1. Russo TA, Johnson JR. Proposal for a new inclusive designation for extraintestinal pathogenic isolates of Escherichia coli: ExPEC. J Infect Dis. 2000; 181: 1753-4. [
DOI:10.1086/315418]
2. Russo TA, Johnson JR. Medical and economic impact of extraintestinal infections due to Escherichia coli: focus on an increasingly important endemic problem. Microb Infect. 2003; 5: 449-56. [
DOI:10.1016/S1286-4579(03)00049-2]
3. Gupta K, Hooton TM, Stamm WE. Increasing antimicrobial resistance and the management of uncomplicated community-acquired urinary tract infections. Ann Intern Med. 2001; 135: 41-50. [
DOI:10.7326/0003-4819-135-1-200107030-00012]
4. Langermann S, Palaszynski S, Barnhart M, et al. Prevention of mucosal Escherichia coli infection by FimH-adhesin-based systemic vaccination. Science. 1997; 276: 607-11. [
DOI:10.1126/science.276.5312.607]
5. Russo TA, McFadden CD, Carlino-MacDonald UB, Beanan JM, Barnard TJ, Johnson JR. IroN functions as a siderophore receptor and is a urovirulence factor in an extraintestinal pathogenic isolate of Escherichia coli. Infect Immun. 2002; 70: 7156-60. [
DOI:10.1128/IAI.70.12.7156-7160.2002]
6. Dozois CM, Daigle F, Curtiss R. Identification of pathogen-specific and conserved genes expressed in vivo by an avian pathogenic Escherichia coli strain. Proc Natl Acad Sci Unit States Am. 2003; 100: 247-52. [
DOI:10.1073/pnas.232686799]
7. Floudas C, Fung H, McAllister S, Mönnigmann M, Rajgaria R. Advances in protein structure prediction and de novo protein design: A review. Chem Eng Sci. 2006; 61:966-88. [
DOI:10.1016/j.ces.2005.04.009]
8. Rahman A, Zomaya AY. An overview of protein-folding techniques: issues and perspectives. Int J Bioinformatics Res Appl. 2005; 1: 121-43. [
DOI:10.1504/IJBRA.2005.006911]
9. Gish W, States DJ. Identification of protein coding regions by database similarity search. Nat Genet. 1993; 3: 266. [
DOI:10.1038/ng0393-266]
10. Fiser A. Protein structure modeling in the proteomics era. Expet Rev Proteonomics. 2004; 1: 97-110. [
DOI:10.1586/14789450.1.1.97]
11. Gasteiger E, Hoogland C, Gattiker A, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook: Springer. 2005: 571-607. [
DOI:10.1385/1-59259-890-0:571]
12. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics. 2007; 8: 4. [
DOI:10.1186/1471-2105-8-4]
13. Yu CS, Cheng CW, Su WC, et al. CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation. PloS one. 2014; 9: e99368. [
DOI:10.1371/journal.pone.0099368]
14. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics. 1995; 11: 681-4. [
DOI:10.1093/bioinformatics/11.6.681]
15. Tsirigos KD, Peters C, Shu N, Käll L, Elofsson A. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res. 2015; 43: W401-W7. [
DOI:10.1093/nar/gkv485]
16. Krogh A, Larsson B, Von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001; 305: 567-80. [
DOI:10.1006/jmbi.2000.4315]
17. Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ. PRED-TMBB: a web server for predicting the topology of β-barrel outer membrane proteins. Nucleic Acids Res. 2004; 32: W400-W4. [
DOI:10.1093/nar/gkh417]
18. Petersen TN, Brunak S, Von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Protocol. 2011; 8: 785. [
DOI:10.1038/nmeth.1701]
19. Chen CC, Hwang JK, Yang JM. 2-v2: template-based protein structure prediction server. Bmc Bioinformatics. 2009; 10: 366. [
DOI:10.1186/1471-2105-10-366]
20. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res. 2003; 31: 3381-5. [
DOI:10.1093/nar/gkg520]
21. Guex N, Peitsch MC. SWISS‐MODEL and the Swiss‐Pdb Viewer: an environment for comparative protein modeling. electrophoresis. 1997; 18: 2714-23. [
DOI:10.1002/elps.1150181505]
22. Wu S, Zhang Y. LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res. 2007; 35: 3375-82. [
DOI:10.1093/nar/gkm251]
23. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protocol. 2015; 10: 845. [
DOI:10.1038/nprot.2015.053]
24. Carugo O, Djinović-Carugo K. Half a century of Ramachandran plots. Acta Crystallographica Section D: Biological Crystallography. 2013; 69: 1333-41. [
DOI:10.1107/S090744491301158X]
25. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J. 2011; 101: 2525-34. [
DOI:10.1016/j.bpj.2011.10.024]
26. Lomize MA, Pogozheva ID, Joo H, Mosberg HI, Lomize AL. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 2011; 40: D370-D6. [
DOI:10.1093/nar/gkr703]
27. Roy A, Yang J, Zhang Y. COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res. 2012; 40: W471-W7. [
