Connor BA, Schwartz E. Typhoid and paratyphoid fever in Travellers. Lancet Infect Dis. 2005;5:623–8. https://doi.org/10.1016/S1473-3099(05)70239-5.
Article
Google Scholar
Zhou Z, McCann A, Weill F-X, Blin C, Nair S, Wain J, et al. Transient Darwinian selection in Salmonella Enterica Serovar Paratyphi a during 450 years of global spread of enteric fever. Proc Natl Acad Sci. 2014;111:12199–204. https://doi.org/10.1073/pnas.1411012111.
Article
CAS
Google Scholar
Gal-Mor O, Boyle EC, Grassl GA. Same species, different diseases: how and why Typhoidal and non-Typhoidal Salmonella Enterica Serovars differ. Front Microbiol. 2014;5. https://doi.org/10.3389/fmicb.2014.00391.
Azmatullah A, Qamar FN, Thaver D, Zaidi AK, Bhutta ZA. Systematic review of the global epidemiology, clinical and laboratory profile of enteric fever. J Glob Health. 2015;5:020407. https://doi.org/10.7189/jogh.05.020407.
Article
Google Scholar
Dougan G, Baker S. Salmonella Enterica Serovar Typhi and the pathogenesis of typhoid fever. Annu Rev Microbiol. 2014;68:317–36. https://doi.org/10.1146/annurev-micro-091313-103739.
Article
CAS
Google Scholar
Bhan M, Bahl R, Bhatnagar S. Typhoid and paratyphoid fever. Lancet. 2005;366:749–62. https://doi.org/10.1016/S0140-6736(05)67181-4.
Article
CAS
Google Scholar
Thielman NM, Guerrant RL. Acute infectious diarrhea. N Engl J Med. 2004;350:38–47. https://doi.org/10.1056/NEJMcp031534.
Article
CAS
Google Scholar
Encyclopedia of Food Microbiology; Batt, C.A., Tortorello, M.L., Eds.; 2. ed.; AP, Academic Press/Elsevier: Amsterdam, 2014; ISBN 978–0–12-384733-1.
Patel BA, Wunderlich RE. Errata: dynamic pressure patterns in the hands of olive baboons (Papio Anubis) during terrestrial locomotion: implications for Cercopithecoid primate hand morphology. Anat Rec. 2010;293:1276. https://doi.org/10.1002/ar.21188.
Article
Google Scholar
Kuvandik C, Karaoglan I, Namiduru M, Baydar I. Predictive value of clinical and laboratory findings in the diagnosis of the enteric fever. New Microbiol. 2009;32:25–30.
Google Scholar
Bakowski MA, Braun V, Brumell JH. Salmonella -containing vacuoles: directing traffic and nesting to grow. Traffic. 2008;9:2022–31. https://doi.org/10.1111/j.1600-0854.2008.00827.x.
Article
CAS
Google Scholar
Raffatellu M, Chessa D, Wilson RP, Tükel C, Akçelik M, Bäumler AJ. Capsule-mediated immune evasion: a new hypothesis explaining aspects of typhoid fever pathogenesis. Infect Immun. 2006;74:19–27. https://doi.org/10.1128/IAI.74.1.19-27.2006.
Article
CAS
Google Scholar
Odoch T, Wasteson Y, L’Abée-Lund T, Muwonge A, Kankya C, Nyakarahuka L, et al. Prevalence, antimicrobial susceptibility and risk factors associated with non-Typhoidal Salmonella on Ugandan layer hen farms. BMC Vet Res. 2017;13:365. https://doi.org/10.1186/s12917-017-1291-1.
Article
Google Scholar
Afema JA, Mather AE, Sischo WM. Antimicrobial resistance profiles and diversity in S Almonella from humans and cattle, 2004–2011. Zoonoses Public Health. 2015;62:506–17. https://doi.org/10.1111/zph.12172.
Article
CAS
Google Scholar
Jajere SM. A review of Salmonella Enterica with particular focus on the pathogenicity and virulence factors, host specificity and antimicrobial resistance including multidrug resistance. Vet World. 2019;12:504–21. https://doi.org/10.14202/vetworld.2019.504-521.
