{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T02:56:16Z","timestamp":1784948176004,"version":"3.55.0"},"reference-count":90,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Vellore Institute of Technology, Vellore"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Streptococcus pneumoniae<\/jats:italic>\n                    (\n                    <jats:italic>S. pneumoniae<\/jats:italic>\n                    ) has developed resistance to \u03b2-lactam antibiotics, largely due to mutations in penicillin-binding protein 2x (PBP2x), particularly within conserved motifs such as STMK and KSG. PBP2x mutations are frequently reported in multidrug-resistant pneumococcal strains associated with pneumonia, meningitis, and septicaemia. especially in serotypes 19A, 19F, and 23F, showing reduced susceptibility to \u03b2-lactam antibiotics. These mutations in the PBP2x disrupt antibiotic binding and enzymatic functions, highlighting the need for alternative therapeutic strategies. This study focused on five clinically relevant PBP2x mutations (T338A\/G\/P and K547G\/T) within its active site. A library of phytocompounds was screened using a machine learning model trained to identify antibacterial compounds. Top candidates were filtered based on ADMET properties, and their electronic characteristics were assessed using HOMO\u2013LUMO analysis and electrostatic potential mapping, through density functional theory (DFT). Glucozaluzanin C, a phytochemical derived from\n                    <jats:italic>Elephantopus scaber<\/jats:italic>\n                    , emerged as a potential candidate. Molecular docking and dynamics simulations revealed strong binding affinity and structural integrity with all PBP2x mutants, over a 100-ns timescale. RMSD, RMSF, and hydrogen bonding analysis confirmed stable interactions, suggesting Glucozaluzanin C may effectively interact with PBP2x mutants. Overall, the study highlights an effective strategy for identifying plant-derived inhibitors against \u03b2-lactam-resistant\n                    <jats:italic>S. pneumoniae.<\/jats:italic>\n                  <\/jats:p>","DOI":"10.1038\/s41598-025-24222-1","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T12:27:19Z","timestamp":1762518439000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning-based virtual screening and density functional theory characterisation of natural inhibitors targeting mutant PBP2x in Streptococcus pneumoniae"],"prefix":"10.1038","volume":"15","author":[{"given":"Avani","family":"Panickar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anand","family":"Manoharan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudha","family":"Ramaiah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"24222_CR1","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/S0140-6736(63)92350-X","volume":"281","author":"W Evans","year":"1963","unstructured":"Evans, W. & Hansman, D. Tetracycline-resistant pneumococcus. Lancet 281, 451 (1963).","journal-title":"Lancet"},{"key":"24222_CR2","first-page":"1927","volume":"59","author":"NIN Zahari","year":"2023","unstructured":"Zahari, N. I. N. et al. A review of the resistance mechanisms for \u03b2-Lactams, macrolides and fluoroquinolones among Streptococcus pneumoniae. Med. (B Aires). 59, 1927 (2023).","journal-title":"Med. (B Aires)"},{"key":"24222_CR3","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1089\/mdr.2020.0204","volume":"27","author":"R Varghese","year":"2021","unstructured":"Varghese, R. et al. Analysis of amino acid sequences of penicillin-Binding proteins 1a, 2b, and 2x in invasive Streptococcus pneumoniae nonsusceptible to penicillin isolated from children in India. Microb. Drug Resist. 27, 311\u2013319 (2021).","journal-title":"Microb. Drug Resist."},{"key":"24222_CR4","doi-asserted-by":"publisher","first-page":"16463","DOI":"10.1074\/jbc.M313492200","volume":"279","author":"L Pernot","year":"2004","unstructured":"Pernot, L. et al. A PBP2x from a clinical isolate of Streptococcus pneumoniae exhibits an alternative mechanism for reduction of susceptibility to \u03b2-Lactam antibiotics. J. Biol. Chem. 279, 16463\u201316470 (2004).","journal-title":"J. Biol. Chem."},{"key":"24222_CR5","doi-asserted-by":"publisher","first-page":"395","DOI":"10.2217\/fmb.12.2","volume":"7","author":"R Hakenbeck","year":"2012","unstructured":"Hakenbeck, R., Br\u00fcckner, R., Denapaite, D. & Maurer, P. Molecular mechanisms of \u03b2-Lactam resistance in Streptococcus pneumoniae. Future Microbiol. 7, 395\u2013410 (2012).","journal-title":"Future Microbiol."