Skip to main content

Advertisement

SpringerLink
Log in

WGS-based screening of the co-chaperone protein DjlA-induced curved DNA binding protein A (CbpA) from a new multidrug-resistant zoonotic mastitis-causing Klebsiella pneumoniae strain: a novel molecular target of selective flavonoids

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

The research aimed to establish a multidrug-resistant Klebsiella pneumoniae-induced genetic model for mastitis considering the alternative mechanisms of the DjlA-mediated CbpA protein regulation. The Whole Genome Sequencing of the newly isolated K. pneumoniae strain was conducted to annotate the frequently occurring antibiotic resistance and virulence factors following PCR and MALDI-TOF mass-spectrophotometry. Co-chaperon DjlA was identified and extracted via restriction digestion on PAGE. Based on the molecular string property analysis of different DnaJ and DnaK type genes, CbpA was identified to be regulated most by the DjlA protein during mastitis. Based on the quantum tunnel-cluster profiles, CbpA was modeled as a novel target for diversified biosynthetic, and chemosynthetic compounds. Pharmacokinetic and pharmacodynamic analyses were conducted to determine the maximal point-specificity of selective flavonoids in complexing with the CbpA macromolecule at molecular docking. The molecular dynamic simulation (100 ns) of each of the flavonoid-protein complexes was studied regarding the parameters RMSD, RMSF, Rg, SASA, MMGBSA, and intramolecular hydrogen bonds; where all of them resulted significantly. To ratify all the molecular dynamic simulation outputs, the potential stability of the flavonoids in complexing with CbpA can be remarked as Quercetin > Biochanin A > Kaempherol > Myricetin, which were all significant in comparison to the control Galangin. Finally, a comprehensive drug-gene interaction pathway for each of the flavonoids was developed to determine the simultaneous and quantitative-synergistic effects of different operons belonging to the DnaJ-type proteins on the metabolism of the tested pharmacophores in CbpA. Considering all the in vitro and in silico parameters, DjlA-mediated CbpA can be a novel target for the tested flavonoids as the potential therapeutics of mastitis as futuristic drugs.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

All necessary data are properly conserved by the corresponding author, which will be shared upon reasonable request with the journal authority.

Abbreviations

CFP:

Cefoperazone

LZ:

Linezolid

PNG:

Penicillin G

CFT:

Ceftaroline fosamil

DC:

Doxycycline

SMA-TMP:

Sulfamethoxazole and trimethoprim

TC:

Tetracycline

CFS:

Cefatrizine

CZD:

Ceftazidime

CTA:

Cefotaxime

IPN:

Imipenem

MTC:

Methicillin

GGI:

Gene–gene interaction

PPI:

Protein–protein interaction

WGS:

Whole genome sequencing

ESBL:

Extended-spectrum beta-lactamase

RMSD:

Root mean square deviation

RMSF:

Root mean square fluctuation

Rg:

Radius of gyration

SASA:

Solvent-accessible surface area

MolSA:

Molecular surface area

MDS:

Molecular dynamic simulation

CbpA:

Curved DNA binding protein A

DGI:

Drug-gene interaction

DPI:

Drug-protein interaction

References

  1. Russo TA, Olson R, Fang C-T et al (2018) Identification of biomarkers for differentiation of hypervirulent Klebsiella pneumoniae from classical K. pneumoniae. J Clin Microbiol 56:e00776-e818. https://doi.org/10.1128/JCM.00776-18

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Piperaki E-T, Syrogiannopoulos GA, Tzouvelekis LS, Daikos GL (2017) Klebsiella pneumoniae: virulence, biofilm and antimicrobial resistance. Pediatr Infect Dis J 36:1002. https://doi.org/10.1097/INF.0000000000001675

    Article  PubMed  Google Scholar 

  3. Paczosa MK, Mecsas J (2016) Klebsiella pneumoniae: going on the offense with a strong defense. Microbiol Mol Biol Rev MMBR 80:629–661. https://doi.org/10.1128/MMBR.00078-15

