OpenLB
Open Library of Bioscience
Advanced Search
Archives of microbiology
Sep 25, 2024;206 (10): 415.

Screening of promising molecules against potential drug targets in Yersinia pestis by integrative pan and subtractive genomics, docking and simulation approach.

Lei Chen, Lihu Zhang, Yanping Li, Liang Qiao, Suresh Kumar
Author Information
  1. Lei Chen: Jiangsu Vocational College of Medicine, Yancheng, China.
  2. Lihu Zhang: Jiangsu Vocational College of Medicine, Yancheng, China.
  3. Yanping Li: Jiangsu Vocational College of Medicine, Yancheng, China.
  4. Liang Qiao: School of Environmental Science and Engineering, Yancheng Institute of Technology, Yancheng, China.
  5. Suresh Kumar: Faculty of Health and Life Sciences, Management and Science University, University Drive, Off Persiaran Olahraga, 40100, Shah Alam, Selangor, Malaysia. sureshkumar@msu.edu.my.
PMID: 39320535 DOI: 10.1007/s00203-024-04140-y

Abstract

This study focuses on Yersinia pestis, the bacterium responsible for plague, which posed a severe threat to public health in history. Despite the availability of antibiotics treatment, the emergence of antibiotic resistance in this pathogen has increased challenges of controlling the infections and plague outbreaks. The development of new drug targets and therapies is urgently needed. This research aims to identify novel protein targets from 28 Y. pestis strains by the integrative pan-genomic and subtractive genomics approach. Additionally, it seeks to screen out potential safe and effective alternative therapies against these targets via high-throughput virtual screening. Targets should lack homology to human, gut microbiota, and known human 'anti-targets', while should exhibit essentiality for pathogen's survival and virulence, druggability, antibiotic resistance, and broad spectrum across multiple pathogenic bacteria. We identified two promising targets: the aminotransferase class I/class II domain-containing protein and 3-oxoacyl-[acyl-carrier-protein] synthase 2. These proteins were modeled using AlphaFold2, validated through several structural analyses, and were subjected to molecular docking and ADMET analysis. Molecular dynamics simulations determined the stability of the ligand-target complexes, providing potential therapeutic options against Y. pestis.

Keywords

Yersinia pestis Drug target identification In silico analysis Novel antibiotics Virtual screening