},{"key":"24222_CR6","doi-asserted-by":"crossref","unstructured":"Chochua, S. et al. Invasive group A streptococcal penicillin binding protein 2\u00d7 variants associated with reduced susceptibility to \u03b2-Lactam antibiotics in the United States, 2015\u20132021. Antimicrob. Agents Chemother. 66, e00802\u2013e00822 (2022).","DOI":"10.1128\/aac.00802-22"},{"key":"24222_CR7","doi-asserted-by":"publisher","first-page":"3529","DOI":"10.1128\/AAC.00429-15","volume":"59","author":"A Jensen","year":"2015","unstructured":"Jensen, A., Vald\u00f3rsson, O., Frimodt-M\u00f8ller, N., Hollingshead, S. & Kilian, M. Commensal streptococci serve as a reservoir for \u03b2-Lactam resistance genes in Streptococcus pneumoniae. Antimicrob. Agents Chemother. 59, 3529\u20133540 (2015).","journal-title":"Antimicrob. Agents Chemother."},{"key":"24222_CR8","doi-asserted-by":"publisher","first-page":"8788","DOI":"10.1038\/s41598-024-59489-3","volume":"14","author":"JO Aribisala","year":"2024","unstructured":"Aribisala, J. O., S\u2019thebe, N. W. & Sabiu, S. In silico exploration of phenolics as modulators of penicillin binding protein (PBP) 2\u00d7 of Streptococcus pneumoniae. Sci. Rep. 14, 8788 (2024).","journal-title":"Sci. Rep."},{"key":"24222_CR9","doi-asserted-by":"publisher","first-page":"45106","DOI":"10.1074\/jbc.M107608200","volume":"276","author":"A Dessen","year":"2001","unstructured":"Dessen, A., Mouz, N., Gordon, E., Hopkins, J. & Dideberg, O. Crystal structure of PBP2x from a highly penicillin-resistant Streptococcus pneumoniae clinical isolate. J. Biol. Chem. 276, 45106\u201345112 (2001).","journal-title":"J. Biol. Chem."},{"key":"24222_CR10","doi-asserted-by":"publisher","first-page":"19175","DOI":"10.1074\/jbc.274.27.19175","volume":"274","author":"N Mouz","year":"1999","unstructured":"Mouz, N. et al. Mutations in the active site of penicillin-binding protein PBP2x from Streptococcus pneumoniae. J. Biol. Chem. 274, 19175\u201319180 (1999).","journal-title":"J. Biol. Chem."},{"key":"24222_CR11","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.jgar.2025.04.016","volume":"43","author":"A Panickar","year":"2025","unstructured":"Panickar, A., Manoharan, A. & Ramaiah, S. Single nucleotide polymorphisms and penicillin non-susceptibility among invasive Streptococcus pneumoniae from Vietnam and India: Insights from a comparative genomics study. J. Glob Antimicrob. Resist. 43, 120\u2013129 (2025).","journal-title":"J. Glob Antimicrob. Resist."},{"key":"24222_CR12","doi-asserted-by":"crossref","unstructured":"Zhou, M. et al. Molecular characterization of penicillin-binding protein2x, 2b and 1a of Streptococcus pneumoniae causing invasive pneumococcal diseases in China: A multicenter study. Front. Microbiol. 13, 838790 (2022).","DOI":"10.3389\/fmicb.2022.838790"},{"key":"24222_CR13","doi-asserted-by":"publisher","first-page":"105694","DOI":"10.1016\/j.micpath.2022.105694","volume":"170","author":"S Basu","year":"2022","unstructured":"Basu, S. et al. Non-steroidal anti-inflammatory drugs ketorolac and etodolac can augment the treatment against pneumococcal meningitis by targeting penicillin-binding proteins. Microb. Pathog. 170, 105694 (2022).","journal-title":"Microb. Pathog"},{"key":"24222_CR14","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1093\/bib\/bbz056","volume":"21","author":"C Zhang","year":"2020","unstructured":"Zhang, C. et al. Systematic analysis of supervised machine learning as an effective approach to predicate \u03b2-lactam resistance phenotype in Streptococcus pneumoniae. Brief. Bioinform. 21, 1347\u20131355 (2020).","journal-title":"Brief. Bioinform"},{"key":"24222_CR15","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1002\/ptr.8365","volume":"39","author":"S Ghosh","year":"2025","unstructured":"Ghosh, S., Basu, S., Anbarasu, A. & Ramaiah, S. A. Comprehensive review of antimicrobial agents against clinically important bacterial pathogens: Prospects for phytochemicals. Phyther Res. 39, 138\u2013161 (2025).","journal-title":"Phyther Res."},{"key":"24222_CR16","doi-asserted-by":"crossref","unstructured":"Kaya, D. E., \u00dclgen, E., Kocag\u00f6z, A. S. & Sezerman, O. U. A comparison of various feature extraction and machine learning methods for antimicrobial resistance prediction in Streptococcus pneumoniae. Front. Antibiot. 2, 1126468 (2023).","DOI":"10.3389\/frabi.2023.1126468"},{"key":"24222_CR17","unstructured":"Gurbych, O., Druchok, M., Yarish, D. & Garkot, S. High throughput screening with machine learning (2020)."