    Article  CAS  PubMed  Google Scholar 

  4. Gonzalez-Ferrer S, Peñaloza HF, Budnick JA et al (2021) Finding order in the chaos: outstanding questions in Klebsiella pneumoniae pathogenesis. Infect Immun 89:e00693-e720. https://doi.org/10.1128/IAI.00693-20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hu Y, Anes J, Devineau S, Fanning S (2021) Klebsiella pneumoniae: prevalence, reservoirs, antimicrobial resistance, pathogenicity, and infection: a hitherto unrecognized zoonotic bacterium. Foodborne Pathog Dis 18:63–84. https://doi.org/10.1089/fpd.2020.2847

    Article  PubMed  Google Scholar 

  6. Mohd Asri NA, Ahmad S, Mohamud R et al (2021) Global prevalence of nosocomial multidrug-resistant Klebsiella pneumoniae: a systematic review and meta-analysis. Antibiotics 10:1508. https://doi.org/10.3390/antibiotics10121508

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. David S, Reuter S, Harris SR et al (2019) Epidemic of carbapenem-resistant Klebsiella pneumoniae in Europe is driven by nosocomial spread. Nat Microbiol 4:1919. https://doi.org/10.1038/s41564-019-0492-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rastegar S, Moradi M, Kalantar-Neyestanaki D et al (2019) Virulence factors, capsular serotypes and antimicrobial resistance of hypervirulent Klebsiella pneumoniae and classical Klebsiella pneumoniae in Southeast Iran. Infect Chemother. https://doi.org/10.3947/ic.2019.0027

    Article  PubMed  Google Scholar 

  9. Holden VI, Breen P, Houle S et al (2016) Klebsiella pneumoniae siderophores induce inflammation, bacterial dissemination, and HIF-1α stabilization during pneumonia. MBio 7:e01397. https://doi.org/10.1128/mBio.01397-16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Navon-Venezia S, Kondratyeva K, Carattoli A (2017) Klebsiella pneumoniae: a major worldwide source and shuttle for antibiotic resistance. FEMS Microbiol Rev 41:252–275. https://doi.org/10.1093/femsre/fux013

    Article  CAS  PubMed  Google Scholar 

  11. Dong N, Yang X, Zhang R et al (2018) Tracking microevolution events among ST11 carbapenemase-producing hypervirulent Klebsiella pneumoniae outbreak strains. Emerg Microbes Infect 7:1–8. https://doi.org/10.1038/s41426-018-0146-6

    Article  CAS  Google Scholar 

  12. Tang M, Kong X, Hao J, Liu J (2020) Epidemiological characteristics and formation mechanisms of multidrug-resistant hypervirulent Klebsiella pneumoniae. Front Microbiol. https://doi.org/10.3389/fmicb.2020.581543

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lee C-R, Lee JH, Park KS et al (2017) Antimicrobial resistance of hypervirulent Klebsiella pneumoniae: epidemiology, hypervirulence-associated determinants, and resistance mechanisms. Front Cell Infect Microbiol. https://doi.org/10.3389/fcimb.2017.00483

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hua Y, Wang J, Huang M et al (2022) Outer membrane vesicles-transmitted virulence genes mediate the emergence of new antimicrobial-resistant hypervirulent Klebsiella pneumoniae. Emerg Microbes Infect 11:1281–1292. https://doi.org/10.1080/22221751.2022.2065935

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hou M, Chen N, Dong L et al (2022) Molecular epidemiology, clinical characteristics and risk factors for bloodstream infection of multidrug-resistant Klebsiella pneumoniae infections in pediatric patients from Tianjin, China. Infect Drug Resist 15:7015–7023. https://doi.org/10.2147/IDR.S389279

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gato E, Vázquez-Ucha JC, Rumbo-Feal S et al (2020) Kpi, a chaperone-usher pili system associated with the worldwide-disseminated high-risk clone Klebsiella pneumoniae ST-15. Proc Natl Acad Sci USA 117:17249–17259. https://doi.org/10.1073/pnas.1921393117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Moo C-L, Osman MA, Yang S-K et al (2021) Antimicrobial activity and mode of action of 1,8-cineol against carbapenemase-producing Klebsiella pneumoniae. Sci Rep 11:20824. https://doi.org/10.1038/s41598-021-00249-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Pranavathiyani G, Prava J, Rajeev AC, Pan A (2020) Novel target exploration from hypothetical proteins of Klebsiella pneumoniae MGH 78578 reveals a protein involved in host-pathogen interaction. Front Cell Infect Microbiol. https://doi.org/10.3389/fcimb.2020.00109