References

  1. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, Huynh W, Nguyen A-LV, Cheng AA, Liu S, Min SY, Miroshnichenko A, Tran H-K, Werfalli RE, Nasir JA, Oloni M, Speicher DJ, Florescu A, Singh B, Faltyn M, Hernandez-Koutoucheva A, Sharma AN, Bordeleau E, Pawlowski AC, Zubyk HL, Dooley D, Griffiths E, Maguire F, Winsor GL, Beiko RG, Brinkman FSL, Hsiao WWL, Domselaar GV, McArthur AG (2019) CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. https://doi.org/10.1093/nar/gkz935 [DOI: 10.1093/nar/gkz935]
  2. Alcock BP, Huynh W, Chalil R, Smith KW, Raphenya AR, Wlodarski MA, Edalatmand A, Petkau A, Syed SA, Tsang KK, Baker SJC, Dave M, McCarthy MC, Mukiri KM, Nasir JA, Golbon B, Imtiaz H, Jiang X, Kaur K, Kwong M, Liang ZC, Niu KC, Shan P, Yang JYJ, Gray KL, Hoad GR, Jia B, Bhando T, Carfrae LA, Farha MA, French S, Gordzevich R, Rachwalski K, Tu MM, Bordeleau E, Dooley D, Griffiths E, Zubyk HL, Brown ED, Maguire F, Beiko RG, Hsiao WWL, Brinkman FSL, Van Domselaar G, McArthur AG (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 [DOI: 10.1093/nar/gkac920]
  3. Ali H, Samad A, Ajmal A, Ali A, Ali I, Danial M, Kamal M, Ullah M, Ullah R, Kalim M (2023) Identification of drug targets and their inhibitors in Yersinia pestis Strain 91001 through subtractive genomics, machine learning, and MD simulation approaches. Pharmaceuticals 16:1124. https://doi.org/10.3390/ph16081124 [DOI: 10.3390/ph16081124]
  4. Ammari MG, Gresham CR, McCarthy FM, Nanduri B (2016) HPIDB 2.0: a curated database for host–pathogen interactions. Database 2016:baw103. https://doi.org/10.1093/database/baw103 [DOI: 10.1093/database/baw103]
  5. Anisimov AP, Amoako KK (2006) Treatment of plague: promising alternatives to antibiotics. J Med Microbiol 55:1461–1475. https://doi.org/10.1099/jmm.0.46697-0 [DOI: 10.1099/jmm.0.46697-0]
  6. Challapa-Mamani MR, Tomás-Alvarado E, Espinoza-Baigorria A, León-Figueroa DA, Sah R, Rodriguez-Morales AJ, Barboza JJ (2023) Molecular docking and molecular dynamics simulations in related to Leishmania donovani: an update and literature review. Trop Med Infect Dis 8:457. https://doi.org/10.3390/tropicalmed8100457 [DOI: 10.3390/tropicalmed8100457]
  7. Chen A, Mindrebo JT, Davis TD, Kim WE, Katsuyama Y, Jiang Z, Ohnishi Y, Noel JP, Burkart MD (2022) Mechanism-based cross-linking probes capture the Escherichia coli ketosynthase FabB in conformationally distinct catalytic states. Acta Crystallogr D Struct Biol 78:1171–1179. https://doi.org/10.1107/S2059798322007434 [DOI: 10.1107/S2059798322007434]
  8. Chen L, Kumar S, Wu H (2023) A review of current antibiotic resistance and promising antibiotics with novel modes of action to combat antibiotic resistance. Arch Microbiol 205:356. https://doi.org/10.1007/s00203-023-03699-2 [DOI: 10.1007/s00203-023-03699-2]
  9. Clabbers MTB, Martynowycz MW, Hattne J, Gonen T (2022) Hydrogens and hydrogen-bond networks in macromolecular MicroED data. J Struct Biol X 6:100078. https://doi.org/10.1016/j.yjsbx.2022.100078 [DOI: 10.1016/j.yjsbx.2022.100078]
  10. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2:1511–1519. https://doi.org/10.1002/pro.5560020916 [DOI: 10.1002/pro.5560020916]
  11. Das M, Sakha Ghosh P, Manna K (2016) A review on platensimycin: a selective FabF inhibitor. Int J Med Chem 2016:1–16. https://doi.org/10.1155/2016/9706753 [DOI: 10.1155/2016/9706753]
  12. Ding W, Baumdicker F, Neher RA (2018) panX: pan-genome analysis and exploration. Nucleic Acids Res 46:e5–e5. https://doi.org/10.1093/nar/gkx977 [DOI: 10.1093/nar/gkx977]
  13. Ditchburn J-L, Hodgkins R (2019) Yersinia pestis, a problem of the past and a re-emerging threat. Biosaf Health 1:65–70. https://doi.org/10.1016/j.bsheal.2019.09.001 [DOI: 10.1016/j.bsheal.2019.09.001]
