Exploring Purine Analogues as Inhibitors against Katanin, a Microtubule Severing Enzyme using Molecular Modeling Approach 

Exploring Purine Analogues as Inhibitors against Katanin, a Microtubule Severing Enzyme using Molecular Modeling Approach | 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 Exploring Purine Analogues as Inhibitors against Katanin, a Microtubule Severing Enzyme using Molecular Modeling Approach Bajarang Kumbhar, Vibhuti Saxena, Pruthanka Patil, Purva Khodke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4742126/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Katanin, a pioneering microtubule-severing enzyme, is a novel AAA-ATPase protein. It severs microtubules by forming hexamers that binds to the C-terminal tails of tubulin, using ATP hydrolysis to generate the force necessary to break the microtubule lattice. Katanin contributes to microtubule amplification and impact the growth of carcinomas. Hence, katanin is a highly promising target for anti-cancer drug development. This study aims to identify potential purine-based inhibitors against katanin by using structure-based virtual screening, PASS and ADME-T prediction, docking, and molecular dynamics simulations. Here, purine-based library of 2,76,280 compounds from the PubChem Database were utilized, and top two purine type inhibitors (PubChem ID: 122589735, and 123629569) were selected based on superior binding energy, ADME-T, and biological activity. Furthermore, molecular docking and molecular dynamics simulations study revealed that 122589735 and 123629569 compounds effectively alter katanin's structure and dynamics as compared to ATP. Besides, binding energy calculations indicate that 122589735 exhibits higher binding affinity with katanin compared to 123629569 and ATP. Thus, our computational study identifies potential purine-based katanin inhibitors that exhibit higher affinity for katanin than ATP and may have implications for various carcinomas. This research paves the way for developing novel, anti-cancer therapies targeting a range of carcinoma types. Katanin Microtubule Purine analogues structure-based drug design Molecular dynamics simulations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Microtubules (MTs) are filamentous proteins that serve as essential components of the cytoskeleton, playing critical roles in mitosis, intracellular transport, cell signalling, and cell shape etc., 1 . They are formed by the polymerization of α tubulin and β tubulin subunit heterodimers. The regulation of MT dynamics involves numerous microtubule-associated proteins which include tau 2 , MAPs, MARKs 3 , and microtubule-severing enzymes such as katanin, spastin, and fidgetin 4 . Among these regulators, the microtubule-severing enzyme, katanin plays a crucial role in the fragmentation and reorganization of MTs (Fig. 1 ), facilitating the generation of new microtubule ends and promoting their remodelling within the cellular environment 5 . Katanin was identified as the first microtubule-severing enzyme 6 . It is AAA-ATPase heterodimeric protein and is made up of the catalytic p60 subunit (KATNA1) and regulatory p80 subunit (KATNB1) 7,8 as shown in Fig. 1 . The acronym "AAA" stands for the family's distinctive conserved amino acid sequence of the ATPase domain, which repeats the amino acids alanine, and arginine 9 . The catalytic p60 subunit has microtubule-stimulated ATPase and severing activity 10 . The p60 subunit is attracted to the site of the microtubule severing by the p80 subunit, which functions as the regulatory component and interacts with microtubules 11 . MTs can be severed by the ATPase subunit p60 alone by ATP hydrolysis 6 , but the p80 subunit makes this activity more effective 8 . Katanin remains in a monomeric state at cellular concentrations and katanin hexamerization is essential for severing activity. katanin hexamerization and the presence of tubulin C-terminal tails are necessary for severing to occur 11 . Katanin severs microtubules through the progressive removal of tubulin subunits 12 , and katanin achieves this by repetitively pulling on the C-terminal tails of tubulin subunits from the MTs 11 . The ATP hydrolysis-driven conformational changes of the hexameric ring enable it to exert force or produce motion that helps to remove the tubulin subunit from the MT 13 . Recently, discrete lattice-based Monte Carlo models incorporating microtubule (MT) dynamics and severing enzyme activity have been developed to elucidate the effects of severing enzymes on tubulin mass, MT quantity, and MT length 14 . However, the breaking and reorganization of MTs by the katanin play a crucial role in ciliogenesis 15 , cell size regulation 4 , plant cell wall biosynthesis and phototropism 16,17 . Besides, the mutation in the katanin subunit leads to neurodegenerative disorders 18,19 . The absence of katanin expression is implicated in various conditions, including ciliopathies 15,20 , impaired corticogenesis and spermiogenesis 21–23 . Moreover, increased expression of katanin has been observed in non-small cell lung cancer (NSCLC), specifically correlating with lymph node metastasis 24 . Also, elevated levels of katanin contribute to enhanced cell proliferation and migration in primary breast cancer tissue 25 . Likewise, in prostate cancer, the expression of katanin P60 enhances the migratory capacity of cells 26,27 . While, in the case of papillary thyroid carcinoma, there is a correlation between katanin expression and worsened tumour characteristics 28 . Hence, katanin is an important target to design a potential anticancer agent against numerous carcinomas. Consequently, previous studies have shown that purine-type compounds induces microtubule fragmentation and promote lung cancer cell death through interactions with katanin 29 . Similarly, Gao and colleagues have designed, synthesized, and evaluated the biological activity of novel diaryl substituted fused heterocycles as dual ligands targeting tubulin and katanin 30 . However, effective medications targeting katanin for treating various types of carcinoma are still unavailable. Hence, present study aimed to identify the potential katanin inhibitors utilizing the purine-type compound library from the PubChem database, further employing the virtual screening, PASS biological activity prediction, ADME-T properties prediction, molecular docking, and molecular dynamics simulation techniques. This study could help to identify the potential purine type inhibitor and pave the way to treat the numerous carcinomas targeting the katanin. Computational methodology Preparation of katanin structure and purine-type drug library: The crystal structure of human katanin (source code: 5ZQM.pdb) 31 was retrieved from RCSB Protein Data Bank. The missing residues in the crystal structure (5ZQM.pdb) from 281 to 284, 314 to 319, 341 to 348, and 400 to 402 were modelled using the Modeller through Chimera 32 . To identify a potential katanin inhibitor, purine type compounds were retrieved from the NCBI PubChem compound ( https://pubchem.ncbi.nlm.nih.gov/ ) database, yielding a total of 2,76,280 purine type compounds. These purine-type compounds are available in the Structured Data File (SDF), were converted to PDBQT file format using OpenBabel software 33 for virtual screening and hit identification. Structure-based virtual screening and hit identification: High-throughput Virtual screening was performed using AutoDock Vina 34 , to identify the higher binding affinity purine type compounds against katanin receptor. Here, ATP binding pocket of katanin was consider as a target site for virtual screening. The grid box of dimensions 40Å × 40Å × 40Å set around the ATP binding pocket. During docking, the protein was kept rigid while the drug molecules were flexible. Further, top 5 compounds with the lowest binding energy were selected using InstaDock 35 software. These compounds were then evaluated for their biological, physicochemical and pharmacokinetics properties. Pass prediction and ADMET: The top five purine-type compounds underwent assessment for their biological characteristics through the Way2drug web server 36 . The accuracy of the biological activity predictions depends on the Pa (probability of activity) and Pi (probability of inactivity) values 36 . If the compounds have Pa greater than Pi, then it is considered to be biologically active, suggesting it could trigger expected pharmacodynamic effects according to the algorithm. This diverse method offers a thorough evaluation of potential drug characteristics, encompassing pharmacokinetics and toxicity, aiding informed choices during drug exploration and advancement 36 . Next, For ADME-T properties prediction Swiss-ADME server 37 and pkCSM web server 38 was used to assess the physicochemical and pharmacokinetic properties of a chosen set of compounds. It effectively identified properties like drug similarity, solubility, lipophilicity, bioavailability and toxicity. Molecular docking of katanin and purine type compounds: To get energetically favourable conformation of selected purine type compounds with katanin, molecular docking was conducted through AutoDock4.2.3 39 . The grid was positioned over the ATP binding pocket utilized in previous virtual screenings to define the docking region. Control docking of ATP with katanin was also performed. The AGS, an ATP analogue bound with katanin (source code: 5ZQM.pdb), was changed to ATP using Discovery Studio Visualizer 40 for the molecular docking. Compounds with the lowest binding energies were then used for molecular dynamics simulations. Further, the bonded and non-bonded intermolecular interactions 40 40 40 40(BIOVIA 2016)(BIOVIA 2016)(BIOVIA, 2016)[ 40 ]of most energetically favourable conformations with katanin were analysed using the BIOVIA 40 . Molecular dynamics simulations: To explore the refined binding mode and affinity of katanin with ATP and purine compounds, molecular dynamics simulations was employed using Gromacs 2021.5 41 . The Amber-ff99SB force field parameters was used for the protein, while Generalized Amber Force Field (GAFF) was used for the drug compounds, similar to a prior study 42,43 . The 'xleap' module of AmberTools22 was employed to set up the simulation systems 44 . A cubic periodic box measuring 10 Å on each side was used, and the necessary Sodium (Na+) ions were added to balance the system charge. Next, the 'Parmed tool' was used to convert 'prmtop' and 'inpcrd' files into Gromacs-compatible 'top' and 'gro' files, following a methodology outlined in prior studies 45 . The simulation complexes were subjected to energy minimization through both the steepest descent (5000 steps) and conjugate gradient (2000 steps) methods 41 . To reach equilibrium, the simulated systems went through 1 ns of NVT and NPT simulation. Subsequently, production MD simulations lasting 500 ns were conducted for all systems. Long-range electrostatic interactions were calculated using the particle mesh Ewald method, employing a cut-off distance of 1.0 nm, a Fourier spacing of 0.16 nm, and an interpolation order of 4. The H-bond length constraints are applied using the LINCS algorithm. Finally, the MD trajectory data were analyzed using Gromacs-provided 'gmx' tools 41 . Principal Component Analysis (PCA): To investigate the conformational changes of proteins during dynamic simulations, Principal Component Analysis (PCA) was employed. The GROMACS tools g_covar and g_anaeig were utilized to compute PCA specifically based on the positional data of Cα atoms. Two distinct analyses were conducted: the first involved calculating atomic displacement covariance matrices, eigenvalues, and eigenvectors using g_covar; the second utilized g_anaeig to project molecular dynamics trajectories onto the resulting eigenvector bases. The protein's motion was determined by projecting onto the first two eigenvectors (ev1 vs e2), capturing the most significant movements 46 . Ultimately, the findings were visualized via a 2D plot generated using the XMGrace tool available at https://plasma-gate.weizmann.ac.il/Grace/ . Free Energy Landscape (FEL): The collective variable free energy landscape (FEL) was constructed by analysing molecular dynamics simulation trajectories, specifically examining the backbone root mean square deviation (RMSD) and the Cα radius of gyration (Rg) through the utilization of the 'g_sham' tool in GROMACS 41 . This FEL provides a means to determine the free energy (G) by evaluating the probability distribution of the system's states. OriginLab software version 2023b (OriginLab Corporation, Northampton, MA, USA) was used to visualized the FEL. Hydrogen bond (H-bond) analysis: Hydrogen bond analysis was performed using the GROMACS v2021.5 suite 41 to quantify the number of hydrogen bonds formed between katanin and drug compounds throughout the molecular dynamics simulation. These hydrogen bonding interactions are essential for molecular recognition and binding affinity 47,48 . Binding energy calculations: For evaluating the binding strength between katanin and purine compounds, we performed binding energy calculations utilizing the 'MMPBSA.py' script available in the AMBER suite's gmx_MMPBSA 49 . The final equilibrated 100 ns (400 ns to 500 ns) MD simulation trajectory for each system were used to compute the binding energy. Entropy contributions were excluded in this study due to their computational intensity, following a similar approach used in a previous study. 