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wickner S, Camberg JL, Doyle SM, Johnston DM (2017) Molecular chaperones. Reference module in life sciences. Elsevier, Amsterdam

    Google Scholar 

  20. Benedetti F, Cocchi F, Latinovic OS et al (2020) Role of mycoplasma chaperone DnaK in cellular transformation. Int J Mol Sci 21:1311. https://doi.org/10.3390/ijms21041311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mayer MP (2021) The Hsp70-chaperone machines in bacteria. Front Mol Biosci. https://doi.org/10.3389/fmolb.2021.694012

    Article  PubMed  PubMed Central  Google Scholar 

  22. Moses MA, Zuehlke AD, Neckers L (2018) Molecular chaperone inhibitors. In: Binder RJ, Srivastava PK (eds) Heat Shock proteins in the immune system. Springer, Cham, pp 21–40

    Chapter  Google Scholar 

  23. Chengolova Z, Ivanov Y, Grigorova G (2021) The relationship of bovine milk somatic cell count to neutrophil level in samples of cow’s milk assessed by an automatic cell counter. J Dairy Res 88:330–333. https://doi.org/10.1017/S0022029921000534

    Article  CAS  PubMed  Google Scholar 

  24. Alhussien MN, Dang AK (2018) Impact of different seasons on the milk somatic and differential cell counts, milk cortisol and neutrophils functionality of three Indian native breeds of cattle. J Therm Biol 78:27–35. https://doi.org/10.1016/j.jtherbio.2018.08.020

    Article  CAS  PubMed  Google Scholar 

  25. Al Azad S, Moazzem Hossain K, Rahman SMM et al (2020) In ovo inoculation of duck embryos with different strains of Bacillus cereus to analyse their synergistic post-hatch anti-allergic potentialities. Vet Med Sci 6:992–999. https://doi.org/10.1002/vms3.279

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Azad SA, Farjana M, Mazumder B et al (2019) Molecular identification of a Bacillus cereus strain from Murrah buffalo milk showed in vitro bioremediation properties on selective heavy metals. J Adv Vet Anim Res 7:62–68. https://doi.org/10.5455/javar.2020.g394

    Article  PubMed  PubMed Central  Google Scholar 

  27. Lou W, Venkataraman S, Zhong G et al (2018) Antimicrobial polymers as therapeutics for treatment of multidrug-resistant Klebsiella pneumoniae lung infection. Acta Biomater 78:78–88. https://doi.org/10.1016/j.actbio.2018.07.038

    Article  CAS  PubMed  Google Scholar 

  28. Saleem M, Syed Khaja AS, Hossain A et al (2022) Molecular characterization and antibiogram of acinetobacter baumannii clinical isolates recovered from the patients with ventilator-associated pneumonia. Healthcare 10:2210. https://doi.org/10.3390/healthcare10112210

    Article  PubMed  PubMed Central  Google Scholar 

  29. Nonnemann B, Lyhs U, Svennesen L et al (2019) Bovine mastitis bacteria resolved by MALDI-TOF mass spectrometry. J Dairy Sci 102:2515–2524. https://doi.org/10.3168/jds.2018-15424

    Article  CAS  PubMed  Google Scholar 

  30. Islam S, Farjana M, Uddin MR et al (2022) Molecular identification, characterization, and antagonistic activity profiling of Bacillus cereus LOCK 1002 along with the in-silico analysis of its presumptive bacteriocins. J Adv Vet Anim Res 9:663–675. https://doi.org/10.5455/javar.2022.i635

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wick RR, Judd LM, Gorrie CL, Holt KE (2017) Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLOS Comput Biol 13:e1005595. https://doi.org/10.1371/journal.pcbi.1005595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Alcock BP, Huynh W, Chalil R et al (2023) CARD 2023: expanded curation, support for machine learning, and resistome prediction at the comprehensive antibiotic resistance database. Nucleic Acids Res 51:D690–D699. https://doi.org/10.1093/nar/gkac920

