. Redkar for her assistance with TEM image acquisition at the Electron Microscopy Facility, ACTREC. We also thank Mr. Sudipta Goswami at Bruker, Bangalore, for his technical assistance with the STD NMR experiments.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHerrero, M. T. \u0026amp; Morelli, M. Multiple mechanisms of neurodegeneration and progression. \u003cem\u003eProg. Neurobiol.\u003c/em\u003e \u003cstrong\u003e155\u003c/strong\u003e, 1 (2017).\u003c/li\u003e\n\u003cli\u003eSoto, C. Unfolding the role of protein misfolding in neurodegenerative diseases. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 49\u0026ndash;60 (2003).\u003c/li\u003e\n\u003cli\u003eLamptey, R. N. L. \u003cem\u003eet al.\u003c/em\u003e A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Nanotherapeutics. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 1851 (2022).\u003c/li\u003e\n\u003cli\u003eHoover, B. R. \u003cem\u003eet al.\u003c/em\u003e Tau Mislocalization to Dendritic Spines Mediates Synaptic Dysfunction Independently of Neurodegeneration. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 1067\u0026ndash;1081 (2010).\u003c/li\u003e\n\u003cli\u003eMilnerwood, A. J. \u0026amp; Raymond, L. A. Early synaptic pathophysiology in neurodegeneration: insights from Huntington\u0026rsquo;s disease. \u003cem\u003eTrends Neurosci.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 513\u0026ndash;523 (2010).\u003c/li\u003e\n\u003cli\u003eScott, D. A. \u003cem\u003eet al.\u003c/em\u003e A Pathologic Cascade Leading to Synaptic Dysfunction in -Synuclein-Induced Neurodegeneration. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 8083\u0026ndash;8095 (2010).\u003c/li\u003e\n\u003cli\u003eSteinmetz, J. D. \u003cem\u003eet al.\u003c/em\u003e Global, regional, and national burden of disorders affecting the nervous system, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet Neurol.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 344\u0026ndash;381 (2024).\u003c/li\u003e\n\u003cli\u003eBamburg, J. R., Minamide, L. S., Wiggan, N., Tahtamouni, L. H. \u0026amp; Kuhn, T. B. Cofilin and actin dynamics: Multiple modes of regulation and their impacts in neuronal development and degeneration. \u003cem\u003emdpi.com\u003c/em\u003e (2021) doi:10.3390/cells10102726.\u003c/li\u003e\n\u003cli\u003eAlsegiani, A. \u0026amp; Shah, Z. The role of cofilin in age-related neuroinflammation. \u003cem\u003eNeural Regen. Res.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1451 (2020).\u003c/li\u003e\n\u003cli\u003eKregel, K. C. \u0026amp; Zhang, H. J. An integrated view of oxidative stress in aging: basic mechanisms, functional effects, and pathological considerations. \u003cem\u003eAm. J. Physiol.-Regul. Integr. Comp. Physiol.\u003c/em\u003e \u003cstrong\u003e292\u003c/strong\u003e, R18\u0026ndash;R36 (2007).\u003c/li\u003e\n\u003cli\u003eFurgerson, M., Fechheimer, M. \u0026amp; Furukawa, R. Model Hirano Bodies Protect against Tau-Independent and Tau-Dependent Cell Death Initiated by the Amyloid Precursor Protein Intracellular Domain. \u003cem\u003ePLoS ONE\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, e44996 (2012).\u003c/li\u003e\n\u003cli\u003eHa, S., Furukawa, R. \u0026amp; Fechheimer, M. Association of AICD and Fe65 with Hirano bodies reduces transcriptional activation and initiation of apoptosis. \u003cem\u003eNeurobiol. Aging\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 2287\u0026ndash;2298 (2011).\u003c/li\u003e\n\u003cli\u003eMinamide, L. S., Striegl, A. M., Boyle, J. A., Meberg, P. J. \u0026amp; Bamburg, J. R. Neurodegenerative stimuli induce persistent ADF/cofilin-actin rods that disrupt distal neurite function. \u003cem\u003eNat. Cell Biol.