42,50 . The comprehensive details on the binding energy calculations mentioned in the earlier study 42,51 . The per-residue decomposition energy calculations were performed to explore the energy impact of individual residues within 4Å distance of katanin's active site. Results and Discussion Structure based virtual screening and hit identification of purine compounds: High-throughput virtual screening utilizing molecular docking was conducted on an entire library of purine-type compounds, aiming at the ATP binding pocket of katanin. The objective was to pinpoint purine compounds exhibiting a stronger binding affinity, employing AutoDock Vina 34 . Within this screening process, we scrutinized 2,76,280 purine-type compounds, examining their interaction specifically with ATP binding site of katanin, as depicted in Fig. 2 . After the initial screening, compounds were sieved based on their binding affinity, culminating in the selection of the top 5 hit compounds exhibiting the lowest binding energy greater than ≤ -10 kcal/mol with katanin, as outlined in Table 1 . This selection process was executed through InstaDock 35 . The notable binding affinity exhibited by these top 5 compounds led us to investigate their potential for the development of drugs. To assess viability of selected compounds, we conducted physicochemical, toxicity, and biological characteristics using ADME-T and pass prediction methodologies. PASS prediction and ADME-T properties: The selected top 5 compounds (PubChem IDs: 123629569, 163388234, 122589735, 163555323, 156185498) were underwent for pass prediction to check its biological activity prediction by using Way2drug web server 36 . The selected purine rich compound from the PubChem database along with their PubChem ID, and 2D structures are mentioned in Table 1 . Here, all the compounds exhibited Pa values below 0.3, Pa values greater than Pi values, suggesting their active nature and listed in Table 2 . Moreover, all these compounds demonstrated anti-neoplastic and anti-metastatic activity, and anti-cancer activity. Table 2 Biological activity of the selected hit compounds using the Way2drug webserver. S.No. PubChem Compound ID Pa Pi Properties 1 123629569 0.213 0.117 Proto-oncogene tyrosine-protein kinase Fgr inhibitor 0.140 0.102 Liver fibrosis treatment 0.254 0.092 Cystic fibrosis treatment 2 163388234 0.315 0.038 Prostate cancer treatment 0.438 0.091 Antineoplastic: sarcoma, lymphoma 3 122589735 0.302 0.222 Antineoplastic (non-Hodgkin's lymphoma) 0.185 0.183 Antimetastatic 0.184 0.084 Prostate cancer treatment 0.258 0.044 Antineoplastic enhancer 4 163555323 0.552 0.056 Antineoplastic 5 156185498 0.489 0.005 Antineoplastic enhancer 0.411 0.020 Prostate cancer treatment 0.263 0.049 Antineoplastic alkaloid 0.123 0.076 Antineoplastic: lymphocytic leukemia, bladder cancer, glioblastoma multiforme, lymphoma, glioma The top five compounds also underwent pharmacokinetic as well as toxicology predictions using SWISS-ADME 37 and pkCSM webserver 38 , respectively. All the compounds molecular weight was > 400kDa that indicates stability. Four compounds show greater lipophilicity that indicated by a larger logP value, which implies that the substance has a stronger propensity to diffuse into lipid-rich environments like cell membranes. Water solubility criteria were categorized as insoluble (<-10), poorly soluble (-6), moderately (-4), soluble (-2), and very soluble (0), with Esol indicating poor solubility for 123629569 and 156185498 and high solubility for 122589735. Lipinski's rule, specifying < 5 hydrogen bond donors, < 10 hydrogen bond acceptors, and molecular weight < 500 Da, showed only one violation, which is acceptable. Bioavailability was predicted as 0.55, indicating neutrality. None of the compounds exhibited PAINS (Pan-Assay Interference Compounds). Synthetic availability ranged between 1 (very easy) to 10 (difficult), with all compounds scoring < 5 as shown in Table 3 . After analysing the top 5 compounds concerning their pass prediction and ADME-T properties, we selected top 3 compounds 123629569, 163388234, and 122589735 for further molecular docking study. Table 3 ADME-T properties of the selected top five compounds ADME Parameters Properties 123629569 163388234 122589735 163555323 156185498 Physiochemical Properties Formula C26H25F3N6O2 C67H44F3N12O C24H33N9O3 C44H28FN5 C39H25N5O Molecular weight (g/mol) 510.51 1090.14 495.58 645.73 579.65 Absorption GI Absorption Low High High Low Low Water Solubility Poorly soluble Insoluble Soluble Insoluble Poorly soluble Distribution BBB Permeation No No No No No Lipophilicity (ILogP) 3.02 0 2.79 4.97 4.84 Metabolism CYP2D6 Substrate/ Inhibitor Yes No Yes No No Excretion OCT2 Substrate No Yes No Yes Yes Toxicity AMES toxicity No Yes No Yes Yes Maximum Tolerance Dose 0.472 0.438 0.644 0.438 0.438 Hepatotoxicity Yes No Yes No No Skin Sensitisation No No No No No Drug likeness and medicinal chemistry Lipinski 1 3 1 2 2 Bioavailability score 0.56 0.17 0.55 0.17 0.17 PAINS 0 0 0 0 0 Synthetic accessibility 4.15 7.39 4.81 4.22 4.09 Interaction of katanin with purine type compounds using docking: First, we performed a control docking of katanin with ATP using AutoDock 4.2.7 39 , and the minimum energy docked conformation of ATP was found to be -4.86 kcal/mol (Fig. 3 B and Table 4 ). The analysis of katanin-ATP complex shows that the ATP is stabilized by the bonded and non-bonded type of interactions as shown in Fig. 3 and Table 4 . ATP forms a conventional hydrogen bonding interaction with residues Gly252 (1.68 and 2.33Å), Thr253 (2.21 Å), Gly254 (2.81 and 2.10 Å), Lys255 (2.52 Å), Thr256 (1.71 and 3.05 Å), Leu257 (2.57 Å), Asp210 (2.41 Å), Thr422 (2.04Å), whereas Leu257 forms a π-sigma, and Leu390 forms π-alkyl type of non-bonded interactions as shown in Fig. 3 A, 3 B and Table 4 . Similarly, molecular docking was performed to explore the binding mode and affinity of selected compounds 122589735, 123629569 and 163388234 with katanin using AutoDock 4.2.7 39 . The least binding energy conformation of compounds 122589735, 123629569 and 163388234 were found to be -8.57, -8.85, and − 8.51 kcal/mol, respectively as shown in Fig. 3 and Table 4 . Notably, compound 122589735 exhibited the lowest binding energy with katanin compared to all other compounds analysed. These findings highlight the considerable potential of these compounds for katanin binding, particularly targeting the ATP site. Therefore, to delve into the mechanisms of bonded and non-bonded interactions with katanin, we conducted additional analyses of docked complexes and thoroughly discuss our findings. Table 4 Analysis of 2D interactions of drug compounds with Katanin receptor after molecular docking. PubChem Compound Id Binding Energy (kcal/mol) Atoms involved in binding Bond type Distance Angle Fig Katanin-ATP -4.86 GLY252:HN - ATP501:O20 THR253:HN - ATP 501:O1B GLY254:HN - ATP 501:O5' GLY254:HN - ATP 501:O1B LYS255:HN - ATP 501:O1B THR256:HN - ATP 501:O3A LEU257:HN - ATP 501:O2A ATP 501:H61 - ASP210:O GLY252:CA - ATP 501:O2G THR256:CB - ATP 501:O1A THR422:HG1 - ATP501 H Bond H Bond H Bond H Bond H Bond H Bond H Bond H Bond H Bond H Bond H Bond 1.68 2.21 2.81 2.10 2.52 3.05 2.57 2.41 2.33 1.71 2.04 164.55 119.63 121.04 143.09 133.19 99.24 127.69 143.99 126.59 154.75 107.42 3B Katanin − 122589735 -8.85 ASN360:ND2 - Drug: O Drug: H - ALA212:O Drug: C - THR422:OG1 Drug: C - ASP308:OD2 Drug: C - THR253:O H Bond H Bond CH Bond CH Bond CH Bond 3.15 2.50 3.16 3.30 3.38 93.24 - 90.31 125.53 94.84 3C Katanin − 123629569 -8.57 GLY252:CA - Drug:O GLY418:CA - Drug: N Drug: C - GLY418:O Drug: C - THR422:OG1 CH Bond CH Bond CH Bond CH Bond 3.66 3.22 3.50 3.19 93.75 105.9 96.9 113.2 3D ϯ Katanin- 163388234 -8.51 - - - - 3E ϯ There are no conventional and CH-type hydrogen bonding interactions with katanin. The analysis of the katanin-122589735 complex (Fig. 3 ) shows that stability of the 122589735 compound is attributed to conventional hydrogen bonding interactions with residues Asn360 (3.15 Å), Ala212 (2.50 Å), whereas CH bonding interactions with residues Thr422 (3.16 Å), Asp308 (3.30 Å), and Thr253 (3.38 Å) as shown in Table 4 and Fig. 3 C. In addition, 122589735 makes an alkyl interaction with Lys255 (5.25 Å and 4.84 Å), Leu257 (5.28 Å), Ala358 (4.32 Å), Pro251 (4.66 Å), and π-Alkyl interactions with Leu257 (4.66 Å), Leu390 (5.47 Å), Ala212 (5.22 Å), Pro382 (4.36 Å), and Leu390 (4.35 Å) as shown in Table 4 and Fig. 3 C. Further analysis of katanin-123629569 complex (Fig. 3 ) shows that the 123629569 compound is stabilized due to CH bonding interactions with residues Gly252 (3.66 Å), Gly418 (3.22 Å and 3.50 Å), and Thr422 (3.19 Å), also the presence of Halogen (fluorine) type interaction was seen for Asp210 (3.42 Å). In addition, 123629569 compound forms π-Sigma interactions with Leu257 (3.62 Å), Leu390 (3.82 Å) and Ala419 (3.84 Å) as shown in Table 4 and Fig. 3 D. Also, Amide-π stacked bonds with Thr253 (4.47 Å), Gly418 (4.24 Å) and Gly418 (5 Å) (Table 4 and Fig. 3 D). Moreover, Alkyl type of interactions were contributed to Ala419 (4.16 Å), Pro382 (3.52 Å), Leu390 (4.26 Å), Leu257 (5.16 Å), and π-Alkyl bonds for Leu257 (5 Å) as well as Ala419 (4.63 Å) as shown in Table 4 and Fig. 3 D. Next, the analysis of the katanin-163388234 complex (Fig. 3 ) shows that the 163388234 compound forms π-Donor hydrogen bond for Thr256 (3.98 Å and 3.74 Å), and Asn272 (3.69 Å) (Table 4 and Fig. 3 E), π-Sigma bond with Ile393 (3.82 Å) and π-Alkyl bonds for Leu257 (4.56 Å), Val206 (5.25 Å), Leu257 (5.24 Å and 4.55 Å) as well as Lys255 (5.32 Å) as shown in Table 4 and Fig. 3 E. The analysis of docking results revealed that katanin with 122589735 complex is stabilized by both bonded and non-bonded types of interactions. However, compound 163388234 forms only non-bonded type of interactions with katanin, as it lacks electron bond donor groups such as O, N, S, etc. In addition, the compound 163388234 shows lower binding affinity with katanin as shown in Table 4 and Fig. 3 E. Hence, to explore the refined binding mode and affinity of katanin with ATP, 122589735 and 123629569 compounds, molecular dynamics simulations were employed. Molecular dynamics (MD) simulation MD simulations were performed to investigate the interaction of katanin with ATP, 122589735 and 123629569 using Gromacs 2021.5 52 . The least energy docked conformation of katanin, katanin-ATP, katanin-123629569 and katanin-122589735 shown in (Fig. 3 ), were considered as starting conformation for MD simulation. The simulations were performed for 500 ns, to obtain detailed conformational and structural changes in the katanin (see supplementary movies 1–4). The stability of the simulation systems were assessed by plotting the root mean square deviation (RMSD) of the C α backbone atoms of protein (Fig. 4 A). RMSD plot