  14. Dörner PJ, Anandakumar H, Röwekamp I, Fiocca Vernengo F, Millet Pascual-Leone B, Krzanowski M, Sellmaier J, Brüning U, Fritsche-Guenther R, Pfannkuch L, Kurth F, Milek M, Igbokwe V, Löber U, Gutbier B, Holstein M, Heinz GA, Mashreghi M-F, Schulte LN, Klatt A-B, Caesar S, Wienhold S-M, Offermanns S, Mack M, Witzenrath M, Jordan S, Beule D, Kirwan JA, Forslund SK, Wilck N, Bartolomaeus H, Heimesaat MM, Opitz B (2024) Clinically used broad-spectrum antibiotics compromise inflammatory monocyte-dependent antibacterial defense in the lung. Nat Commun 15:2788. https://doi.org/10.1038/s41467-024-47149-z [DOI: 10.1038/s41467-024-47149-z]
  15. Durmuş Tekir S, Çakır T, Ardıç E, Sayılırbaş AS, Konuk G, Konuk M, Sarıyer H, Uğurlu A, Karadeniz İ, Özgür A, Sevilgen FE, Ülgen KÖ (2013) PHISTO: pathogen–host interaction search tool. Bioinformatics 29:1357–1358. https://doi.org/10.1093/bioinformatics/btt137 [DOI: 10.1093/bioinformatics/btt137]
  16. Espeland LO, Georgiou C, Klein R, Bhukya H, Haug BE, Underhaug J, Mainkar PS, Brenk R (2021) An experimental toolbox for structure-based hit discovery for P. aeruginosa FabF, a promising target for antibiotics. ChemMedChem 16:2715–2726. https://doi.org/10.1002/cmdc.202100302 [DOI: 10.1002/cmdc.202100302]
  17. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes. J Med Chem 49:6177–6196. https://doi.org/10.1021/jm051256o [DOI: 10.1021/jm051256o]
  18. Gage KL, Kosoy MY (2005) Natural history of plague: perspectives from more than a century of research. Annu Rev Entomol 50:505–528. https://doi.org/10.1146/annurev.ento.50.071803.130337 [DOI: 10.1146/annurev.ento.50.071803.130337]
  19. Gerber JS, Ross RK, Bryan M, Localio AR, Szymczak JE, Wasserman R, Barkman D, Odeniyi F, Conaboy K, Bell L, Zaoutis TE, Fiks AG (2017) Association of broad- vs narrow-spectrum antibiotics with treatment failure, adverse events, and quality of life in children with acute respiratory tract infections. JAMA 318:2325. https://doi.org/10.1001/jama.2017.18715 [DOI: 10.1001/jama.2017.18715]
  20. Gomaa EZ (2020) Human gut microbiota/microbiome in health and diseases: a review. Antonie Van Leeuwenhoek 113:2019–2040. https://doi.org/10.1007/s10482-020-01474-7 [DOI: 10.1007/s10482-020-01474-7]
  21. Huang Y, Niu B, Gao Y, Fu L, Li W (2010) CD-HIT suite: a web server for clustering and comparing biological sequences. Bioinformatics 26:680–682. https://doi.org/10.1093/bioinformatics/btq003 [DOI: 10.1093/bioinformatics/btq003]
  22. Islam J, Sarkar H, Hoque H, Hasan MdN, Jewel GMNA (2022a) In-silico approach of identifying novel therapeutic targets against Yersinia pestis using pan and subtractive genomic analysis. Comput Biol Chem 101:107784. https://doi.org/10.1016/j.compbiolchem.2022.107784 [DOI: 10.1016/j.compbiolchem.2022.107784]
  23. Islam MA, Dudekula DB, Rallabandi VPS, Srinivasan S, Natarajan S, Chung H, Park J (2022b) Identification of potential cytochrome P450 3A5 inhibitors: an extensive virtual screening through molecular docking, negative image-based screening, machine learning and molecular dynamics simulation studies. Int J Mol Sci 23:9374. https://doi.org/10.3390/ijms23169374 [DOI: 10.3390/ijms23169374]
  24. Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL (2008) NCBI BLAST: a better web interface. Nucleic Acids Res 36:W5–W9. https://doi.org/10.1093/nar/gkn201 [DOI: 10.1093/nar/gkn201]
  25. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589. https://doi.org/10.1038/s41586-021-03819-2 [DOI: 10.1038/s41586-021-03819-2]
  26. Keller LM, Weber-Ban E (2023) An emerging class of nucleic acid-sensing regulators in bacteria: WYL domain-containing proteins. Curr Opin Microbiol 74:102296. https://doi.org/10.1016/j.mib.2023.102296 [DOI: 10.1016/j.mib.2023.102296]
  27. Kirsch N, Ha J, Kang H-T, Frisch T, Yoo JW, Grossman C, Oroomchi N, Shigemitsu H, Cross CL, Kioka MJ (2021) Factors associated with the appropriate use of ultra-broad spectrum antibiotics, meropenem, for suspected healthcare-associated pneumonia. Medicine 100:e27488. https://doi.org/10.1097/MD.0000000000027488 [DOI: 10.1097/MD.0000000000027488]