    Article  CAS  PubMed  Google Scholar 

  33. Chen C-Y, Clark CG, Langner S et al (2020) Detection of antimicrobial resistance using proteomics and the comprehensive antibiotic resistance database: a case study. Proteom Clin Appl 14:e1800182. https://doi.org/10.1002/prca.201800182

    Article  CAS  Google Scholar 

  34. Morshed AKMH, Al Azad S, Mia MdAR et al (2022) Oncoinformatic screening of the gene clusters involved in the HER2-positive breast cancer formation along with the in silico pharmacodynamic profiling of selective long-chain omega-3 fatty acids as the metastatic antagonists. Mol Divers. https://doi.org/10.1007/s11030-022-10573-8

    Article  PubMed  PubMed Central  Google Scholar 

  35. Huang JK, Carlin DE, Yu MK et al (2018) Systematic evaluation of molecular networks for discovery of disease genes. Cell Syst 6:484-495.e5. https://doi.org/10.1016/j.cels.2018.03.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Jabin A, Uddin MF, Al Azad S et al (2023) Target-specificity of different amyrin subunits in impeding HCV influx mechanism inside the human cells considering the quantum tunnel profiles and molecular strings of the CD81 receptor: a combined in silico and in vivo study. Silico Pharmacol 11:8. https://doi.org/10.1007/s40203-023-00144-6

    Article  Google Scholar 

  37. Sharif MA, Hossen MS, Shaikat MM, et al (2021) Molecular optimization, docking and dynamic simulation study of selective natural aromatic components to block E2-CD81 complex formation in predating protease inhibitor resistant HCV influx. Int J Pharm Res. https://doi.org/10.31838/ijpr/2021.13.02.408

    Article  Google Scholar 

  38. Chen L, Zheng D, Liu B et al (2016) VFDB 2016: hierarchical and refined dataset for big data analysis–10 years on. Nucleic Acids Res 44:D694-697. https://doi.org/10.1093/nar/gkv1239

    Article  CAS  PubMed  Google Scholar 

  39. Liu B, Zheng D, Zhou S et al (2021) VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res 50:D912–D917. https://doi.org/10.1093/nar/gkab1107

    Article  CAS  PubMed Central  Google Scholar 

  40. Azad S, Ahmed S, Biswas P et al (2022) Quantitative analysis of the factors influencing IDA and TSH downregulation in correlation to the fluctuation of activated vitamin D3 in women. J Adv Biotechnol Exp Ther 5:320. https://doi.org/10.5455/jabet.2022.d118

    Article  Google Scholar 

  41. Lemoine F, Correia D, Lefort V et al (2019) NGPhylogeny.fr: new generation phylogenetic services for non-specialists. Nucleic Acids Res 47:W260–W265. https://doi.org/10.1093/nar/gkz303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gialama D, Delivoria DC, Michou M et al (2017) Functional requirements for DjlA- and RraA-mediated enhancement of recombinant membrane protein production in the engineered Escherichia coli strains SuptoxD and SuptoxR. J Mol Biol 429:1800–1816. https://doi.org/10.1016/j.jmb.2017.05.003

    Article  CAS  PubMed  Google Scholar 

  43. Dey D, Paul PK, Al Azad S et al (2021) Molecular optimization, docking, and dynamic simulation profiling of selective aromatic phytochemical ligands in blocking the SARS-CoV-2 S protein attachment to ACE2 receptor: an in silico approach of targeted drug designing. J Adv Vet Anim Res 8:24–35. https://doi.org/10.5455/javar.2021.h481

    Article  PubMed  PubMed Central  Google Scholar 

  44. Arefin A, Ismail Ema T, Islam T et al (2021) Target specificity of selective bioactive compounds in blocking α-dystroglycan receptor to suppress Lassa virus infection: an in silico approach. J Biomed Res 35:459–473. https://doi.org/10.7555/JBR.35.20210111

    Article  PubMed  PubMed Central  Google Scholar 

  45. Ferdausi N, Islam S, Rimti FH et al (2022) Point-specific interactions of isovitexin with the neighboring amino acid residues of the hACE2 receptor as a targeted therapeutic agent in suppressing the SARS-CoV-2 influx mechanism. J Adv Vet Anim Res 9:230–240. https://doi.org/10.5455/javar.2022.i588