Article
CAS
Google Scholar
Hassan SS, Tiwari S, Guimarães LC, Jamal SB, Folador E, Sharma NB, et al. Proteome scale comparative modeling for conserved drug and vaccine targets identification in Corynebacterium Pseudotuberculosis. BMC Genomics. 2014;15(Suppl 7):S3. https://doi.org/10.1186/1471-2164-15-S7-S3.
Article
Google Scholar
Jamal SB, Hassan SS, Tiwari S, Viana MV, Benevides L d J, Ullah A, et al. An integrative in-Silico approach for therapeutic target identification in the human pathogen Corynebacterium Diphtheriae. PLoS One. 2017;12:e0186401. https://doi.org/10.1371/journal.pone.0186401.
Article
CAS
Google Scholar
Mourenza Á, Gil JA, Mateos LM, Letek M. Novel treatments against mycobacterium tuberculosis based on drug repurposing. Antibiotics (Basel). 2020;9. https://doi.org/10.3390/antibiotics9090550.
Reddy TBK, Thomas AD, Stamatis D, Bertsch J, Isbandi M, Jansson J, et al. The Genomes OnLine Database (GOLD) v.5: A Metadata Management System Based on a Four Level (Meta) Genome Project Classification. Nucleic Acids Res. 2015;43:D1099–106. https://doi.org/10.1093/nar/gku950.
Article
CAS
Google Scholar
Stecher G, Tamura K, Kumar S. Molecular evolutionary genetics analysis (MEGA) for MacOS. Mol Biol Evol. 2020;37:1237–9. https://doi.org/10.1093/molbev/msz312.
Article
CAS
Google Scholar
Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9. https://doi.org/10.1093/molbev/msy096.
Article
CAS
Google Scholar
Blom J, Kreis J, Spänig S, Juhre T, Bertelli C, Ernst C, et al. EDGAR 2.0: an enhanced software platform for comparative gene content analyses. Nucleic Acids Res. 2016;44:W22–8. https://doi.org/10.1093/nar/gkw255.
Article
CAS
Google Scholar
Gao F, Luo H, Zhang C-T, Zhang R. Gene Essentiality Analysis Based on DEG 10, an Updated Database of Essential Genes. In: Lu LJ, editor. Gene Essentiality; Methods in Molecular Biology, vol. 1279. New York: Springer New York; 2015. p. 219–33. ISBN 978–1–4939-2397-7.
Google Scholar
Rossi AD, Oliveira PHE, Siqueira DG, Reis VCC, Dardenne LE, Goliatt PVZC. MHOLline 2.0: workflow for automatic large-scale modeling and analysis of proteins. MUNDI ETG. 2020;5. https://doi.org/10.21575/25254782rmetg2020vol5n61325.
Eisenberg D, Lüthy R, Bowie JU. VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol. 1997;277:396–404. https://doi.org/10.1016/s0076-6879(97)77022-8.
Article
CAS
Google Scholar
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING Database in 2017: Quality-Controlled Protein–Protein Association Networks, Made Broadly Accessible. Nucleic Acids Res. 2017;45:D362–8. https://doi.org/10.1093/nar/gkw937.
Article
CAS
Google Scholar
Yu C-S, Cheng C-W, Su W-C, Chang K-C, Huang S-W, Hwang J-K, et al. CELLO2GO: a web server for protein SubCELlular LOcalization prediction with functional gene ontology annotation. PLoS One. 2014;9:e99368. https://doi.org/10.1371/journal.pone.0099368.
Article
CAS
Google Scholar
Liu B, Zheng D, Jin Q, Chen L, Yang J. VFDB 2019: a comparative Pathogenomic platform with an interactive web Interface. Nucleic Acids Res. 2019;47:D687–92. https://doi.org/10.1093/nar/gky1080.