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 628\u0026ndash;636 (2000).\u003c/li\u003e\n\u003cli\u003eGibson, P. H. \u0026amp; Tomlinson, B. E. Numbers of Hirano bodies in the hippocampus of normal and demented people with Alzheimer\u0026rsquo;s disease. \u003cem\u003eJ. Neurol. Sci.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 199\u0026ndash;206 (1977).\u003c/li\u003e\n\u003cli\u003eKang, D. E. \u0026amp; Woo, J. A. Cofilin, a Master Node Regulating Cytoskeletal Pathogenesis in Alzheimer\u0026rsquo;s Disease. \u003cem\u003eJ. Alzheimers Dis.\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, S131\u0026ndash;S144 (2019).\u003c/li\u003e\n\u003cli\u003eSpears, W. \u003cem\u003eet al.\u003c/em\u003e Hirano bodies differentially modulate cell death induced by tau and the amyloid precursor protein intracellular domain. \u003cem\u003eBMC Neurosci.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1\u0026ndash;20 (2014).\u003c/li\u003e\n\u003cli\u003eBamburg, J. R. \u0026amp; Bernstein, B. W. Actin dynamics and cofilin-actin rods in alzheimer disease. \u003cem\u003eCytoskeleton\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, 477\u0026ndash;497 (2016).\u003c/li\u003e\n\u003cli\u003eFurgerson, M. \u003cem\u003eet al.\u003c/em\u003e Hirano body expression impairs spatial working memory in a novel mouse model. \u003cem\u003eActa Neuropathol. Commun.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 131 (2014).\u003c/li\u003e\n\u003cli\u003eJang, D.-H. \u003cem\u003eet al.\u003c/em\u003e Cofilin expression induces cofilin-actin rod formation and disrupts synaptic structure and function in \u003cem\u003eAplysia\u003c/em\u003e synapses. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 16072\u0026ndash;16077 (2005).\u003c/li\u003e\n\u003cli\u003eTanaka, J., Blain, J. C. \u0026amp; Allingham, J. S. Actin-binding toxin \u0026lsquo;tail\u0026rsquo; wags the dog. \u003cem\u003eChem. Biol.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 205\u0026ndash;207 (2008).\u003c/li\u003e\n\u003cli\u003ePathak, S., Deori, N., Sharma, A., Nagotu, S. \u0026amp; Kale, A. In vitro, in vivo and in silico rationale for the muscle loss due to therapeutic drugs used in the treatment of Mycobacterium tuberculosis infection. \u003cem\u003eJ. Biomol. Struct. Dyn.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 44\u0026ndash;60 (2022).\u003c/li\u003e\n\u003cli\u003ePathak, S. \u003cem\u003eet al.\u003c/em\u003e The role of Colchicine on actin polymerization dynamics: as a potent anti-angiogenic factor. \u003cem\u003eJ. Biomol. Struct. Dyn.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 11729\u0026ndash;11743 (2022).\u003c/li\u003e\n\u003cli\u003eDesrayaud, S., Guntz, P., Scherrmann, J.-M. \u0026amp; Lemaire, M. Effect of the P-glycoprotein inhibitor, SDZ PSC 833, on the blood and brain pharmacokinetics of colchicine. \u003cem\u003eLife Sci.\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 153\u0026ndash;163 (1997).\u003c/li\u003e\n\u003cli\u003eLiu, L. \u003cem\u003eet al.\u003c/em\u003e Modulation of P-glycoprotein at the Human Blood-Brain Barrier by Quinidine or Rifampin Treatment: A Positron Emission Tomography Imaging Study. \u003cem\u003eDrug Metab. Dispos. Biol. Fate Chem.\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 1795\u0026ndash;1804 (2015).\u003c/li\u003e\n\u003cli\u003eAlffenaar, J. W. C. \u003cem\u003eet al.\u003c/em\u003e Plasma and cerebrospinal fluid pharmacokinetics of moxifloxacin in a patient with tuberculous meningitis. \u003cem\u003eAntimicrob. Agents Chemother.\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 2293\u0026ndash;2295 (2008).