},{"key":"24222_CR18","doi-asserted-by":"publisher","first-page":"e1010727","DOI":"10.1371\/journal.ppat.1010727","volume":"18","author":"PS Gibson","year":"2022","unstructured":"Gibson, P. S., Bexkens, E., Zuber, S., Cowley, L. A. & Veening, J. W. The acquisition of clinically relevant amoxicillin resistance in Streptococcus pneumoniae requires ordered horizontal gene transfer of four loci. PLOS Pathog. 18, e1010727 (2022).","journal-title":"PLOS Pathog"},{"key":"24222_CR19","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1128\/AAC.40.4.829","volume":"40","author":"T Grebe","year":"1996","unstructured":"Grebe, T. & Hakenbeck, R. Penicillin-binding proteins 2b and 2x of Streptococcus pneumoniae are primary resistance determinants for different classes of beta-lactam antibiotics. Antimicrob. Agents Chemother. 40, 829\u2013834 (1996).","journal-title":"Antimicrob. Agents Chemother."},{"key":"24222_CR20","doi-asserted-by":"publisher","first-page":"3345","DOI":"10.1007\/s11030-025-11125-6","volume":"29","author":"S Mathpal","year":"2025","unstructured":"Mathpal, S., Joshi, T., Priyamvada, P., Ramaiah, S. & Anbarasu, A. Machine learning and cheminformatics-based identification of lichen-derived compounds targeting mutant PBP4R200L in Staphylococcus aureus. Mol. Divers. 29, 3345\u20133370. https:\/\/doi.org\/10.1007\/s11030-025-11125-6 (2025).","journal-title":"Mol. Divers."},{"key":"24222_CR21","doi-asserted-by":"publisher","first-page":"2745","DOI":"10.1093\/bioinformatics\/btv195","volume":"31","author":"Y Choi","year":"2015","unstructured":"Choi, Y. & Chan, A. P. PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31, 2745\u20132747 (2015).","journal-title":"Bioinformatics"},{"issue":"Suppl 3","key":"24222_CR22","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1186\/1471-2164-14-S3-S2","volume":"14","author":"E Capriotti","year":"2013","unstructured":"Capriotti, E., Altman, R. B. & Bromberg, Y. Collective judgment predicts disease-associated single nucleotide variants. BMC Genom. 14(Suppl 3), S2 (2013).","journal-title":"BMC Genom."},{"key":"24222_CR23","doi-asserted-by":"publisher","first-page":"W344","DOI":"10.1093\/nar\/gkw408","volume":"44","author":"H Ashkenazy","year":"2016","unstructured":"Ashkenazy, H. et al. ConSurf 2016: An improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344\u2013W350 (2016).","journal-title":"Nucleic Acids Res."},{"key":"24222_CR24","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1002\/pro.3942","volume":"30","author":"CHM Rodrigues","year":"2021","unstructured":"Rodrigues, C. H. M., Pires, D. E. V. & Ascher, D. B. DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations Protein Sci. 30, 60\u201369 (2021).","journal-title":"Protein Sci."},{"key":"24222_CR25","doi-asserted-by":"publisher","first-page":"4329","DOI":"10.1038\/s41598-018-22631-z","volume":"8","author":"K Mohanraj","year":"2018","unstructured":"Mohanraj, K. et al. IMPPAT: A curated database of Indian medicinal plants, phytochemistry and therapeutics. Sci. Rep. 8, 4329 (2018).","journal-title":"Sci. Rep."},{"key":"24222_CR26","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1002\/jcc.21707","volume":"32","author":"CW Yap","year":"2011","unstructured":"Yap, C. W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 32, 1466\u20131474 (2011).","journal-title":"J. Comput. Chem."},{"key":"24222_CR27","first-page":"157","volume":"3","author":"IAE Guyon","year":"2003","unstructured":"Guyon, I. A. E. An introduction of variable and feature selection. Ournal Mach. Learn. Res. 3, 157\u20131182 (2003).","journal-title":"Ournal Mach. Learn. Res."},{"key":"24222_CR28","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall, M. et al. The WEKA data mining software. ACM SIGKDD Explor. Newsl. 11, 10\u201318 (2009).","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"24222_CR29","doi-asserted-by":"publisher","unstructured":"Frank, E., Hall, M. A. & Witten, I. H. The WEKA workbench. Data Min. https:\/\/doi.org\/10.1016\/b978-0-12-804291-5.00024-6 (2017).","DOI":"10.1016\/b978-0-12-804291-5.00024-6"},{"key":"24222_CR30","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/1471-2105-7-91","volume":"7","author":"S Varma","year":"2006","unstructured":"Varma, S. & Simon, R. Bias in error estimation when using cross-validation for model selection. BMC Bioinform. 7, 91 (2006).","journal-title":"BMC Bioinform."