revealed that all the simulation systems reached their equilibrium after 300ns (Fig. 4 A). Overall, katanin with ATP and drug complexes show lower RMSD value compared to the katanin in apo form (Fig. 4 A), this shows that the ATP and drug compound has profound effect on structure and dynamics of katanin (see supplementary movie 1–4). The katanin-123629569 as it shows the higher fluctuations compared to all other katanin complexes after 300ns (Fig. 4 A). To gain more insight on the impact of drug binding and conformational changes in the katanin structure, root mean square fluctuations (RMSF) of C α atoms were performed (Fig. 4 B). It is revealed that katanin bound to ATP and 122589735 compounds showed a lower fluctuations (Fig. 4 B) compared to the katanin and katanin-123629569. The ATP binding site residues of katanin (141 to 171) shows reduced fluctuations of katanin 122589735 compared to the katanin alone and katanin with ATP and 123629569 (Fig. 4 B). Moreover, katanin-122589735 complex revealed lower conformational changes in 122589735 compound (Supplementary Movie 3) while 123629569 compound show higher conformational fluctuations (Supplementary Movie 4). This might be because compound 122589735 forms two hydrogen bonds with Asn360 and Ala212, along with three CH bonds with Thr422, Asp308, and Thr253 of katanin (Fig. 4 B- 4 C and Table 4 ). In contrast, compound 123629569 only engages in non-bonded interactions. Altogether, katanin with compound 122589735 show a profound effect on the structure and dynamics of katanin. To further check the compactness of the protein, radius of gyration (Rg) (Fig. 4 C) and solvent-accessible surface area (SASA) (Fig. 4 D) were calculated. The Rg plot analysis revealed that katanin-ATP and katanin-122589735 complexes exhibit a lower Rg value compared to katanin and katanin-123629569 complexes (Fig. 4 C). This suggests that katanin adopts a more compact conformation when bound to ATP and compound 122589735 (Fig. 4 C). Similarly, the SASA plot complements Rg analysis by focusing on the surface area of the protein accessible to the solvent molecule. The collective SASA values of all the systems ranged between 160-180nm 2 (Fig. 4 D). To comprehensively characterize the protein's conformational changes upon binding to ATP and drug compounds throughout the simulation, we employed a multi-pronged approach which includes principal component analysis (PCA), free energy landscape, hydrogen bonding interaction, and binding energy calculations. Principle component analysis (PCA): PCA was carried out using the gmx_covar and gmx_anaeig modules of Gromacs 2021.5 to understand the essential motions of the katanin, katanin-ATP and katanin-122589735, and katanin-123629569 complexes. Here, the eigenvectors, known as PCs (principal components) were investigated as shown in Fig. 4 E. PCA analysis revealed that there was a higher diversity of conformations when katanin was in an unbound state during the simulation while, for the katanin-ATP, katanin-122589735 and katanin-123629569 complexes showed lower conformational diversity as shown in Fig. 4 E. PCA analysis showed the highest diversity of conformations for katanin during the simulation (PCA2:7, PCA1:7) compared to other complexes. As evidenced by the PCA scores, the katanin-122589735 complex scored (PCA2: 4, PCA1: 8) and the katanin-123629569 complex scored (PCA2: 4, PCA1: 10). However, the katanin-ATP showed the least diversity in the conformations. Overall, katanin in its unbound state has higher variations in the conformations over the time of 500 ns but, upon drug binding the dynamics of katanin are affected thus leading to a stable state. To gain a deeper understanding of the energetics involved, we employed free energy landscape analysis to visualize the various energetic states of katanin, katanin bound to ATP, and katanin in complex with the drug compounds. Free energy landscape (FEL) FEL analysis shows the protein conformational space concerning energy and time 53 . This allowed us to visualize the most stable conformations and identify potential energy barriers for transitions between varying states. For all the landscapes (Fig. 5 A- 5 H), the initial minimum represents the filtering of the most stable conformation and has the lowest energy with a conical termination. As observed in the MD analysis results and PCA, katanin in unbound nature displays large conformation states and takes time to achieve the least energy conformation (Fig. 5 A and 5 B), similarly, a greater number of high energy states are observed as seen in the contour map plot of katanin (Fig. 5 B). The scenario changes when the katanin is bound to ATP, and stabilizes the katanin as observed in the energy funnel with confined basin (Fig. 5 C and 5 D). Similar observations are made concerning katanin-drug complexes. Wherein, katanin-122589735 complex had the least energy range between 0-1.62 kJ/mol for the deep energy minima (Fig. 5 E and 5 F). The katanin-123629569 shows single minima but undergoes more variations/transition state in the structural folding as compared to katanin-122589735 to achieve the least energy state (Fig. 5 G and 5 H). These results infer that the katanin-122589735 complex achieves the global state conformation sooner than the katanin-123629569 complex (Fig. 5 E- 5 H). To gain a deeper understanding of the interactions between katanin and drug compounds, we specifically investigated hydrogen bonding through a dedicated analysis. Hydrogen bond interaction analysis This analysis quantified and characterized the hydrogen bond formation between katanin and ATP, 122589735 and 123629569 compounds during the 500 ns time steps (Supplementary Fig. 1). Katanin-ATP complex shows more stable and frequent hydrogen bond formation (Supplementary Fig. 1A). Further, hydrogen bond analysis shows that the katanin-122589735 complex (Supplementary Fig. 1B) forms stable hydrogen bonds as compared to the katanin-123629569 complex (Supplementary Fig. 1C). Whereas the number of hydrogen bonds increases after 300ns for the katanin-122589735 complex indicating stronger interaction between the drug and katanin (Supplementary Fig. 1A). The hydrogen bond analysis shows that 122589735 is stable at the ATP binding pocket of katanin and from constant the number of hydrogen bonds is over time (Supplementary Fig. 1B). Hence, to investigate the binding affinity of drug compounds, we employed the binding energy calculations and residue per decomposition analysis using the gmx_MMPBSA tool 49 . Binding energy calculations Table 5 Binding energy calculation of katanin with ATP and drug compounds. All energies are in kcal/mol. Katanin-drug complex ΔE vdw ΔE ele ΔE gas ΔE sol ΔE bind Katanin-ATP -40.74 215.51 174.76 -197.46 -22.70 Katanin-122589735 -45.45 -20.63 -66.08 32.49 -33.58 Katanin-123629569 -39.20 -13.73 -52.93 25.39 -27.54 To investigate the binding affinity of katanin with ATP and drug compounds, we employed binding energy calculations using the MM-GBSA method through gmx_MMPBSA tool 49 . The equilibrated last 100 ns (a total of 5000 frames) were considered for the binding energy calculations. The energy data analysis revealed that katanin had a higher binding affinity with compound 122589735 compared to ATP and compound 123629569 as shown in Table 5 . The order of binding affinity decreases in the order of 122589735 (-33.58 kcal/mol) > 123629569 (-27.54 kcal/mol) > ATP (-22.70 kcal/mol). Furthermore, the analysis revealed that the Van der Waals and electrostatic interactions play a significant role in the binding of the 122589735 compounds with katanin whereas, the loss of these interactions reduces the affinity of 123629569 for katanin. To further investigate the contribution of binding site residues, we performed per-residue energy decomposition analysis. Additionally, the per-residue contributions of katanin interacting with the drug and influencing binding affinity were determined using decomposition analysis with the gmx_MMPBSA tool (Supplementary Fig. 2). The residues decomposition analysis of katanin-ATP complex shows that Ile28, Leu31, Thr70, Gly71, Leu74, Gly235, Ala236, Ile238, and Thr239 are involved in the binding with ATP (Supplementary Fig. 2A). Whereas katanin-122589735 complex had a relatively higher number of active residues contributing to energy than the other complexes (Supplementary Fig. 2B). Notably, Leu74, Ala236, and Thr239 exhibited the highest energy contributions. In katanin-123629569 complex, Gly71, Gly235, and Thr239 were found to have significant roles in drug binding (Supplementary Fig. 2C). Overall, residue decomposition analysis suggests that residues in the ATP binding site, such as Thr70, Gly71, Leu74, Gly235, Ala236, and Thr239, are common active site residues of katanin that interact with both ATP and the drugs (Supplementary Figs. 2A-2C). Conclusion In the present study, we employed structure-based virtual screening, ADME-T analysis, molecular docking, and MD simulation to identify promising purine-type inhibitors for the microtubule-severing enzyme, katanin. Katanin's activity depends on the formation of katanin hexamers and the presence of the tubulin C-terminal, consecutively removing tubulin dimers to amplify microtubules and influence the proliferation of various cancer cells. Previous, study shown that purine type compounds are highly effective in controlling different carcinomas by inducing microtubule fragmentation through their interactions with katanin 29 . Therefore, we leveraged the PubChem purine library to identify potential katanin inhibitors. Docking analysis revealed significant binding affinities for katanin with purine type compounds PubChem IDs; 122589735, 123629569, and 163388234. Among these, compounds 122589735 and 123629569 were further assessed for their dynamic stability through molecular dynamics simulations. MD simulation analysis indicated that compound 122589735 demonstrated the most robust interaction with katanin. Further binding energy studies revealed that 122589735 has a higher binding affinity (-33.58 kcal/mol) for katanin compared to compound 123629569 (-27.54 kcal/mol) and ATP (-22.70 kcal/mol). These findings suggest that compound 122589735 could potentially serve as a katanin inhibitor, influencing its microtubule-severing function. Further in vitro and in vivo studies are required to investigate the efficacy of compound 122589735 against katanin and to support its preclinical development. Consequently, our computational investigation not only identifies a potential inhibitor of the microtubule-severing enzyme katanin but also establishes a foundation for developing treatments for various carcinomas. Declarations Acknowledgment: Pruthanka Patil is thankful to UGC New Delhi for awarding Savitribai Jyotirao Phule single girl child fellowship for doctoral study. Bajarang Kumbhar is thankful to SVKM’s Narsee Monjee Institute of Management Studies (NMIMS) Deemed-to-be University for providing computational facilities. All authors have seen and agree with the contents of the manuscript, and there is no conflict of interest. 