\u003c/li\u003e\n\u003cli\u003eBergogne-B\u0026eacute;r\u0026eacute;zin, E. Clinical role of protein binding of quinolones. \u003cem\u003eClin. Pharmacokinet.\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 741\u0026ndash;750 (2002).\u003c/li\u003e\n\u003cli\u003ePathak, S., Parkar, H., Tripathi, S. \u0026amp; Kale, A. Ofloxacin as a Disruptor of Actin Aggresome \u0026ldquo;Hirano Bodies\u0026rdquo;: A Potential Repurposed Drug for the Treatment of Neurodegenerative Diseases. \u003cem\u003eFront. Aging Neurosci.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1\u0026ndash;18 (2020).\u003c/li\u003e\n\u003cli\u003ePathak, S. \u003cem\u003eet al.\u003c/em\u003e Effect of tetracycline family of antibiotics on actin aggregation, resulting in the formation of Hirano bodies responsible for neuropathological disorders. \u003cem\u003eJ. Biomol. Struct. Dyn.\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 236\u0026ndash;253 (2021).\u003c/li\u003e\n\u003cli\u003eMiles, A. J., Janes, R. W. \u0026amp; Wallace, B. A. Tools and methods for circular dichroism spectroscopy of proteins: a tutorial review. \u003cem\u003eChem. Soc. Rev.\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 8400\u0026ndash;8413 (2021).\u003c/li\u003e\n\u003cli\u003eTolchard, J. \u003cem\u003eet al.\u003c/em\u003e The intrinsically disordered Tarp protein from chlamydia binds actin with a partially preformed helix. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 1\u0026ndash;11 (2018).\u003c/li\u003e\n\u003cli\u003eFarkas, P., Szatm\u0026aacute;ri, D., K\u0026ouml;ncz\u0026ouml;l, F. \u0026amp; Lőrinczy, D. Cyclophosphamide treatment evoked side effect on skeletal muscle actin, monitored by DSC. \u003cem\u003eJ. Therm. Anal. Calorim.\u003c/em\u003e \u003cstrong\u003e147\u003c/strong\u003e, 3609\u0026ndash;3614 (2022).\u003c/li\u003e\n\u003cli\u003eLe Bihan, T. \u0026amp; Gicquaud, C. Stabilization of actin by phalloidin: a differential scanning calorimetric study. \u003cem\u003eBiochem. Biophys. Res. Commun.\u003c/em\u003e \u003cstrong\u003e181\u003c/strong\u003e, 542\u0026ndash;547 (1991).\u003c/li\u003e\n\u003cli\u003eB\u0026eacute;n\u0026eacute;detti, H., Raths, S., Crausaz, F. \u0026amp; Riezman, H. The END3 gene encodes a protein that is required for the internalization step of endocytosis and for actin cytoskeleton organization in yeast. \u003cem\u003eMol. Biol. Cell\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 1023 (1994).\u003c/li\u003e\n\u003cli\u003eCeron, R. H. \u003cem\u003eet al.\u003c/em\u003e Molecular mechanisms linking missense ACTG2 mutations to visceral myopathy. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, eadn6615 (2024).\u003c/li\u003e\n\u003cli\u003eChiappori, F., Palma, F. D., Cavalli, A., de Rosa, M. \u0026amp; Viti, F. Dynamical features of smooth muscle actin pathological mutants: The arginine-257(258)-Cysteine cases. \u003cem\u003eComput. Struct. Biotechnol. J.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 753\u0026ndash;764 (2025).\u003c/li\u003e\n\u003cli\u003eThwaites, G. E. \u003cem\u003eet al.\u003c/em\u003e Randomized pharmacokinetic and pharmacodynamic comparison of fluoroquinolones for tuberculous meningitis. \u003cem\u003eAntimicrob. Agents Chemother.\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 3244\u0026ndash;3253 (2011).\u003c/li\u003e\n\u003cli\u003eThwaites, G. E., Van Toorn, R. \u0026amp; Schoeman, J. Tuberculous meningitis: more questions, still too few answers. \u003cem\u003eLancet Neurol.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 999\u0026ndash;1010 (2013).\u003c/li\u003e\n\u003cli\u003ede Lange, E. C. \u003cem\u003eet al.