    Article  PubMed  PubMed Central  Google Scholar 

  46. Nipun TS, Ema TI, Mia MdAR et al (2021) Active site-specific quantum tunneling of hACE2 receptor to assess its complexing poses with selective bioactive compounds in co-suppressing SARS-CoV-2 influx and subsequent cardiac injury. J Adv Vet Anim Res 8:540–556. https://doi.org/10.5455/javar.2021.h544

    Article  PubMed  PubMed Central  Google Scholar 

  47. Paul PK, Al Azad S, Rahman MH et al (2022) Catabolic profiling of selective enzymes in the saccharification of non-food lignocellulose parts of biomass into functional edible sugars and bioenergy: an in silico bioprospecting. J Adv Vet Anim Res 9:19–32. https://doi.org/10.5455/javar.2022.i565

    Article  PubMed  PubMed Central  Google Scholar 

  48. Akter KM, Tushi T, Jahan Mily S et al (2020) RT-PCR mediated identification of SARS-CoV-2 patients from particular regions of Bangladesh and the multi-factorial analysis considering their pre and post infection health conditions. Biotechnol J Int 24:43–56. https://doi.org/10.9734/bji/2020/v24i630121

    Article  CAS  Google Scholar 

  49. Hossain A, Proma TS, Raju R et al (2022) Employment-related musculoskeletal complications experienced by the physical therapists in Bangladesh: a comprehensive cross-sectional case study. Bull Fac Phys Ther 27:36. https://doi.org/10.1186/s43161-022-00096-6

    Article  Google Scholar 

  50. Islam R, Akter KM, Rahman A et al (2021) The serological basis of the correlation between iron deficiency anemia and thyroid disorders in women: a community based study. J Pharm Res Int 33:69–81. https://doi.org/10.9734/jpri/2021/v33i19A31330

    Article  Google Scholar 

  51. Mohammad Rashaduzzaman M, Mohammad Kamrujjaman M, Mohammad Ariful Islam MA et al (2019) An experimental analysis of different point specific musculoskeletal pain among selected adolescent-club cricketers in Dhaka City. Eur J Clin Exp Med. https://doi.org/10.15584/ejcem.2019.4.4

    Article  Google Scholar 

  52. Akther T, Rony MKK, Anowar A et al (2021) Comparative analysis of the government investments and revenue from different sectors in Bangladesh and its impact on the development of HRM sectors: a 20 years of study. Int J Bus Manag Soc Res. https://doi.org/10.18801/ijbmsr.100120.58

    Article  Google Scholar 

  53. Paul PK, Swadhin HR, Tushi T et al (2022) The Pros and cons of selective renewable energy technologies for generating electricity in the perspective of Bangladesh: A survey-based profiling of issues. Eur J Energy Res 2:1–8. https://doi.org/10.24018/ejenergy.2022.2.2.33

    Article  Google Scholar 

  54. He M, Li H, Zhang Z et al (2022) Microbiological characteristics and pathogenesis of Klebsiella pneumoniae isolated from hainan black goat. Vet Sci 9:471. https://doi.org/10.3390/vetsci9090471

    Article  PubMed  PubMed Central  Google Scholar 

  55. Ackers L, Ackers-Johnson G, Welsh J et al (2020) The role of microbiology testing in controlling infection and promoting antimicrobial stewardship. In: Ackers L, Ackers-Johnson G, Welsh J et al (eds) Anti-microbial resistance in global perspective. Springer, Cham, pp 81–102

    Chapter  Google Scholar 

  56. Chakraborty S, Mohsina K, Sarker PK et al (2016) Prevalence, antibiotic susceptibility profiles and ESBL production in Klebsiella pneumoniae and Klebsiella oxytoca among hospitalized patients. Period Biol. https://doi.org/10.18054/pb.v118i1.3160

    Article  Google Scholar 

  57. Tascini C, Sozio E, Viaggi B, Meini S (2016) Reading and understanding an antibiogram. Ital J Med 10:289–300. https://doi.org/10.4081/itjm.2016.794