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Supplementary Files Table1.docx SupplementaryInformation.docx SupplementaryMovie1Katanin.mp4 SupplementaryMovie2KataninATP.mp4 SupplementaryMovie3Katanin735.mp4 SupplementaryMovie4Katanin569.mp4 Cite Share Download PDF Status: Published Journal Publication published 30 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Oct, 2024 Reviews received at journal 05 Oct, 2024 Reviewers agreed at journal 03 Oct, 2024 Reviews received at journal 24 Aug, 2024 Reviewers agreed at journal 17 Aug, 2024 Reviewers agreed at journal 19 Jul, 2024 Reviewers agreed at journal 19 Jul, 2024 Reviewers invited by journal 18 Jul, 2024 Editor assigned by journal 17 Jul, 2024 Editor invited by journal 17 Jul, 2024 Submission checks completed at journal 17 Jul, 2024 First submitted to journal 15 Jul, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4742126","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":337475707,"identity":"ba2a463d-d062-4059-8743-e069d44b14a6","order_by":0,"name":"Bajarang Kumbhar","email":"data:image/png;base64,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","orcid":"","institution":"Department of Biological Science, Sunandan Divatia School of Science, SVKM's NMIMS (Deemed-to-be) University, India","correspondingAuthor":true,"prefix":"","firstName":"Bajarang","middleName":"","lastName":"Kumbhar","suffix":""},{"id":337475708,"identity":"f0ffec2c-5527-4687-a2f3-1ae9f26382ca","order_by":1,"name":"Vibhuti Saxena","email":"","orcid":"","institution":"Department of Biological Science, Sunandan Divatia School of Science, SVKM's NMIMS (Deemed-to-be) University, India","correspondingAuthor":false,"prefix":"","firstName":"Vibhuti","middleName":"","lastName":"Saxena","suffix":""},{"id":337475709,"identity":"17b0abe8-34cd-42a6-b1d4-f956c2e910df","order_by":2,"name":"Pruthanka Patil","email":"","orcid":"","institution":"Department of Biological Science, Sunandan Divatia School of Science, SVKM's NMIMS (Deemed-to-be) University, India","correspondingAuthor":false,"prefix":"","firstName":"Pruthanka","middleName":"","lastName":"Patil","suffix":""},{"id":337475710,"identity":"8a5ae0c1-2987-4846-ac16-3f26dc7f2dd4","order_by":3,"name":"Purva Khodke","email":"","orcid":"","institution":"Department of Biological Science, Sunandan Divatia School of Science, SVKM's NMIMS (Deemed-to-be) University, India","correspondingAuthor":false,"prefix":"","firstName":"Purva","middleName":"","lastName":"Khodke","suffix":""}],"badges":[],"createdAt":"2024-07-15 10:17:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4742126/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4742126/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-83723-7","type":"published","date":"2024-12-30T15:57:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62108093,"identity":"0ad4b525-188c-4db8-8469-c061358061a8","added_by":"auto","created_at":"2024-08-09 11:12:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":710369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of katanin and its mechanism of microtubule severing. \u003c/strong\u003e(A) show the p80 subunit (red) and p60 subunit (blue) of katanin, (B) shows the detailed domains for p60 and p80 subunits. The N-terminal MT-interacting and trafficking domain (p60-MIT) of the catalytic p60 subunit interacts with p80-CTD, while the C-terminal AAA+ domain binds and hydrolyzes ATP. MT-severing function requires hexamerization of katanin and hydrolysis of ATP to ADP and Pi. (C) Shows the monomeric katanin assembles around the tubulin C-terminal tail (green) to further pull and cause microtubule severing (light green and dark green circles). The tubulin dimer is deformed and the interdimer connections are loosened by the AAA ATPase using ATP hydrolysis to release the tubulin dimer from the microtubule.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/120e64fc8e1eb996f5f5d1d8.png"},{"id":62108094,"identity":"778f0588-e49a-42eb-ac39-0a7223cca413","added_by":"auto","created_at":"2024-08-09 11:12:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":788171,"visible":true,"origin":"","legend":"\u003cp\u003eComputational methodology for identifying potential inhibitors against Katanin. Image created by Biorender.com.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/2cfe7b4fee5a600dee39e9b8.png"},{"id":62108096,"identity":"af6323d0-3aac-4672-8edd-59da30b92cf6","added_by":"auto","created_at":"2024-08-09 11:12:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1765571,"visible":true,"origin":"","legend":"\u003cp\u003eBinding modes and 2D interaction of Katanin with ATP and purine-type compounds using molecular docking. Here, (A) shows the least energy-docked conformation of ATP (cyan) is depicted, along with compounds 122589735 (blue), 123629569 (yellow), and 163388234 (green), located at the ATP binding pocket (residues 141-171) of Katanin (The figure includes an enlarged view of ATP and the purine-type compounds). (B) shows the interaction network of the katanin-ATP complex (C) shows the 2D interaction network with the katanin-122589735 complex, (D) shows the interaction network with the katanin-123629569 complex, (E) shows the 2D interaction network with the katanin-163388234 complex after docking. The 2D interaction analysis was performed using the Discovery Studio Visualizer \u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/4e9b2710c7c5d3cb6b7ec8b1.png"},{"id":62108100,"identity":"373e5e34-83da-4669-82bb-0a8f54a1f1e3","added_by":"auto","created_at":"2024-08-09 11:12:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1422497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMD simulation analysis of katanin and drug complexes. \u003c/strong\u003eHere, katanin is represented in black, the katanin-ATP complex in orange, whereas the katanin-122589735 complex and Katanin-123629569 complex are represented in blue and green respectively. Panel (\u003cstrong\u003eA\u003c/strong\u003e) shows the RMSD plot, (\u003cstrong\u003eB\u003c/strong\u003e) displays the RMSF plot, (\u003cstrong\u003eC\u003c/strong\u003e) represents the Rg, (\u003cstrong\u003eD\u003c/strong\u003e) shows the (SASA) of the Katanin and katanin-drug complexes, and \u003cstrong\u003e(E)\u003c/strong\u003eshows the PCA plot where katanin is represented in black, katanin with ATP in orange, katanin-122589735 complex in blue, and katanin-123629569 in green.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/7d7c59f890b4551d29f6d22c.png"},{"id":62108527,"identity":"2085e892-8330-40bc-b224-e4f6412673c9","added_by":"auto","created_at":"2024-08-09 11:20:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1579619,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFree energy landscape (FEL) of katanin with ATP and drug compounds: \u003c/strong\u003eFEL is plotted to trace the stability of the protein with or without drug molecules concerning free energy and to understand the critical conformational states with least energy. Plot \u003cstrong\u003e(A)\u003c/strong\u003e and \u003cstrong\u003e(B)\u003c/strong\u003e shows the FEL and contour plot of katanin. Plot \u003cstrong\u003e(C)\u003c/strong\u003e and (D) represents the FEL and contour plot of katanin-ATP, \u003cstrong\u003e(E)\u003c/strong\u003e and \u003cstrong\u003e(F)\u003c/strong\u003e shows the FEL and contour plot of katanin-122589735 complex and \u003cstrong\u003e(G)\u003c/strong\u003eand \u003cstrong\u003e(H)\u003c/strong\u003e show FEL and contour plot of katanin-123629569 complex, respectively.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/0ba6201b71e5996cde03c265.png"},{"id":73093300,"identity":"077e88ce-d9ec-4481-8d0c-edcbd4c27d21","added_by":"auto","created_at":"2025-01-06 16:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10210787,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/b83bc8e8-0862-4162-8e5b-2a7e1820cfc1.pdf"},{"id":62108095,"identity":"a09fa43c-5bb9-4f6c-bc04-7711e8f71676","added_by":"auto","created_at":"2024-08-09 11:12:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":191524,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/807a53d62597fc5a21f6b84b.docx"},{"id":62108526,"identity":"feb183b8-3c07-4db3-9ad1-45a23799b82c","added_by":"auto","created_at":"2024-08-09 11:20:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":688038,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/48d7c8d9591edf08ed34defa.docx"},{"id":62108102,"identity":"51064111-437b-4a41-8243-d23d4fddba52","added_by":"auto","created_at":"2024-08-09 11:12:47","extension":"mp4","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":21422345,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMovie1Katanin.mp4","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/804f8b285004f2556be2458a.mp4"},{"id":62108099,"identity":"ee5e0d6d-5896-4ed7-87da-446b90b73a00","added_by":"auto","created_at":"2024-08-09 11:12:47","extension":"mp4","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19139066,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMovie2KataninATP.mp4","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/a919a311c75aef300e371d26.mp4"},{"id":62108528,"identity":"3f5c7d71-e26a-4820-9eec-2f176301c837","added_by":"auto","created_at":"2024-08-09 11:20:47","extension":"mp4","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":23470603,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMovie3Katanin735.mp4","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/77a54a416b92ab50db8db3d5.mp4"},{"id":62108101,"identity":"4c55791b-4ebe-4071-9a2d-9c192f5eb81d","added_by":"auto","created_at":"2024-08-09 11:12:47","extension":"mp4","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":22442355,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMovie4Katanin569.mp4","url":"https://assets-eu.researchsquare.com/files/rs-4742126/v1/e63161b14706a39fe432ddd2.mp4"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Purine Analogues as Inhibitors against Katanin, a Microtubule Severing Enzyme using Molecular Modeling Approach ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicrotubules (MTs) are filamentous proteins that serve as essential components of the cytoskeleton, playing critical roles in mitosis, intracellular transport, cell signalling, and cell shape etc., \u003csup\u003e1\u003c/sup\u003e. They are formed by the polymerization of α tubulin and β tubulin subunit heterodimers. The regulation of MT dynamics involves numerous microtubule-associated proteins which include tau \u003csup\u003e2\u003c/sup\u003e, MAPs, MARKs \u003csup\u003e3\u003c/sup\u003e, and microtubule-severing enzymes such as katanin, spastin, and fidgetin \u003csup\u003e4\u003c/sup\u003e. Among these regulators, the microtubule-severing enzyme, katanin plays a crucial role in the fragmentation and reorganization of MTs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), facilitating the generation of new microtubule ends and promoting their remodelling within the cellular environment \u003csup\u003e5\u003c/sup\u003e. Katanin was identified as the first microtubule-severing enzyme \u003csup\u003e6\u003c/sup\u003e. It is AAA-ATPase heterodimeric protein and is made up of the catalytic p60 subunit (KATNA1) and regulatory p80 subunit (KATNB1) \u003csup\u003e7,8\u003c/sup\u003e as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The acronym \"AAA\" stands for the family's distinctive conserved amino acid sequence of the ATPase domain, which repeats the amino acids alanine, and arginine \u003csup\u003e9\u003c/sup\u003e. The catalytic p60 subunit has microtubule-stimulated ATPase and severing activity \u003csup\u003e10\u003c/sup\u003e. The p60 subunit is attracted to the site of the microtubule severing by the p80 subunit, which functions as the regulatory component and interacts with microtubules \u003csup\u003e11\u003c/sup\u003e. MTs can be severed by the ATPase subunit p60 alone by ATP hydrolysis \u003csup\u003e6\u003c/sup\u003e, but the p80 subunit makes this activity more effective \u003csup\u003e8\u003c/sup\u003e. Katanin remains in a monomeric state at cellular concentrations and katanin hexamerization is essential for severing activity. katanin hexamerization and the presence of tubulin C-terminal tails are necessary for severing to occur \u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKatanin severs microtubules through the progressive removal of tubulin subunits \u003csup\u003e12\u003c/sup\u003e, and katanin achieves this by repetitively pulling on the C-terminal tails of tubulin subunits from the MTs \u003csup\u003e11\u003c/sup\u003e. The ATP hydrolysis-driven conformational changes of the hexameric ring enable it to exert force or produce motion that helps to remove the tubulin subunit from the MT \u003csup\u003e13\u003c/sup\u003e. Recently, discrete lattice-based Monte Carlo models incorporating microtubule (MT) dynamics and severing enzyme activity have been developed to elucidate the effects of severing enzymes on tubulin mass, MT quantity, and MT length \u003csup\u003e14\u003c/sup\u003e. However, the breaking and reorganization of MTs by the katanin play a crucial role in ciliogenesis \u003csup\u003e15\u003c/sup\u003e, cell size regulation \u003csup\u003e4\u003c/sup\u003e, plant cell wall biosynthesis and phototropism \u003csup\u003e16,17\u003c/sup\u003e. Besides, the mutation in the katanin subunit leads to neurodegenerative disorders \u003csup\u003e18,19\u003c/sup\u003e. The absence of katanin expression is implicated in various conditions, including ciliopathies \u003csup\u003e15,20\u003c/sup\u003e, impaired corticogenesis and spermiogenesis \u003csup\u003e21\u0026ndash;23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, increased expression of katanin has been observed in non-small cell lung cancer (NSCLC), specifically correlating with lymph node metastasis \u003csup\u003e24\u003c/sup\u003e. Also, elevated levels of katanin contribute to enhanced cell proliferation and migration in primary breast cancer tissue \u003csup\u003e25\u003c/sup\u003e. Likewise, in prostate cancer, the expression of katanin P60 enhances the migratory capacity of cells \u003csup\u003e26,27\u003c/sup\u003e. While, in the case of papillary thyroid carcinoma, there is a correlation between katanin expression and worsened tumour characteristics \u003csup\u003e28\u003c/sup\u003e. Hence, katanin is an important target to design a potential anticancer agent against numerous carcinomas.