\u003c/em\u003e In vitro and in vivo investigations on fluoroquinolones; effects of the P-glycoprotein efflux transporter on brain distribution of sparfloxacin. \u003cem\u003eEur. J. Pharm. Sci. Off. J. Eur. Fed. Pharm. Sci.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 85\u0026ndash;93 (2000).\u003c/li\u003e\n\u003cli\u003ePardee, J. D. \u0026amp; Spudich, J. A. Purification of muscle actin. \u003cem\u003eMethods Cell Biol\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 271\u0026ndash;289 (1982).\u003c/li\u003e\n\u003cli\u003eHundt, N. \u003cem\u003eet al.\u003c/em\u003e Direct observation of the molecular mechanism underlying protein polymerization. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eZimmerle, C. T. \u0026amp; Frieden, C. Effect of Temperature on the Mechanism of Actin Polymerization. \u003cem\u003eBiochemistry\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 6432\u0026ndash;6438 (1986).\u003c/li\u003e\n\u003cli\u003eOhi, M., Li, Y., Cheng, Y. \u0026amp; Walz, T. Negative Staining and Image Classification \u0026ndash; Powerful Tools in Modern Electron Microscopy. \u003cem\u003eBiol. Proced. Online\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 23 (2004).\u003c/li\u003e\n\u003cli\u003eKremneva, E. V., Nikolaeva, O. P., Gusev, N. B. \u0026amp; Levitsky, D. I. Effects of Troponin on Thermal Unfolding of Actin-Bound Tropomyosin. \u003cem\u003eBiochem. Mosc.\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 802\u0026ndash;809 (2003).\u003c/li\u003e\n\u003cli\u003eLevitsky, D. I., Pivovarova, A. V., Mikhailova, V. V. \u0026amp; Nikolaeva, O. P. Thermal unfolding and aggregation of actin: Stabilization and destabilization of actin filaments. \u003cem\u003eFEBS J.\u003c/em\u003e \u003cstrong\u003e275\u003c/strong\u003e, 4280\u0026ndash;4295 (2008).\u003c/li\u003e\n\u003cli\u003eOosterheert, W., Klink, B. U., Belyy, A., Pospich, S. \u0026amp; Raunser, S. Structural basis of actin filament assembly and aging. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e611\u003c/strong\u003e, 374\u0026ndash;379 (2022).\u003c/li\u003e\n\u003cli\u003eEmsley, P. \u0026amp; Cowtan, K. Coot: model-building tools for molecular graphics. \u003cem\u003eBiol. Crystallogr.\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 2126\u0026ndash;2132 (2004).\u003c/li\u003e\n\u003cli\u003eJo, S., Kim, T., Iyer, V. G. \u0026amp; Im, W. CHARMM‐GUI: A web‐based graphical user interface for CHARMM. \u003cem\u003eJ. Comput. Chem.\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1859\u0026ndash;1865 (2008).\u003c/li\u003e\n\u003cli\u003ePark, S.-J., Kern, N., Brown, T., Lee, J. \u0026amp; Im, W. CHARMM-GUI \u003cem\u003ePDB Manipulator\u003c/em\u003e: Various PDB Structural Modifications for Biomolecular Modeling and Simulation. \u003cem\u003eJ. Mol. Biol.\u003c/em\u003e \u003cstrong\u003e435\u003c/strong\u003e, 167995 (2023).\u003c/li\u003e\n\u003cli\u003eLee, J. \u003cem\u003eet al.\u003c/em\u003e CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. \u003cem\u003eJ. Chem. Theory Comput.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 405\u0026ndash;413 (2016).\u003c/li\u003e\n\u003cli\u003eEssmann, U. \u003cem\u003eet al.\u003c/em\u003e A smooth particle mesh Ewald method. \u003cem\u003eJ. Chem. Phys.\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 8577\u0026ndash;8593 (1995).\u003c/li\u003e\n\u003cli\u003eHuang, J. \u003cem\u003eet al.\u003c/em\u003e CHARMM36m: an improved force field for folded and intrinsically disordered proteins. \u003cem\u003eNat. Methods\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 71\u0026ndash;73 (2017).