},{"key":"24222_CR31","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.3390\/molecules23092208","volume":"23","author":"R Chen","year":"2018","unstructured":"Chen, R., Liu, X., Jin, S., Lin, J. & Liu, J. Machine learning for drug-target interaction prediction. Molecules 23, 2208 (2018).","journal-title":"Molecules"},{"key":"24222_CR32","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.drudis.2014.10.012","volume":"20","author":"A Lavecchia","year":"2015","unstructured":"Lavecchia, A. Machine-learning approaches in drug discovery: Methods and applications. Drug Discov. Today 20, 318\u2013331 (2015).","journal-title":"Drug Discov. Today"},{"key":"24222_CR33","doi-asserted-by":"publisher","first-page":"14668","DOI":"10.1038\/s41598-025-98835-x","volume":"15","author":"A Roy","year":"2025","unstructured":"Roy, A. & Anbarasu, A. Unveiling Berberine analogues as potential inhibitors of Escherichia coli FtsZ through machine learning molecular docking and molecular dynamics approach. Sci. Rep. 15, 14668 (2025).","journal-title":"Sci. Rep."},{"key":"24222_CR34","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1038\/nrd1032","volume":"2","author":"H van de Waterbeemd","year":"2003","unstructured":"van de Waterbeemd, H. & Gifford, E. ADMET in silico modelling: Towards prediction paradise? Nat. Rev. Drug Discov. 2, 192\u2013204 (2003).","journal-title":"Nat. Rev. Drug Discov"},{"key":"24222_CR35","doi-asserted-by":"publisher","first-page":"W422","DOI":"10.1093\/nar\/gkae236","volume":"52","author":"L Fu","year":"2024","unstructured":"Fu, L. et al. ADMETlab 3.0: An updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Res. 52, W422\u2013W431 (2024).","journal-title":"Nucleic Acids Res."},{"key":"24222_CR36","doi-asserted-by":"publisher","first-page":"3099","DOI":"10.1021\/ci300367a","volume":"52","author":"F Cheng","year":"2012","unstructured":"Cheng, F. et al. AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 52, 3099\u20133105 (2012).","journal-title":"J. Chem. Inf. Model."},{"key":"24222_CR37","doi-asserted-by":"publisher","first-page":"W513","DOI":"10.1093\/nar\/gkae303","volume":"52","author":"P Banerjee","year":"2024","unstructured":"Banerjee, P., Kemmler, E., Dunkel, M. & Preissner, R. ProTox 3.0: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 52, W513\u2013W520 (2024).","journal-title":"Nucleic Acids Res."},{"key":"24222_CR38","doi-asserted-by":"publisher","first-page":"4066","DOI":"10.1021\/acs.jmedchem.5b00104","volume":"58","author":"DEV Pires","year":"2015","unstructured":"Pires, D. E. V., Blundell, T. L. & Ascher, D. B. PkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem. 58, 4066\u20134072 (2015).","journal-title":"J. Med. Chem."},{"key":"24222_CR39","doi-asserted-by":"publisher","first-page":"4989","DOI":"10.1039\/c3cs60456j","volume":"43","author":"P Geerlings","year":"2014","unstructured":"Geerlings, P., Fias, S., Boisdenghien, Z. & De Proft, F. Conceptual DFT: Chemistry from the linear response function. Chem. Soc. Rev. 43, 4989 (2014).","journal-title":"Chem. Soc. Rev."},{"key":"24222_CR40","doi-asserted-by":"crossref","unstructured":"Chai, J. D. & Head-Gordon, M. Systematic optimization of long-range corrected hybrid density functionals. J Chem. Phys 128, 084106 (2008).","DOI":"10.1063\/1.2834918"},{"key":"24222_CR41","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1039\/b802832j","volume":"105","author":"PK Chattaraj","year":"2009","unstructured":"Chattaraj, P. K. & Giri, S. Electrophilicity index within a conceptual DFT framework. Annu. Rep. Sect. \u2018C\u2019 (Phys. Chem.) 105, 13 (2009).","journal-title":"Annu. Rep. Sect. \u2018C\u2019 (Phys. Chem.)"},{"key":"24222_CR42","doi-asserted-by":"publisher","first-page":"4271","DOI":"10.1016\/j.csbj.2022.08.017","volume":"20","author":"A Naha","year":"2022","unstructured":"Naha, A. et al. Network metrics, structural dynamics and density functional theory calculations identified a novel ursodeoxycholic acid derivative against therapeutic target parkin for Parkinson\u2019s disease. Comput. Struct. Biotechnol. J. 20, 4271\u20134287 (2022).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"24222_CR43","doi-asserted-by":"publisher","first-page":"2785","DOI":"10.1002\/jcc.21256","volume":"30","author":"GM Morris","year":"2009","unstructured":"Morris, G. M. et al. AutoDock4 and AutoDockTools4: Automated Docking with selective receptor flexibility. J. Comput. Chem. 30, 2785\u20132791 (2009).","journal-title":"J. Comput. Chem."},{"key":"24222_CR44","doi-asserted-by":"publisher","first-page":"2371","DOI":"10.1021\/ci500209e","volume":"54","author":"D Santos-Martins","year":"2014","unstructured":"Santos-Martins, D., Forli, S., Ramos, M. J. & Olson, A. J. AutoDock4zn: an improved AutoDock force field for small-molecule docking to zinc metalloproteins. J. Chem. Inf. Model. 