Article
CAS
Google Scholar
Volkamer A, Kuhn D, Rippmann F, Rarey M. DoGSiteScorer: a web server for automatic binding site prediction, Analysis and Druggability Assessment. Bioinformatics. 2012;28:2074–5. https://doi.org/10.1093/bioinformatics/bts310.
Article
CAS
Google Scholar
Sterling T, Irwin JJ. ZINC 15--Ligand Discovery for Everyone. J Chem Inf Model. 2015;55:2324–37. https://doi.org/10.1021/acs.jcim.5b00559.
Article
CAS
Google Scholar
Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. CTMC. 2008;8:1555–72. https://doi.org/10.2174/156802608786786624.
Article
CAS
Google Scholar
Scholz C, Knorr S, Hamacher K, Schmidt B. DOCKTITE—A highly versatile step-by-step workflow for covalent docking and virtual screening in the molecular operating environment. J Chem Inf Model. 2015;55:398–406. https://doi.org/10.1021/ci500681r.
Article
CAS
Google Scholar
Basharat Z, Jahanzaib M, Yasmin A, Khan IA. Pan-genomics, drug candidate mining and ADMET profiling of natural product inhibitors screened against Yersinia Pseudotuberculosis. Genomics. 2021;113:238–44. https://doi.org/10.1016/j.ygeno.2020.12.015.
Article
CAS
Google Scholar
Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys. 2020;153:044130. https://doi.org/10.1063/5.0014475.
Article
CAS
Google Scholar
Lee J, Hitzenberger M, Rieger M, Kern NR, Zacharias M, Im W. CHARMM-GUI supports the Amber force fields. J Chem Phys. 2020;153:035103. https://doi.org/10.1063/5.0012280.
Article
CAS
Google Scholar
Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput. 2016;12:405–13. https://doi.org/10.1021/acs.jctc.5b00935.
Article
CAS
Google Scholar
Jo S, Kim T, Iyer VG, Im W. CHARMM-GUI: a web-based graphical user Interface for CHARMM. J Comput Chem. 2008;29:1859–65. https://doi.org/10.1002/jcc.20945.
Article
CAS
Google Scholar
Gowers R, Linke M, Barnoud J, Reddy T, Melo M, Seyler S, et al. MDAnalysis: a Python package for the rapid analysis of molecular dynamics simulations. Austin: Texas; 2016. p. 98–105.
Google Scholar
Michaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O. MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem. 2011;32:2319–27. https://doi.org/10.1002/jcc.21787.
Article
CAS
Google Scholar
Adasme MF, Linnemann KL, Bolz SN, Kaiser F, Salentin S, Haupt VJ, et al. PLIP 2021: Expanding the Scope of the Protein–Ligand Interaction Profiler to DNA and RNA. Nucleic Acids Res. 2021;49:W530–4. https://doi.org/10.1093/nar/gkab294.
Article
CAS
Google Scholar
Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, et al. End-point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design. Chem Rev. 2019;119:9478–508. https://doi.org/10.1021/acs.chemrev.9b00055.
Article
CAS
Google Scholar
Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res. 2000;33:889–97. https://doi.org/10.1021/ar000033j.
Article
CAS
Google Scholar
Liu H, Hou T. CaFE: a tool for binding affinity prediction using end-point free energy methods. Bioinformatics. 2016;32:2216–8. https://doi.org/10.1093/bioinformatics/btw215.
Article
CAS
Google Scholar
Humphrey W, Dalke A, Schulten K. VMD: Visual Molecular Dynamics. J Mol Graph. 1996;14:33–8. https://doi.org/10.1016/0263-7855(96)00018-5.
Article
CAS
Google Scholar
Nei M. Molecular evolution and Phylogenetics; 2000.
Google Scholar
Felsenstein J. Confidence limits on PHYLOGENIES: an approach using the bootstrap. Evolution. 1985;39:783–91. https://doi.org/10.1111/j.1558-5646.1985.tb00420.x.
Article
Google Scholar
Felsenstein J. Inferring Phylogenies. Sunderland: Sinauer Associates; 2004.
