Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis

Bitew, A., Mengist, A., Belew, H., Aschale, Y. & Reta, A. The prevalence, antibiotic resistance pattern, and associated factors of bacterial vaginosis among women of the reproductive age group from felege hiwot referral hospital, Ethiopia. Infect. Drug Resist. 5, 2685–2696 (2021).Article 

Google Scholar 
Schellenberg, J. J., Patterson, M. H. & Hill, J. E. Gardnerella vaginalis diversity and ecology in relation to vaginal symptoms. Res. Microbiol. 168, 837–844 (2017).Article 
CAS 
PubMed 

Google Scholar 
Qin, H. & Xiao, B. Research progress on the correlation between Gardnerella typing and bacterial vaginosis. Front. Cell. Infect. Microbiol. 12, 858155 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Ma, X. et al. Biofilm and pathogenic factor analysis of Gardnerella vaginalis associated with bacterial vaginosis in Northeast China. Front. Microbiol. 13, 1033040 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Bhujel, R., Mishra, S. K., Yadav, S. K., Bista, K. D. & Parajuli, K. Comparative study of Amsel’s criteria and Nugent scoring for diagnosis of bacterial vaginosis in a tertiary care hospital, Nepal. BMC Infect. Dis. 21, 1–6 (2021).Article 

Google Scholar 
Nenadić, D. B., Pavlović, M. D. & Motrenko, T. A novel microscopic method for analyzing Gram-stained vaginal smears in the diagnosis of disorders of vaginal microflora. Vojnosanit. Pregl. 72, 670–676 (2015).Article 
PubMed 

Google Scholar 
Numanović, F. et al. Importance of isolation and biotypization of Gardnerella vaginalis in diagnosis of bacterial vaginosis. Bosn. J. Basic Med. Sci. 8, 270 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Redelinghuys, M. J., Geldenhuys, J., Jung, H. & Kock, M. M. J. Bacterial vaginosis: Current diagnostic avenues and future opportunities. Front. Cell. Infect. Microbiol. 10, 354 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Janulaitiene, M. et al. Prevalence and distribution of Gardnerella vaginalis subgroups in women with and without bacterial vaginosis. BMC Infect. Dis. 17, 1–9 (2017).Article 

Google Scholar 
Pleckaityte, M. Cholesterol-dependent cytolysins produced by vaginal bacteria: Certainties and controversies. Front. Cell. Infect. Microbiol. 9, 452 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hardy, L. et al. The presence of the putative Gardnerella vaginalis sialidase A gene in vaginal specimens is associated with bacterial vaginosis biofilm. PLoS ONE 12, e0172522 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Abbe, C. & Mitchell, C. M. Bacterial vaginosis: A review of approaches to treatment and prevention. Front. Reprod. Health 5, 1100029 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Wu, S., Hugerth, L. W., Schuppe-Koistinen, I. & Du, J. The right bug in the right place: opportunities for bacterial vaginosis treatment. npj Biofilms Microbiomes 8, 34 (2022).Munoz-Barreno, A., Cabezas-Mera, F., Tejera, E. & Machado, A. Comparative effectiveness of treatments for bacterial vaginosis: A network meta-analysis. Antibiotics 10, 978 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Owens, D. K. et al. Screening for bacterial vaginosis in pregnant persons to prevent preterm delivery: US preventive services task force recommendation statement. Jama 323, 1286–1292 (2020).Article 
PubMed 

Google Scholar 
Bradshaw, C. S. & Sobel, J. D. Current treatment of bacterial vaginosis—Limitations and need for innovation. J. Infect. Dis. 214, S14–S20 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Alturki, N. A. et al. Therapeutic target identification and inhibitor screening against riboflavin synthase of colorectal cancer associated fusobacterium nucleatum. Cancers 14, 6260 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sinoliya, P., Solanki, P. S., Niraj, R. R. K. & Sharma, V. Computational study of antimicrobial peptides for promising therapeutic applications against methicillin-resistant Staphylococcus aureus. Curr. Comput. Aided Drug Des. https://doi.org/10.2174/0115734099285473240101111303 (2024).Article 
PubMed 

Google Scholar 
Sinoliya, P., Solanki, P. S., Piplani, S., Kumar Niraj, R. R. & Sharma, V. Anti-microbial peptides against methicillin-resistant Staphylococcus aureus: Promising therapeutics. Curr. Protein Pept. Sci. 24, 156–177. https://doi.org/10.2174/1389203724666221216115850 (2023).Article 
CAS 
PubMed 