    Article  Google Scholar 

  58. Liu L, Li F, Xu L et al (2020) Cyclic AMP-CRP modulates the cell morphology of Klebsiella pneumoniae in high-glucose environment. Front Microbiol. https://doi.org/10.3389/fmicb.2019.02984

    Article  PubMed  PubMed Central  Google Scholar 

  59. Sugimoto S, Yamanaka K, Niwa T et al (2021) Hierarchical model for the role of J-domain proteins in distinct cellular functions. J Mol Biol 433:166750. https://doi.org/10.1016/j.jmb.2020.166750

    Article  CAS  PubMed  Google Scholar 

  60. Fay A, Philip J, Saha P et al (2021) The DnaK chaperone system buffers the fitness cost of antibiotic resistance mutations in mycobacteria. MBio 12:e00123. https://doi.org/10.1128/mBio.00123-21

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Min Y, Xu W, Xiao Y et al (2021) Biomineralization improves the stability of a Streptococcus pneumoniae protein vaccine at high temperatures. Nanomed 16:1747–1761. https://doi.org/10.2217/nnm-2021-0023

    Article  CAS  Google Scholar 

  62. Chae C, Sharma S, Hoskins JR, Wickner S (2004) CbpA, a DnaJ homolog, is a DnaK co-chaperone, and its activity is modulated by CbpM*♦. J Biol Chem 279:33147–33153. https://doi.org/10.1074/jbc.M404862200

    Article  CAS  PubMed  Google Scholar 

  63. Rezanejad M, Karimi S, Momtaz H (2019) Phenotypic and molecular characterization of antimicrobial resistance in Trueperella pyogenes strains isolated from bovine mastitis and metritis. BMC Microbiol 19:305. https://doi.org/10.1186/s12866-019-1630-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zastempowska E, Lassa H (2012) Genotypic characterization and evaluation of an antibiotic resistance of Trueperella pyogenes (Arcanobacterium pyogenes) isolated from milk of dairy cows with clinical mastitis. Vet Microbiol 161:153–158. https://doi.org/10.1016/j.vetmic.2012.07.018

    Article  CAS  PubMed  Google Scholar 

  65. Dang Y, Lin G, Xie Y et al (2014) Quantitative determination of myricetin in rat plasma by ultra performance liquid chromatography tandem mass spectrometry and its absolute bioavailability. Drug Res 64:516–522. https://doi.org/10.1055/s-0033-1363220

    Article  CAS  Google Scholar 

  66. Fatima S, Gupta P, Sharma S et al (2020) ADMET profiling of geographically diverse phytochemical using chemoinformatic tools. Future Med Chem 12:69–87. https://doi.org/10.4155/fmc-2019-0206

    Article  CAS  PubMed  Google Scholar 

  67. Parikesit AA, Nurdiansyah R (2021) Natural products repurposing of the H5N1-based lead compounds for the most fit inhibitors against 3C-like protease of SARS-CoV-2. J Pharm Pharmacogn Res 9:730–745

    Article  CAS  Google Scholar 

  68. Khelfaoui H, Harkati D, Saleh BA (2020) Molecular docking, molecular dynamics simulations and reactivity, studies on approved drugs library targeting ACE2 and SARS-CoV-2 binding with ACE2. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2020.1803967

    Article  PubMed  PubMed Central  Google Scholar 

  69. Sepunaru L, Refaely-Abramson S, Lovrinčić R et al (2015) Electronic transport via homopeptides: the role of side chains and secondary structure. J Am Chem Soc 137:9617–9626. https://doi.org/10.1021/jacs.5b03933

    Article  CAS  PubMed  Google Scholar 

  70. Ostermann AI, Koch E, Rund KM et al (2020) Targeting esterified oxylipins by LC–MS—effect of sample preparation on oxylipin pattern. Prostaglandins Other Lipid Mediat 146:106384. https://doi.org/10.1016/j.prostaglandins.2019.106384