54, 2371\u20132379 (2014).","journal-title":"J. Chem. Inf. Model."},{"key":"24222_CR45","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1002\/jcc.20634","volume":"28","author":"R Huey","year":"2007","unstructured":"Huey, R., Morris, G. M., Olson, A. J. & Goodsell, D. S. A semiempirical free energy force field with charge-based desolvation. J. Comput. Chem. 28, 1145\u20131152 (2007).","journal-title":"J. Comput. Chem."},{"key":"24222_CR46","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s13205-023-03858-8","volume":"14","author":"S Mathpal","year":"2024","unstructured":"Mathpal, S. et al. In Silico screening of chalcone derivatives as promising EGFR-TK inhibitors for the clinical treatment of cancer. 3 Biotech. 14, 18 (2024).","journal-title":"3 Biotech."},{"key":"24222_CR47","doi-asserted-by":"publisher","first-page":"146","DOI":"10.2174\/157340911795677602","volume":"7","author":"XY Meng","year":"2011","unstructured":"Meng, X. Y., Zhang, H. X., Mezei, M. & Cui, M. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Comput. Aided-Drug Des. 7, 146\u2013157 (2011).","journal-title":"Curr. Comput. Aided-Drug Des."},{"key":"24222_CR48","doi-asserted-by":"publisher","first-page":"3731","DOI":"10.1080\/07391102.2020.1850356","volume":"40","author":"T Joshi","year":"2022","unstructured":"Joshi, T. et al. Repurposing of FDA approved drugs against Salmonella enteric serovar Typhi by targeting dihydrofolate reductase: an in silico study. J. Biomol. Struct. Dyn. 40, 3731\u20133744 (2022).","journal-title":"J. Biomol. Struct. Dyn."},{"key":"24222_CR49","doi-asserted-by":"publisher","first-page":"3110","DOI":"10.1021\/acschembio.2c00503","volume":"17","author":"PL Flanders","year":"2022","unstructured":"Flanders, P. L. et al. Combined structural analysis and molecular dynamics reveal penicillin-binding protein Inhibition mode with \u03b2-Lactones. ACS Chem. Biol. 17, 3110\u20133120 (2022).","journal-title":"ACS Chem. Biol."},{"key":"24222_CR50","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.softx.2015.06.001","volume":"1","author":"MJ Abraham","year":"2015","unstructured":"Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 1, 19\u201325 (2015).","journal-title":"SoftwareX"},{"key":"24222_CR51","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.bbrc.2021.09.076","volume":"580","author":"BK Savita","year":"2021","unstructured":"Savita, B. K. et al. Characterization of recombinant pumpkin 2S albumin and mutation studies to unravel potential DNA\/RNA binding site. Biochem. Biophys. Res. Commun. 580, 28\u201334 (2021).","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"24222_CR52","doi-asserted-by":"publisher","first-page":"3481","DOI":"10.1080\/07391102.2020.1847687","volume":"40","author":"TU da Silva","year":"2022","unstructured":"da Silva, T. U., Pougy, K., de Albuquerque, C., da Silva Lima, M. G., de Machado, S. & C. H. & Development of parameters compatible with the CHARMM36 force field for [Fe4S4]2+\u2009clusters and molecular dynamics simulations of adenosine-5\u2019-phosphosulfate reductase in GROMACS 2019. J. Biomol. Struct. Dyn. 40, 3481\u20133491 (2022).","journal-title":"J. Biomol. Struct. Dyn."},{"key":"24222_CR53","doi-asserted-by":"publisher","first-page":"9885","DOI":"10.1080\/07391102.2021.1936183","volume":"40","author":"S Mathpal","year":"2022","unstructured":"Mathpal, S. et al. Screening of potential bio-molecules from Moringa olifera against SARS-CoV-2 main protease using computational approaches. J. Biomol. Struct. Dyn. 40, 9885\u20139896 (2022).","journal-title":"J. Biomol. Struct. Dyn."},{"key":"24222_CR54","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1093\/bioinformatics\/btt055","volume":"29","author":"S Pronk","year":"2013","unstructured":"Pronk, S. et al. GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29, 845\u2013854 (2013).","journal-title":"Bioinformatics"},{"key":"24222_CR55","doi-asserted-by":"publisher","unstructured":"Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101. https:\/\/doi.org\/10.1063\/1.2408420 (2007)","DOI":"10.1063\/1.2408420"},{"key":"24222_CR56","doi-asserted-by":"publisher","first-page":"7182","DOI":"10.1063\/1.328693","volume":"52","author":"M Parrinello","year":"1981","unstructured":"Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 52, 7182\u20137190 (1981).","journal-title":"J. Appl. Phys."