Google Scholar 
Gupta, S. R. et al. Comparative proteome analysis of Mycobacterium tuberculosis strains-H37Ra, H37Rv, CCDC5180, and CAS/NITR204: A step forward to identify novel drug targets. Lett. Drug Des. Discov. 17, 1422–1431 (2020).Article 
CAS 

Google Scholar 
Zaidi, S., Bhardwaj, T., Somvanshi, P. & Khan, A. U. Proteomic characterization and target identification against Streptococcus mutans under bacitracin stress conditions using LC–MS and subtractive proteomics. Protein J. 41, 166–178 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Öztürk, H., Özgür, A. & Ozkirimli, E. DeepDTA: Deep drug-target binding affinity prediction. Bioinformatics 34, i821–i829 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Alzamami, A., Alturki, N. A., Khan, K., Basharat, Z. & Mashraqi, M. M. Screening inhibitors against the Ef-Tu of Fusobacterium nucleatum: A docking, ADMET and PBPK assessment study. Mol. Divers. 1–18 (2024).Korf, I., Yandell, M. & Bedell, J. Blast. (O’Reilly Media, Inc., 2003).Liu, S. et al. CEG 2.0: An updated database of clusters of essential genes including eukaryotic organisms. Database 2020, baaa112 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, R., Ou, H. Y. & Zhang, C. T. DEG: A database of essential genes. Nucleic Acids Res. 32, D271–D272 (2004).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Basharat, Z., Jahanzaib, M., Yasmin, A. & Khan, I. A. Pan-genomics, drug candidate mining and ADMET profiling of natural product inhibitors screened against Yersinia pseudotuberculosis. Genomics 113, 238–244 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wishart, D. S. et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).Article 
CAS 
PubMed 

Google Scholar 
Grasso, D., Galderisi, S., Santucci, A. & Bernini, A. Pharmacological chaperones and protein conformational diseases: Approaches of computational structural biology. Int. J. Mol. Sci. 24, 5819 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bull, S. C. & Doig, A. J. Properties of protein drug target classes. PloS one 10, e0117955 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Gasteiger, E. et al. Protein Identification and Analysis Tools on the ExPASy Server. (Humana Press, 2005).Hallgren, J. et al. DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. BioRxiv: 2022.2004.2008.487609 (2022).Saha, S. & Raghava, G. P. BTXpred: Prediction of bacterial toxins. In Silico Biol. 7, 405–412 (2007).CAS 
PubMed 

Google Scholar 
Sharma, N. et al. AlgPred 2.0: An improved method for predicting allergenic proteins and mapping of IgE epitopes. Brief. Bioinform. 22, 294 (2021).Article 

Google Scholar 
Kiefer, F., Arnold, K., Künzli, M., Bordoli, L. & Schwede, T. The SWISS-MODEL repository and associated resources. Nucleic Acids Res. 37, D387–D392 (2009).Article 
CAS 
PubMed 

Google Scholar 
Bæk, K. T. & Kepp, K. P. Assessment of AlphaFold2 for human proteins via residue solvent exposure. J. Chem. Inf. Model. 62, 3391–3400 (2022).Article 
PubMed 

Google Scholar 
Schwede, T., Kopp, J., Guex, N. & Peitsch, M. C. SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Res. 31, 3381–3385 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Perrakis, A. & Sixma, T. K. AI revolutions in biology: The joys and perils of AlphaFold. EMBO Rep. 22, e54046 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mirdita, M. et al. ColabFold: Making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Heo, L., Park, H. & Seok, C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res. 41, W384–W388 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Laskowski, R. A. et al. PDBsum: A web-based database of summaries and analyses of all PDB structures. Trends Biochem. Sci. 22, 488–490 (1997).Article 
CAS 
PubMed 

Google Scholar 
Dallakyan, S. & Olson, A. J. Small-molecule library screening by docking with PyRx. Chem. Biol. Methods Protoc. 243–250 (2015).Sireesha, R. et al. Unveiling the anticancer mechanism of 1, 2, 3-triazole-incorporated thiazole-pyrimidine-isoxazoles: Insights from docking and molecular dynamics simulations. J. Biomol. Struct. Dyn. 1–13 (2023).Laskowski, R. A. & Swindells, M. B. LigPlot+: Multiple ligand–protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 51, 2778–2786 (2011).Article 
CAS 
PubMed 