\u003c/p\u003e \u003cp\u003eConsequently, previous studies have shown that purine-type compounds induces microtubule fragmentation and promote lung cancer cell death through interactions with katanin \u003csup\u003e29\u003c/sup\u003e. Similarly, Gao and colleagues have designed, synthesized, and evaluated the biological activity of novel diaryl substituted fused heterocycles as dual ligands targeting tubulin and katanin \u003csup\u003e30\u003c/sup\u003e. However, effective medications targeting katanin for treating various types of carcinoma are still unavailable. Hence, present study aimed to identify the potential katanin inhibitors utilizing the purine-type compound library from the PubChem database, further employing the virtual screening, PASS biological activity prediction, ADME-T properties prediction, molecular docking, and molecular dynamics simulation techniques. This study could help to identify the potential purine type inhibitor and pave the way to treat the numerous carcinomas targeting the katanin.\u003c/p\u003e"},{"header":"Computational methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of katanin structure and purine-type drug library:\u003c/h2\u003e \u003cp\u003eThe crystal structure of human katanin (source code: 5ZQM.pdb) \u003csup\u003e31\u003c/sup\u003e was retrieved from RCSB Protein Data Bank. The missing residues in the crystal structure (5ZQM.pdb) from 281 to 284, 314 to 319, 341 to 348, and 400 to 402 were modelled using the Modeller through Chimera \u003csup\u003e32\u003c/sup\u003e. To identify a potential katanin inhibitor, purine type compounds were retrieved from the NCBI PubChem compound (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database, yielding a total of 2,76,280 purine type compounds. These purine-type compounds are available in the Structured Data File (SDF), were converted to PDBQT file format using OpenBabel software \u003csup\u003e33\u003c/sup\u003e for virtual screening and hit identification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStructure-based virtual screening and hit identification:\u003c/h2\u003e \u003cp\u003eHigh-throughput Virtual screening was performed using AutoDock Vina \u003csup\u003e34\u003c/sup\u003e, to identify the higher binding affinity purine type compounds against katanin receptor. Here, ATP binding pocket of katanin was consider as a target site for virtual screening. The grid box of dimensions 40\u0026Aring; \u0026times; 40\u0026Aring; \u0026times; 40\u0026Aring; set around the ATP binding pocket. During docking, the protein was kept rigid while the drug molecules were flexible. Further, top 5 compounds with the lowest binding energy were selected using InstaDock \u003csup\u003e35\u003c/sup\u003e software. These compounds were then evaluated for their biological, physicochemical and pharmacokinetics properties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePass prediction and ADMET:\u003c/h2\u003e \u003cp\u003eThe top five purine-type compounds underwent assessment for their biological characteristics through the Way2drug web server \u003csup\u003e36\u003c/sup\u003e. The accuracy of the biological activity predictions depends on the Pa (probability of activity) and Pi (probability of inactivity) values \u003csup\u003e36\u003c/sup\u003e. If the compounds have Pa greater than Pi, then it is considered to be biologically active, suggesting it could trigger expected pharmacodynamic effects according to the algorithm. This diverse method offers a thorough evaluation of potential drug characteristics, encompassing pharmacokinetics and toxicity, aiding informed choices during drug exploration and advancement \u003csup\u003e36\u003c/sup\u003e. Next, For ADME-T properties prediction Swiss-ADME server \u003csup\u003e37\u003c/sup\u003e and pkCSM web server \u003csup\u003e38\u003c/sup\u003e was used to assess the physicochemical and pharmacokinetic properties of a chosen set of compounds. It effectively identified properties like drug similarity, solubility, lipophilicity, bioavailability and toxicity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking of katanin and purine type compounds:\u003c/h2\u003e \u003cp\u003eTo get energetically favourable conformation of selected purine type compounds with katanin, molecular docking was conducted through AutoDock4.2.3 \u003csup\u003e39\u003c/sup\u003e. The grid was positioned over the ATP binding pocket utilized in previous virtual screenings to define the docking region. Control docking of ATP with katanin was also performed. The AGS, an ATP analogue bound with katanin (source code: 5ZQM.pdb), was changed to ATP using Discovery Studio Visualizer \u003csup\u003e40\u003c/sup\u003e for the molecular docking. Compounds with the lowest binding energies were then used for molecular dynamics simulations. Further, the bonded and non-bonded intermolecular interactions\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026thinsp;\u0026gt;\u0026thinsp;40\u0026lt;/sup\u0026thinsp;\u0026gt;\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026thinsp;\u0026gt;\u0026thinsp;40\u0026lt;/sup\u0026thinsp;\u0026gt;\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026thinsp;\u0026gt;\u0026thinsp;40\u0026lt;/sup\u0026thinsp;\u0026gt;\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026thinsp;\u0026gt;\u0026thinsp;40\u0026lt;/sup\u0026gt;(BIOVIA 2016)(BIOVIA 2016)(BIOVIA, 2016)[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]of most energetically favourable conformations with katanin were analysed using the BIOVIA \u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMolecular dynamics simulations:\u003c/h2\u003e \u003cp\u003eTo explore the refined binding mode and affinity of katanin with ATP and purine compounds, molecular dynamics simulations was employed using Gromacs 2021.5 \u003csup\u003e41\u003c/sup\u003e. The Amber-ff99SB force field parameters was used for the protein, while Generalized Amber Force Field (GAFF) was used for the drug compounds, similar to a prior study \u003csup\u003e42,43\u003c/sup\u003e. The 'xleap' module of AmberTools22 was employed to set up the simulation systems \u003csup\u003e44\u003c/sup\u003e. A cubic periodic box measuring 10 \u0026Aring; on each side was used, and the necessary Sodium (Na+) ions were added to balance the system charge. Next, the 'Parmed tool' was used to convert 'prmtop' and 'inpcrd' files into Gromacs-compatible 'top' and 'gro' files, following a methodology outlined in prior studies \u003csup\u003e45\u003c/sup\u003e. The simulation complexes were subjected to energy minimization through both the steepest descent (5000 steps) and conjugate gradient (2000 steps) methods \u003csup\u003e41\u003c/sup\u003e. To reach equilibrium, the simulated systems went through 1 ns of NVT and NPT simulation. Subsequently, production MD simulations lasting 500 ns were conducted for all systems. Long-range electrostatic interactions were calculated using the particle mesh Ewald method, employing a cut-off distance of 1.0 nm, a Fourier spacing of 0.16 nm, and an interpolation order of 4. The H-bond length constraints are applied using the LINCS algorithm. Finally, the MD trajectory data were analyzed using Gromacs-provided 'gmx' tools \u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Component Analysis (PCA):\u003c/h2\u003e \u003cp\u003eTo investigate the conformational changes of proteins during dynamic simulations, Principal Component Analysis (PCA) was employed. The GROMACS tools g_covar and g_anaeig were utilized to compute PCA specifically based on the positional data of Cα atoms. Two distinct analyses were conducted: the first involved calculating atomic displacement covariance matrices, eigenvalues, and eigenvectors using g_covar; the second utilized g_anaeig to project molecular dynamics trajectories onto the resulting eigenvector bases. The protein's motion was determined by projecting onto the first two eigenvectors (ev1 vs e2), capturing the most significant movements \u003csup\u003e46\u003c/sup\u003e. Ultimately, the findings were visualized via a 2D plot generated using the XMGrace tool available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plasma-gate.weizmann.ac.il/Grace/\u003c/span\u003e\u003cspan address=\"https://plasma-gate.weizmann.ac.il/Grace/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFree Energy Landscape (FEL):\u003c/h2\u003e \u003cp\u003eThe collective variable free energy landscape (FEL) was constructed by analysing molecular dynamics simulation trajectories, specifically examining the backbone root mean square deviation (RMSD) and the Cα radius of gyration (Rg) through the utilization of the 'g_sham' tool in GROMACS \u003csup\u003e41\u003c/sup\u003e. This FEL provides a means to determine the free energy (G) by evaluating the probability distribution of the system's states. OriginLab software version 2023b (OriginLab Corporation, Northampton, MA, USA) was used to visualized the FEL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHydrogen bond (H-bond) analysis:\u003c/h2\u003e \u003cp\u003eHydrogen bond analysis was performed using the GROMACS v2021.5 suite \u003csup\u003e41\u003c/sup\u003e to quantify the number of hydrogen bonds formed between katanin and drug compounds throughout the molecular dynamics simulation. These hydrogen bonding interactions are essential for molecular recognition and binding affinity \u003csup\u003e47,48\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBinding energy calculations:\u003c/h2\u003e \u003cp\u003eFor evaluating the binding strength between katanin and purine compounds, we performed binding energy calculations utilizing the 'MMPBSA.py' script available in the AMBER suite's gmx_MMPBSA \u003csup\u003e49\u003c/sup\u003e. The final equilibrated 100 ns (400 ns to 500 ns) MD simulation trajectory for each system were used to compute the binding energy. Entropy contributions were excluded in this study due to their computational intensity, following a similar approach used in a previous study. \u003csup\u003e42,50\u003c/sup\u003e. The comprehensive details on the binding energy calculations mentioned in the earlier study \u003csup\u003e42,51\u003c/sup\u003e. The per-residue decomposition energy calculations were performed to explore the energy impact of individual residues within 4\u0026Aring; distance of katanin's active site.