\u003c/li\u003e\n\u003cli\u003eJorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. \u0026amp; Klein, M. L. Comparison of simple potential functions for simulating liquid water. \u003cem\u003eJ. Chem. Phys.\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 926\u0026ndash;935 (1983).\u003c/li\u003e\n\u003cli\u003eMacKerell, A. D. Jr. \u003cem\u003eet al.\u003c/em\u003e All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. \u003cem\u003eJ. Phys. Chem. B\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 3586\u0026ndash;3616 (1998).\u003c/li\u003e\n\u003cli\u003eBussi, G., Donadio, D. \u0026amp; Parrinello, M. Canonical sampling through velocity rescaling. \u003cem\u003eJ. Chem. Phys.\u003c/em\u003e \u003cstrong\u003e126\u003c/strong\u003e, 014101 (2007).\u003c/li\u003e\n\u003cli\u003eBernetti, M. \u0026amp; Bussi, G. Pressure control using stochastic cell rescaling. \u003cem\u003eJ. Chem. Phys.\u003c/em\u003e \u003cstrong\u003e153\u003c/strong\u003e, 114107 (2020).\u003c/li\u003e\n\u003cli\u003eBussi, G. \u0026amp; Parrinello, M. Accurate sampling using Langevin dynamics. \u003cem\u003ePhys. Rev. E\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 056707 (2007).\u003c/li\u003e\n\u003cli\u003eHess, B., Bekker, H., Berendsen, H. J. C. \u0026amp; Fraaije, J. G. E. M. LINCS: A linear constraint solver for molecular simulations. \u003cem\u003eJ. Comput. Chem.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1463\u0026ndash;1472 (1997).\u003c/li\u003e\n\u003cli\u003eFletcher, R. \u003cem\u003ePractical Methods of Optimization\u003c/em\u003e. (Wiley, 2000). doi:10.1002/9781118723203.\u003c/li\u003e\n\u003cli\u003eRappe, A. K., Casewit, C. J., Colwell, K. S., Goddard III, W. A. \u0026amp; Skiff, W. M. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. \u003cem\u003eACS Publications\u003c/em\u003e https://pubs.acs.org/doi/pdf/10.1021/ja00051a040 (2002) doi:10.1021/ja00051a040.\u003c/li\u003e\n\u003cli\u003eMorris, G. M. \u003cem\u003eet al.\u003c/em\u003e AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. \u003cem\u003eJ. Comput. Chem.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 2785\u0026ndash;2791 (2009).\u003c/li\u003e\n\u003cli\u003eHet\u0026eacute;nyi, C. \u0026amp; van der Spoel, D. Blind docking of drug-sized compounds to proteins with up to a thousand residues. \u003cem\u003eFEBS Lett.\u003c/em\u003e \u003cstrong\u003e580\u003c/strong\u003e, 1447\u0026ndash;1450 (2006).\u003c/li\u003e\n\u003cli\u003eTrott, O. \u0026amp; Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. \u003cem\u003eJ. Comput. Chem.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 455\u0026ndash;461 (2010).\u003c/li\u003e\n\u003cli\u003eVanommeslaeghe, K. \u003cem\u003eet al.\u003c/em\u003e CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. \u003cem\u003eJ. Comput. Chem.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 671\u0026ndash;690 (2010).\u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;dinger, LLC. The PyMOL Molecular Graphics System, Version 1.8. (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"F-actin, Fluoroquinolones, drug repurposing, neurodegenerative disorders, STD NMR, molecular dynamics","lastPublishedDoi":"10.21203/rs.3.rs-6693104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6693104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeurodegenerative disorders involving actin dysregulation demand safer yet potent actin-disrupting compounds. Previous studies have reported the actin-destabilizing effects of fluoroquinolones (FQs), suggesting their potential for repurposing. FQs are widely prescribed broad-spectrum antibiotics known for their