    Article  CAS  PubMed  Google Scholar 

  71. Chen D, Oezguen N, Urvil P et al (2016) Regulation of protein-ligand binding affinity by hydrogen bond pairing. Sci Adv 2:e1501240. https://doi.org/10.1126/sciadv.1501240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Li M, Li D, Tang Y et al (2017) CytoCluster: a cytoscape plugin for cluster analysis and visualization of biological networks. Int J Mol Sci 18:1880. https://doi.org/10.3390/ijms18091880

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Naha A, Banerjee S, Debroy R et al (2022) Network metrics, structural dynamics and density functional theory calculations identified a novel ursodeoxycholic acid derivative against therapeutic target Parkin for Parkinson’s disease. Comput Struct Biotechnol J 20:4271–4287. https://doi.org/10.1016/j.csbj.2022.08.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Malik FK, Guo J (2022) Insights into protein–DNA interactions from hydrogen bond energy-based comparative protein–ligand analyses. Proteins 90:1303–1314. https://doi.org/10.1002/prot.26313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Semwal DK, Semwal RB, Combrinck S, Viljoen A (2016) Myricetin: A dietary molecule with diverse biological activities. Nutrients 8:90. https://doi.org/10.3390/nu8020090

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Mishra A, Ranganathan S, Jayaram B, Sattar A (2018) Role of solvent accessibility for aggregation-prone patches in protein folding. Sci Rep 8:12896. https://doi.org/10.1038/s41598-018-31289-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Jiang M, Zhu M, Wang L, Yu S (2019) Anti-tumor effects and associated molecular mechanisms of myricetin. Biomed Pharmacother 120:109506. https://doi.org/10.1016/j.biopha.2019.109506

    Article  CAS  PubMed  Google Scholar 

  78. Bhargava P, Mahanta D, Kaul A et al (2021) Experimental evidence for therapeutic potentials of propolis. Nutrients 13:2528. https://doi.org/10.3390/nu13082528

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Bennett S, Fliss I, Ben Said L et al (2022) Efficacy of bacteriocin-based formula for reducing staphylococci, streptococci, and total bacterial counts on teat skin of dairy cows. J Dairy Sci 105:4498–4507. https://doi.org/10.3168/jds.2021-21381

    Article  CAS  PubMed  Google Scholar 

  80. Dey D, Ema T, Biswas P et al (2021) Antiviral effects of bacteriocin against animal-to-human transmittable mutated SARS-COV-2: a systematic review. Front Agric Sci Eng. https://doi.org/10.15302/J-FASE-2021397

    Article  Google Scholar 

  81. Al-Mamun M, Hasan M, Azad S et al (2016) Evaluation of potential probiotic characteristics of isolated lactic acid bacteria from goat milk. Br Biotechnol J 14:1–7. https://doi.org/10.9734/BBJ/2016/26397

    Article  Google Scholar 

  82. Azad SA, Mamun MAA, Mondal KJ et al (2016) Range of various fungal infections to local and hybrid varieties of non-germinated lentil seed in Bangladesh. J Biosci Agric Res 9:775–781. https://doi.org/10.18801/jbar.090116.93

    Article  Google Scholar 

  83. Azad SA, Shahriyar S, Mondal KJ (2016) Opsonin and its mechanism of action in secondary immune response. J Mol Stud Med Res 1:48–56. https://doi.org/10.18801/jmsmr.010216.06

    Article  Google Scholar 

  84. Azad SA, Khan I, Salauddin AA, Khan I (2019) HAMLET (human alpha-lactalbumin made lethal to tumor cells)—a hope for the cancer patients. Adv Pharmacol Clin Trials 4(1):000152

    Google Scholar 

  85. Biswas P, Dey D, Biswas PK et al (2022) A comprehensive analysis and anti-cancer activities of quercetin in ROS-mediated cancer and cancer stem cells. Int J Mol Sci 23:11746. https://doi.org/10.3390/ijms231911746

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors are grateful to BAS-USDA Endowment program; Bangladesh Agricultural University, Mymensingh-2202; Department of Livestock Services (DLS), GoB; QC Lab, DLS, Dhaka; and National Institute of Biotechnology, Savar, Dhaka for their unconditional supports to smooth and successful completion of the research.