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eStructure based virtual screening and hit identification of purine compounds:\u003c/h2\u003e\n \u003cp\u003eHigh-throughput virtual screening utilizing molecular docking was conducted on an entire library of purine-type compounds, aiming at the ATP binding pocket of katanin. The objective was to pinpoint purine compounds exhibiting a stronger binding affinity, employing AutoDock Vina \u003csup\u003e34\u003c/sup\u003e. Within this screening process, we scrutinized 2,76,280 purine-type compounds, examining their interaction specifically with ATP binding site of katanin, as depicted in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. After the initial screening, compounds were sieved based on their binding affinity, culminating in the selection of the top 5 hit compounds exhibiting the lowest binding energy greater than \u0026le; -10 kcal/mol with katanin, as outlined in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. This selection process was executed through InstaDock \u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe notable binding affinity exhibited by these top 5 compounds led us to investigate their potential for the development of drugs. To assess viability of selected compounds, we conducted physicochemical, toxicity, and biological characteristics using ADME-T and pass prediction methodologies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003ePASS prediction and ADME-T properties:\u003c/h2\u003e\n \u003cp\u003eThe selected top 5 compounds (PubChem IDs: 123629569, 163388234, 122589735, 163555323, 156185498) were underwent for pass prediction to check its biological activity prediction by using Way2drug web server \u003csup\u003e36\u003c/sup\u003e. The selected purine rich compound from the PubChem database along with their PubChem ID, and 2D structures are mentioned in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. Here, all the compounds exhibited Pa values below 0.3, Pa values greater than Pi values, suggesting their active nature and listed in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. Moreover, all these compounds demonstrated anti-neoplastic and anti-metastatic activity, and anti-cancer activity.\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBiological activity of the selected hit compounds using the Way2drug webserver.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePubChem\u003c/p\u003e\n \u003cp\u003eCompound ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePi\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProperties\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e123629569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProto-oncogene tyrosine-protein kinase Fgr inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver fibrosis treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCystic fibrosis treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e163388234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstate cancer treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntineoplastic: sarcoma, lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e122589735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntineoplastic (non-Hodgkin\u0026apos;s lymphoma)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntimetastatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstate cancer treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntineoplastic enhancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163555323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntineoplastic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e156185498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntineoplastic enhancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstate cancer treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntineoplastic alkaloid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntineoplastic: lymphocytic leukemia, bladder cancer, glioblastoma multiforme, lymphoma, glioma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe top five compounds also underwent pharmacokinetic as well as toxicology predictions using SWISS-ADME \u003csup\u003e37\u003c/sup\u003e and pkCSM webserver \u003csup\u003e38\u003c/sup\u003e, respectively. All the compounds molecular weight was \u0026gt;\u0026thinsp;400kDa that indicates stability. Four compounds show greater lipophilicity that indicated by a larger logP value, which implies that the substance has a stronger propensity to diffuse into lipid-rich environments like cell membranes. Water solubility criteria were categorized as insoluble (\u0026lt;-10), poorly soluble (-6), moderately (-4), soluble (-2), and very soluble (0), with Esol indicating poor solubility for 123629569 and 156185498 and high solubility for 122589735. Lipinski\u0026apos;s rule, specifying\u0026thinsp;\u0026lt;\u0026thinsp;5 hydrogen bond donors, \u0026lt;\u0026thinsp;10 hydrogen bond acceptors, and molecular weight\u0026thinsp;\u0026lt;\u0026thinsp;500 Da, showed only one violation, which is acceptable. Bioavailability was predicted as 0.55, indicating neutrality. None of the compounds exhibited PAINS (Pan-Assay Interference Compounds). Synthetic availability ranged between 1 (very easy) to 10 (difficult), with all compounds scoring\u0026thinsp;\u0026lt;\u0026thinsp;5 as shown in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. After analysing the top 5 compounds concerning their pass prediction and ADME-T properties, we selected top 3 compounds 123629569, 163388234, and 122589735 for further molecular docking study.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eADME-T properties of the selected top five compounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADME Parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProperties\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e123629569\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e163388234\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e122589735\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e163555323\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e156185498\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysiochemical Properties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC26H25F3N6O2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC67H44F3N12O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC24H33N9O3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC44H28FN5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC39H25N5O\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolecular weight\u003c/p\u003e\n \u003cp\u003e(g/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e510.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1090.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e495.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e645.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e579.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAbsorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI\u003c/p\u003e\n \u003cp\u003eAbsorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003cp\u003eSolubility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorly soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsoluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsoluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorly soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDistribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBB\u003c/p\u003e\n \u003cp\u003ePermeation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipophilicity (ILogP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP2D6 Substrate/ Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcretion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOCT2\u003c/p\u003e\n \u003cp\u003eSubstrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eToxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMES toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum Tolerance Dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin Sensitisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eDrug likeness and medicinal chemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipinski\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioavailability score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAINS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSynthetic accessibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eInteraction of katanin with purine type compounds using docking:\u003c/h2\u003e\n \u003cp\u003eFirst, we performed a control docking of katanin with ATP using AutoDock 4.2.7 \u003csup\u003e39\u003c/sup\u003e, and the minimum energy docked conformation of ATP was found to be -4.86 kcal/mol (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eB and Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). The analysis of katanin-ATP complex shows that the ATP is stabilized by the bonded and non-bonded type of interactions as shown in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e. ATP forms a conventional hydrogen bonding interaction with residues Gly252 (1.68 and 2.33\u0026Aring;), Thr253 (2.21 \u0026Aring;), Gly254 (2.81 and 2.10 \u0026Aring;), Lys255 (2.52 \u0026Aring;), Thr256 (1.71 and 3.05 \u0026Aring;), Leu257 (2.57 \u0026Aring;), Asp210 (2.41 \u0026Aring;), Thr422 (2.04\u0026Aring;), whereas Leu257 forms a \u0026pi;-sigma, and Leu390 forms \u0026pi;-alkyl type of non-bonded interactions as shown in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eA, \u003cspan\u003e3\u003c/span\u003eB and Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eSimilarly, molecular docking was performed to explore the binding mode and affinity of selected compounds 122589735, 123629569 and 163388234 with katanin using AutoDock 4.2.7 \u003csup\u003e39\u003c/sup\u003e. The least binding energy conformation of compounds 122589735, 123629569 and 163388234 were found to be -8.57, -8.85, and \u0026minus;\u0026thinsp;8.51 kcal/mol, respectively as shown in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eNotably, compound 122589735 exhibited the lowest binding energy with katanin compared to all other compounds analysed. These findings highlight the considerable potential of these compounds for katanin binding, particularly targeting the ATP site. Therefore, to delve into the mechanisms of bonded and non-bonded interactions with katanin, we conducted additional analyses of docked complexes and thoroughly discuss our findings.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAnalysis of 2D interactions of drug compounds with Katanin receptor after molecular docking.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePubChem Compound Id\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinding Energy\u003c/p\u003e\n \u003cp\u003e(kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAtoms involved in binding\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBond type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAngle\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFig\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKatanin-ATP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLY252:HN - ATP501:O20\u003c/p\u003e\n \u003cp\u003eTHR253:HN - ATP 501:O1B\u003c/p\u003e\n \u003cp\u003eGLY254:HN - ATP 501:O5\u0026apos;\u003c/p\u003e\n \u003cp\u003eGLY254:HN - ATP 501:O1B\u003c/p\u003e\n \u003cp\u003eLYS255:HN - ATP 501:O1B\u003c/p\u003e\n \u003cp\u003eTHR256:HN - ATP 501:O3A\u003c/p\u003e\n \u003cp\u003eLEU257:HN - ATP 501:O2A\u003c/p\u003e\n \u003cp\u003eATP 501:H61 - ASP210:O\u003c/p\u003e\n \u003cp\u003eGLY252:CA - ATP 501:O2G\u003c/p\u003e\n \u003cp\u003eTHR256:CB - ATP 501:O1A\u003c/p\u003e\n \u003cp\u003eTHR422:HG1 - ATP501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164.55\u003c/p\u003e\n \u003cp\u003e119.63\u003c/p\u003e\n \u003cp\u003e121.04\u003c/p\u003e\n \u003cp\u003e143.09\u003c/p\u003e\n \u003cp\u003e133.19\u003c/p\u003e\n \u003cp\u003e99.24\u003c/p\u003e\n \u003cp\u003e127.69\u003c/p\u003e\n \u003cp\u003e143.99\u003c/p\u003e\n \u003cp\u003e126.59\u003c/p\u003e\n \u003cp\u003e154.75\u003c/p\u003e\n \u003cp\u003e107.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKatanin \u0026minus;\u0026thinsp;122589735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASN360:ND2 - Drug: O\u003c/p\u003e\n \u003cp\u003eDrug: H - ALA212:O\u003c/p\u003e\n \u003cp\u003eDrug: C - THR422:OG1\u003c/p\u003e\n \u003cp\u003eDrug: C - ASP308:OD2\u003c/p\u003e\n \u003cp\u003eDrug: C - THR253:O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eH Bond\u003c/p\u003e\n \u003cp\u003eCH Bond\u003c/p\u003e\n \u003cp\u003eCH Bond\u003c/p\u003e\n \u003cp\u003eCH Bond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.24\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e90.31\u003c/p\u003e\n \u003cp\u003e125.53\u003c/p\u003e\n \u003cp\u003e94.