Google Scholar
Hall BG. Building phylogenetic trees from molecular data with MEGA. Mol Biol Evol. 2013;30:1229–35. https://doi.org/10.1093/molbev/mst012.
Article
CAS
Google Scholar
Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4:406–25. https://doi.org/10.1093/oxfordjournals.molbev.a040454.
Article
CAS
Google Scholar
Gascuel O, Steel M. Neighbor-joining revealed. Mol Biol Evol. 2006;23:1997–2000. https://doi.org/10.1093/molbev/msl072.
Article
CAS
Google Scholar
Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics. 2011;27:343–50. https://doi.org/10.1093/bioinformatics/btq662.
Article
CAS
Google Scholar
Mariani V, Biasini M, Barbato A, Schwede T. LDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics. 2013;29:2722–8. https://doi.org/10.1093/bioinformatics/btt473.
Article
CAS
Google Scholar
Studer G, Rempfer C, Waterhouse AM, Gumienny R, Haas J, Schwede T. QMEANDisCo-distance constraints applied on model quality estimation. Bioinformatics. 2020;36:1765–71. https://doi.org/10.1093/bioinformatics/btz828.
Article
CAS
Google Scholar
Morris AL, MacArthur MW, Hutchinson EG, Thornton JM. Stereochemical quality of protein structure coordinates. Proteins. 1992;12:345–64. https://doi.org/10.1002/prot.340120407.
Article
CAS
Google Scholar
Ramachandran GN, Ramakrishnan C, Sasisekharan V. Stereochemistry of polypeptide chain configurations. J Mol Biol. 1963;7:95–9. https://doi.org/10.1016/s0022-2836(63)80023-6.
Article
CAS
Google Scholar
Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the Stereochemical quality of protein structures. J Appl Crystallogr. 1993;26:283–91. https://doi.org/10.1107/S0021889892009944.
Article
CAS
Google Scholar
Daina A, Michielin O, Zoete V. ILOGP: a simple, robust, and efficient description of n-Octanol/water partition coefficient for drug design using the GB/SA approach. J Chem Inf Model. 2014;54:3284–301. https://doi.org/10.1021/ci500467k.
Article
CAS
Google Scholar
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46:3–26. https://doi.org/10.1016/s0169-409x(00)00129-0.
Article
CAS
Google Scholar
Potts RO, Guy RH. Predicting skin permeability. Pharm Res. 1992;9:663–9. https://doi.org/10.1023/a:1015810312465.
Article
CAS
Google Scholar
Moreno-Cinos C, Goossens K, Salado IG, Van Der Veken P, De Winter H, Augustyns K. ClpP protease, a promising antimicrobial target. Int J Mol Sci. 2019;20. https://doi.org/10.3390/ijms20092232.
Raju RM, Unnikrishnan M, Rubin DHF, Krishnamoorthy V, Kandror O, Akopian TN, et al. Mycobacterium tuberculosis ClpP1 and ClpP2 function together in protein degradation and are required for viability in vitro and during infection. PLoS Pathog. 2012;8:e1002511. https://doi.org/10.1371/journal.ppat.1002511.
Article
CAS
Google Scholar
Culp E, Wright GD. Bacterial proteases, untapped antimicrobial drug targets. J Antibiot (Tokyo). 2017;70:366–77. https://doi.org/10.1038/ja.2016.138.
Article
CAS
Google Scholar
Frees D, Ingmer H. ClpP participates in the degradation of Misfolded protein in Lactococcus Lactis. Mol Microbiol. 1999;31:79–87. https://doi.org/10.1046/j.1365-2958.1999.01149.x.
Article
CAS
Google Scholar
Thomsen LE, Olsen JE, Foster JW, Ingmer H. ClpP is involved in the stress response and degradation of Misfolded proteins in Salmonella Enterica Serovar Typhimurium. Microbiology (Reading). 2002;148:2727–33. https://doi.org/10.1099/00221287-148-9-2727.