Funding

The research is fully funded by the Bangladesh Academy of Science (BAS) and the United States Department of Agriculture (USDA) Endowment Program under the Grant ID: BAS-USDA LS-26/2020 (4th phase). The in silico part of this project is fully sponsored by the RPG Interface Lab (Registration No. 05-060-06021), under the Grant ID: Category-E4-GRP-2021/22 (Phase-2).

Author information

Authors and Affiliations

  1. Molecular Microbiology and Vaccinology Lab, Department of Microbiology and Hygiene, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh

    Mohammad Habibur Rahman, Mohammad Ferdousur Rahman Khan & Md. Bahanur Rahman

  2. Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, Jiangsu, People’s Republic of China

    Salauddin Al Azad

  3. College of Material Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, People’s Republic of China

    Mohammad Fahim Uddin

  4. School of Medicine, Dentistry and Nursing, University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK

    Maisha Farzana

  5. Department of Mathematics & Natural Sciences, School of Data Sciences, BRAC University, Dhaka, 1212, Bangladesh

    Iffat Ara Sharmeen

  6. Department of Biochemistry and Microbiology, North South University, Dhaka, 1229, Bangladesh

    Kaifi Sultana Kabbo, Anika Jabin, Ashfaque Rahman & Samiur Rahman

  7. Department of Pharmacy, University of Asia Pacific, Farmgate, Dhaka, 1205, Bangladesh

    Farhan Jamil

  8. School of Pharmacy, BRAC University, 66 Mohakhali, Dhaka, 1212, Bangladesh

    Sanjida Ahmed Srishti & Fahmida Haque Riya

  9. Department of Medicine, Comilla Medical College, Kuchaitoli, Comilla, 3500, Bangladesh

    Towhid Khan

  10. School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, TS1 3BX, UK

    Rasel Ahmed

  11. Department of Mathematics, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh

    Nurunnahar

  12. Immunoinformatics and Vaccinomics Research Unit, RPG Interface Lab, Jashore, 7400, Bangladesh

    Salauddin Al Azad, Mohammad Fahim Uddin, Anika Jabin, Ashfaque Rahman & Samiur Rahman

Authors
  1. Mohammad Habibur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salauddin Al Azad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Fahim Uddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maisha Farzana
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Iffat Ara Sharmeen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kaifi Sultana Kabbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Anika Jabin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ashfaque Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Farhan Jamil
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sanjida Ahmed Srishti
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Fahmida Haque Riya
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Towhid Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Rasel Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Nurunnahar
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Samiur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Mohammad Ferdousur Rahman Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Md. Bahanur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MHR, SAA made the conceptualization, methodology, and formal analysis. MHR, SAA, MFU, MF, IAS, and KSK prepared the original draft. SAA, AJ, AR, FJ, SAS, FHR, TK conducted the in silico data validation, data analysis, and visualization. SAA, MHR, FHR, and TK conducted the wet-lab works including WGS, PCR, PAGE, MALDI-TOF MS. SAA, MHR, FHR, TK accomplished the in silico data curation, and visualization. SAA, RA, N, SR studied the complicated structural modeling and statistical analysis. SAA, RA, N developed the Infection pathway modeling. MHR, SAA, MFU, AJ checked the literature review, and manuscript editing. MFRK, MBR managed the administrative procedures and ethical clearance. MBR, MFRK, managed the funds for wet-lab activities, while SAA funded the in silico parts. MBR was the Supervisor of the project and playing roles as a corresponding author.

Corresponding author

Correspondence to Md. Bahanur Rahman.

Ethics declarations

Competing interest

The authors have no conflict of interest at all with the others.

Ethical approval

The ethical approval is authorized by AWEC, Bangladesh Agricultural University, Mymesningh regarding the project titled—“Polyvalent Vaccine Development to Prevent Mastitis in Dairy Cow” with the Grant ID: BAS-USDA LS-26/2020 (4th phase).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahman, M.H., Al Azad, S., Uddin, M.F. et al. WGS-based screening of the co-chaperone protein DjlA-induced curved DNA binding protein A (CbpA) from a new multidrug-resistant zoonotic mastitis-causing Klebsiella pneumoniae strain: a novel molecular target of selective flavonoids. Mol Divers (2023). https://doi.org/10.1007/s11030-023-10731-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11030-023-10731-6

Keywords

Search

Navigation