104
Guirimand T, Delmotte S, Navratil V (2015) VirHostNet 2.0: surfing on the web of virus/host
molecular interactions data. Nucleic Acids Res 43(Database issue):D583–D587. https://doi.
org/10.1093/nar/gku1121
Gupta R, Pradhan D, Jain AK, Rai CS (2017) TiD: standalone software for mining putative
drug targets from bacterial proteome. Genomics 109(1):51–57. https://doi.org/10.1016/j.
ygeno.2016.11.005
Hansen EE, Lozupone CA, Rey FE, Wu M, Guruge JL, Narra A et al (2011) Pan-genome of the
dominant human gut-associated archaeon, Methanobrevibacter smithii, studied in twins. Proc
Natl Acad Sci U S A 108(Suppl 1):4599–4606. https://doi.org/10.1073/pnas.1000071108
Hassan SS, Tiwari S, Guimaraes LC, Jamal SB, Folador E, Sharma NB et al (2014) Proteome
scale comparative modeling for conserved drug and vaccine targets identification in
Corynebacterium pseudotuberculosis. BMC Genomics 15(Suppl 7):S3. https://doi.org/10.118
6/1471-2164-15-S7-S3
Hassan D, Omolo CA, Gannimani R, Waddad AY, Mocktar C, Rambharose S et al (2019) Delivery
of novel vancomycin nanoplexes for combating methicillin resistant Staphylococcus aureus
(MRSA) infections. Int J Pharm 558:143–156. https://doi.org/10.1016/j.ijpharm.2019.01.010
Hauser AR, Mecsas J, Moir DT (2016) Beyond antibiotics: new therapeutic approaches for
bacterial infections. Clin Infect Dis Off Publ Infect Dis Soc Am 63(1):89–95. https://doi.
org/10.1093/cid/ciw200
Holmes AH, Moore LSP, Sundsfjord A, Steinbakk M, Regmi S, Karkey A et al (2016)
Understanding the mechanisms and drivers of antimicrobial resistance. Lancet 387(10014):
176–187. https://doi.org/10.1016/S0140-6736(15)00473-0
Hosen MI, Tanmoy AM, Mahbuba D-A, Salma U, Nazim M, Islam MT et al (2014) Application
of a subtractive genomics approach for in silico identification and characterization of novel
drug targets in Mycobacterium tuberculosis F11. Interdiscipl Sci Comput Life Sci 6(1):48–56.
https://doi.org/10.1007/s12539-014-0188-y
Hossain T, Kamruzzaman M, Choudhury TZ, Mahmood HN, Nabi A, Hosen MI (2017) Application
of the subtractive genomics and molecular docking analysis for the identification of novel
putative drug targets against Salmonella entericasubsp.enterica serovarPoona. Biomed Res Int
2017:3783714–3783719. https://doi.org/10.1155/2017/3783714
Jamal SB, Hassan SS, Tiwari S, Viana MV, Benevides LdJ, Ullah A et al (2017) An integrative
in-silico approach for therapeutic target identification in the human pathogen Corynebacterium
diphtheriae. PLoS One 12(10):e0186401. https://doi.org/10.1371/journal.pone.0186401
Jamshidi N, Palsson BO (2007) Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC
Syst Biol 1:26. https://doi.org/10.1186/1752-0509-1-26
Jernberg C, Löfmark S, Edlund C, Jansson JK (2007) Long-term ecological impacts of antibiotic
administration on the human intestinal microbiota. ISME J 1(1):56–66. https://doi.org/10.1038/
ismej.2007.3
Kaltdorf M, Srivastava M, Gupta SK, Liang C, Binder J, Dietl AM et al (2016) Systematic identification of anti-fungal drug targets by a metabolic network approach. Front Mol Biosci 3:22.
https://doi.org/10.3389/fmolb.2016.00022
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M et al (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36(Database issue):D480–D484.
https://doi.org/10.1093/nar/gkm882
Koch A, Mizrahi V (2018) Mycobacterium tuberculosis. Trends Microbiol 26(6):555–556. https://
doi.org/10.1016/j.tim.2018.02.012
Konig R, Zhou Y, Elleder D, Diamond TL, Bonamy GM, Irelan JT et al (2008) Global analysis
of host-pathogen interactions that regulate early-stage HIV-1 replication. Cell 135(1):49–60.
https://doi.org/10.1016/j.cell.2008.07.032
Kumar Jaiswal A, Tiwari S, Jamal SB, Barh D, Azevedo V, Soares SC (2017) An in silico identification of common putative vaccine candidates against Treponema pallidum: a reverse
M. Santana et al.
Guirimand T, Delmotte S, Navratil V (2015) VirHostNet 2.0: surfing on the web of virus/host
molecular interactions data. Nucleic Acids Res 43(Database issue):D583–D587. https://doi.