Google Scholar 
Daina, A., Michielin, O. & Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 7, 42717 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Pires, D. E., Blundell, T. L. & Ascher, D. B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem. 58, 4066–4072 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ertl, P. & Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 1, 1–11 (2009).Article 

Google Scholar 
Van Der Spoel, D. et al. GROMACS: Fast, flexible, and free. J. Comput. Chem. 26, 1701–1718 (2005).Article 
PubMed 

Google Scholar 
Huai, Z., Shen, Z. & Sun, Z. Binding thermodynamics and interaction patterns of inhibitor-major urinary protein-I binding from extensive free-energy calculations: Benchmarking AMBER force fields. J. Chem. Inf. Model. 61, 284–297 (2020).Article 
PubMed 

Google Scholar 
Eskandari, A., Leow, T. C., Rahman, M. B. A. & Oslan, S. N. Molecular dynamics-guided insight into the adsorption–inhibition mechanism for controlling ice growth/melt of antifreeze protein type IV mutant from longhorn sculpin fish. Chem. Pap. 1–18 (2024).Wang, E. et al. End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chem. Rev. 119, 9478–9508 (2019).Article 
CAS 
PubMed 

Google Scholar 
Yokoyama, R., Kleven, B., Gupta, A., Wang, Y. & Maeda, H. A. 3-Deoxy-D-arabino-heptulosonate 7-phosphate synthase as the gatekeeper of plant aromatic natural product biosynthesis. Curr. Opin. Plant Biol. 67, 102219 (2022).Article 
CAS 
PubMed 

Google Scholar 
Liu, S., Xu, J.-Z. & Zhang, W.-G. Advances and prospects in metabolic engineering of Escherichia coli for l-tryptophan production. World J. Microbiol. Biotechnol. 38, 22 (2022).Article 
CAS 
PubMed 

Google Scholar 
Singh, P. et al. Biofuel from Microbes and Plants. 189–209 (CRC Press, 2021).Almeida, A. M. et al. Revisiting the Shikimate Pathway and Highlighting Their Enzyme Inhibitors. 1–37 (2023).Castro, J. I. R. Adhesion of Vaginal Microorganisms to Epithelial Cells and Its Association with Bacterial Vaginosis. (Universidade do Minho (Portugal), 2012).Anton, L. et al. Gardnerella vaginalis alters cervicovaginal epithelial cell function through microbe-specific immune responses. Microbiome 10, 119 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Niu, A. Toward Transition State Analysis of DAHP Synthase (2020).Jeckelmann, J.-M. & Erni, B. Carbohydrate transport by group translocation: the bacterial phosphoenolpyruvate: Sugar phosphotransferase system. In Bacterial Cell Walls Membranes. 223–274 (2019).Agoni, C., Olotu, F. A., Ramharack, P. & Soliman, M. E. Druggability and drug-likeness concepts in drug design: Are biomodelling and predictive tools having their say?. J. Mol. Model. 26, 1–11 (2020).Article 

Google Scholar 
Amabebe, E. & Anumba, D. O. Mechanistic insights into immune suppression and evasion in bacterial vaginosis. Curr. Microbiol. 79, 84 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wong, Y. P. et al. Gardnerella vaginalis infection in pregnancy: Effects on placental development and neonatal outcomes. Placenta 120, 79–87 (2022).Article 
PubMed 

Google Scholar 
Daskalakis, G. et al. Maternal infection and preterm birth: from molecular basis to clinical implications. Children 10, 907 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Menard, J.-P. Antibacterial treatment of bacterial vaginosis: Current and emerging therapies. Int. J. Women’s Health 295–305 (2011).Owen, M. K. & Clenney, T. L. Management of vaginitis. Am. Fam. Phys. 70, 2125–2132 (2004).
Google Scholar 
Pentikis, H., Adetoro, N., Tipping, D. & Levy, S. An integrated efficacy and safety analysis of single-dose secnidazole 2 g in the treatment of bacterial vaginosis. Reprod. Sci. 27, 523–528 (2020).Article 
PubMed 

Google Scholar 
Al-Kraety, I. A. A., Al-Muhanna, S. G., Banoon, S. R. & Ghasemian, A. Bacterial vaginosis pattern and antibiotic susceptibility testing in female patients using high vaginal swabs. Biodivers. J. Biol. Divers. 23, 34 (2022).Article 