Article
CAS
Google Scholar
Chaudhuri BN, Lange SC, Myers RS, Chittur SV, Davisson VJ, Smith JL. Crystal structure of imidazole glycerol phosphate synthase. Structure. 2001;9:987–97. https://doi.org/10.1016/S0969-2126(01)00661-X.
Article
CAS
Google Scholar
Klem TJ, Chen Y, Davisson VJ. Subunit interactions and glutamine utilization by Escherichia Coli imidazole glycerol phosphate synthase. J Bacteriol. 2001;183:989–96. https://doi.org/10.1128/JB.182.3.989-996.2001.
Article
CAS
Google Scholar
Rivalta I, Sultan MM, Lee N-S, Manley GA, Loria JP, Batista VS. Allosteric pathways in imidazole glycerol phosphate synthase. Proc Natl Acad Sci. 2012;109:E1428–36. https://doi.org/10.1073/pnas.1120536109.
Article
Google Scholar
Griffith EC, Wallace MJ, Wu Y, Kumar G, Gajewski S, Jackson P, et al. The structural and functional basis for recurring sulfa drug resistance mutations in staphylococcus aureus Dihydropteroate synthase. Front Microbiol. 2018;9:1369. https://doi.org/10.3389/fmicb.2018.01369.
Article
Google Scholar
Achari A, Somers DO, Champness JN, Bryant PK, Rosemond J, Stammers DK. Crystal structure of the anti-bacterial sulfonamide drug target Dihydropteroate synthase. Nat Struct Biol. 1997;4:490–7. https://doi.org/10.1038/nsb0697-490.
Article
CAS
Google Scholar
Dhamodharan R, Hoti SL, Sankari T. Characterization of cofactor-independent Phosphoglycerate Mutase Isoform-1 (Wb-IPGM) gene: a drug and diagnostic target from human lymphatic filarial parasite, Wuchereria Bancrofti. Infect Genet Evol. 2012;12:957–65. https://doi.org/10.1016/j.meegid.2012.02.005.
Article
CAS
Google Scholar
Mercaldi GF, Pereira HM, Cordeiro AT, Michels PAM, Thiemann OH. Structural role of the active-site metal in the conformation of Trypanosoma Brucei Phosphoglycerate Mutase. FEBS J. 2012;279:2012–21. https://doi.org/10.1111/j.1742-4658.2012.08586.x.
Article
CAS
Google Scholar
Lokhande KB, Banerjee T, Swamy KV, Ghosh P, Deshpande M. An in Silico scientific basis for LL-37 as a therapeutic for Covid-19. Proteins. 2022;90:1029–43. https://doi.org/10.1002/prot.26198.
Article
CAS
Google Scholar
Pulakuntla S, Lokhande KB, Padmavathi P, Pal M, Swamy KV, Sadasivam J, et al. Mutational analysis in international isolates and drug repurposing against SARS-CoV-2 spike protein: molecular docking and simulation approach. Virusdisease. 2021;32:690–702. https://doi.org/10.1007/s13337-021-00720-4.
Article
CAS
Google Scholar
Gandhi SP, Lokhande KB, Swamy VK, Nanda RK, Chitlange SS. Computational data of Phytoconstituents from hibiscus Rosa-Sinensis on various anti-obesity targets. Data Brief. 2019;24:103994. https://doi.org/10.1016/j.dib.2019.103994.
Article
Google Scholar
Mansuri A, Lokhande K, Kore S, Gaikwad S, Nawani N, Swamy KV, et al. Antioxidant, anti-quorum sensing, biofilm inhibitory activities and chemical composition of patchouli essential oil: in vitro and in silico approach. J Biomol Struct Dyn. 2022;40:154–65. https://doi.org/10.1080/07391102.2020.1810124.
Article
CAS
Google Scholar
Lokhande KB, Ghosh P, Nagar S, Venkateswara Swamy K, Novel B. C-ring truncated Deguelin derivatives reveals as potential inhibitors of Cyclin D1 and Cyclin E using molecular docking and molecular dynamic simulation. Mol Divers. 2022;26:2295–309. https://doi.org/10.1007/s11030-021-10334-z.
Article
CAS
Google Scholar