efficacy against bacterial infections. The newer generation FQs are also known to effectively cross the Blood Brain Barrier (BBB). In this study, we demonstrate that FQs irreversibly disrupts F-actin filaments in a concentration-dependent manner using scattering based assay. Electron microscopy and gel filtration confirms generation-dependent disruption activity. In particular, Gen3 and Gen4 FQs reduces actin aggregates in more than 60% yeast cells. FQ treatment alters the thermal stability of F-actin at 1:30 and 1:50 molar ratios with minor secondary structural changes. While exploring the molecular insights of FQs interaction with F-actin, STD NMR combined with MD simulations revealed the importance of the fluorinated quinolone core, which is common to all FQs. These studies also highlight the involvement of R5 amino, bulky piperazine and azabicyclo rings at the R7 position in F-actin\u0026rsquo;s intermonomer interface binding, ultimately leading to the disruption of F-actin. We propose modifying the identified positions on the quinolone core to enhance the potency of F-actin disruption. For targeting actin mis-aggregation related. neuro degenerative diseases (NDs), these positions can be modified with the functional groups that might increase the lipophilicity of the molecule to cross the BBB.\u003c/p\u003e","manuscriptTitle":"From binding to breakdown: biophysical and molecular insights into fluoroquinolone induced F-actin perturbation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 13:45:04","doi":"10.21203/rs.3.rs-6693104/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-11T04:53:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T04:01:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104678373765921905654352883902351956050","date":"2025-07-24T23:44:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T19:53:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-11T20:19:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39789958054497775554921690192521224033","date":"2025-07-03T11:02:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60413499848848806462749517137559354196","date":"2025-06-27T13:28:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T12:33:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-05T04:12:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-23T10:35:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-23T10:34:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"512db58d-bbb2-4e86-a9e3-28bf692807d2","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49685809,"name":"Health sciences/Diseases/Neurological disorders"},{"id":49685810,"name":"Biological sciences/Biophysics"},{"id":49685811,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":49685812,"name":"Biological sciences/Computational biology and bioinformatics/Protein analysis/Protein sequence analyses"},{"id":49685813,"name":"Biological sciences/Structural biology/Molecular modelling"},{"id":49685814,"name":"Biological sciences/Structural biology/Nmr spectroscopy/Solution state nmr"}],"tags":[],"updatedAt":"2026-02-23T16:10:36+00:00","versionOfRecord":{"articleIdentity":"rs-6693104","link":"https://doi.org/10.1038/s41598-026-36089-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-18 15:58:24","publishedOnDateReadable":"February 18th, 2026"},"versionCreatedAt":"2025-06-09 13:45:04","video":"","vorDoi":"10.1038/s41598-026-36089-x","vorDoiUrl":"https://doi.org/10.1038/s41598-026-36089-x","workflowStages":[]},"version":"v1","identity":"rs-6693104","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6693104","identity":"rs-6693104","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}