},{"key":"24222_CR57","doi-asserted-by":"publisher","first-page":"110164","DOI":"10.1016\/j.abb.2024.110164","volume":"761","author":"S Panchalingam","year":"2024","unstructured":"Panchalingam, S., Jayaraman, M., Jeyaraman, J. & Kasivelu, G. Harnessing marine natural products to inhibit PAD4 triple mutant: A structure-based virtual screening approach for rheumatoid arthritis therapy. Arch. Biochem. Biophys. 761, 110164 (2024).","journal-title":"Arch. Biochem. Biophys."},{"key":"24222_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/s12602-025-10608-0","author":"EA George","year":"2025","unstructured":"George, E. A. et al. Pharmacokinetics Screening, molecular Docking, and dynamics simulations revealed novel antimicrobial peptide NKLF2 mutants as potent inhibitors of Mycobacterium tuberculosis. Probiotics Antimicrob. Proteins. https:\/\/doi.org\/10.1007\/s12602-025-10608-0 (2025).","journal-title":"Probiotics Antimicrob. Proteins"},{"key":"24222_CR59","doi-asserted-by":"publisher","first-page":"107991","DOI":"10.1016\/j.compbiolchem.2023.107991","volume":"108","author":"M Jayaraman","year":"2024","unstructured":"Jayaraman, M., Gosu, V., Kumar, R. & Jeyaraman, J. Computational insights into potential marine natural products as selective inhibitors of Mycobacterium tuberculosis inha: A structure-based virtual screening study. Comput. Biol. Chem. 108, 107991 (2024).","journal-title":"Comput. Biol. Chem."},{"key":"24222_CR60","doi-asserted-by":"publisher","first-page":"144082","DOI":"10.1016\/j.gene.2019.144082","volume":"720","author":"M Jayaraman","year":"2019","unstructured":"Jayaraman, M., Rajendra, S. K. & Ramadas, K. Structural insight into conformational dynamics of non-active site mutations in kasa: A Mycobacterium tuberculosis target protein. Gene 720, 144082 (2019).","journal-title":"Gene"},{"key":"24222_CR61","doi-asserted-by":"publisher","first-page":"6281","DOI":"10.1021\/acs.jctc.1c00645","volume":"17","author":"MS Vald\u00e9s-Tresanco","year":"2021","unstructured":"Vald\u00e9s-Tresanco, M. S., Vald\u00e9s-Tresanco, M. E., Valiente, P. A. & Moreno, E. gmx_MMPBSA: A new tool to perform End-State free energy calculations with GROMACS. J. Chem. Theory Comput. 17, 6281\u20136291 (2021).","journal-title":"J. Chem. Theory Comput."},{"key":"24222_CR62","doi-asserted-by":"publisher","first-page":"28046","DOI":"10.1021\/acsomega.4c00929","volume":"9","author":"T Joshi","year":"2024","unstructured":"Joshi, T. et al. Identifying novel therapeutics for the resistant mutant F533L in PBP3 of Pseudomonas aeruginosa using ML techniques. ACS Omega. 9, 28046\u201328060 (2024).","journal-title":"ACS Omega"},{"key":"24222_CR63","doi-asserted-by":"publisher","first-page":"38448","DOI":"10.1021\/acsomega.2c03671","volume":"7","author":"V Singh","year":"2022","unstructured":"Singh, V. et al. Drug-Repurposing approach to combat Staphylococcus aureus: biomolecular and binding interaction study. ACS Omega. 7, 38448\u201338458 (2022).","journal-title":"ACS Omega"},{"key":"24222_CR64","doi-asserted-by":"publisher","first-page":"2409","DOI":"10.1021\/acs.jcim.2c00057","volume":"62","author":"V Dalal","year":"2022","unstructured":"Dalal, V., Golemi-Kotra, D. & Kumar, P. Quantum mechanics\/molecular mechanics studies on the catalytic mechanism of a novel esterase (FmtA) of Staphylococcus aureus. J. Chem. Inf. Model. 62, 2409\u20132420 (2022).","journal-title":"J. Chem. Inf. Model."},{"key":"24222_CR65","doi-asserted-by":"publisher","first-page":"3249","DOI":"10.1021\/ct1001413","volume":"6","author":"A Skliros","year":"2010","unstructured":"Skliros, A., Jernigan, R. L. & Kloczkowski, A. Models to approximate the motions of protein loops. J. Chem. Theory Comput. 6, 3249\u20133258 (2010).","journal-title":"J. Chem. Theory Comput."},{"key":"24222_CR66","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1074\/jbc.M805761200","volume":"284","author":"AJ Powell","year":"2009","unstructured":"Powell, A. J., Tomberg, J., Deacon, A. M., Nicholas, R. A. & Davies, C. Crystal structures of penicillin-binding protein 2 from penicillin-susceptible and -resistant strains of neisseria gonorrhoeae reveal an unexpectedly subtle mechanism for antibiotic resistance. J. Biol. Chem. 284, 1202\u20131212 (2009).","journal-title":"J. Biol. Chem."},{"key":"24222_CR67","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1128\/AAC.01107-08","volume":"53","author":"I Zerfa\u00df","year":"2009","unstructured":"Zerfa\u00df, I., Hakenbeck, R. & Denapaite, D. An important site in PBP2x of penicillin-resistant clinical isolates of Streptococcus pneumoniae: mutational analysis of Thr338. Antimicrob. Agents Chemother. 