org/10.1093/nar/gku1121
Gupta R, Pradhan D, Jain AK, Rai CS (2017) TiD: standalone software for mining putative
drug targets from bacterial proteome. Genomics 109(1):51–57. https://doi.org/10.1016/j.
ygeno.2016.11.005
Hansen EE, Lozupone CA, Rey FE, Wu M, Guruge JL, Narra A et al (2011) Pan-genome of the
dominant human gut-associated archaeon, Methanobrevibacter smithii, studied in twins. Proc
Natl Acad Sci U S A 108(Suppl 1):4599–4606. https://doi.org/10.1073/pnas.1000071108
Hassan SS, Tiwari S, Guimaraes LC, Jamal SB, Folador E, Sharma NB et al (2014) Proteome
scale comparative modeling for conserved drug and vaccine targets identification in
Corynebacterium pseudotuberculosis. BMC Genomics 15(Suppl 7):S3. https://doi.org/10.118
6/1471-2164-15-S7-S3
Hassan D, Omolo CA, Gannimani R, Waddad AY, Mocktar C, Rambharose S et al (2019) Delivery
of novel vancomycin nanoplexes for combating methicillin resistant Staphylococcus aureus
(MRSA) infections. Int J Pharm 558:143–156. https://doi.org/10.1016/j.ijpharm.2019.01.010
Hauser AR, Mecsas J, Moir DT (2016) Beyond antibiotics: new therapeutic approaches for
bacterial infections. Clin Infect Dis Off Publ Infect Dis Soc Am 63(1):89–95. https://doi.
org/10.1093/cid/ciw200
Holmes AH, Moore LSP, Sundsfjord A, Steinbakk M, Regmi S, Karkey A et al (2016)
Understanding the mechanisms and drivers of antimicrobial resistance. Lancet 387(10014):
176–187. https://doi.org/10.1016/S0140-6736(15)00473-0
Hosen MI, Tanmoy AM, Mahbuba D-A, Salma U, Nazim M, Islam MT et al (2014) Application
of a subtractive genomics approach for in silico identification and characterization of novel
drug targets in Mycobacterium tuberculosis F11. Interdiscipl Sci Comput Life Sci 6(1):48–56.
https://doi.org/10.1007/s12539-014-0188-y
Hossain T, Kamruzzaman M, Choudhury TZ, Mahmood HN, Nabi A, Hosen MI (2017) Application
of the subtractive genomics and molecular docking analysis for the identification of novel
putative drug targets against Salmonella entericasubsp.enterica serovarPoona. Biomed Res Int
2017:3783714–3783719. https://doi.org/10.1155/2017/3783714
Jamal SB, Hassan SS, Tiwari S, Viana MV, Benevides LdJ, Ullah A et al (2017) An integrative
in-silico approach for therapeutic target identification in the human pathogen Corynebacterium
diphtheriae. PLoS One 12(10):e0186401. https://doi.org/10.1371/journal.pone.0186401
Jamshidi N, Palsson BO (2007) Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC
Syst Biol 1:26. https://doi.org/10.1186/1752-0509-1-26
Jernberg C, Löfmark S, Edlund C, Jansson JK (2007) Long-term ecological impacts of antibiotic
administration on the human intestinal microbiota. ISME J 1(1):56–66. https://doi.org/10.1038/
ismej.2007.3
Kaltdorf M, Srivastava M, Gupta SK, Liang C, Binder J, Dietl AM et al (2016) Systematic identification of anti-fungal drug targets by a metabolic network approach. Front Mol Biosci 3:22.
https://doi.org/10.3389/fmolb.2016.00022
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M et al (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36(Database issue):D480–D484.
https://doi.org/10.1093/nar/gkm882
Koch A, Mizrahi V (2018) Mycobacterium tuberculosis. Trends Microbiol 26(6):555–556. https://
doi.org/10.1016/j.tim.2018.02.012
Konig R, Zhou Y, Elleder D, Diamond TL, Bonamy GM, Irelan JT et al (2008) Global analysis
of host-pathogen interactions that regulate early-stage HIV-1 replication. Cell 135(1):49–60.
https://doi.org/10.1016/j.cell.2008.07.032
Kumar Jaiswal A, Tiwari S, Jamal SB, Barh D, Azevedo V, Soares SC (2017) An in silico identification of common putative vaccine candidates against Treponema pallidum: a reverse
M. Santana et al.