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKatanin \u0026minus;\u0026thinsp;123629569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLY252:CA - Drug:O\u003c/p\u003e\n \u003cp\u003eGLY418:CA - Drug: N\u003c/p\u003e\n \u003cp\u003eDrug: C - GLY418:O\u003c/p\u003e\n \u003cp\u003eDrug: C - THR422:OG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCH Bond\u003c/p\u003e\n \u003cp\u003eCH Bond\u003c/p\u003e\n \u003cp\u003eCH Bond\u003c/p\u003e\n \u003cp\u003eCH Bond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.75\u003c/p\u003e\n \u003cp\u003e105.9\u003c/p\u003e\n \u003cp\u003e96.9\u003c/p\u003e\n \u003cp\u003e113.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003eϯ\u003c/sup\u003eKatanin- 163388234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003eϯ\u003c/sup\u003e There are no conventional and CH-type hydrogen bonding interactions with katanin.\u003c/p\u003e\n \u003cp\u003eThe analysis of the katanin-122589735 complex (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e) shows that stability of the 122589735 compound is attributed to conventional hydrogen bonding interactions with residues Asn360 (3.15 \u0026Aring;), Ala212 (2.50 \u0026Aring;), whereas CH bonding interactions with residues Thr422 (3.16 \u0026Aring;), Asp308 (3.30 \u0026Aring;), and Thr253 (3.38 \u0026Aring;) as shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eC. In addition, 122589735 makes an alkyl interaction with Lys255 (5.25 \u0026Aring; and 4.84 \u0026Aring;), Leu257 (5.28 \u0026Aring;), Ala358 (4.32 \u0026Aring;), Pro251 (4.66 \u0026Aring;), and \u0026pi;-Alkyl interactions with Leu257 (4.66 \u0026Aring;), Leu390 (5.47 \u0026Aring;), Ala212 (5.22 \u0026Aring;), Pro382 (4.36 \u0026Aring;), and Leu390 (4.35 \u0026Aring;) as shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eC.\u003c/p\u003e\n \u003cp\u003eFurther analysis of katanin-123629569 complex (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e) shows that the 123629569 compound is stabilized due to CH bonding interactions with residues Gly252 (3.66 \u0026Aring;), Gly418 (3.22 \u0026Aring; and 3.50 \u0026Aring;), and Thr422 (3.19 \u0026Aring;), also the presence of Halogen (fluorine) type interaction was seen for Asp210 (3.42 \u0026Aring;). In addition, 123629569 compound forms \u0026pi;-Sigma interactions with Leu257 (3.62 \u0026Aring;), Leu390 (3.82 \u0026Aring;) and Ala419 (3.84 \u0026Aring;) as shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eD. Also, Amide-\u0026pi; stacked bonds with Thr253 (4.47 \u0026Aring;), Gly418 (4.24 \u0026Aring;) and Gly418 (5 \u0026Aring;) (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eD). Moreover, Alkyl type of interactions were contributed to Ala419 (4.16 \u0026Aring;), Pro382 (3.52 \u0026Aring;), Leu390 (4.26 \u0026Aring;), Leu257 (5.16 \u0026Aring;), and \u0026pi;-Alkyl bonds for Leu257 (5 \u0026Aring;) as well as Ala419 (4.63 \u0026Aring;) as shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eD. Next, the analysis of the katanin-163388234 complex (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e) shows that the 163388234 compound forms \u0026pi;-Donor hydrogen bond for Thr256 (3.98 \u0026Aring; and 3.74 \u0026Aring;), and Asn272 (3.69 \u0026Aring;) (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eE), \u0026pi;-Sigma bond with Ile393 (3.82 \u0026Aring;) and \u0026pi;-Alkyl bonds for Leu257 (4.56 \u0026Aring;), Val206 (5.25 \u0026Aring;), Leu257 (5.24 \u0026Aring; and 4.55 \u0026Aring;) as well as Lys255 (5.32 \u0026Aring;) as shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eE.\u003c/p\u003e\n \u003cp\u003eThe analysis of docking results revealed that katanin with 122589735 complex is stabilized by both bonded and non-bonded types of interactions. However, compound 163388234 forms only non-bonded type of interactions with katanin, as it lacks electron bond donor groups such as O, N, S, etc. In addition, the compound 163388234 shows lower binding affinity with katanin as shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eE. Hence, to explore the refined binding mode and affinity of katanin with ATP, 122589735 and 123629569 compounds, molecular dynamics simulations were employed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eMolecular dynamics (MD) simulation\u003c/h2\u003e\n \u003cp\u003eMD simulations were performed to investigate the interaction of katanin with ATP, 122589735 and 123629569 using Gromacs 2021.5 \u003csup\u003e52\u003c/sup\u003e. The least energy docked conformation of katanin, katanin-ATP, katanin-123629569 and katanin-122589735 shown in (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e), were considered as starting conformation for MD simulation. The simulations were performed for 500 ns, to obtain detailed conformational and structural changes in the katanin (see supplementary movies 1\u0026ndash;4). The stability of the simulation systems were assessed by plotting the root mean square deviation (RMSD) of the C\u003csub\u003e\u0026alpha;\u003c/sub\u003e backbone atoms of protein (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA). RMSD plot revealed that all the simulation systems reached their equilibrium after 300ns (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA). Overall, katanin with ATP and drug complexes show lower RMSD value compared to the katanin in apo form (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA), this shows that the ATP and drug compound has profound effect on structure and dynamics of katanin (see supplementary movie 1\u0026ndash;4). The katanin-123629569 as it shows the higher fluctuations compared to all other katanin complexes after 300ns (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA). To gain more insight on the impact of drug binding and conformational changes in the katanin structure, root mean square fluctuations (RMSF) of C\u003csub\u003e\u0026alpha;\u003c/sub\u003e atoms were performed (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eIt is revealed that katanin bound to ATP and 122589735 compounds showed a lower fluctuations (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB) compared to the katanin and katanin-123629569. The ATP binding site residues of katanin (141 to 171) shows reduced fluctuations of katanin 122589735 compared to the katanin alone and katanin with ATP and 123629569 (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB). Moreover, katanin-122589735 complex revealed lower conformational changes in 122589735 compound (Supplementary Movie 3) while 123629569 compound show higher conformational fluctuations (Supplementary Movie 4). This might be because compound 122589735 forms two hydrogen bonds with Asn360 and Ala212, along with three CH bonds with Thr422, Asp308, and Thr253 of katanin (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB-\u003cspan\u003e4\u003c/span\u003eC and Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). In contrast, compound 123629569 only engages in non-bonded interactions. Altogether, katanin with compound 122589735 show a profound effect on the structure and dynamics of katanin.\u003c/p\u003e\n \u003cp\u003eTo further check the compactness of the protein, radius of gyration (Rg) (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eC) and solvent-accessible surface area (SASA) (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eD) were calculated. The Rg plot analysis revealed that katanin-ATP and katanin-122589735 complexes exhibit a lower Rg value compared to katanin and katanin-123629569 complexes (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eC). This suggests that katanin adopts a more compact conformation when bound to ATP and compound 122589735 (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eC). Similarly, the SASA plot complements Rg analysis by focusing on the surface area of the protein accessible to the solvent molecule. The collective SASA values of all the systems ranged between 160-180nm\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eD). To comprehensively characterize the protein\u0026apos;s conformational changes upon binding to ATP and drug compounds throughout the simulation, we employed a multi-pronged approach which includes principal component analysis (PCA), free energy landscape, hydrogen bonding interaction, and binding energy calculations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003ePrinciple component analysis (PCA):\u003c/h2\u003e\n \u003cp\u003ePCA was carried out using the gmx_covar and gmx_anaeig modules of Gromacs 2021.5 to understand the essential motions of the katanin, katanin-ATP and katanin-122589735, and katanin-123629569 complexes. Here, the eigenvectors, known as PCs (principal components) were investigated as shown in Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eE. PCA analysis revealed that there was a higher diversity of conformations when katanin was in an unbound state during the simulation while, for the katanin-ATP, katanin-122589735 and katanin-123629569 complexes showed lower conformational diversity as shown in Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eE. PCA analysis showed the highest diversity of conformations for katanin during the simulation (PCA2:7, PCA1:7) compared to other complexes. As evidenced by the PCA scores, the katanin-122589735 complex scored (PCA2: 4, PCA1: 8) and the katanin-123629569 complex scored (PCA2: 4, PCA1: 10). However, the katanin-ATP showed the least diversity in the conformations. Overall, katanin in its unbound state has higher variations in the conformations over the time of 500 ns but, upon drug binding the dynamics of katanin are affected thus leading to a stable state. To gain a deeper understanding of the energetics involved, we employed free energy landscape analysis to visualize the various energetic states of katanin, katanin bound to ATP, and katanin in complex with the drug compounds.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003eFree energy landscape (FEL)\u003c/h2\u003e\n \u003cp\u003eFEL analysis shows the protein conformational space concerning energy and time \u003csup\u003e53\u003c/sup\u003e. This allowed us to visualize the most stable conformations and identify potential energy barriers for transitions between varying states. For all the landscapes (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eA-\u003cspan\u003e5\u003c/span\u003eH), the initial minimum represents the filtering of the most stable conformation and has the lowest energy with a conical termination. As observed in the MD analysis results and PCA, katanin in unbound nature displays large conformation states and takes time to achieve the least energy conformation (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eA and \u003cspan\u003e5\u003c/span\u003eB), similarly, a greater number of high energy states are observed as seen in the contour map plot of katanin (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eB). The scenario changes when the katanin is bound to ATP, and stabilizes the katanin as observed in the energy funnel with confined basin (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eC and \u003cspan\u003e5\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eSimilar observations are made concerning katanin-drug complexes. Wherein, katanin-122589735 complex had the least energy range between 0-1.62 kJ/mol for the deep energy minima (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eE and \u003cspan\u003e5\u003c/span\u003eF). The katanin-123629569 shows single minima but undergoes more variations/transition state in the structural folding as compared to katanin-122589735 to achieve the least energy state (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eG and \u003cspan\u003e5\u003c/span\u003eH). These results infer that the katanin-122589735 complex achieves the global state conformation sooner than the katanin-123629569 complex (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eE-\u003cspan\u003e5\u003c/span\u003eH). To gain a deeper understanding of the interactions between katanin and drug compounds, we specifically investigated hydrogen bonding through a dedicated analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003eHydrogen bond interaction analysis\u003c/h2\u003e\n \u003cp\u003eThis analysis quantified and characterized the hydrogen bond formation between katanin and ATP, 122589735 and 123629569 compounds during the 500 ns time steps (Supplementary Fig.