53, 1107\u20131115 (2009).","journal-title":"Antimicrob. Agents Chemother."},{"key":"24222_CR68","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1128\/AAC.05658-11","volume":"56","author":"SH Kim","year":"2012","unstructured":"Kim, S. H. et al. Changing trends in antimicrobial resistance and serotypes of Streptococcus pneumoniae isolates in Asian countries: An Asian network for surveillance of resistant pathogens (ANSORP) study. Antimicrob. Agents Chemother. 56, 1418\u20131426 (2012).","journal-title":"Antimicrob. Agents Chemother."},{"key":"24222_CR69","doi-asserted-by":"crossref","unstructured":"Varghese, R. et al. Emergence of meropenem resistance among cefotaxime Non-susceptible Streptococcus pneumoniae: Evidence and challenges. Front. Microbiol. 12, 810414 (2022).","DOI":"10.3389\/fmicb.2021.810414"},{"key":"24222_CR70","doi-asserted-by":"publisher","first-page":"3173","DOI":"10.3390\/plants13223173","volume":"13","author":"N Savickiene","year":"2024","unstructured":"Savickiene, N. & Raudone, L. Trends in plants phytochemistry and bioactivity analysis. Plants 13, 3173 (2024).","journal-title":"Plants"},{"key":"24222_CR71","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/s42452-025-06772-1","volume":"7","author":"S Ghosh","year":"2025","unstructured":"Ghosh, S. et al. Computational advancements to facilitate therapeutic application of phytochemicals: Where do we stand? Discov Appl. Sci. 7, 491 (2025).","journal-title":"Discov Appl. Sci."},{"key":"24222_CR72","doi-asserted-by":"publisher","first-page":"101511","DOI":"10.1016\/j.cegh.2024.101511","volume":"28","author":"S Basu","year":"2024","unstructured":"Basu, S., Veeraraghavan, B. & Anbarasu, A. Anti-bacterial compounds from Indian curry-leaf tree Murraya koenigii have potential to inhibit carbapenem-resistant Streptococcus pneumoniae. Clin. Epidemiol. Glob. Health 28, 101511 (2024).","journal-title":"Clin. Epidemiol. Glob. Health"},{"key":"24222_CR73","doi-asserted-by":"publisher","first-page":"562","DOI":"10.3390\/antibiotics9090562","volume":"9","author":"HA Alhadrami","year":"2020","unstructured":"Alhadrami, H. A. et al. Flavonoids as potential anti-MRSA agents through modulation of PBP2a: A computational and experimental study. Antibiotics 9, 562 (2020).","journal-title":"Antibiotics"},{"key":"24222_CR74","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.foodchem.2017.07.074","volume":"240","author":"C Araya-Cloutier","year":"2018","unstructured":"Araya-Cloutier, C., Vincken, J. P., van Ederen, R., den Besten, H. M. W. & Gruppen, H. Rapid membrane permeabilization of Listeria monocytogenes and Escherichia coli induced by antibacterial prenylated phenolic compounds from legumes. Food Chem. 240, 147\u2013155 (2018).","journal-title":"Food Chem."},{"key":"24222_CR75","doi-asserted-by":"crossref","unstructured":"Gawalska, A. et al. Application of automated machine learning in the identification of multi-target\u2010directed ligands blocking PDE4B, PDE8A, and TRPA1 with potential use in the treatment of asthma and COPD. Mol. Inform. 42, 2200214 (2023).","DOI":"10.1002\/minf.202200214"},{"key":"24222_CR76","doi-asserted-by":"publisher","first-page":"e1010613","DOI":"10.1371\/journal.pcbi.1010613","volume":"18","author":"ASMZ Rahman","year":"2022","unstructured":"Rahman, A. S. M. Z. et al. A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery. PLOS Comput. Biol. 18, e1010613 (2022).","journal-title":"PLOS Comput. Biol."},{"key":"24222_CR77","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.csbj.2025.01.006","volume":"27","author":"H Bilal","year":"2025","unstructured":"Bilal, H. et al. The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance. Comput. Struct. Biotechnol. J. 27, 423\u2013439 (2025).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"24222_CR78","doi-asserted-by":"publisher","first-page":"101812","DOI":"10.1016\/j.apmt.2023.101812","volume":"32","author":"N Usharani","year":"2023","unstructured":"Usharani, N. et al. Green synthesis and characterization of water soluble nanocarnosine: A prospective drug delivery system. Appl. Mater. Today. 32, 101812 (2023).","journal-title":"Appl. Mater. Today"},{"key":"24222_CR79","doi-asserted-by":"publisher","first-page":"21328","DOI":"10.1039\/D4RA04302B","volume":"14","author":"S Basu","year":"2024","unstructured":"Basu, S. et al. Cefiderocol susceptibility endows hope in treating carbapenem-resistant Pseudomonas aeruginosa: insights from in vitro and in Silico evidence. RSC Adv. 14, 21328\u201321341 (2024).","journal-title":"RSC Adv."