DOI:10.1093/nar/gks372]
28. Berezin C, Glaser F, Rosenberg J, Paz I, Pupko T, Fariselli P, et al. ConSeq: the identification of functionally and structurally important residues in protein sequences. Bioinformatics, 2004; 20 (8): 1322-4. [
DOI:10.1093/bioinformatics/bth070]
29. Negi SS, Schein CH, Oezguen N, Power TD, Braun W. InterProSurf: a web server for predicting interacting sites on protein surfaces. Bioinformatics. 2007; 23: 3397-9. [
DOI:10.1093/bioinformatics/btm474]
30. Kawabata T. Detection of multiscale pockets on protein surfaces using mathematical morphology. Proteins: Structure, Function, and Bioinformatics. 2010; 78: 1195-211. [
DOI:10.1002/prot.22639]
31. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J. CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res. 2006; 34: W116-W8. [
DOI:10.1093/nar/gkl282]
32. Tan KP, Nguyen TB, Patel S, Varadarajan R, Madhusudhan MS. Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins. Nucleic Acids Res. 2013; 41: W314-W21. [
DOI:10.1093/nar/gkt503]
33. Vita R, Overton JA, Greenbaum JA, et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 2014; 43: D405-D12. [
DOI:10.1093/nar/gku938]
34. Saha S, Raghava G. BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. In: ICARIS. Springer: 197-204. [
DOI:10.1007/978-3-540-30220-9_16]
35. Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017; 45: W24-W9. [
DOI:10.1093/nar/gkx346]
36. Yao B, Zhang L, Liang S, Zhang C. SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PloS one. 2012; 7: e45152. [
DOI:10.1371/journal.pone.0045152]
37. Davies MN, Flower DR. Harnessing bioinformatics to discover new vaccines. Drug Discov Today. 2007; 12: 389-95. [
DOI:10.1016/j.drudis.2007.03.010]
38. Ponomarenko J, Bui HH, Li W, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC bioinformatics. 2008; 9: 514. [
DOI:10.1186/1471-2105-9-514]
39. Uehling DT, Hopkins WJ, Elkahwaji JE, Schmidt DM, Leverson GE. Phase 2 clinical trial of a vaginal mucosal vaccine for urinary tract infections. J Urol. 2003; 170: 867-9. [
DOI:10.1097/01.ju.0000075094.54767.6e]
40. Bauer HW, Alloussi S, Egger G, et al. A long-term, multicenter, double-blind study of an Escherichia coli extract (OM-89) in female patients with recurrent urinary tract infections. J Urol. 2005; 47: 542-8. [
DOI:10.1016/j.eururo.2004.12.009]
41. Goluszko P, Goluszko E, Nowicki B, Nowicki S, Popov V, Wang H-Q. Vaccination with purified Dr Fimbriae reduces mortality associated with chronic urinary tract infection due to Escherichia coli bearing Dr adhesin. Infect Immun. 2005; 73: 627-31. [
DOI:10.1128/IAI.73.1.627-631.2005]
42. Jahangiri A, Rasooli I, Gargari SLM, et al. An in silico DNA vaccine against Listeria monocytogenes. Vaccine. 2011; 29: 6948-58. [
DOI:10.1016/j.vaccine.2011.07.040]
43. Sefid F, Rasooli I, Jahangiri A. In silico determination and validation of baumannii acinetobactin utilization a structure and ligand binding site. Biomed Res. 2013; 2013. [
DOI:10.1155/2013/172784]
44. Payandeh Z, Rajabibazl M, Mortazavi Y, Rahimpour A, Taromchi AH. Ofatumumab monoclonal antibody affinity maturation through in silico modeling. IBJ. 2018; 22: 180.
45. Payandeh Z, Rajabibazl M, Mortazavi Y, Rahimpour A. In Silico Analysis for Determination and Validation of Human CD20 Antigen 3D Structure. Int J Pept Res Therapeut. 2019; 25: 123-35. [
DOI:10.1007/s10989-017-9654-9]
46. Payandeh Z, Rajabibazl M, Mortazavi Y, Rahimpour A, Taromchi AH, Dastmalchi S. Affinity maturation and characterization of the ofatumumab monoclonal antibody. J Cell Biochem. 2019; 120: 940-50. [
DOI:10.1002/jcb.27457]
47. Kleywegt GJ, Jones TA. Databases in protein crystallography. Acta Crystallographica Section D: Biological Crystallography. 1998; 54: 1119-31. [
DOI:10.1107/S0907444998007100]
48. Mislin GL, Schalk IJ. Siderophore-dependent iron uptake systems as gates for antibiotic Trojan horse strategies against Pseudomonas aeruginosa. Metallomics. 2014; 6: 408-20. [
DOI:10.1039/C3MT00359K]
49. Maiorov VN, Crippen GM. Significance of root-mean-square deviation in comparing three-dimensional structures of globular proteins. 1994. [
DOI:10.1006/jmbi.1994.1017]
50. Bagos PG, Liakopoulos TD, Hamodrakas SJ. Evaluation of methods for predicting the topology of β-barrel outer membrane proteins and a consensus prediction method. BMC bioinformatics. 2005; 6:7. [
DOI:10.1186/1471-2105-6-7]