\u0026nbsp;1). Katanin-ATP complex shows more stable and frequent hydrogen bond formation (Supplementary Fig.\u0026nbsp;1A). Further, hydrogen bond analysis shows that the katanin-122589735 complex (Supplementary Fig.\u0026nbsp;1B) forms stable hydrogen bonds as compared to the katanin-123629569 complex (Supplementary Fig.\u0026nbsp;1C). Whereas the number of hydrogen bonds increases after 300ns for the katanin-122589735 complex indicating stronger interaction between the drug and katanin (Supplementary Fig.\u0026nbsp;1A). The hydrogen bond analysis shows that 122589735 is stable at the ATP binding pocket of katanin and from constant the number of hydrogen bonds is over time (Supplementary Fig.\u0026nbsp;1B). Hence, to investigate the binding affinity of drug compounds, we employed the binding energy calculations and residue per decomposition analysis using the gmx_MMPBSA tool \u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003eBinding energy calculations\u003c/h2\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBinding energy calculation of katanin with ATP and drug compounds. All energies are in kcal/mol.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKatanin-drug complex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;E\u003csub\u003evdw\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;E\u003csub\u003eele\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;E\u003csub\u003egas\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;E\u003csub\u003esol\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;E\u003csub\u003ebind\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eKatanin-ATP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-40.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-197.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-22.70\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eKatanin-122589735\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-45.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-20.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-66.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-33.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eKatanin-123629569\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-39.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-52.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-27.54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo investigate the binding affinity of katanin with ATP and drug compounds, we employed binding energy calculations using the MM-GBSA method through gmx_MMPBSA tool \u003csup\u003e49\u003c/sup\u003e. The equilibrated last 100 ns (a total of 5000 frames) were considered for the binding energy calculations. The energy data analysis revealed that katanin had a higher binding affinity with compound 122589735 compared to ATP and compound 123629569 as shown in Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e. The order of binding affinity decreases in the order of 122589735 (-33.58 kcal/mol)\u0026thinsp;\u0026gt;\u0026thinsp;123629569 (-27.54 kcal/mol)\u0026thinsp;\u0026gt;\u0026thinsp;ATP (-22.70 kcal/mol). Furthermore, the analysis revealed that the Van der Waals and electrostatic interactions play a significant role in the binding of the 122589735 compounds with katanin whereas, the loss of these interactions reduces the affinity of 123629569 for katanin. To further investigate the contribution of binding site residues, we performed per-residue energy decomposition analysis. Additionally, the per-residue contributions of katanin interacting with the drug and influencing binding affinity were determined using decomposition analysis with the gmx_MMPBSA tool (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eThe residues decomposition analysis of katanin-ATP complex shows that Ile28, Leu31, Thr70, Gly71, Leu74, Gly235, Ala236, Ile238, and Thr239 are involved in the binding with ATP (Supplementary Fig.\u0026nbsp;2A). Whereas katanin-122589735 complex had a relatively higher number of active residues contributing to energy than the other complexes (Supplementary Fig.\u0026nbsp;2B). Notably, Leu74, Ala236, and Thr239 exhibited the highest energy contributions. In katanin-123629569 complex, Gly71, Gly235, and Thr239 were found to have significant roles in drug binding (Supplementary Fig.\u0026nbsp;2C). Overall, residue decomposition analysis suggests that residues in the ATP binding site, such as Thr70, Gly71, Leu74, Gly235, Ala236, and Thr239, are common active site residues of katanin that interact with both ATP and the drugs (Supplementary Figs.\u0026nbsp;2A-2C).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present study, we employed structure-based virtual screening, ADME-T analysis, molecular docking, and MD simulation to identify promising purine-type inhibitors for the microtubule-severing enzyme, katanin. Katanin's activity depends on the formation of katanin hexamers and the presence of the tubulin C-terminal, consecutively removing tubulin dimers to amplify microtubules and influence the proliferation of various cancer cells. Previous, study shown that purine type compounds are highly effective in controlling different carcinomas by inducing microtubule fragmentation through their interactions with katanin \u003csup\u003e29\u003c/sup\u003e. Therefore, we leveraged the PubChem purine library to identify potential katanin inhibitors. Docking analysis revealed significant binding affinities for katanin with purine type compounds PubChem IDs; 122589735, 123629569, and 163388234. Among these, compounds 122589735 and 123629569 were further assessed for their dynamic stability through molecular dynamics simulations. MD simulation analysis indicated that compound 122589735 demonstrated the most robust interaction with katanin. Further binding energy studies revealed that 122589735 has a higher binding affinity (-33.58 kcal/mol) for katanin compared to compound 123629569 (-27.54 kcal/mol) and ATP (-22.70 kcal/mol). These findings suggest that compound 122589735 could potentially serve as a katanin inhibitor, influencing its microtubule-severing function. Further in vitro and in vivo studies are required to investigate the efficacy of compound 122589735 against katanin and to support its preclinical development. Consequently, our computational investigation not only identifies a potential inhibitor of the microtubule-severing enzyme katanin but also establishes a foundation for developing treatments for various carcinomas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePruthanka Patil is thankful to UGC New Delhi for awarding Savitribai Jyotirao Phule single girl child fellowship for doctoral study. Bajarang Kumbhar is thankful to SVKM\u0026rsquo;s Narsee Monjee Institute of Management Studies (NMIMS) Deemed-to-be University for providing computational facilities.\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have seen and agree with the contents of the manuscript, and there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVibhuti Saxena:\u003c/strong\u003e Methodology, Analysis of results, Validation, Writing-original draft, review and editing, \u003cstrong\u003ePruthanka Patil:\u003c/strong\u003e Methodology, Analysis of results, Validation, Writing-original draft, Writing-review and editing, \u003cstrong\u003ePurva Khodke:\u003c/strong\u003e Methodology, Data Curation, Writing-review and editing, \u003cstrong\u003eBajarang Kumbhar:\u003c/strong\u003e Supervision, Conceptualization, Methodology, Data Curation, Analysis, Validation, Writing-original draft, Writing-review and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have seen and agree with the contents of the manuscript, and there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eParker, A. 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Substrate-induced changes in dynamics and molecular motions of cuticle-degrading serine protease PL646: a molecular dynamics study. \u003cem\u003eRSC Adv.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 42094\u0026ndash;42104 (2017).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"Katanin, Microtubule, Purine analogues, structure-based drug design, Molecular dynamics simulations","lastPublishedDoi":"10.21203/rs.3.rs-4742126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4742126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKatanin, a pioneering microtubule-severing enzyme, is a novel AAA-ATPase protein. It severs microtubules by forming hexamers that binds to the C-terminal tails of tubulin, using ATP hydrolysis to generate the force necessary to break the microtubule lattice. Katanin contributes to microtubule amplification and impact the growth of carcinomas. Hence, katanin is a highly promising target for anti-cancer drug development. This study aims to identify potential purine-based inhibitors against katanin by using structure-based virtual screening, PASS and ADME-T prediction, docking, and molecular dynamics simulations. Here, purine-based library of 2,76,280 compounds from the PubChem Database were utilized, and top two purine type inhibitors (PubChem ID: 122589735, and 123629569) were selected based on superior binding energy, ADME-T, and biological activity. Furthermore, molecular docking and molecular dynamics simulations study revealed that 122589735 and 123629569 compounds effectively alter katanin's structure and dynamics as compared to ATP. Besides, binding energy calculations indicate that 122589735 exhibits higher binding affinity with katanin compared to 123629569 and ATP. Thus, our computational study identifies potential purine-based katanin inhibitors that exhibit higher affinity for katanin than ATP and may have implications for various carcinomas. This research paves the way for developing novel, anti-cancer therapies targeting a range of carcinoma types.\u003c/p\u003e","manuscriptTitle":"Exploring Purine Analogues as Inhibitors against Katanin, a Microtubule Severing Enzyme using Molecular Modeling Approach ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 11:12:41","doi":"10.21203/rs.3.rs-4742126/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-07T08:58:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-06T01:47:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4085402612034133191208370965805151260","date":"2024-10-04T03:28:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-24T22:42:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135685564135104434892661842325451148541","date":"2024-08-17T12:58:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242921126413876330121239397599922193184","date":"2024-07-19T04:31:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333912538002030038389081756749690786697","date":"2024-07-19T04:02:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-19T02:23:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-17T10:31:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-17T09:05:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-17T08:58:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-15T10:15:57+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":"771ee9e0-a035-4d03-abbd-476eca57f923","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T16:02:16+00:00","versionOfRecord":{"articleIdentity":"rs-4742126","link":"https://doi.org/10.1038/s41598-024-83723-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-12-30 15:57:34","publishedOnDateReadable":"December 30th, 2024"},"versionCreatedAt":"2024-08-09 11:12:41","video":"","vorDoi":"10.1038/s41598-024-83723-7","vorDoiUrl":"https://doi.org/10.1038/s41598-024-83723-7","workflowStages":[]},"version":"v1","identity":"rs-4742126","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4742126","identity":"rs-4742126","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}