  28. Koper K, Han S-W, Pastor DC, Yoshikuni Y, Maeda HA (2022) Evolutionary origin and functional diversification of aminotransferases. J Biol Chem 298:102122. https://doi.org/10.1016/j.jbc.2022.102122 [DOI: 10.1016/j.jbc.2022.102122]
  29. Krogh A, Larsson B, von Heijne G, Sonnhammer ELL (2001) Predicting transmembrane protein topology with a hidden markov model: application to complete genomes Edited by F. Cohen. J Mol Biol 305:567–580. https://doi.org/10.1006/jmbi.2000.4315 [DOI: 10.1006/jmbi.2000.4315]
  30. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291. https://doi.org/10.1107/S0021889892009944 [DOI: 10.1107/S0021889892009944]
  31. Lei C, Kumar S (2022) Yersinia pestis antibiotic resistance: a systematic review. Osong Public Health Res Perspect 13:24–36. https://doi.org/10.24171/j.phrp.2021.0288 [DOI: 10.24171/j.phrp.2021.0288]
  32. Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1:337–341. https://doi.org/10.1016/j.ddtec.2004.11.007 [DOI: 10.1016/j.ddtec.2004.11.007]
  33. Luo H, Lin Y, Gao F, Zhang C-T, Zhang R (2014) DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements: Table 1. Nucleic Acids Res 42:D574–D580. https://doi.org/10.1093/nar/gkt1131 [DOI: 10.1093/nar/gkt1131]
  34. Luo Q, Li M, Fu H, Meng Q, Gao H (2016) Shewanella oneidensis FabB: a β-ketoacyl-ACP synthase that works with C16:1-ACP. Front Microbiol 7:327. https://doi.org/10.3389/fmicb.2016.00327 [DOI: 10.3389/fmicb.2016.00327]
  35. Lv Q, Chen G, He H, Yang Z, Zhao L, Zhang K, Chen CY-C (2023) TCMBank-the largest TCM database provides deep learning-based Chinese-Western medicine exclusion prediction. Signal Transduct Target Ther 8:127. https://doi.org/10.1038/s41392-023-01339-1 [DOI: 10.1038/s41392-023-01339-1]
  36. Moche M, Dehesh K, Edwards P, Lindqvist Y (2001) The crystal structure of β-ketoacyl-acyl carrier protein synthase II from Synechocystis sp. at 1.54 Å resolution and its relationship to other condensing enzymes Edited by R. Huber. J Mol Biol 305:491–503. https://doi.org/10.1006/jmbi.2000.4272 [DOI: 10.1006/jmbi.2000.4272]
  37. Okuda K, Kato S, Ito T, Shiraki S, Kawase Y, Goto M, Kawashima S, Hemmi H, Fukada H, Yoshimura T (2015) Role of the aminotransferase domain in Bacillus subtilis GabR, a pyridoxal 5′-phosphate-dependent transcriptional regulator. Mol Microbiol 95:245–257. https://doi.org/10.1111/mmi.12861 [DOI: 10.1111/mmi.12861]
  38. Paysan-Lafosse T, Blum M, Chuguransky S, Grego T, Pinto BL, Salazar GA, Bileschi ML, Bork P, Bridge A, Colwell L, Gough J, Haft DH, Letunić I, Marchler-Bauer A, Mi H, Natale DA, Orengo CA, Pandurangan AP, Rivoire C, Sigrist CJA, Sillitoe I, Thanki N, Thomas PD, Tosatto SCE, Wu CH, Bateman A (2023) InterPro in 2022. Nucleic Acids Res 51:D418–D427. https://doi.org/10.1093/nar/gkac993 [DOI: 10.1093/nar/gkac993]
  39. Raman K, Yeturu K, Chandra N (2008) targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol 2:109. https://doi.org/10.1186/1752-0509-2-109 [DOI: 10.1186/1752-0509-2-109]
  40. Ramírez D, Caballero J (2018) Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data? Molecules 23:1038. https://doi.org/10.3390/molecules23051038 [DOI: 10.3390/molecules23051038]
  41. Sebbane F, Lemaître N (2021) Antibiotic therapy of plague: a review. Biomolecules 11:724. https://doi.org/10.3390/biom11050724 [DOI: 10.3390/biom11050724]
  42. Sha S, Ni L, Stefil M, Dixon M, Mouraviev V (2020) The human gastrointestinal microbiota and prostate cancer development and treatment. Investig Clin Urol 61:S43. https://doi.org/10.4111/icu.2020.61.S1.S43 [DOI: 10.4111/icu.2020.61.S1.S43]
  43. Shanmugham B, Pan A (2013) Identification and characterization of potential therapeutic candidates in emerging human pathogen Mycobacterium abscessus: a novel hierarchical in silico approach. PLoS ONE 8:e59126. https://doi.org/10.1371/journal.pone.0059126 [DOI: 10.1371/journal.pone.0059126]
  44. Sharma A, Pan A (2012) Identification of potential drug targets in Yersinia pestis using metabolic pathway analysis: MurE ligase as a case study. Eur J Med Chem 57:185–195. https://doi.org/10.1016/j.ejmech.2012.09.018 [DOI: 10.1016/j.ejmech.2012.09.018]