},{"key":"24222_CR80","first-page":"38","volume":"2","author":"A Jenny","year":"2012","unstructured":"Jenny, A. et al. Antibacterial activity of aerial part of extract of Elephantopus scaber Linn. Bull. Pharm. Res. 2, 38\u201341 (2012).","journal-title":"Bull. Pharm. Res."},{"key":"24222_CR81","first-page":"6","volume":"23","author":"SS Kumar","year":"2004","unstructured":"Kumar, S. S., Perumal, P., Suresh, B., Antibacterial studies on leaf extract of Elephantopus scaber Linn. Anc. Sci. Life 23, 6\u20138 (2004).","journal-title":"Anc. Sci. Life"},{"key":"24222_CR82","unstructured":"Subhash, S. & Panda, P. K. Therapeutic effects of Elephantopus scaber Linn.: A review. J. Ayurveda Holist. Med. 11, 69\u201376 (2023)."},{"key":"24222_CR83","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1055\/s-2000-8607","volume":"66","author":"JC Meng","year":"2000","unstructured":"Meng, J. C., Zhu, Q. X. & Tan, R. X. New antimicrobial mono-and sesquiterpenes from Soroseris hookeriana Subsp. Erysimoides. Planta Med. 66, 541\u2013544 (2000).","journal-title":"Planta Med."},{"key":"24222_CR84","doi-asserted-by":"publisher","first-page":"106183","DOI":"10.1016\/j.bioorg.2022.106183","volume":"129","author":"QL Yan","year":"2022","unstructured":"Yan, Q. L. et al. Sesquiterpene lactones from Elephantopus scaber exhibit cytotoxic effects on glioma cells by targeting GSTP1. Bioorg. Chem. 129, 106183 (2022).","journal-title":"Bioorg. Chem."},{"key":"24222_CR85","doi-asserted-by":"publisher","first-page":"4977","DOI":"10.1021\/jm4004285","volume":"57","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov, A. et al. QSAR modeling: Where have you been? Where are you going to? J. Med. Chem. 57, 4977\u20135010 (2014).","journal-title":"J. Med. Chem."},{"key":"24222_CR86","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1016\/j.drudis.2020.03.003","volume":"25","author":"S D\u2019Souza","year":"2020","unstructured":"D\u2019Souza, S., Prema, K. V. & Balaji, S. Machine learning models for drug\u2013target interactions: Current knowledge and future directions. Drug Discov. Today. 25, 748\u2013756 (2020).","journal-title":"Drug Discov. Today"},{"key":"24222_CR87","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.proenv.2011.10.070","volume":"8","author":"HJ Luo","year":"2011","unstructured":"Luo, H. J., Wang, J. Z., Deng, W. Q. & Zou, K. DFT calculations and docking study on sesquiterpene lactones: Inhibition of aromatase. Procedia Environ. Sci. 8, 446\u2013450 (2011).","journal-title":"Procedia Environ. Sci."},{"key":"24222_CR88","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s00894-024-06124-7","volume":"30","author":"YS Liu","year":"2024","unstructured":"Liu, Y. S., Yuan, W. S., Liu, Q. J., Liu, F. S. & Liu, Z. T. Structural, mechanical, electronic, vibrational properties and hydrogen bonding of a novel energetic ionic 5, 5\u2032-dinitroamino-3, 3\u2032-azo-oxadiazole 4, 7-diaminopyridazino [4, 5-c] Furoxan salt. J. Mol. Model. 30, 321 (2024).","journal-title":"J. Mol. Model."},{"key":"24222_CR89","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1016\/j.jmb.2007.12.058","volume":"376","author":"P Maurer","year":"2008","unstructured":"Maurer, P. et al. Penicillin-binding protein 2x of Streptococcus pneumoniae: Three new mutational pathways for remodelling an essential enzyme into a resistance determinant. J. Mol. Biol. 376, 1403\u20131416 (2008).","journal-title":"J. Mol. Biol."},{"key":"24222_CR90","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.3390\/microorganisms9081685","volume":"9","author":"K Peters","year":"2021","unstructured":"Peters, K., Schweizer, I., Hakenbeck, R. & Denapaite, D. New insights into beta-lactam resistance of Streptococcus pneumoniae: Serine protease HtrA degrades altered penicillin-binding protein 2x. Microorganisms 9, 1685 (2021).","journal-title":"Microorganisms"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24222-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24222-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24222-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T14:03:26Z","timestamp":1762524206000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24222-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":90,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["24222"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-24222-1","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]},"assertion":[{"value":"2 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"39164"}}