Google Scholar 
Qian, Z., Zhao, D., Yin, Y., Zhu, H. & Chen, D. Antibacterial activity of Lactobacillus strains isolated from Mongolian yogurt against Gardnerella vaginalis. BioMed Res. Int. 2020 (2020).Ahmad, S. S. & Ali, F. A. Detection of ESBL, AmpC and metallo beta-lactamase mediated resistance in Gram-negative bacteria isolated from women with genital tract infection. Eur. Sci. J. 10 (2014).Wang, S., Liu, D., Bilal, M., Wang, W. & Zhang, X. Uncovering the role of phzc as DAHP synthase in shikimate pathway of Pseudomonas chlororaphis HT66. Biology 11, 86 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tohge, T. & R Fernie, A. An overview of compounds derived from the shikimate and phenylpropanoid pathways and their medicinal importance. Mini Rev. Med. Chem. 17, 1013–1027 (2017).Almeida, A. M. et al. Revisiting the shikimate pathway and highlighting their enzyme inhibitors. Phytochem. Rev. 1–37 (2023).Dev, A., Tapas, S., Pratap, S. & Kumar, P. Structure and function of enzymes of shikimate pathway. Curr. Bioinform. 7, 374–391 (2012).Article 
CAS 

Google Scholar 
Shumilin, I. A., Bauerle, R., Wu, J., Woodard, R. W. & Kretsinger, R. H. Crystal structure of the reaction complex of 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase from Thermotoga maritima refines the catalytic mechanism and indicates a new mechanism of allosteric regulation. J. Mol. Biol. 341, 455–466 (2004).Article 
CAS 
PubMed 

Google Scholar 
Cui, D. et al. Molecular basis for feedback inhibition of tyrosine-regulated 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase from Escherichia coli. J. Struct. Biol. 206, 322–334 (2019).Article 
CAS 
PubMed 

Google Scholar 
Balachandran, N. et al. Potent inhibition of 3-deoxy-d-arabinoheptulosonate-7-phosphate (DAHP) synthase by DAHP oxime, a phosphate group mimic. Biochemistry 55, 6617–6629 (2016).Article 
CAS 
PubMed 

Google Scholar 
de Oliveira, M. D., Araujo, J. D. O., Galúcio, J. M., Santana, K. & Lima, A. H. Targeting shikimate pathway: In silico analysis of phosphoenolpyruvate derivatives as inhibitors of EPSP synthase and DAHP synthase. J. Mol. Graph. Model. 101, 107735 (2020).Article 
PubMed 

Google Scholar 
Stegemann, S., Leveiller, F., Franchi, D., De Jong, H. & Lindén, H. When poor solubility becomes an issue: From early stage to proof of concept. Eur. J. Pharmaceut. Sci. 31, 249–261 (2007).Article 
CAS 

Google Scholar 
Fang, Y. Ligand–receptor interaction platforms and their applications for drug discovery. Exp. Opin. Drug Discov. 7, 969–988 (2012).Article 
CAS 

Google Scholar 
He, J. et al. Binding properties of the natural red dye carthamin with human serum albumin: Surface plasmon resonance, isothermal titration microcalorimetry, and molecular docking analysis. Food Chem. 221, 650–656 (2017).Article 
CAS 
PubMed 

Google Scholar 
Kairys, V., Baranauskiene, L., Kazlauskiene, M., Matulis, D. & Kazlauskas, E. Binding affinity in drug design: Experimental and computational techniques. Expert Opin Drug Discov. 14, 755–768 (2019).Article 
CAS 
PubMed 

Google Scholar 
Taskar, K. S., Harada, I. & Alluri, R. V. Physiologically-based pharmacokinetic (PBPK) modelling of transporter mediated drug absorption, clearance and drug–drug interactions. Curr. Drug Metab. 22, 523–531 (2021).Article 
CAS 
PubMed 

Google Scholar 
Alrubia, S., Mao, J., Chen, Y., Barber, J. & Rostami-Hodjegan, A. Altered bioavailability and pharmacokinetics in Crohn’s disease: Capturing systems parameters for PBPK to assist with predicting the fate of orally administered drugs. Clin. Pharmacokinet. 61, 1365–1392 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hartmanshenn, C., Scherholz, M. & Androulakis, I. P. Physiologically-based pharmacokinetic models: Approaches for enabling personalized medicine. J. Pharmacokinet. Pharmacodyn. 43, 481–504 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rowland Yeo, K., Aarabi, M., Jamei, M. & Rostami-Hodjegan, A. Modeling and predicting drug pharmacokinetics in patients with renal impairment. Expert Rev. Clin. Pharmacol. 4, 261–274 (2011).Article 
PubMed 

Google Scholar