  45. Soares da Costa TP, Nanson JD, Forwood JK (2017) Structural characterisation of the fatty acid biosynthesis enzyme FabF from the pathogen Listeria monocytogenes. Sci Rep 7:39277. https://doi.org/10.1038/srep39277 [DOI: 10.1038/srep39277]
  46. Spyrou MA, Musralina L, Gnecchi Ruscone GA, Kocher A, Borbone P-G, Khartanovich VI, Buzhilova A, Djansugurova L, Bos KI, Kühnert D, Haak W, Slavin P, Krause J (2022) The source of the Black Death in fourteenth-century central Eurasia. Nature 606:718–724. https://doi.org/10.1038/s41586-022-04800-3 [DOI: 10.1038/s41586-022-04800-3]
  47. Urban M, Pant R, Raghunath A, Irvine AG, Pedro H, Hammond-Kosack KE (2015) The pathogen-host interactions database (PHI-base): additions and future developments. Nucleic Acids Res 43:D645–D655. https://doi.org/10.1093/nar/gku1165 [DOI: 10.1093/nar/gku1165]
  48. Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A, Žídek A, Green T, Tunyasuvunakool K, Petersen S, Jumper J, Clancy E, Green R, Vora A, Lutfi M, Figurnov M, Cowie A, Hobbs N, Kohli P, Kleywegt G, Birney E, Hassabis D, Velankar S (2022) AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50:D439–D444. https://doi.org/10.1093/nar/gkab1061 [DOI: 10.1093/nar/gkab1061]
  49. Weber OC, Uversky VN (2017) How accurate are your simulations? Effects of confined aqueous volume and AMBER FF99SB and CHARMM22/CMAP force field parameters on structural ensembles of intrinsically disordered proteins: amyloid-β in water. Intrinsically Disord Proteins 5:e1377813. https://doi.org/10.1080/21690707.2017.1377813 [DOI: 10.1080/21690707.2017.1377813]
  50. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:W407–W410. https://doi.org/10.1093/nar/gkm290 [DOI: 10.1093/nar/gkm290]
  51. Wilkins MR, Gasteiger E, Bairoch A, Sanchez J-C, Williams KL, Appel RD, Hochstrasser DF (1999) Protein identification and analysis tools in the ExPASy server. Methods Mol Biol 112:531–552 [PMID: 10027275]
  52. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082. https://doi.org/10.1093/nar/gkx1037 [DOI: 10.1093/nar/gkx1037]
  53. Xiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C, Yin M, Zeng X, Wu C, Lu A, Chen X, Hou T, Cao D (2021) ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res 49:W5–W14. https://doi.org/10.1093/nar/gkab255 [DOI: 10.1093/nar/gkab255]
  54. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, Dao P, Sahinalp SC, Ester M, Foster LJ, Brinkman FSL (2010a) PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26:1608–1615. https://doi.org/10.1093/bioinformatics/btq249 [DOI: 10.1093/bioinformatics/btq249]
  55. Yu J, Zhou Y, Tanaka I, Yao M (2010b) Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere. Bioinformatics 26:46–52. https://doi.org/10.1093/bioinformatics/btp599 [DOI: 10.1093/bioinformatics/btp599]
  56. Zhang S, Yan Z, Huang Y, Liu L, He D, Wang W, Fang X, Zhang X, Wang F, Wu H, Wang H (2022) HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer. Bioinformatics 38:3444–3453. https://doi.org/10.1093/bioinformatics/btac342 [DOI: 10.1093/bioinformatics/btac342]
  57. Zhou Y, Zhang Y, Lian X, Li F, Wang C, Zhu F, Qiu Y, Chen Y (2022) Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Res 50:D1398–D1407. https://doi.org/10.1093/nar/gkab953 [DOI: 10.1093/nar/gkab953]
  58. Burrows RL (2021) The third plague pandemic and British India: a transformation of science, policy, and Indian Society, vol 10. Tenor of Our Times
  59. Li T, Yin Y (2022) Critical assessment of pan-genomic analysis of metagenome-assembled genomes. Brief Bioinform 23. https://doi.org/10.1093/bib/bbac413
  60. Piret J, Boivin G (2021) Pandemics throughout history. Front Microbiol 11. https://doi.org/10.3389/fmicb.2020.631736
  61. World Health Organization (2022) Plague. https://www.who.int/news-room/fact-sheets/detail/plague . Accessed 8 Aug 2023
  62. World Health Organizaiton (2024) Plague in the African Region

Grants

  1. xjr2020020/the Funding for school-level research projects of Yancheng Institute of Technology

MeSH Term

Yersinia pestis
Molecular Docking Simulation
Anti-Bacterial Agents
Bacterial Proteins
Plague
Genomics
Humans
Molecular Dynamics Simulation

Chemicals

Anti-Bacterial Agents
Bacterial Proteins
Journal Article

Word Cloud

Created with Highcharts 10.0.0pestistargetsYersiniapotentialplagueantibioticsantibioticresistancedrugtherapiesproteinYintegrativesubtractivegenomicsapproachscreeninghumanpromisingdockinganalysisstudyfocusesbacteriumresponsibleposedseverethreatpublichealthhistoryDespiteavailabilitytreatmentemergencepathogenincreasedchallengescontrollinginfectionsoutbreaksdevelopmentnewurgentlyneededresearchaimsidentifynovel28strainspan-genomicAdditionallyseeksscreensafeeffectivealternativeviahigh-throughputvirtualTargetslackhomologygutmicrobiotaknown'anti-targets'exhibitessentialitypathogen'ssurvivalvirulencedruggabilitybroadspectrumacrossmultiplepathogenicbacteriaidentifiedtwotargets:aminotransferaseclassI/classIIdomain-containing3-oxoacyl-[acyl-carrier-protein]synthase2proteinsmodeledusingAlphaFold2validatedseveralstructuralanalysessubjectedmolecularADMETMoleculardynamicssimulationsdeterminedstabilityligand-targetcomplexesprovidingtherapeuticoptionsScreeningmoleculespansimulationDrugtargetidentificationsilicoNovelVirtual

Similar Articles

Cited By